NC_BackgroundParametersEst_Clumping.html

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<title>NC_BackgroundParametersEst_Clumping</title>

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  vertical-align: baseline;
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audio:not([controls]) {
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[hidden],
template {
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a {
  background-color: transparent;
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a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
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b,
strong {
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dfn {
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h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
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  color: #000;
}
small {
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sub,
sup {
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  position: relative;
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sup {
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sub {
  bottom: -0.25em;
}
img {
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svg:not(:root) {
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figure {
  margin: 1em 40px;
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hr {
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pre {
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}
button,
input,
optgroup,
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textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
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}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
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  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
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input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
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input[type="search"] {
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  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
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}
fieldset {
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  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
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legend {
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textarea {
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optgroup {
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}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
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@media print {
  *,
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  *:after {
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    box-shadow: none !important;
    text-shadow: none !important;
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  a:visited {
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    content: " (" attr(title) ")";
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  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
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  thead {
    display: table-header-group;
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  img {
    page-break-inside: avoid;
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    display: none;
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  .dropup > .btn > .caret {
    border-top-color: #000 !important;
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    border: 1px solid #000;
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@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
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}
.glyphicon-arrow-left:before {
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}
.glyphicon-arrow-right:before {
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}
.glyphicon-arrow-up:before {
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}
.glyphicon-arrow-down:before {
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}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
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}
.notification_widget.success:active,
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fieldset[disabled] .notification_widget.success:hover,
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fieldset[disabled] .notification_widget.success:focus,
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fieldset[disabled] .notification_widget.success.focus {
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}
.notification_widget.info {
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  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
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}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
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}
.notification_widget.info:active,
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  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
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.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
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fieldset[disabled] .notification_widget.info:hover,
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fieldset[disabled] .notification_widget.info:focus,
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fieldset[disabled] .notification_widget.info.focus {
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}
.notification_widget.info .badge {
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  background-color: #fff;
}
.notification_widget.danger {
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  background-color: #d9534f;
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.notification_widget.danger:active:hover,
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fieldset[disabled] .notification_widget.danger.focus {
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.notification_widget.danger .badge {
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div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
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  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
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div#pager pre {
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div#pager #pager-contents {
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div#pager #pager-contents #pager-container {
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  -webkit-box-sizing: border-box;
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div#pager .ui-resizable-handle {
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div#pager .ui-resizable-handle::after {
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}
.quickhelp {
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  display: -webkit-box;
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  -webkit-box-align: stretch;
  display: -moz-box;
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  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
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  /* Modern browsers */
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  align-items: stretch;
  line-height: 1.8em;
}
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}
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  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
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  margin-top: 4px;
  display: flex;
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  align-items: baseline;
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  flex: 1;
}
span.save_widget span.filename {
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  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
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}
span.save_widget span.filename:hover {
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}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
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    font-size: small;
    padding: 0 0 0 5px;
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  span.checkpoint_status,
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}
@media (min-width: 768px) and (max-width: 991px) {
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.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
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.toolbar label {
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  display: inline;
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  margin-right: 0.3em;
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}
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}
.toolbar .btn-group {
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  margin-left: 5px;
}
.toolbar-btn-label {
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}
#maintoolbar {
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  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
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  to {
    opacity: 0;
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}
@-moz-keyframes fadeIn {
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}
@-webkit-keyframes fadeIn {
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  to {
    opacity: 1;
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}
/*properties of tooltip after "expand"*/
.bigtooltip {
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  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
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  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
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}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
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  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
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}
.ipython_tooltip .tooltiptext pre {
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  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
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  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
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  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
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  margin-right: -10px;
}
ul.typeahead-list {
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ul.typeahead-list > li > a {
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[dir="rtl"] .typeahead-list {
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.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
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  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
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  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
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[dir="rtl"] .edit-shortcut.pull-right {
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#find-and-replace #replace-preview .insert {
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[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
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  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
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  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
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  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
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  margin-top: 20px;
}
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/* Overrides of notebook CSS for static HTML export */
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<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Trait-pairs-between-26-complex-traits-and-five-negative-control-outcomes-(26*5=130-pairs)">Trait pairs between 26 complex traits and five negative control outcomes (26*5=130 pairs)<a class="anchor-link" href="#Trait-pairs-between-26-complex-traits-and-five-negative-control-outcomes-(26*5=130-pairs)">&#182;</a></h1>
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<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
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<p>Note: you may need additional files to perform the following analysis.</p>
<ol>
<li>For estimation of background parameters by LDSC, you need an LD score reference panel.<br>
In our analysis, we used the 1000G European LD scores(<a href="https://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2">"eur_w_ld_chr"</a>), which are provided by the LDSC software.</li>
<li>Users can choose to perform LD clumping locally, or using the server.<br>
If you choose to perform LD clumping locally, you may need the following two types of files: a user-specified LD reference panel and a packaged plink binary file. In our analysis, we performed LD clumping locally. Files used in our analysis can be downloaded <a href="https://gohkust-my.sharepoint.com/:u:/g/personal/maxhu_ust_hk/ERK5HkrHdAtGlKnSLEoLlUwB5fpKxYfWlAoXby4fHSqxOw?e=1ogfrO">here</a>.</li>
</ol>

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<div class=" highlight hl-r"><pre><span></span><span class="nf">library</span><span class="p">(</span><span class="n">MRAPSS</span><span class="p">)</span>
<span class="nf">library</span><span class="p">(</span><span class="n">readr</span><span class="p">)</span>
</pre></div>

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<div class=" highlight hl-r"><pre><span></span><span class="n">ts1</span> <span class="o">=</span> <span class="nf">c</span><span class="p">(</span><span class="s">&quot;AD&quot;</span><span class="p">,</span> <span class="s">&quot;ASD&quot;</span><span class="p">,</span> <span class="s">&quot;Daytime_Sleepiness&quot;</span><span class="p">,</span> <span class="s">&quot;Height_UKB&quot;</span><span class="p">,</span>  <span class="s">&quot;Intelligence&quot;</span><span class="p">,</span> <span class="s">&quot;RA&quot;</span><span class="p">,</span>      
        <span class="s">&quot;T2D&quot;</span><span class="p">,</span> <span class="s">&quot;Alcohol&quot;</span><span class="p">,</span> <span class="s">&quot;BMI&quot;</span><span class="p">,</span> <span class="s">&quot;Depression&quot;</span><span class="p">,</span> <span class="s">&quot;IBD&quot;</span><span class="p">,</span> <span class="s">&quot;MDD&quot;</span><span class="p">,</span> <span class="s">&quot;SCZ&quot;</span><span class="p">,</span> <span class="s">&quot;Angina&quot;</span><span class="p">,</span> 
        <span class="s">&quot;CAD&quot;</span><span class="p">,</span> <span class="s">&quot;HBP&quot;</span><span class="p">,</span> <span class="s">&quot;Income&quot;</span><span class="p">,</span> <span class="s">&quot;NEB&quot;</span><span class="p">,</span> <span class="s">&quot;Smoking&quot;</span><span class="p">,</span> <span class="s">&quot;Urate&quot;</span><span class="p">,</span> <span class="s">&quot;Anorexia&quot;</span><span class="p">,</span> 
        <span class="s">&quot;CD&quot;</span><span class="p">,</span> <span class="s">&quot;Height_GIANT&quot;</span><span class="p">,</span> <span class="s">&quot;Insomnia&quot;</span><span class="p">,</span> <span class="s">&quot;Neuroticism&quot;</span><span class="p">,</span> <span class="s">&quot;SWB&quot;</span><span class="p">)</span>

<span class="n">ts2</span> <span class="o">=</span> <span class="nf">c</span><span class="p">(</span><span class="s">&quot;Hair_Light_Brown&quot;</span><span class="p">,</span> <span class="s">&quot;Hair_Dark_Brown&quot;</span><span class="p">,</span>  <span class="s">&quot;Hair_Black&quot;</span><span class="p">,</span>  <span class="s">&quot;Hair_Blonde&quot;</span><span class="p">,</span>  <span class="s">&quot;Tanning&quot;</span><span class="p">)</span>


<span class="nf">for</span><span class="p">(</span> <span class="n">exposure</span> <span class="n">in</span> <span class="n">ts1</span><span class="p">){</span>
  <span class="nf">for</span><span class="p">(</span> <span class="n">outcome</span> <span class="n">in</span> <span class="n">ts2</span><span class="p">){</span>
    
    
    <span class="c1"># Start the clock!</span>
    <span class="n">start</span> <span class="o">=</span> <span class="nf">proc.time</span><span class="p">()</span>
    
    <span class="c1"># read in formatted GWAS data</span>
    <span class="n">trait1.dir</span> <span class="o">=</span> <span class="nf">paste0</span><span class="p">(</span><span class="s">&quot;./GWAS_26and5_formatted/&quot;</span><span class="p">,</span> <span class="n">exposure</span><span class="p">)</span>
    <span class="n">trait2.dir</span> <span class="o">=</span> <span class="nf">paste0</span><span class="p">(</span><span class="s">&quot;./GWAS_26and5_formatted/&quot;</span><span class="p">,</span> <span class="n">outcome</span><span class="p">)</span>
    
    
    <span class="nf">cat</span><span class="p">(</span><span class="n">exposure</span><span class="p">,</span><span class="s">&quot;~&quot;</span><span class="p">,</span><span class="n">outcome</span><span class="p">,</span><span class="s">&quot;\n&quot;</span><span class="p">)</span>
    
