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coloc.R
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library("readr")
library("tidyverse")
library(coloc)
library(tidyverse)
library(dplyr)
library(stringr)
library(GWAS.utils)
rm(list=ls())
gc()
clump_r2 = 0.01
maf_threshold = 0.01
presso_needed=FALSE
server<-'home'
har_list<-list.files('.../2_20/result_save/umr_clump_0.01/ukb-d-30040_irnt',pattern='final_har.RData$',full.names=TRUE,recursive=TRUE)
data_list<-list.files('.../2_20/data/figgen',pattern='*.RData$',full.names=TRUE,recursive=TRUE)
whole_folder<-paste0('.../2_20/result/coloc_clump_',clump_r2)
data_table<-'.../2_20/data_table.csv'
source('.../2_20/code/umr_tool.R')
bfile<-".../plink_reference/EUR"
print(rep(1,20))
#mr_package_input_snp_corr<-ld_matrix(raddat$SNP, with_alleles = TRUE, pop = "EUR")
#unlink(whole_folder,recursive = TRUE)
dir.create(whole_folder, showWarnings = TRUE, recursive = TRUE)
ptm <- proc.time()
#source(mvmr_tool)
id_exposure <- c("ukb-d-30040_irnt")
id_outcome<-c( "finn-b-ST19_TRAUMAT_SUBAR_HAEMORRHAGE",
"finn-b-I9_SAH", "finn-b-I9_INTRACRA",
"finn-b-I9_ICH")
MCV_vcf<-data_list[str_detect(data_list,id_exposure)]
final<-d_fun(data_list,data_table,id=c(id_outcome))
har_list<-data.frame(har_list)
har_list$id<-unlist(lapply(strsplit(har_list$har_list,split='/'),path_fun1))
final<-inner_join(final, har_list,
c("id" = "id"))%>%rename(out_vcf=data_list)
final$exp_vcf<-MCV_vcf
rm(har_list)
gc()
#i<-1
##################################
RCDW_vcf<-data_list[str_detect(data_list,'ebi-a-GCST006804')]
RCDW_final<-final[final$id=="finn-b-ST19_TRAUMAT_SUBAR_HAEMORRHAGE",]
RCDW_final$exp_vcf<-RCDW_vcf
RCDW_final$har_list<-NA
final<-rbind(final,RCDW_final)
##########################
ptm <- proc.time()
exp_id<-"ukb-d-30040_irnt"
#exp_id<-'ebi-a-GCST006804'###############################################################################
if (exp_id=="ukb-d-30040_irnt"){
exp_N<-350473
exp_trait<-'MCV'
loop<-1:length(final$out_vcf)
} else if (exp_id=='ebi-a-GCST006804'){
exp_N<-116666
exp_trait<-'RCDW'
loop<-length(final$out_vcf):length(final$out_vcf)
}
#for (i in loop){print(i)}
for (i in loop){#1:length(final$data_list)
print(paste('The ', i, ' trial！'))
# re_log_folder<-'radial_outliner.log'
# unlink(re_log_folder,recursive = TRUE)
# dir.create(f, showWarnings = TRUE, recursive = TRUE)
# con <- file(re_log_folder) #
# sink(con, append=TRUE) #
# sink(con, append=TRUE, type="message") #
load(final$out_vcf[i])
vcf_dat_out<-l%>% distinct(rsids, .keep_all = TRUE)%>%filter(.,maf<1,0<maf,pval>0)%>%drop_na(rsids)
vcf_dat_out<-vcf_dat_out[startsWith(vcf_dat_out$rsids, 'rs'),]#
vcf_dat_out<-vcf_dat_out[str_count(vcf_dat_out[,c('rsids')], c("_rs"))==0,]
rm(l)
load(final$exp_vcf[i])
vcf_dat_exp<-l%>% distinct(ID, .keep_all = TRUE)%>%transform(., AF=as.numeric(AF))%>%filter(.,0<AF,pval>0,AF<1)%>%transform(., MAF = pmin(AF, 1-AF),POS=as.numeric(POS))
vcf_dat_exp
rm(l)
gc()
out_id<-final$id[i]
folder<-paste(whole_folder,exp_id,out_id,sep='/')
# unlink(folder,recursive = TRUE)
dir.create(folder, showWarnings = TRUE, recursive = TRUE)
setwd(folder)
out_trait<-final$trait[i]
print(paste('Exp: ',exp_trait,'; ID ',exp_id))
print(paste('Outcome: ',out_trait,'; ID ',out_id))
##################
r2_thresh = 0.01
pval_thresh = 1e-3
X <- gwas_merge(vcf_dat_exp,vcf_dat_out, snp_name_cols = c("ID","rsids"),
