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Traits_8MRmethods.r
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Traits_8MRmethods.r
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# TwosampleMR
# install.packages("remotes")
# remotes::install_github("MRCIEU/TwoSampleMR")
# dIVW
# install.packages("devtools")
# devtools::install_github("tye27/mr.divw")
# MRcML
# devtools::install_github("xue-hr/MRcML")
# MRMix
# devtools::install_github("gqi/MRMix")
ts1 = c("AD", "ASD", "Daytime_Sleepiness", "Height_UKB", "Intelligence", "RA",
"T2D", "Alcohol", "BMI", "Depression", "IBD", "MDD", "SCZ", "Angina",
"CAD", "HBP", "Income", "NEB", "Smoking", "Urate", "Anorexia",
"CD", "Height_GIANT", "Insomnia", "Neuroticism", "SWB")
ts2 = ts1
Threshold = 5e-08 # note that the default IV threshold for the eight methods is 5e-08
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome," ")
dat= try(read.table(paste0("./MRdat/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
# IVW
res.IVW <- try(TwoSampleMR::mr_ivw_fe(dat$b.exp[indx], dat$b.out[indx], dat$se.exp[indx], dat$se.out[indx]))
#cat("IVW: beta.hat=", res.IVW$b, " se=", res.IVW$se, "pval=", res.IVW$pval, "\n")
if(inherits(res.IVW,"try-error")) next
write.table(data.frame(exposure = exposure, outcome = outcome, method = "IVW", Threshold = Threshold,
nsnp = nsnp, beta = res.IVW$b, se = res.IVW$se, pval = res.IVW$pval),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
print(proc.time()-start0)
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome," ")
dat= try(read.table(paste0("./MRdat/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
## dIVW
lambda.opt = mr.divw::mr.eo(0, dat$b.exp[indx], dat$b.out[indx],
dat$se.exp[indx], dat$se.out[indx],
pval.selection=dat$pval.exp[indx])$lambda.opt
res.dIVW = mr.divw::mr.divw(dat$b.exp[indx], dat$b.out[indx],
dat$se.exp[indx], dat$se.out[indx],
pval.selection=dat$pval.exp[indx], lambda=lambda.opt)
#cat("dIVW: beta.hat=", res.dIVW$beta.hat, " se=", res.dIVW$beta.se, "\n")
if(inherits(res.dIVW,"try-error")) next
write.table(data.frame(exposure = exposure, outcome = outcome, method = "dIVW", Threshold = Threshold,
nsnp = nsnp, beta = res.dIVW$beta.hat, se = res.dIVW$beta.se,
pval = pchisq(res.dIVW$beta.hat^2/res.dIVW$beta.se^2, 1, lower.tail = F)),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
print(proc.time()-start0)
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome," ")
dat= try(read.table(paste0("./MRdat/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
## RAPS
raps.df = data.frame(beta.exposure = dat$b.exp[indx], beta.outcome = dat$b.out[indx],
se.exposure = dat$se.exp[indx], se.outcome = dat$se.out[indx])
res.raps <- try(suppressWarnings(mr.raps::mr.raps(raps.df, diagnostics=F)))
#cat("RAPS: beta.hat=", res.raps$beta.hat, " se=", res.raps$beta.se, "\n")
if(inherits(res.raps,"try-error")) next
write.table(data.frame(exposure = exposure, outcome = outcome, method = "RAPS", Threshold = Threshold,
nsnp = nsnp, beta = res.raps$beta.hat, se = res.raps$beta.se,
pval = pchisq(res.raps$beta.hat^2/res.raps$beta.se^2, 1, lower.tail = F)),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
print(proc.time()-start0)
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome," ")
dat= try(read.table(paste0("./MRdat/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
## Egger
res.egger <- try(TwoSampleMR::mr_egger_regression(dat$b.exp[indx], dat$b.out[indx],
dat$se.exp[indx], dat$se.out[indx]))
#cat("Egger: beta.hat=", res.egger$b, " se=", res.egger$se, "pval=", res.egger$pval, "\n")
if(inherits(res.egger,"error")) next
write.table(data.frame(exposure = exposure, outcome = outcome, method = "Egger", Threshold = Threshold,
nsnp = nsnp, beta = res.egger$b, se = res.egger$se, pval = res.egger$pval),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
print(proc.time()-start0)
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome, " ")
dat= try(read.table(paste0("./MRdat/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
## MRmix
res.MRMix = try(MRMix::MRMix(dat[indx,]$b.exp, dat[indx,]$b.out, dat[indx,]$se.exp, dat[indx,]$se.out))
#cat("MRMix: beta.hat=", res.MRMix$theta, " se=", res.MRMix$SE_theta, "pval=", res.MRMix$pvalue_theta, "\n")
if(inherits(res.MRMix,"try-error")) next
write.table(data.frame(exposure = exposure, outcome = outcome, method = "MRMix", Threshold = Threshold,
nsnp = nsnp, beta = res.MRMix$theta, se = res.MRMix$SE_theta, pval = res.MRMix$pvalue_theta),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
print(proc.time()-start0)