    <span class="n">trait1</span> <span class="o">=</span> <span class="n">readr</span><span class="o">::</span><span class="nf">read_delim</span><span class="p">(</span><span class="n">trait1.dir</span><span class="p">,</span> <span class="n">delim</span> <span class="o">=</span> <span class="s">&quot;\t&quot;</span><span class="p">,</span> <span class="n">escape_double</span> <span class="o">=</span> <span class="kc">FALSE</span><span class="p">,</span> <span class="n">trim_ws</span> <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span> <span class="n">progress</span> <span class="o">=</span> <span class="bp">F</span><span class="p">)</span>
    
    <span class="n">trait2</span> <span class="o">=</span> <span class="n">readr</span><span class="o">::</span><span class="nf">read_delim</span><span class="p">(</span><span class="n">trait2.dir</span><span class="p">,</span> <span class="n">delim</span> <span class="o">=</span> <span class="s">&quot;\t&quot;</span><span class="p">,</span> <span class="n">escape_double</span> <span class="o">=</span> <span class="kc">FALSE</span><span class="p">,</span> <span class="n">trim_ws</span> <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span> <span class="n">progress</span> <span class="o">=</span> <span class="bp">F</span><span class="p">)</span>
    
    <span class="c1"># estimate parameters for the background model</span>
    <span class="n">paras</span> <span class="o">=</span> <span class="nf">est_paras</span><span class="p">(</span><span class="n">dat1</span> <span class="o">=</span> <span class="n">trait1</span><span class="p">,</span>
                      <span class="n">dat2</span> <span class="o">=</span> <span class="n">trait2</span><span class="p">,</span>
                      <span class="n">trait1.name</span> <span class="o">=</span> <span class="n">exposure</span><span class="p">,</span>
                      <span class="n">trait2.name</span> <span class="o">=</span> <span class="n">outcome</span><span class="p">,</span>
                      <span class="n">h2.fix.intercept</span> <span class="o">=</span> <span class="bp">F</span><span class="p">,</span>
                      <span class="n">LDSC</span> <span class="o">=</span> <span class="bp">T</span><span class="p">,</span>
                      <span class="n">ldscore.dir</span> <span class="o">=</span> <span class="s">&quot;eur_w_ld_chr&quot;</span><span class="p">)</span>
    
    <span class="nf">write.table</span><span class="p">(</span><span class="nf">data.frame</span><span class="p">(</span><span class="n">exposure</span><span class="p">,</span>
                           <span class="n">outcome</span><span class="p">,</span>
                           <span class="nf">nrow</span><span class="p">(</span><span class="n">paras</span><span class="o">$</span><span class="n">dat</span><span class="p">),</span>
                           <span class="n">rg</span> <span class="o">=</span> <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">rg</span><span class="p">,</span>
                           <span class="n">rg.se</span> <span class="o">=</span> <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">rg.se</span><span class="p">,</span>
                           <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">I[1</span><span class="p">,</span><span class="m">1</span><span class="n">]</span><span class="p">,</span>
                           <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">I[2</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span>
                           <span class="n">rho</span> <span class="o">=</span> <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">I[1</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span>
                           <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">I.se[1</span><span class="p">,</span><span class="m">1</span><span class="n">]</span><span class="p">,</span>
                           <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">I.se[2</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span>
                           <span class="n">rho</span> <span class="o">=</span> <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">I.se[1</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span>
                           <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">cov[1</span><span class="p">,</span><span class="m">1</span><span class="n">]</span><span class="p">,</span>  <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">cov[1</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span>  <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">cov[2</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span>
                           <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">cov.se[1</span><span class="p">,</span><span class="m">1</span><span class="n">]</span><span class="p">,</span>  <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">cov.se[1</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">,</span>  <span class="n">paras</span><span class="o">$</span><span class="n">ldsc_res</span><span class="o">$</span><span class="n">cov.se[2</span><span class="p">,</span><span class="m">2</span><span class="n">]</span><span class="p">),</span>
                <span class="n">file</span> <span class="o">=</span> <span class="s">&quot;pairs_ldsc_res&quot;</span><span class="p">,</span> <span class="n">col.names</span> <span class="o">=</span> <span class="bp">F</span><span class="p">,</span> <span class="n">row.names</span> <span class="o">=</span> <span class="bp">F</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="bp">F</span><span class="p">,</span><span class="n">append</span> <span class="o">=</span> <span class="bp">T</span><span class="p">)</span>
    
    <span class="nf">if</span><span class="p">(</span><span class="nf">inherits</span><span class="p">(</span><span class="n">paras</span><span class="p">,</span> <span class="s">&#39;try-error&#39;</span><span class="p">))</span> <span class="n">next</span>
    
    <span class="nf">write.table</span><span class="p">(</span><span class="nf">matrix</span><span class="p">(</span><span class="nf">as.vector</span><span class="p">(</span><span class="n">paras</span><span class="o">$</span><span class="n">Omega</span><span class="p">),</span> <span class="n">nrow</span><span class="o">=</span><span class="m">1</span><span class="p">),</span> <span class="nf">paste0</span><span class="p">(</span><span class="s">&quot;./pairs_bg_paras/&quot;</span><span class="p">,</span> <span class="n">exposure</span><span class="p">,</span> <span class="s">&quot;~&quot;</span><span class="p">,</span> <span class="n">outcome</span><span class="p">,</span> <span class="s">&quot;_Omega&quot;</span><span class="p">),</span>
                <span class="n">row.names</span> <span class="o">=</span> <span class="bp">F</span><span class="p">,</span> <span class="n">col.names</span> <span class="o">=</span> <span class="bp">F</span><span class="p">,</span> <span class="n">quote</span> <span class="o">=</span> <span class="bp">F</span><span class="p">)</span>
      
    <span class="nf">write.table</span><span class="p">(</span><span class="nf">matrix</span><span class="p">(</span><span class="nf">as.vector</span><span class="p">(</span><span class="n">paras</span><span class="o">$</span><span class="n">C</span><span class="p">),</span> <span class="n">nrow</span><span class="o">=</span><span class="m">1</span><span class="p">),</span> <span class="nf">paste0</span><span class="p">(</span><span class="s">&quot;./pairs_bg_paras/&quot;</span><span class="p">,</span> <span class="n">exposure</span><span class="p">,</span> <span class="s">&quot;~&quot;</span><span class="p">,</span> <span class="n">outcome</span><span class="p">,</span> <span class="s">&quot;_C&quot;</span><span class="p">),</span>
                <span class="n">row.names</span> <span class="o">=</span> <span class="bp">F</span><span class="p">,</span> <span class="n">col.names</span> <span class="o">=</span> <span class="bp">F</span><span class="p">,</span> <span class="n">quote</span> <span class="o">=</span> <span class="bp">F</span><span class="p">)</span>
    
    <span class="c1"># clumping      </span>
    <span class="nf">cat</span><span class="p">(</span><span class="s">&quot;Begin clumping ...\n &quot;</span><span class="p">)</span>
    
    <span class="n">clumped</span> <span class="o">=</span>  <span class="nf">clump</span><span class="p">(</span><span class="n">paras</span><span class="o">$</span><span class="n">dat</span><span class="p">,</span>
                     <span class="n">IV.Threshold</span> <span class="o">=</span> <span class="m">5e-05</span><span class="p">,</span>
                     <span class="n">SNP_col</span> <span class="o">=</span> <span class="s">&quot;SNP&quot;</span><span class="p">,</span>
                     <span class="n">pval_col</span> <span class="o">=</span> <span class="s">&quot;pval.exp&quot;</span><span class="p">,</span>
                     <span class="n">clump_kb</span> <span class="o">=</span> <span class="m">1000</span><span class="p">,</span>
                     <span class="n">clump_r2</span> <span class="o">=</span> <span class="m">0.001</span><span class="p">,</span>
                     <span class="n">bfile</span> <span class="o">=</span> <span class="s">&quot;all_1000G_EUR_Phase3&quot;</span><span class="p">,</span>
                     <span class="n">plink_bin</span> <span class="o">=</span> <span class="s">&quot;plink&quot;</span><span class="p">)</span>
    
    <span class="c1"># For convenience, users can perform LD clumping through API and do not need to provide a reference panel, i.e.,</span>
    <span class="c1"># clumped =  clump(paras$dat,</span>
    <span class="c1">#                  IV.Threshold = 5e-05,</span>
    <span class="c1">#                  SNP_col = &quot;SNP&quot;,</span>
    <span class="c1">#                  pval_col = &quot;pval.exp&quot;,</span>
    <span class="c1">#                  clump_kb = 1000,</span>
    <span class="c1">#                  clump_r2 = 0.001)</span>
    

    <span class="nf">if</span><span class="p">(</span><span class="nf">inherits</span><span class="p">(</span><span class="n">clumped</span> <span class="p">,</span> <span class="s">&#39;try-error&#39;</span><span class="p">))</span> <span class="n">next</span>
    
    
    <span class="nf">write.table</span><span class="p">(</span><span class="n">clumped</span><span class="p">,</span> <span class="n">file</span> <span class="o">=</span> <span class="nf">paste0</span><span class="p">(</span><span class="s">&quot;./MRdat/&quot;</span><span class="p">,</span> <span class="n">exposure</span><span class="p">,</span><span class="s">&quot;~&quot;</span><span class="p">,</span><span class="n">outcome</span><span class="p">),</span> <span class="n">col.names</span><span class="o">=</span><span class="bp">T</span><span class="p">,</span> <span class="n">row.names</span><span class="o">=</span><span class="bp">F</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="bp">F</span><span class="p">)</span>
    
    <span class="c1"># Stop the clock</span>
    <span class="nf">proc.time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span>
    
  <span class="p">}</span>
  
<span class="p">}</span>
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<pre>AD ~ Hair_Light_Brown 
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1148476</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1133.

