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main_run_MR_methods.R
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main_run_MR_methods.R
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source("./Rfuncs/Run_Methods_func.R", echo=TRUE)
source("./Rfuncs/ReadSummaryStat_thisstudy.R", echo=TRUE)
source("./Rfuncs/EstRho_thisstudy.R", echo=TRUE)
source("./Rfuncs/MRConMix.R", echo=TRUE)
source("./Rfuncs/mr_lasso.R", echo=TRUE)
setwd("MR-benchmarking-data/Dataset1") # Change the directory for different datasets
##### Run MR-APSS #####
library(MRAPSS)
IV.Threshold = c(5e-05,5e-06,5e-07,5e-08)
# The exposure and outcome pairs to be analyzed
pairs=read.table("TestedPairs", header=T)
for( i in 1:nrow(pairs)){
exposure = as.character(pairs[i, "exposure"])
outcome = as.character(pairs[i, "outcome"])
cat("Pair: ", exposure,"~", outcome,"\n")
# read in GWAS summary data for IVs
clumped = try(read.table(paste0("./MRdata/",exposure,"~",outcome), header = T))
# read in background parameters Omega and C
C = try(as.matrix(read.table(paste0("./bg_paras/", exposure, "~", outcome, "_C"),
header = F)))
Omega = try(as.matrix(read.table(paste0("./bg_paras/", exposure, "~", outcome, "_Omega"),
header = F)))
if(inherits(clumped , 'try-error')) next
if(inherits(C, 'try-error')) next
if(inherits(Omega, 'try-error')) next
if(nrow(clumped) < 3 ) next
Omega = matrix(Omega[nrow(Omega), ], 2, 2)
C = matrix(C[nrow(C), ], 2,2)
# The p-value threshold for selection bias correction
Threshold = ifelse(IV.Threshold==5e-05, unique(clumped$Threshold), IV.Threshold)
# MR-APSS
cat("Run MR-APSS ... \n")
res = run_APSS_func(clumped = clumped,
exposure = exposure,
outcome = outcome,
C = C,
Omega=Omega,
IV.Threshold = IV.Threshold,
Threshold = Threshold,
Cor.SelectionBias = T)
# saving resuts
write.table(res, "MRAPSS.MRres", quote=F, col.names = F, append = T,row.names = F)
}
##### run 11 methods #####
set.seed(1234)
methods = c("IVW_fe", "IVW", "RAPS","Egger", "MRMix","Weighted-median", "Weighted-mode", "MR-Robust","MR-Lasso", "cML-MA", "MR-PRESSO")
# The exposure and outcome pairs to be analyzed
pairs=read.table("TestedPairs", header=T)
for(Thresh in c(5e-08,5e-07,5e-06,5e-05)){
for( i in 1:nrow(pairs)){
exposure = as.character(pairs[i, "exposure"])
outcome = as.character(pairs[i, "outcome"])
# read in GWAS summary data for IVs
clumped = try(read.table(paste0("./MRdat/1kgRef_", exposure,"~",outcome), header = T))
ldmat = NULL
if(inherits(clumped,"try-error")) next
for(i in 1:length(methods)){
res = runMRmethods(dat=clumped, ldmat=ldmat, Threshold=Thresh,
exposure=exposure, outcome=outcome, methods.list = methods[i])
write.table(res, "MRmethods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
}
##### Run MR-ConMix #####
set.seed(1234)
# The exposure and outcome pairs to be analyzed
pairs=read.table("TestedPairs", header=T)
for(Thresh in c(5e-08,5e-07,5e-06,5e-05)){
for( i in 1:nrow(pairs)){
exposure = as.character(pairs[i, "exposure"])
outcome = as.character(pairs[i, "outcome"])
# read in GWAS summary data for IVs
clumped = try(read.table(paste0("./MRdat/1kgRef_", exposure,"~",outcome), header = T))
ldmat = NULL
if(inherits(clumped,"try-error")) next
res = runMRmethods(dat=clumped, ldmat=ldmat, Threshold=Thresh,
exposure=exposure, outcome=outcome, ConMixonly = T)
write.table(res, "ConMix.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
##### Run CAUSE #####
library(readr)
library(cause)
start = proc.time()
# The exposure and outcome pairs to be analyzed
pairs=read.table("TestedPairs", header=T)