# Weighted-mode uses bootstrap to estimate the standard error.
# The standard errors and p values could be slightly different from the MR-APSS paper since we have not set the random seed.
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome, " ")
dat= try(read.table(paste0("./MRdat_order/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
## MRmix
res.median <- try(TwoSampleMR::mr_weighted_median(dat$b.exp[indx], dat$b.out[indx], dat$se.exp[indx], dat$se.out[indx]))
#cat("Weighted-median: beta.hat=", res.median$b, " se=", res.median$se, "pval=", res.median$pval, "\n")
if(inherits(res.median,"try-error")) next
write.table(data.frame(exposure = exposure, outcome = outcome, method = "Weighted-median", Threshold = Threshold,
nsnp = nsnp, beta = res.median$b, se = res.median$se, pval = res.median$pval),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
print(proc.time()-start0)
# Weighted-mode uses bootstrap to estimate the standard error.
# The standard errors and p values could be slightly different from the MR-APSS paper since we have not set the random seed.
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome, " ")
dat= try(read.table(paste0("./MRdat_order/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
res.mode <- try(TwoSampleMR::mr_weighted_mode(dat$b.exp[indx], dat$b.out[indx], dat$se.exp[indx], dat$se.out[indx]))
#cat("Weighted-mode: beta.hat=", res.mode$b, " se=", res.mode$se, "pval=", res.mode$pval, "\n")
if(inherits(res.mode,"try-error")) next
write.table(data.frame(exposure = exposure, outcome = outcome, method = "Weighted-mode", Threshold = Threshold,
nsnp = nsnp, beta = res.mode$b, se = res.mode$se, pval = res.mode$pval),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
}
}
print(proc.time()-start0)
start0 = proc.time()
for( exposure in ts1 ){
for( outcome in ts2 ){
if(exposure==outcome) next
# reading in data
#cat("*********************************\n")
#cat("Pair: ", exposure,"~", outcome, " ")
dat= try(read.table(paste0("./MRdat/", exposure,"~", outcome), header = T))
if(inherits(dat , 'try-error')) next
indx = which(dat$pval.exp <= Threshold)
nsnp = length(indx)
if(nsnp < 4) next
## cML-MA
final_dat = dat[indx,]
n= min(median(1/final_dat$se.exp^2), median(1/final_dat$se.out^2))
cML_result = try(suppressWarnings(MRcML::mr_cML_DP(final_dat$b.exp,
final_dat$b.out,
final_dat$se.exp,
final_dat$se.out,
n = n,
random_start = 10,
random_start_pert = 10,
random_seed = 1,
num_pert = 200)))
#cat("cML-MA: beta.hat=", cML_result$MA_BIC_DP_theta, " se=", cML_result$MA_BIC_DP_se, "pval=", cML_result$MA_BIC_DP_p, "\n")
write.table(data.frame(exposure = exposure, outcome = outcome, method = "cML-MA", Threshold = Threshold,
nsnp = nsnp, beta = cML_result$MA_BIC_DP_theta, se = cML_result$MA_BIC_DP_se, pval = cML_result$MA_BIC_DP_p),
"Traits_8methods.MRres", quote=F, col.names = F, append = T,row.names = F)
if(inherits(cML_result,"try-error")) next
}
}
print(proc.time()-start0)
# It takes about two weeks for cML-MA to test the trait pairs between 26 traits using the above code.
# Noted we set num_pert = 200 for cML-MA.
MRmethods_res = read.table("Traits_8methods.MRres", header = F)
colnames(MRmethods_res) = c("exposure","outcome","Method", "Threshold", "nsnp", "beta.hat","se", "pval")
write.table(MRmethods_res, "Traits_8methods.MRres", quote=F, col.names = T, append = F,row.names = F)
head(MRmethods_res)