Intercept: 1.0379(0.0099).

Total Observed scale h2:0.0664(0.0131).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6086.

Intercept: 1.3001 (0.014).

Total Observed scale h2:0.0383 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0131 (0.0069).

Total Observed scale gencov: 5e-04 (0.0036).

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<pre>Begin clumping ...
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<pre>API: public: http://gwas-api.mrcieu.ac.uk/

Clumping FkbBDg, 557 variants, using EUR population reference

Removing 488 of 557 variants due to LD with other variants or absence from LD reference panel

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<pre>AD ~ Hair_Dark_Brown 
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1148476</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1133.

Intercept: 1.0384(0.0099).

Total Observed scale h2:0.066(0.0134).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1931.

Intercept: 1.4611 (0.0186).

Total Observed scale h2:0.0887 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0098 (0.007).

Total Observed scale gencov: -5e-04 (0.0039).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping A9OXkz, 557 variants, using EUR population reference

Removing 488 of 557 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>AD ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1148476</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1132.

Intercept: 1.0382(0.0101).

Total Observed scale h2:0.0662(0.0132).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.096.

Intercept: 1.6301 (0.0175).

Total Observed scale h2:0.0578 (0.0077).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0188 (0.0079).

Total Observed scale gencov: 0.0039 (0.0041).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping scufZx, 557 variants, using EUR population reference

Removing 488 of 557 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>AD ~ Hair_Blonde 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1148476</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1133.

Intercept: 1.0381(0.01).

Total Observed scale h2:0.0663(0.0132).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8986.

Intercept: 1.2005 (0.0175).

Total Observed scale h2:0.0884 (0.0105).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0095 (0.0065).

Total Observed scale gencov: 0.001 (0.0035).

</pre>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping VfwiGk, 557 variants, using EUR population reference

Removing 488 of 557 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>AD ~ Tanning 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1148476</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1133.

Intercept: 1.0382(0.01).

Total Observed scale h2:0.0663(0.0135).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6625.

Intercept: 1.8085 (0.0227).

Total Observed scale h2:0.1072 (0.0142).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0067 (0.0082).

Total Observed scale gencov: 0.0069 (0.0041).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping CjmB18, 557 variants, using EUR population reference

Removing 488 of 557 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>ASD ~ Hair_Light_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1076873</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0779.

Intercept: 0.9717(0.0076).

Total Observed scale h2:0.3197(0.0386).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6118.

Intercept: 1.3009 (0.0145).

Total Observed scale h2:0.0386 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0021 (0.0063).

Total Observed scale gencov: -0.0063 (0.006).

</pre>
</div>
</div>

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<pre>Begin clumping ...
 </pre>
</div>
</div>

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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping yCOmQD, 97 variants, using EUR population reference

Removing 58 of 97 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>ASD ~ Hair_Dark_Brown 
</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1076873</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.078.

Intercept: 0.9716(0.0077).

Total Observed scale h2:0.3203(0.0384).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1972.

Intercept: 1.4639 (0.019).

Total Observed scale h2:0.0889 (0.0119).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0066 (0.0074).

Total Observed scale gencov: 0.0172 (0.007).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping uFSt7V, 97 variants, using EUR population reference

Removing 58 of 97 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<pre>ASD ~ Hair_Black 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1076873</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0779.

Intercept: 0.9717(0.0076).

Total Observed scale h2:0.3199(0.0385).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1008.

Intercept: 1.6338 (0.0169).

Total Observed scale h2:0.058 (0.008).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.018 (0.007).

Total Observed scale gencov: -0.0167 (0.0064).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping qpSfw4, 97 variants, using EUR population reference

Removing 58 of 97 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<pre>ASD ~ Hair_Blonde 
</pre>
</div>
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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1076873</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0779.

Intercept: 0.972(0.0074).

Total Observed scale h2:0.3186(0.0378).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9031.

Intercept: 1.2016 (0.0175).

Total Observed scale h2:0.0888 (0.0109).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0028 (0.0056).

Total Observed scale gencov: -0.001 (0.0058).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping VUXlKO, 97 variants, using EUR population reference

Removing 58 of 97 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>ASD ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1076873</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.078.

Intercept: 0.9708(0.0074).

Total Observed scale h2:0.3234(0.0377).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6698.

Intercept: 1.8138 (0.0231).

Total Observed scale h2:0.1073 (0.0147).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0088 (0.0089).

Total Observed scale gencov: -0.0216 (0.008).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping 6jFknW, 97 variants, using EUR population reference

Removing 58 of 97 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Daytime_Sleepiness ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065141</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4833.

Intercept: 1.0327(0.0104).

Total Observed scale h2:0.048(0.0025).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6091.

Intercept: 1.2955 (0.014).

Total Observed scale h2:0.039 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0029 (0.0074).

Total Observed scale gencov: 8e-04 (0.0015).

</pre>
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<pre>Begin clumping ...
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping YUh2gR, 2803 variants, using EUR population reference

Removing 2560 of 2803 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Daytime_Sleepiness ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065141</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4829.

Intercept: 1.0331(0.0106).

Total Observed scale h2:0.0479(0.0025).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.193.

Intercept: 1.453 (0.0195).

Total Observed scale h2:0.0899 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0051 (0.0083).

Total Observed scale gencov: -8e-04 (0.0018).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 7iNN7d, 2797 variants, using EUR population reference

Removing 2555 of 2797 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Daytime_Sleepiness ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065141</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4832.

Intercept: 1.0325(0.0105).

Total Observed scale h2:0.048(0.0025).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0972.

Intercept: 1.6247 (0.0177).

Total Observed scale h2:0.0587 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0131 (0.0079).

Total Observed scale gencov: -0.0014 (0.0014).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping PzfCWj, 2803 variants, using EUR population reference

Removing 2560 of 2803 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Daytime_Sleepiness ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065141</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4833.

Intercept: 1.0332(0.0104).

Total Observed scale h2:0.0479(0.0025).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9006.

Intercept: 1.1988 (0.0176).

Total Observed scale h2:0.0889 (0.0107).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0029 (0.0071).

Total Observed scale gencov: 0.0011 (0.0015).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping H86xm1, 2803 variants, using EUR population reference

Removing 2560 of 2803 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Daytime_Sleepiness ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065141</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4828.

Intercept: 1.0324(0.0104).

Total Observed scale h2:0.048(0.0025).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.66.

Intercept: 1.7987 (0.0225).

Total Observed scale h2:0.1083 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0143 (0.0078).

Total Observed scale gencov: -0.0012 (0.0018).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping 1wJJIh, 2797 variants, using EUR population reference

Removing 2555 of 2797 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Height_UKB ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037320</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:5.4776.

Intercept: 1.9701(0.0401).

Total Observed scale h2:0.4183(0.0176).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6052.

Intercept: 1.3049 (0.0145).

Total Observed scale h2:0.0374 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.3299 (0.0114).

Total Observed scale gencov: 0.0054 (0.0031).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping niY72C, 71718 variants, using EUR population reference

Removing 69492 of 71718 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Height_UKB ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037320</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:5.4702.

Intercept: 1.9698(0.0391).

Total Observed scale h2:0.4179(0.0176).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1795.

Intercept: 1.4644 (0.0189).

Total Observed scale h2:0.0873 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.3539 (0.0126).

Total Observed scale gencov: 0.0028 (0.0041).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping H5MmvX, 71569 variants, using EUR population reference

Removing 69345 of 71569 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Height_UKB ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037320</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:5.4774.

Intercept: 1.9705(0.0397).

Total Observed scale h2:0.4182(0.0175).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0932.

Intercept: 1.6378 (0.0172).

Total Observed scale h2:0.0566 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.4838 (0.0124).

Total Observed scale gencov: -0.0199 (0.0037).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping Vlb9d2, 71715 variants, using EUR population reference

Removing 69488 of 71715 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Height_UKB ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037320</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:5.476.

Intercept: 1.9701(0.0391).

Total Observed scale h2:0.4181(0.0175).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8894.

Intercept: 1.2032 (0.0181).

Total Observed scale h2:0.0873 (0.0108).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.2127 (0.0106).

Total Observed scale gencov: 0.0026 (0.0035).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 1G1ZQ3, 71652 variants, using EUR population reference

Removing 69427 of 71652 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<div class="output_subarea output_stream output_stdout output_text">
<pre>Height_UKB ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037320</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:5.4392.

Intercept: 1.9717(0.0392).

Total Observed scale h2:0.4167(0.0177).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6434.

Intercept: 1.8121 (0.0224).

Total Observed scale h2:0.1061 (0.0148).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.3609 (0.0139).

Total Observed scale gencov: -0.0067 (0.0046).

</pre>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping PEeFVQ, 71366 variants, using EUR population reference

Removing 69139 of 71366 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Intelligence ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6916.

Intercept: 1.1088(0.0128).

Total Observed scale h2:0.2192(0.011).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.609.

Intercept: 1.2991 (0.0142).

Total Observed scale h2:0.0386 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.074 (0.0077).

Total Observed scale gencov: -0.0062 (0.0031).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping a8izSx, 4746 variants, using EUR population reference

Removing 4344 of 4746 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Intelligence ~ Hair_Dark_Brown 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6914.

Intercept: 1.1094(0.0125).

Total Observed scale h2:0.2188(0.0107).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1917.

Intercept: 1.4599 (0.0191).

Total Observed scale h2:0.089 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0265 (0.0077).