for(Thresh in c(5e-08,5e-07,5e-06,5e-05)){
for( i in 1:nrow(pairs)){
exposure = as.character(pairs[i, "exposure"])
outcome = as.character(pairs[i, "outcome"])
if(exposure==outcome) next
# read GWAS summary statistics
cat(exposure,"~",outcome,"\n")
fileexp = paste0("./Formatted/exposure.set", as.character(exposure))
fileout = paste0("./Formatted/outcome.set", as.character(outcome))
X1 = suppressMessages(readr::read_delim(fileexp, delim="\t",
escape_double = FALSE, trim_ws = TRUE, progress = F))
X2 = suppressMessages(readr::read_delim(fileout, delim="\t",
escape_double = FALSE, trim_ws = TRUE, progress = F))
X1$b = X1$Z/sqrt(X1$N)
X2$b = X2$Z/sqrt(X2$N)
X1$se = 1/sqrt(X1$N)
X2$se = 1/sqrt(X2$N)
X <- try(gwas_merge(X1, X2,
snp_name_cols = c("SNP", "SNP"),
beta_hat_cols = c("b", "b"),
se_cols = c("se", "se"),
A1_cols = c("A1", "A1"),
A2_cols = c("A2", "A2")))
if(inherits(X , 'try-error')) next
d0 = X1[, c("SNP", "P")]
colnames(d0) = c("snp", "pval.exp")
X0 = merge(X, d0, by="snp")
SNPs = try(read.table(paste0("./MRdat/1kgRef_", exposure,"~",outcome), header = T))$SNP
clumped0 = subset(X0, snp %in% SNPs)
varlist <- with(X, sample(snp, size=min(nrow(X), 1000000), replace=FALSE))
params <- try(est_cause_params(X, varlist))
if(inherits(params , 'try-error')) next
if(inherits(clumped0 , 'try-error')) next
for(Thresh in c(5e-05, 5e-06,5e-07,5e-08)){
clumped = subset(clumped0, pval.exp <= Thresh)
# cause
if(!is.null(clumped)){
top_ldl_pruned_vars =intersect(as.character(X$snp), as.character(clumped$snp))
cause_res <- try(cause(X=X, variants = top_ldl_pruned_vars , param_ests = params, force=TRUE))
if(inherits( cause_res , 'try-error')) next
res_elpd <- data.frame(exposure,
outcome,
Thresh,
length(top_ldl_pruned_vars),
cause_res$elpd)
res.cause.est = summary(cause_res, ci_size=0.95)
res_est = data.frame(exposure, outcome,
Thresh,length(top_ldl_pruned_vars),
matrix(c(res.cause.est$quants[[2]][,1],
res.cause.est$quants[[2]][,2],
res.cause.est$quants[[2]][,3]), nrow=1))
colnames(res_elpd) = c("exposure","outcome","Threshold","nsnp","model1","model2","delta_elpd", "se_delta_elpd", "Z")
colnames(res_est) = c("exposure","outcome","Threshold","nsnp", "beta.hat","b_l","b_u","eta","eta_l","eta_u","q","q_l","q_u")
res_elpd = unique(subset(res_elpd, model1=="sharing"&model2=="causal"))
res_elpd$pval = pnorm(res_elpd$Z)
res_est = unique(res_est[, c("exposure","outcome","Threshold","nsnp", "beta.hat","b_l","b_u")])
res_est$se = (res_est$b_u - res_est$b_l)/2/1.96
cause_res = unique(merge(unique(res_elpd[, c("exposure","outcome","pval")]),
res_est[, c("exposure","outcome","Threshold","nsnp", "beta.hat","se")],
by=c("exposure","outcome")))
cause_res$Method = "CAUSE"
write.table(cause_res, file="CAUSE.MRres", append=T, col.names = F, row.names = F, quote = F)
rm(top_ldl_pruned_vars)
rm(res_est)
rm(res_elpd)
rm(res.cause.est)
rm(cause_res)
}
}
}
print(proc.time()-start)
##### run MR-CUE #####
library(MRAPSS)
load("snps.hm3.RData")
set.seed(1234)
library(MR.CUE)
library(ggplot2)
filepan <- vector("list", 22)
NumChr = 22
for(i in 1:NumChr){
filepan[[i]] <- paste0("MR-benchmarking-data/dataset_mrcue/UK10KCHR", i, "LDhm3.RDS")
}
snpinfo = "MR-benchmarking-data/dataset_mrcue/UK10Ksnpinforhm3.RDS"
ld_r2_thresh = 0.001
lambda = 0.85
pth = 1.96
# The exposure and outcome pairs to be analyzed
pairs=read.table("TestedPairs", header=T)
for( i in 1:nrow(pairs)){
exposure = as.character(pairs[i, "exposure"])
outcome = as.character(pairs[i, "outcome"])
cat(exposure, "~", outcome, " ")
fileexp = paste0("./Formatted/", as.character(exposure))