Total Observed scale gencov: 0.015 (0.0038).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping S3vCcK, 4745 variants, using EUR population reference

Removing 4344 of 4745 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Intelligence ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6916.

Intercept: 1.1092(0.0127).

Total Observed scale h2:0.2189(0.011).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0975.

Intercept: 1.6309 (0.0175).

Total Observed scale h2:0.0581 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.1374 (0.0089).

Total Observed scale gencov: -0.0147 (0.0034).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping YnFRKY, 4746 variants, using EUR population reference

Removing 4344 of 4746 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Intelligence ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6917.

Intercept: 1.1089(0.0128).

Total Observed scale h2:0.2191(0.011).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8984.

Intercept: 1.1989 (0.0174).

Total Observed scale h2:0.089 (0.0105).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.017 (0.0077).

Total Observed scale gencov: -0.0035 (0.0035).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping GVCfaF, 4746 variants, using EUR population reference

Removing 4344 of 4746 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Intelligence ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6913.

Intercept: 1.1094(0.0126).

Total Observed scale h2:0.2188(0.0108).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6612.

Intercept: 1.8083 (0.0226).

Total Observed scale h2:0.1075 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.1004 (0.0094).

Total Observed scale gencov: -0.027 (0.0039).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping ocPdJ0, 4745 variants, using EUR population reference

Removing 4344 of 4745 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>RA ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1029912</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1118.

Intercept: 0.9564(0.0097).

Total Observed scale h2:0.1286(0.0138).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6092.

Intercept: 1.2975 (0.0144).

Total Observed scale h2:0.0387 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0057 (0.006).

Total Observed scale gencov: 0.0108 (0.0033).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping hcmxl3, 1449 variants, using EUR population reference

Removing 1314 of 1449 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>RA ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1029912</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1117.

Intercept: 0.9568(0.0097).

Total Observed scale h2:0.1282(0.0139).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1924.

Intercept: 1.4592 (0.0189).

Total Observed scale h2:0.0889 (0.0119).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0045 (0.0075).

Total Observed scale gencov: -0.0102 (0.0039).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping 3yB2SL, 1447 variants, using EUR population reference

Removing 1312 of 1447 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>RA ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1029912</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1117.

Intercept: 0.9566(0.0098).

Total Observed scale h2:0.1283(0.014).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0974.

Intercept: 1.6295 (0.0176).

Total Observed scale h2:0.0581 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 6e-04 (0.0071).

Total Observed scale gencov: -0.0086 (0.0039).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping oKFCql, 1447 variants, using EUR population reference

Removing 1312 of 1447 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>RA ~ Hair_Blonde 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1029912</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1118.

Intercept: 0.9564(0.0097).

Total Observed scale h2:0.1285(0.0139).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8985.

Intercept: 1.1985 (0.0176).

Total Observed scale h2:0.0887 (0.0107).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0078 (0.0066).

Total Observed scale gencov: 0.0024 (0.0036).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping XyW86z, 1447 variants, using EUR population reference

Removing 1312 of 1447 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>RA ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1029912</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1115.

Intercept: 0.9561(0.0098).

Total Observed scale h2:0.1287(0.0138).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.66.

Intercept: 1.8053 (0.0224).

Total Observed scale h2:0.1075 (0.0148).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.003 (0.0083).

Total Observed scale gencov: -0.0061 (0.0042).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping m5sxXz, 1446 variants, using EUR population reference

Removing 1311 of 1446 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>T2D ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1048310</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.01.

Intercept: 1.1126(0.0188).

Total Observed scale h2:0.1849(0.0073).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6092.

Intercept: 1.2997 (0.0144).

Total Observed scale h2:0.0385 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.024 (0.0072).

Total Observed scale gencov: 8e-04 (0.0025).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping y4sZ6W, 11075 variants, using EUR population reference

Removing 10423 of 11075 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>T2D ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1048310</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0097.

Intercept: 1.1144(0.0181).

Total Observed scale h2:0.1843(0.0069).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1916.

Intercept: 1.4628 (0.0195).

Total Observed scale h2:0.0885 (0.0117).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0288 (0.0079).

Total Observed scale gencov: -0.0058 (0.0029).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping X8oGf9, 11072 variants, using EUR population reference

Removing 10420 of 11072 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>T2D ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1048310</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.01.

Intercept: 1.1129(0.0189).

Total Observed scale h2:0.1848(0.0072).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0964.

Intercept: 1.6251 (0.0179).

Total Observed scale h2:0.0585 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0297 (0.0082).

Total Observed scale gencov: 0.004 (0.0031).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping PYTmfI, 11075 variants, using EUR population reference

Removing 10423 of 11075 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>T2D ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1048310</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.01.

Intercept: 1.1136(0.0188).

Total Observed scale h2:0.1846(0.0072).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9003.

Intercept: 1.2018 (0.0179).

Total Observed scale h2:0.0884 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.014 (0.008).

Total Observed scale gencov: 0.002 (0.0028).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping CtLcoA, 11075 variants, using EUR population reference

Removing 10423 of 11075 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>T2D ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1048310</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0067.

Intercept: 1.1129(0.0193).

Total Observed scale h2:0.1845(0.007).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6588.

Intercept: 1.8029 (0.0227).

Total Observed scale h2:0.1081 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0241 (0.0099).

Total Observed scale gencov: -0.0077 (0.0035).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping r8KUsf, 10999 variants, using EUR population reference

Removing 10347 of 10999 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Alcohol ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1084534</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6281.

Intercept: 1.0193(0.0113).

Total Observed scale h2:0.0694(0.0033).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6096.

Intercept: 1.3011 (0.0142).

Total Observed scale h2:0.0383 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0124 (0.008).

Total Observed scale gencov: -5e-04 (0.0018).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping bYtJhu, 4407 variants, using EUR population reference

Removing 4064 of 4407 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Alcohol ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1084534</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6282.

Intercept: 1.0198(0.0112).

Total Observed scale h2:0.0693(0.0033).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1933.

Intercept: 1.4634 (0.0194).

Total Observed scale h2:0.0886 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0039 (0.0085).

Total Observed scale gencov: -0.0013 (0.0022).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping 0H3LMz, 4407 variants, using EUR population reference

Removing 4064 of 4407 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Alcohol ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1084534</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6282.

Intercept: 1.0191(0.0109).

Total Observed scale h2:0.0694(0.0033).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0978.

Intercept: 1.6309 (0.0177).

Total Observed scale h2:0.058 (0.0077).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0122 (0.0087).

Total Observed scale gencov: -0.001 (0.0022).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping SemUTe, 4407 variants, using EUR population reference

Removing 4064 of 4407 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Alcohol ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1084534</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6281.

Intercept: 1.0192(0.0108).

Total Observed scale h2:0.0694(0.0032).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9001.

Intercept: 1.2011 (0.0174).

Total Observed scale h2:0.0887 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 4e-04 (0.0077).

Total Observed scale gencov: 0.004 (0.0019).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping HJBSi5, 4407 variants, using EUR population reference

Removing 4064 of 4407 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Alcohol ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1084534</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6269.

Intercept: 1.0196(0.0113).

Total Observed scale h2:0.0693(0.0034).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.662.

Intercept: 1.8104 (0.0228).

Total Observed scale h2:0.1071 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0741 (0.0092).

Total Observed scale gencov: 0.0107 (0.0024).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping Q1KnCq, 4400 variants, using EUR population reference

Removing 4058 of 4400 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>BMI ~ Hair_Light_Brown 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037799</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.9412.

Intercept: 1.1797(0.0196).

Total Observed scale h2:0.2169(0.0066).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.609.

Intercept: 1.2989 (0.0143).

Total Observed scale h2:0.0387 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0276 (0.0087).

Total Observed scale gencov: -0.0014 (0.0021).

</pre>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping SdKd5s, 28069 variants, using EUR population reference

Removing 26768 of 28069 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>BMI ~ Hair_Dark_Brown 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037799</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.9409.

Intercept: 1.1798(0.0202).

Total Observed scale h2:0.2168(0.0066).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1917.

Intercept: 1.4596 (0.0192).

Total Observed scale h2:0.0891 (0.0119).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0767 (0.0109).

Total Observed scale gencov: -0.0076 (0.0026).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping sruacp, 28044 variants, using EUR population reference

Removing 26743 of 28044 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>BMI ~ Hair_Black 
</pre>
</div>
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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037799</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.9411.

Intercept: 1.1801(0.0201).

Total Observed scale h2:0.2168(0.0066).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0976.

Intercept: 1.6301 (0.0175).

Total Observed scale h2:0.0582 (0.0081).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0365 (0.0095).

Total Observed scale gencov: 0.0066 (0.0027).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping aAwPVw, 28069 variants, using EUR population reference

Removing 26768 of 28069 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>BMI ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037799</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.9406.

Intercept: 1.1798(0.0203).

Total Observed scale h2:0.2168(0.0066).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8984.

Intercept: 1.1979 (0.0179).

Total Observed scale h2:0.0891 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0161 (0.0085).

Total Observed scale gencov: 0.0019 (0.0023).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping dujdVi, 28039 variants, using EUR population reference

Removing 26739 of 28039 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>BMI ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037799</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.9404.

Intercept: 1.1806(0.0206).

Total Observed scale h2:0.2167(0.0065).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6612.

Intercept: 1.81 (0.0226).

Total Observed scale h2:0.1072 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.146 (0.0117).

Total Observed scale gencov: -0.0054 (0.0031).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping nhCjSN, 28023 variants, using EUR population reference

Removing 26722 of 28023 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Depression ~ Hair_Light_Brown 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1026744</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3826.

Intercept: 1.0179(0.0105).

Total Observed scale h2:0.0466(0.0029).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6099.

Intercept: 1.2958 (0.0139).

Total Observed scale h2:0.0391 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0172 (0.0067).

Total Observed scale gencov: 4e-04 (0.0015).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping GPXnWI, 1376 variants, using EUR population reference

Removing 1179 of 1376 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Depression ~ Hair_Dark_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1026744</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3825.

Intercept: 1.0182(0.0103).

Total Observed scale h2:0.0465(0.0029).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1945.

Intercept: 1.4555 (0.0194).

Total Observed scale h2:0.0897 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0035 (0.0076).

Total Observed scale gencov: -8e-04 (0.0018).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping pPgoZR, 1376 variants, using EUR population reference

Removing 1179 of 1376 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Depression ~ Hair_Black 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1026744</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3826.

Intercept: 1.0182(0.0105).

Total Observed scale h2:0.0465(0.0029).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.098.

Intercept: 1.6249 (0.0173).

Total Observed scale h2:0.0588 (0.0079).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0224 (0.0077).

Total Observed scale gencov: 0.0015 (0.0018).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping csrYfV, 1376 variants, using EUR population reference

Removing 1179 of 1376 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Depression ~ Hair_Blonde 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1026744</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3826.

Intercept: 1.0177(0.0104).

Total Observed scale h2:0.0466(0.0029).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9015.

Intercept: 1.1991 (0.0176).

Total Observed scale h2:0.0889 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0025 (0.0069).

Total Observed scale gencov: -0.0015 (0.0016).

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Begin clumping ...
 </pre>
</div>
</div>

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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping KKdlpq, 1376 variants, using EUR population reference

Removing 1179 of 1376 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Depression ~ Tanning 
</pre>
</div>
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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1026744</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3821.

Intercept: 1.0189(0.0101).

Total Observed scale h2:0.0464(0.0029).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6617.

Intercept: 1.8008 (0.0225).

Total Observed scale h2:0.1081 (0.015).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0247 (0.0092).

Total Observed scale gencov: 0.0047 (0.0021).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping lWVdwG, 1376 variants, using EUR population reference

Removing 1179 of 1376 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>IBD ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004597</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4925.

Intercept: 1.1559(0.0128).

Total Observed scale h2:0.2636(0.023).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6094.

Intercept: 1.2985 (0.0142).

Total Observed scale h2:0.0387 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0143 (0.0073).

Total Observed scale gencov: -0.0118 (0.0041).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping pijopg, 5087 variants, using EUR population reference

Removing 4726 of 5087 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>IBD ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004597</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4927.

Intercept: 1.157(0.013).

Total Observed scale h2:0.2628(0.0229).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1924.

Intercept: 1.4604 (0.0194).

Total Observed scale h2:0.089 (0.0119).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.016 (0.0085).

Total Observed scale gencov: 0.0124 (0.0049).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping CeYgle, 5087 variants, using EUR population reference

Removing 4726 of 5087 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>IBD ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004597</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4925.

Intercept: 1.1556(0.013).

Total Observed scale h2:0.2639(0.0232).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0976.

Intercept: 1.6335 (0.0185).

Total Observed scale h2:0.0577 (0.0077).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0012 (0.0081).

Total Observed scale gencov: 0.0024 (0.0048).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping yizNpi, 5087 variants, using EUR population reference

Removing 4726 of 5087 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>IBD ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004597</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4925.

Intercept: 1.1564(0.0131).

Total Observed scale h2:0.2632(0.0234).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8998.

Intercept: 1.201 (0.0184).

Total Observed scale h2:0.0889 (0.0105).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0018 (0.0075).

Total Observed scale gencov: -0.003 (0.0049).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping jZNa5R, 5087 variants, using EUR population reference

Removing 4726 of 5087 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>IBD ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004597</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4926.

Intercept: 1.1575(0.0127).

Total Observed scale h2:0.2623(0.0229).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6598.

Intercept: 1.8093 (0.0223).

Total Observed scale h2:0.107 (0.0148).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0356 (0.0084).

Total Observed scale gencov: -6e-04 (0.0056).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping nFdIEV, 5087 variants, using EUR population reference

Removing 4726 of 5087 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>MDD ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0672.

Intercept: 0.9848(0.0072).

Total Observed scale h2:0.0169(0.0025).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.609.

Intercept: 1.2988 (0.014).

Total Observed scale h2:0.0387 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0063 (0.0061).

Total Observed scale gencov: -1e-04 (0.0014).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping Ao0WeV, 98 variants, using EUR population reference

Removing 76 of 98 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>MDD ~ Hair_Dark_Brown 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0671.

Intercept: 0.985(0.0068).

Total Observed scale h2:0.0168(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1917.

Intercept: 1.4591 (0.0193).

Total Observed scale h2:0.0892 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0108 (0.0068).

Total Observed scale gencov: -2e-04 (0.0017).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping MshcTV, 98 variants, using EUR population reference

Removing 76 of 98 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>MDD ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0672.

Intercept: 0.9853(0.0071).

Total Observed scale h2:0.0168(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0975.

Intercept: 1.6313 (0.0176).

Total Observed scale h2:0.058 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0037 (0.0078).

Total Observed scale gencov: 0.0019 (0.0019).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping 2wsq0f, 98 variants, using EUR population reference

Removing 76 of 98 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>MDD ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0673.

Intercept: 0.9848(0.007).

Total Observed scale h2:0.0169(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8984.

Intercept: 1.1989 (0.0175).

Total Observed scale h2:0.089 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0055 (0.0063).

Total Observed scale gencov: -0.0019 (0.0017).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping rMys8l, 98 variants, using EUR population reference

Removing 76 of 98 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>MDD ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.0669.

Intercept: 0.9852(0.0068).

Total Observed scale h2:0.0167(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6612.

Intercept: 1.808 (0.0226).

Total Observed scale h2:0.1075 (0.015).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0085 (0.0071).

Total Observed scale gencov: 0.0039 (0.0019).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping yvXmGM, 98 variants, using EUR population reference

Removing 76 of 98 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>SCZ ~ Hair_Light_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1047564</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0124.

Intercept: 1.0964(0.0143).

Total Observed scale h2:0.4064(0.0153).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6025.

Intercept: 1.302 (0.0144).

Total Observed scale h2:0.0371 (0.0058).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0319 (0.0083).

Total Observed scale gencov: -0.0037 (0.0034).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping X49WkQ, 9758 variants, using EUR population reference

Removing 9093 of 9758 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>SCZ ~ Hair_Dark_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1047564</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0126.

Intercept: 1.0961(0.0144).

Total Observed scale h2:0.4067(0.0156).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1567.

Intercept: 1.4631 (0.018).

Total Observed scale h2:0.0833 (0.0107).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.022 (0.0093).

Total Observed scale gencov: 0.0032 (0.0044).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 0f1dHf, 9758 variants, using EUR population reference

Removing 9093 of 9758 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>SCZ ~ Hair_Black 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1047564</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0124.

Intercept: 1.0963(0.0142).

Total Observed scale h2:0.4065(0.0152).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0778.

Intercept: 1.626 (0.0164).

Total Observed scale h2:0.056 (0.0077).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0691 (0.0105).

Total Observed scale gencov: -0.0014 (0.0042).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping CZ6FZA, 9758 variants, using EUR population reference

Removing 9093 of 9758 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>SCZ ~ Hair_Blonde 
</pre>
</div>
</div>

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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1047564</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0126.

Intercept: 1.0966(0.0145).

Total Observed scale h2:0.4064(0.0155).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8824.

Intercept: 1.1972 (0.0173).

Total Observed scale h2:0.0865 (0.0105).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0268 (0.0078).

Total Observed scale gencov: 0.0055 (0.0039).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping v9xE1S, 9758 variants, using EUR population reference

Removing 9093 of 9758 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>SCZ ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1047564</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0114.

Intercept: 1.0969(0.0144).

Total Observed scale h2:0.4061(0.0154).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6305.

Intercept: 1.8016 (0.0228).

Total Observed scale h2:0.1028 (0.014).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0233 (0.0098).

Total Observed scale gencov: 0.009 (0.0048).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping WN63z0, 9741 variants, using EUR population reference

Removing 9076 of 9741 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Angina ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1717.

Intercept: 1.0297(0.0086).

Total Observed scale h2:0.0239(0.0027).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.609.

Intercept: 1.299 (0.014).

Total Observed scale h2:0.0386 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0212 (0.0066).

Total Observed scale gencov: -0.0013 (0.0016).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping Q15hL1, 664 variants, using EUR population reference

Removing 584 of 664 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Angina ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1714.

Intercept: 1.0304(0.0086).

Total Observed scale h2:0.0238(0.0027).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1917.

Intercept: 1.4591 (0.0192).

Total Observed scale h2:0.0892 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0146 (0.0073).

Total Observed scale gencov: -9e-04 (0.002).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping QpeI54, 664 variants, using EUR population reference

Removing 584 of 664 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Angina ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1717.

Intercept: 1.03(0.0088).

Total Observed scale h2:0.0239(0.0028).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0975.

Intercept: 1.6314 (0.0176).

Total Observed scale h2:0.058 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0344 (0.0082).

Total Observed scale gencov: 0.0039 (0.0018).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping my3fAc, 664 variants, using EUR population reference

Removing 584 of 664 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Angina ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1716.

Intercept: 1.0301(0.0088).

Total Observed scale h2:0.0239(0.0027).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8984.