fileout = paste0("./Formatted/", as.character(outcome))
datexp = read.table(fileexp, header=T)
datout = read.table(fileout, header = T)
paras = est_paras(dat1 = datexp[, !names(datexp) %in% c("chr", "BP")],
dat2 = datout[, !names(datout) %in% c("chr", "BP")],
trait1.name = exposure,
trait2.name = outcome,
h2.fix.intercept = F,
LDSC = F,
ldscore.dir = "dat/eur_w_ld_chr")
dat = merge(paras$dat, snps.hm3[, c("SNP", "chr", "BP")], by="SNP")
datexp = dat[, c("SNP", "chr", "BP", "A1", "A2", "b.exp", "se.exp", "pval.exp")]
datout = dat[, c("SNP", "chr", "BP", "A1", "A2", "b.out", "se.out", "pval.out")]
colnames(datexp) = colnames(datout) = c("SNP", "chr", "BP", "A1", "A2", "beta", "se", "pvalue")
write.table(datexp[, c("SNP", "chr", "BP", "A1", "A2", "beta", "se", "pvalue")], file = paste0(exposure, ".cuedat"), quote = F, col.names = T, row.names = F, sep = "\t")
write.table(datout[, c("SNP", "chr", "BP", "A1", "A2", "beta", "se", "pvalue")], file = paste0(outcome, ".cuedat"), quote = F, col.names = T, row.names = F, sep = "\t")
fileexp = paste0(exposure, ".cuedat")
fileout = paste0(outcome, ".cuedat")
rho=0
RhoEst = try(EstRho_thisstudy(fileexp, fileout, filepan, snpinfo, ld_r2_thresh, lambda, pth))
if(!inherits(RhoEst,"try-error")) {
rho = mean(RhoEst$Rhores)
}
for(pva_cutoff in c(5e-05, 5e-06,5e-07, 5e-08)){
cat(pva_cutoff, "\n")
data <- try(ReadSummaryStat_thisstudy(fileexp, fileout, filepan, snpinfo, pva_cutoff, lambda))
if(inherits(data,"try-error")) next
F4gammah <- data$ResF4gammah;
F4Gammah <- data$ResF4Gammah;
F4se1 <- data$ResF4se1;
F4se2 <- data$ResF4se2;
F4Rblock <- data$ResF4Rblock;
F4SNPs <- data$ResF4SNPchr
opt = list(agm = 0, bgm = 0, atau1 = 0, btau1 = 0,
atau2 = 0, btau2 = 0,
a = 2, b = length(F4gammah), maxIter = 5000, thin = 10, burnin = 5000);
RealRes = try(MRCUE(F4gammah, F4Gammah, F4se1, F4se2, F4Rblock, rho=rho, coreNum = 30, opt=opt))
if(!inherits(RealRes,"try-error")) {
cat("MR-CUE: beta.hat=", RealRes$beta.hat, " se=", RealRes$beta.se, "pval=", RealRes$beta.p.value, "\n")
write.table(data.frame(exposure = exposure, outcome = outcome, method = "MR-CUE", Threshold = pva_cutoff,
nsnp = length(unlist(F4gammah)), beta = RealRes$beta.hat, se = RealRes$beta.se, pval = RealRes$beta.p.value),
file = "MRCUE.MRres",
quote=F, col.names = F, append = T,row.names = F)
}
}
unlink(paste0(exposure, "~", outcome, "_exp", ".cuedat"))
unlink(paste0(exposure, "~", outcome, "_out", ".cuedat"))
}
### Run dIVW(lambda=0)
### run dIVW
library(mr.divw)
set.seed(1234)
pairs=read.table("TestedPairs", header=T)
for(Thresh in c(5e-08,5e-07,5e-06,5e-05)){
for( i in 1:nrow(pairs)){
exposure = as.character(pairs[i, "exposure"])
outcome = as.character(pairs[i, "outcome"])
# read in GWAS summary data for IVs
clumped = try(read.table(paste0("./clumped/UKB600/MRdat/UKB226/1kgRef_", exposure,"~",outcome), header = T))
if(inherits(clumped,"try-error")) next
for(Thresh in c(5e-08,5e-07,5e-06,5e-05)){
data = subset(clumped, pval.exp<=Thresh)
res = mr.divw(beta.exposure=data$b.exp, beta.outcome=data$b.out,
se.exposure=data$se.exp, se.outcome=data$se.out,
diagnostics=F, over.dispersion=T)
divw.res = data.frame(exposure = exposure, outcome=outcome,
method = "dIVW", Threshold = Thresh,
nsnp = res$n.IV , beta = res$beta.hat, se = res$beta.se,
pval = pchisq(res$beta.hat^2/res$beta.se^2, 1, lower.tail = F))
write.table(divw.res, "/import/home/share/xhu/MR-Benchmarking-AJHG-revison-r1/add-dIVW/AtlasUKB_NCO_1kgRef_dIVW.MRres",
quote=F, col.names = F, append = T,row.names = F)
}
}
}