Intercept: 1.199 (0.0175).

Total Observed scale h2:0.0889 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0114 (0.0069).

Total Observed scale gencov: -4e-04 (0.0018).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping LGWF71, 664 variants, using EUR population reference

Removing 584 of 664 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Angina ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1702.

Intercept: 1.0305(0.0086).

Total Observed scale h2:0.0236(0.0027).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6612.

Intercept: 1.808 (0.0226).

Total Observed scale h2:0.1075 (0.015).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0084 (0.0084).

Total Observed scale gencov: 0.0014 (0.0021).

</pre>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping eCkdeI, 641 variants, using EUR population reference

Removing 561 of 641 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<pre>CAD ~ Hair_Light_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1112977</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1465.

Intercept: 0.8952(0.0095).

Total Observed scale h2:0.0651(0.005).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6101.

Intercept: 1.3017 (0.0143).

Total Observed scale h2:0.0383 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0115 (0.0063).

Total Observed scale gencov: 0.0046 (0.002).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping Nibdxj, 1583 variants, using EUR population reference

Removing 1430 of 1583 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>CAD ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1112977</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1463.

Intercept: 0.8957(0.0096).

Total Observed scale h2:0.0649(0.005).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1935.

Intercept: 1.4623 (0.0191).

Total Observed scale h2:0.0887 (0.0119).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0118 (0.0065).

Total Observed scale gencov: -0.0086 (0.0024).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping p1BX9o, 1583 variants, using EUR population reference

Removing 1430 of 1583 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>CAD ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1112977</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1466.

Intercept: 0.8954(0.0096).

Total Observed scale h2:0.0651(0.005).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0987.

Intercept: 1.6313 (0.0178).

Total Observed scale h2:0.0581 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.028 (0.0079).

Total Observed scale gencov: -0.0035 (0.0024).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping zjct1N, 1583 variants, using EUR population reference

Removing 1430 of 1583 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>CAD ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1112977</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1466.

Intercept: 0.895(0.0096).

Total Observed scale h2:0.0652(0.0049).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8999.

Intercept: 1.2012 (0.0177).

Total Observed scale h2:0.0886 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0129 (0.0065).

Total Observed scale gencov: 0.0048 (0.0022).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping LM8f4B, 1583 variants, using EUR population reference

Removing 1430 of 1583 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>CAD ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1112977</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.146.

Intercept: 0.8958(0.0096).

Total Observed scale h2:0.0649(0.0049).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6634.

Intercept: 1.8066 (0.0226).

Total Observed scale h2:0.1078 (0.015).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0075 (0.009).

Total Observed scale gencov: -0.0019 (0.0028).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping opLzwo, 1583 variants, using EUR population reference

Removing 1430 of 1583 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<pre>HBP ~ Hair_Light_Brown 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0463.

Intercept: 1.1249(0.0166).

Total Observed scale h2:0.1137(0.0046).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.609.

Intercept: 1.2979 (0.0141).

Total Observed scale h2:0.0388 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0292 (0.008).

Total Observed scale gencov: 0.0014 (0.0019).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<pre>Clumping myETE2, 12353 variants, using EUR population reference

Removing 11669 of 12353 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>HBP ~ Hair_Dark_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0444.

Intercept: 1.1255(0.017).

Total Observed scale h2:0.1135(0.0046).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1917.

Intercept: 1.4584 (0.0193).

Total Observed scale h2:0.0893 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0619 (0.0089).

Total Observed scale gencov: -0.0048 (0.0021).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping jKGO25, 12317 variants, using EUR population reference

Removing 11633 of 12317 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>HBP ~ Hair_Black 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.046.

Intercept: 1.1256(0.0165).

Total Observed scale h2:0.1135(0.0045).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0975.

Intercept: 1.6313 (0.0173).

Total Observed scale h2:0.058 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0042 (0.0093).

Total Observed scale gencov: 0 (0.0023).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping tP0VMo, 12351 variants, using EUR population reference

Removing 11667 of 12351 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>HBP ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0461.

Intercept: 1.1255(0.0163).

Total Observed scale h2:0.1136(0.0046).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8984.

Intercept: 1.1993 (0.0172).

Total Observed scale h2:0.0889 (0.0107).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.017 (0.0073).

Total Observed scale gencov: 0.0014 (0.0021).

</pre>
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<pre>Begin clumping ...
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping Usi8p3, 12352 variants, using EUR population reference

Removing 11668 of 12352 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>HBP ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0442.

Intercept: 1.1249(0.0165).

Total Observed scale h2:0.1136(0.0045).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6612.

Intercept: 1.8093 (0.0229).

Total Observed scale h2:0.1073 (0.015).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.098 (0.0092).

Total Observed scale gencov: -0.0073 (0.0025).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 2K0IEc, 12314 variants, using EUR population reference

Removing 11630 of 12314 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Income ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1208814</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4824.

Intercept: 1.0465(0.0108).

Total Observed scale h2:0.0733(0.0036).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6088.

Intercept: 1.2987 (0.014).

Total Observed scale h2:0.0386 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0056 (0.0075).

Total Observed scale gencov: -0.0025 (0.0021).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping GrLXuD, 2607 variants, using EUR population reference

Removing 2348 of 2607 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Income ~ Hair_Dark_Brown 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1208814</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4818.

Intercept: 1.0477(0.0105).

Total Observed scale h2:0.0729(0.0036).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1915.

Intercept: 1.4597 (0.0192).

Total Observed scale h2:0.0891 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0097 (0.0079).

Total Observed scale gencov: 0.0079 (0.0024).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping X3mCcp, 2607 variants, using EUR population reference

Removing 2348 of 2607 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Income ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1208814</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4823.

Intercept: 1.0474(0.0109).

Total Observed scale h2:0.073(0.0036).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0973.

Intercept: 1.6312 (0.0176).

Total Observed scale h2:0.058 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0061 (0.008).

Total Observed scale gencov: -0.0035 (0.0022).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping PcY7ym, 2607 variants, using EUR population reference

Removing 2348 of 2607 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Income ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1208814</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4824.

Intercept: 1.0468(0.011).

Total Observed scale h2:0.0732(0.0036).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8986.

Intercept: 1.1994 (0.0174).

Total Observed scale h2:0.0889 (0.0105).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0034 (0.0071).

Total Observed scale gencov: -0.0049 (0.0021).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping kjQC40, 2607 variants, using EUR population reference

Removing 2348 of 2607 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Income ~ Tanning 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1208814</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Mean Chi2:2.6603.

Intercept: 1.8082 (0.0224).

Total Observed scale h2:0.1074 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0128 (0.0098).

Total Observed scale gencov: -0.0052 (0.0026).

</pre>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 9UVgAe, 2601 variants, using EUR population reference

Removing 2344 of 2601 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>NEB ~ Hair_Light_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">984184</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1427.

Intercept: 0.9787(0.0085).

Total Observed scale h2:0.023(0.0021).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6021.

Intercept: 1.2964 (0.0148).

Total Observed scale h2:0.0381 (0.0058).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0125 (0.0065).

Total Observed scale gencov: -9e-04 (0.0016).

</pre>
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<div class="output_subarea output_stream output_stdout output_text">
<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping COFsXm, 275 variants, using EUR population reference

Removing 213 of 275 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>NEB ~ Hair_Dark_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">984184</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1423.

Intercept: 0.9784(0.0086).

Total Observed scale h2:0.023(0.0021).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1708.

Intercept: 1.454 (0.0195).

Total Observed scale h2:0.0873 (0.0111).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0245 (0.0067).

Total Observed scale gencov: -0.001 (0.0018).

</pre>
</div>
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<div class="output_subarea output_stream output_stdout output_text">
<pre>Begin clumping ...
 </pre>
</div>
</div>

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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping bGNDpH, 271 variants, using EUR population reference

Removing 210 of 271 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>NEB ~ Hair_Black 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">984184</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1425.

Intercept: 0.9787(0.0086).

Total Observed scale h2:0.023(0.0021).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0845.

Intercept: 1.6201 (0.0175).

Total Observed scale h2:0.0581 (0.0079).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0016 (0.0074).

Total Observed scale gencov: -3e-04 (0.0018).

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Begin clumping ...
 </pre>
</div>
</div>

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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping y2pYuS, 272 variants, using EUR population reference

Removing 210 of 272 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>NEB ~ Hair_Blonde 
</pre>
</div>
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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">984184</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1425.

Intercept: 0.9784(0.0086).

Total Observed scale h2:0.0231(0.0021).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8862.

Intercept: 1.1998 (0.0183).

Total Observed scale h2:0.0877 (0.0108).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0022 (0.0078).

Total Observed scale gencov: 0.0026 (0.0016).

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Begin clumping ...
 </pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping XCaZYZ, 274 variants, using EUR population reference

Removing 212 of 274 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>NEB ~ Tanning 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">984184</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1418.

Intercept: 0.9797(0.0085).

Total Observed scale h2:0.0228(0.0021).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6457.

Intercept: 1.8018 (0.0215).

Total Observed scale h2:0.1059 (0.0142).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.039 (0.0086).

Total Observed scale gencov: 0.0045 (0.002).

</pre>
</div>
</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Begin clumping ...
 </pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping qoaERd, 271 variants, using EUR population reference

Removing 210 of 271 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Smoking ~ Hair_Light_Brown 
</pre>
</div>
</div>

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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1043420</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3301.

Intercept: 0.9743(0.0098).

Total Observed scale h2:0.0687(0.0039).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6101.

Intercept: 1.3014 (0.0144).

Total Observed scale h2:0.0384 (0.0058).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0291 (0.0069).

Total Observed scale gencov: 0.0017 (0.0018).

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Begin clumping ...
 </pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping iJHpP3, 1370 variants, using EUR population reference

Removing 1228 of 1370 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Smoking ~ Hair_Dark_Brown 
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1043420</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3299.

Intercept: 0.9745(0.0096).

Total Observed scale h2:0.0686(0.0038).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1953.

Intercept: 1.4652 (0.0191).

Total Observed scale h2:0.0887 (0.0119).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0155 (0.0074).

Total Observed scale gencov: -0.0018 (0.0021).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping SQpTpy, 1370 variants, using EUR population reference

Removing 1228 of 1370 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Smoking ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1043420</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3301.

Intercept: 0.9744(0.0096).

Total Observed scale h2:0.0687(0.0039).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0968.

Intercept: 1.6305 (0.0169).

Total Observed scale h2:0.0582 (0.0077).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0481 (0.0075).

Total Observed scale gencov: -0.0019 (0.002).

</pre>
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<pre>Begin clumping ...
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping J8YAfV, 1370 variants, using EUR population reference

Removing 1228 of 1370 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Smoking ~ Hair_Blonde 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1043420</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3302.

Intercept: 0.9742(0.0095).

Total Observed scale h2:0.0687(0.0038).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9027.

Intercept: 1.2014 (0.0167).

Total Observed scale h2:0.0892 (0.0108).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0119 (0.0068).

Total Observed scale gencov: 0.0011 (0.0019).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping m1kvxB, 1370 variants, using EUR population reference

Removing 1228 of 1370 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Smoking ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1043420</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3299.

Intercept: 0.9752(0.0097).

Total Observed scale h2:0.0685(0.0039).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6675.

Intercept: 1.81 (0.0229).

Total Observed scale h2:0.1079 (0.015).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0242 (0.0075).

Total Observed scale gencov: 0.0176 (0.0023).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping QB7wFw, 1370 variants, using EUR population reference

Removing 1228 of 1370 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Urate ~ Hair_Light_Brown 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1060305</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1202.

Intercept: 0.9046(0.0091).

Total Observed scale h2:0.0949(0.0093).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6016.

Intercept: 1.2999 (0.0142).

Total Observed scale h2:0.0377 (0.0058).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0046 (0.0067).

Total Observed scale gencov: 0.0035 (0.0026).

</pre>
</div>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping pzItb1, 1225 variants, using EUR population reference

Removing 1132 of 1225 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Urate ~ Hair_Dark_Brown 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1060305</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1198.

Intercept: 0.9054(0.0091).

Total Observed scale h2:0.0944(0.0093).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.171.

Intercept: 1.4619 (0.0189).

Total Observed scale h2:0.0863 (0.0113).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0158 (0.0073).

Total Observed scale gencov: -0.0055 (0.0029).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping xxHAl7, 1225 variants, using EUR population reference

Removing 1132 of 1225 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Urate ~ Hair_Black 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1060305</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1202.

Intercept: 0.9044(0.0091).

Total Observed scale h2:0.095(0.0093).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0846.

Intercept: 1.6252 (0.0178).

Total Observed scale h2:0.0576 (0.008).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0168 (0.0076).

Total Observed scale gencov: 0.0046 (0.0031).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping pdVXFe, 1225 variants, using EUR population reference

Removing 1132 of 1225 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Urate ~ Hair_Blonde 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1060305</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1201.

Intercept: 0.9042(0.0092).

Total Observed scale h2:0.0951(0.0093).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8851.

Intercept: 1.2015 (0.0185).

Total Observed scale h2:0.0874 (0.011).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.015 (0.007).

Total Observed scale gencov: -5e-04 (0.0028).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping MhSHZW, 1225 variants, using EUR population reference

Removing 1132 of 1225 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Urate ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1060305</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1193.

Intercept: 0.9063(0.0091).

Total Observed scale h2:0.0938(0.0095).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6424.

Intercept: 1.8079 (0.0222).

Total Observed scale h2:0.1054 (0.0144).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0342 (0.0085).

Total Observed scale gencov: -0.0065 (0.0036).

</pre>
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<pre>Begin clumping ...
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping eCKlGa, 1225 variants, using EUR population reference

Removing 1132 of 1225 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Anorexia ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">910374</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3179.

Intercept: 1.0232(0.012).

Total Observed scale h2:0.1824(0.0135).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6295.

Intercept: 1.3059 (0.0166).

Total Observed scale h2:0.037 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.017 (0.0084).

Total Observed scale gencov: -0.0033 (0.0037).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping bIKhdQ, 1086 variants, using EUR population reference

Removing 972 of 1086 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Anorexia ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">910374</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.318.

Intercept: 1.0223(0.0118).

Total Observed scale h2:0.1832(0.0132).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.2488.

Intercept: 1.4666 (0.021).

Total Observed scale h2:0.0869 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0224 (0.009).

Total Observed scale gencov: 0.0072 (0.004).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping E6idCs, 1086 variants, using EUR population reference

Removing 972 of 1086 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Anorexia ~ Hair_Black 
</pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">910374</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.318.

Intercept: 1.0232(0.012).

Total Observed scale h2:0.1825(0.0134).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1266.

Intercept: 1.6361 (0.0201).

Total Observed scale h2:0.0561 (0.0079).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0241 (0.0103).

Total Observed scale gencov: -0.0012 (0.0041).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping LdzjuR, 1086 variants, using EUR population reference

Removing 972 of 1086 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Anorexia ~ Hair_Blonde 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">910374</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.3179.

Intercept: 1.0225(0.0117).

Total Observed scale h2:0.183(0.0131).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9522.

Intercept: 1.2026 (0.0195).

Total Observed scale h2:0.0873 (0.0109).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0124 (0.0089).

Total Observed scale gencov: -0.0031 (0.0038).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping PpJvNK, 1086 variants, using EUR population reference

Removing 972 of 1086 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>Anorexia ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">910374</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.318.

Intercept: 1.0234(0.0119).

Total Observed scale h2:0.1824(0.0133).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.7093.

Intercept: 1.8135 (0.025).

Total Observed scale h2:0.1038 (0.0144).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0239 (0.0104).

Total Observed scale gencov: 0.0071 (0.0051).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping xZRm2b, 1086 variants, using EUR population reference

Removing 972 of 1086 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>CD ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004565</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4329.

Intercept: 1.122(0.0124).

Total Observed scale h2:0.3602(0.0344).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6095.

Intercept: 1.2988 (0.0143).

Total Observed scale h2:0.0387 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0175 (0.0073).

Total Observed scale gencov: -0.0124 (0.0049).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping k9Y5cl, 4426 variants, using EUR population reference

Removing 4131 of 4426 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>CD ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004565</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4329.

Intercept: 1.1228(0.0121).

Total Observed scale h2:0.3592(0.0336).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1926.

Intercept: 1.4603 (0.0193).

Total Observed scale h2:0.089 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0174 (0.0085).

Total Observed scale gencov: 0.0161 (0.0059).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping ilt00f, 4426 variants, using EUR population reference

Removing 4131 of 4426 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>CD ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004565</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4329.

Intercept: 1.1216(0.0125).

Total Observed scale h2:0.3608(0.0343).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0976.

Intercept: 1.6328 (0.0188).

Total Observed scale h2:0.0578 (0.0077).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0254 (0.0083).

Total Observed scale gencov: 0.0085 (0.0059).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping Nj4Gpx, 4426 variants, using EUR population reference

Removing 4131 of 4426 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>CD ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004565</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.4329.

Intercept: 1.1226(0.0126).

Total Observed scale h2:0.3595(0.0342).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9001.

Intercept: 1.2006 (0.0184).

Total Observed scale h2:0.0889 (0.0107).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0108 (0.0079).

Total Observed scale gencov: -0.0088 (0.0058).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 9v34zC, 4426 variants, using EUR population reference

Removing 4131 of 4426 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>CD ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1004565</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.433.

Intercept: 1.1222(0.0122).

Total Observed scale h2:0.36(0.0336).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6598.

Intercept: 1.8091 (0.0223).

Total Observed scale h2:0.1071 (0.0148).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0297 (0.0088).

Total Observed scale gencov: 0.0019 (0.0065).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping TIhQQj, 4426 variants, using EUR population reference

Removing 4131 of 4426 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Height_GIANT ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">961042</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:3.0502.

Intercept: 1.353(0.0254).

Total Observed scale h2:0.3084(0.014).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.602.

Intercept: 1.3025 (0.015).

Total Observed scale h2:0.037 (0.0056).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.1611 (0.01).

Total Observed scale gencov: 0.0039 (0.003).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping eyQDzI, 26716 variants, using EUR population reference

Removing 25527 of 26716 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Height_GIANT ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">961042</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:3.0467.

Intercept: 1.3531(0.0254).

Total Observed scale h2:0.308(0.0137).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1707.

Intercept: 1.4664 (0.0198).

Total Observed scale h2:0.085 (0.011).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.1677 (0.0113).

Total Observed scale gencov: -0.0012 (0.0036).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping ywyCn1, 26622 variants, using EUR population reference

Removing 25433 of 26622 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Height_GIANT ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">961042</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:3.0501.

Intercept: 1.3533(0.0254).

Total Observed scale h2:0.3083(0.0139).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0851.

Intercept: 1.6233 (0.0171).

Total Observed scale h2:0.0573 (0.0076).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.2138 (0.0108).

Total Observed scale gencov: -0.017 (0.0036).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping 1hZtbN, 26712 variants, using EUR population reference

Removing 25523 of 26712 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Height_GIANT ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">961042</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:3.0489.

Intercept: 1.3532(0.0254).

Total Observed scale h2:0.3082(0.0139).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8864.

Intercept: 1.2098 (0.0185).

Total Observed scale h2:0.0856 (0.0109).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0859 (0.0096).

Total Observed scale gencov: 0.0071 (0.0033).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping e2ytSs, 26682 variants, using EUR population reference

Removing 25493 of 26682 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Height_GIANT ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">961042</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:3.0341.

Intercept: 1.3538(0.0262).

Total Observed scale h2:0.3072(0.0142).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.645.

Intercept: 1.8064 (0.0226).

Total Observed scale h2:0.105 (0.0144).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.1721 (0.0116).

Total Observed scale gencov: -0.0071 (0.0042).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping KGLtMz, 26501 variants, using EUR population reference

Removing 25313 of 26501 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Insomnia ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065149</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6124.

Intercept: 1.0349(0.0111).

Total Observed scale h2:0.0604(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6091.

Intercept: 1.2953 (0.0141).

Total Observed scale h2:0.0391 (0.006).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0015 (0.0074).

Total Observed scale gencov: -7e-04 (0.0016).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping yjj9h2, 3997 variants, using EUR population reference

Removing 3649 of 3997 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Insomnia ~ Hair_Dark_Brown 
</pre>
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    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065149</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6122.

Intercept: 1.0355(0.0112).

Total Observed scale h2:0.0604(0.0025).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.193.

Intercept: 1.4525 (0.0195).

Total Observed scale h2:0.09 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0038 (0.0085).

Total Observed scale gencov: -0.0022 (0.002).

</pre>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping zVlFep, 3997 variants, using EUR population reference

Removing 3649 of 3997 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Insomnia ~ Hair_Black 
</pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065149</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6124.

Intercept: 1.035(0.0112).

Total Observed scale h2:0.0604(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0971.

Intercept: 1.6247 (0.0177).

Total Observed scale h2:0.0587 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0104 (0.0079).

Total Observed scale gencov: 7e-04 (0.0018).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping z4Gvfx, 3997 variants, using EUR population reference

Removing 3649 of 3997 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Insomnia ~ Hair_Blonde 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065149</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6125.

Intercept: 1.0349(0.0113).

Total Observed scale h2:0.0604(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.9006.

Intercept: 1.1985 (0.0177).

Total Observed scale h2:0.089 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0091 (0.0074).

Total Observed scale gencov: 0.0019 (0.0017).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping Hq5n9s, 3997 variants, using EUR population reference

Removing 3649 of 3997 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Insomnia ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1065149</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6117.

Intercept: 1.0351(0.011).

Total Observed scale h2:0.0604(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6599.

Intercept: 1.7985 (0.0225).

Total Observed scale h2:0.1083 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0038 (0.0094).

Total Observed scale gencov: 0.004 (0.002).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping acWYuC, 3997 variants, using EUR population reference

Removing 3649 of 3997 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Neuroticism ~ Hair_Light_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.7542.

Intercept: 1.0464(0.0128).

Total Observed scale h2:0.1086(0.0045).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.609.

Intercept: 1.2988 (0.014).

Total Observed scale h2:0.0387 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0283 (0.0079).

Total Observed scale gencov: -0.0013 (0.002).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping waQLKH, 6226 variants, using EUR population reference

Removing 5764 of 6226 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>Neuroticism ~ Hair_Dark_Brown 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.7534.

Intercept: 1.0462(0.0124).

Total Observed scale h2:0.1085(0.0046).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1917.

Intercept: 1.4592 (0.0192).

Total Observed scale h2:0.0892 (0.0118).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0382 (0.0081).

Total Observed scale gencov: -0.0015 (0.0022).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping niUKFl, 6190 variants, using EUR population reference

Removing 5728 of 6190 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Neuroticism ~ Hair_Black 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.7542.

Intercept: 1.0464(0.013).

Total Observed scale h2:0.1085(0.0045).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0975.

Intercept: 1.631 (0.0177).

Total Observed scale h2:0.0581 (0.0078).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0489 (0.0093).

Total Observed scale gencov: 0.0024 (0.0024).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 3UayXr, 6226 variants, using EUR population reference

Removing 5764 of 6226 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
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<pre>Neuroticism ~ Hair_Blonde 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.7543.

Intercept: 1.0462(0.0128).

Total Observed scale h2:0.1086(0.0044).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8984.

Intercept: 1.1995 (0.0174).

Total Observed scale h2:0.0889 (0.0106).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0247 (0.0073).

Total Observed scale gencov: 0.0013 (0.002).

</pre>
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<pre>Begin clumping ...
 </pre>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping mQ3QpO, 6226 variants, using EUR population reference

Removing 5764 of 6226 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Neuroticism ~ Tanning 
</pre>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037824</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.7524.

Intercept: 1.0466(0.0127).

Total Observed scale h2:0.1084(0.0045).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6612.

Intercept: 1.8085 (0.0224).

Total Observed scale h2:0.1074 (0.0149).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0044 (0.01).

Total Observed scale gencov: 0.004 (0.0028).

</pre>
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<pre>Begin clumping ...
 </pre>
</div>
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<div class="output_subarea output_stream output_stderr output_text">
<pre>Clumping 98rE6W, 6179 variants, using EUR population reference

Removing 5718 of 6179 variants due to LD with other variants or absence from LD reference panel

</pre>
</div>
</div>

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<pre>SWB ~ Hair_Light_Brown 
</pre>
</div>
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">975109</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037742</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1593.

Intercept: 0.9958(0.008).

Total Observed scale h2:0.026(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.6046.

Intercept: 1.2991 (0.0147).

Total Observed scale h2:0.0381 (0.0059).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0027 (0.0071).

Total Observed scale gencov: 0 (0.0015).

</pre>
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<pre>Begin clumping ...
 </pre>
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<pre>Clumping 1NoeH2, 287 variants, using EUR population reference

Removing 222 of 287 variants due to LD with other variants or absence from LD reference panel

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<pre>SWB ~ Hair_Dark_Brown 
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">975109</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037381</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1591.

Intercept: 0.9963(0.0081).

Total Observed scale h2:0.0259(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.1765.

Intercept: 1.4643 (0.0199).

Total Observed scale h2:0.0865 (0.0112).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: -0.0185 (0.008).

Total Observed scale gencov: 4e-04 (0.0017).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping wb5ug8, 287 variants, using EUR population reference

Removing 222 of 287 variants due to LD with other variants or absence from LD reference panel

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<pre>SWB ~ Hair_Black 
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">975109</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037702</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1593.

Intercept: 0.9962(0.0078).

Total Observed scale h2:0.0259(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.0874.

Intercept: 1.6279 (0.0176).

Total Observed scale h2:0.0574 (0.008).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0 (0.0078).

Total Observed scale gencov: 0.0018 (0.0016).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping Sck1wS, 287 variants, using EUR population reference

Removing 222 of 287 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>SWB ~ Hair_Blonde 
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">975109</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037533</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1594.

Intercept: 0.9961(0.0078).

Total Observed scale h2:0.026(0.0024).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.8881.

Intercept: 1.204 (0.0181).

Total Observed scale h2:0.0871 (0.0108).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0103 (0.0063).

Total Observed scale gencov: -0.0013 (0.0015).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping qX8oJw, 287 variants, using EUR population reference

Removing 222 of 287 variants due to LD with other variants or absence from LD reference panel

</pre>
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<pre>SWB ~ Tanning 
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<pre><span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">975109</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

<span class="ansi-bold">Rows: </span><span class="ansi-blue-fg">1037026</span> <span class="ansi-bold">Columns: </span><span class="ansi-blue-fg">7</span>

<span class="ansi-cyan-fg">──</span> <span class="ansi-bold">Column specification</span> <span class="ansi-cyan-fg">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>
<span class="ansi-bold">Delimiter:</span> &#34;\t&#34;
<span class="ansi-red-fg">chr</span> (3): SNP, A1, A2
<span class="ansi-green-fg">dbl</span> (4): Z, N, chi2, P


<span class="ansi-cyan-fg">ℹ</span> Use <span class="ansi-black-fg ansi-white-bg">`spec()`</span> to retrieve the full column specification for this data.
<span class="ansi-cyan-fg">ℹ</span> Specify the column types or set <span class="ansi-black-fg ansi-white-bg">`show_col_types = FALSE`</span> to quiet this message.

Merge dat1 and dat2 by SNP ...

Harmonize the direction of SNP effects of exposure and outcome

Read in LD scores ... 

Add LD scores to the harmonized data set...

The Harmonized data set will also be used for MR analysis 


Begin estimation of C and Omega using LDSC ...

Estimate heritability for trait 1 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:1.1584.

Intercept: 0.9964(0.0079).

Total Observed scale h2:0.0258(0.0023).


Estimate heritability for trait 2 ...

Using two-step estimator with cutoff at 30.

Mean Chi2:2.6479.

Intercept: 1.8084 (0.0219).

Total Observed scale h2:0.1058 (0.0142).


Estimate genetic covariance ...

Using two-step estimator with cutoff at 30.

Intercept: 0.0085 (0.0086).

Total Observed scale gencov: -1e-04 (0.002).

</pre>
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<pre>Begin clumping ...
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<pre>Clumping 3P4nZw, 285 variants, using EUR population reference

Removing 221 of 285 variants due to LD with other variants or absence from LD reference panel

</pre>
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