beta_hat_cols = c("ES","beta"),
se_cols = c("SE","sebeta"),
A1_cols = c( "REF","ref"),
A2_cols = c( "ALT","alt"),
pval_cols = c("pval", "pval"))%>%inner_join(vcf_dat_out[,c('rsids','maf','chrom','pos')],
c("snp" = "rsids"))%>%inner_join(vcf_dat_exp[,c('ID','MAF')],
c("snp" = "ID"))%>%transform(.,varbe1=seb1*seb1,varbe2=seb2*seb2)%>%rename(MAF2=MAF,MAF1=maf)
save(X,file='merge_exp_out.RData')
rm(vcf_dat_out,vcf_dat_exp)
gc()
# clump_path<-paste(folder,paste0('X_clump_r_',r2_thresh,'_p_',pval_thresh,'.RData'),sep='/')
# # if(!file.exists(clump_path)){
# X_clump <- X %>%
# rename(rsid = snp,
# pval = p1) %>%
# ieugwasr::ld_clump(dat = .,
# clump_r2 = r2_thresh,
# clump_p = pval_thresh,
# #clump_kb = kb_thresh,
# plink_bin = genetics.binaRies::get_plink_binary(),
# bfile = bfile,
# pop = "EUR")
# save(X_clump,file=clump_path)
# # } else {
# # load(clump_path)
# # }
# # rm(X)
# gc()
#
# save(X_clump,file='merge_clump_exp_out.RData')
# if(server=='hpc'){
# presso_needed<-TRUE
# }
# out_exp_har<-filter_fun(X_clump,folder,out_id=out_id,exp_id=exp_id,out_trait=out_trait,exp_trait=exp_trait,bfile,presso_needed=presso_needed,pattern='merge_clump')
# save(out_exp_har,file='merge_clump_filter_exp_out.RData')
# rm(out_exp_har)
X<-X%>%drop_na(p1,p2)%>%arrange(p1,p2)
row.names(X)<-NULL
result_whole <- coloc.abf(dataset1=list(pvalues=X$p1, type="quant", N=exp_N,snp=X$snp), dataset2=list(pvalues=X$p2, type="cc", s=final$ncase[i]/final$sample_size[i],N=final$sample_size[i],snp=X$snp), MAF=X$MAF1)
save(result_whole,file=paste0('merge_exp_out_whole_MAFexp_abf',nrow(X),'.RData'))
X_1M<-X%>%filter(.,pos<(X$pos[1]+1000000))%>%filter(.,pos>=(X$pos[1]-1000000))
row.names(X_1M)<-NULL
save(X_1M,file='merge_exp_out_1M.RData')
X_3M<-X%>%filter(.,pos<(X$pos[1]+3000000))%>%filter(.,pos>=(X$pos[1]-3000000))
row.names(X_3M)<-NULL
save(X_3M,file='merge_exp_out_3M.RData')
X_200K<-X%>%filter(.,pos<(X$pos[1]+200000))%>%filter(.,pos>=(X$pos[1]-200000))
row.names(X_200K)<-NULL
save(X_200K,file='merge_exp_out_200K.RData')
rm(X)
gc()
abf_fun<-function(X,d=NA){
X<-X%>%drop_na(p1,p2)%>%arrange(p1,p2)
if (!is.na(d)){
print(paste0('Cut regions'))
X<-X%>%filter(.,pos<(X$pos[1]+d))%>%filter(.,pos>=(X$pos[1]-d))}
row.names(X)<-NULL
# save(X_cut,file=paste0('merge_exp_out_',d,'.RData'))
result <- coloc.abf(dataset1=list(pvalues=X$p1, type="quant", N=exp_N,snp=X$snp), dataset2=list(pvalues=X$p2, type="cc", s=final$ncase[i]/final$sample_size[i],N=final$sample_size[i],snp=X$snp), MAF=X$MAF1)
#save(result2,file=paste0('merge_exp_out_d',d,'_MAFexp_abf_snp',nrow(X_cut),'.RData'))
return(list('X'=X,'result'=result))
}
abf_fun(X,1000000)
result1 <- coloc.abf(dataset1=list(pvalues=X_1M$p1, type="quant", N=exp_N,snp=X_1M$snp), dataset2=list(pvalues=X_1M$p2, type="cc", s=final$ncase[i]/final$sample_size[i],N=final$sample_size[i],snp=X_1M$snp), MAF=X_1M$MAF1)
save(result1,file=paste0('merge_exp_out_1M_MAFexp_abf',nrow(X_1M),'.RData'))
print(paste0('1M abf:',result1))
rm(result1)
result2 <- coloc.abf(dataset1=list(pvalues=X_3M$p1, type="quant", N=exp_N,snp=X_3M$snp), dataset2=list(pvalues=X_3M$p2, type="cc", s=final$ncase[i]/final$sample_size[i],N=final$sample_size[i],snp=X_3M$snp), MAF=X_3M$MAF1)
save(result2,file=paste0('merge_exp_out_3M_MAFexp_abf',nrow(X_3M),'.RData'))
print(paste0('3M abf:',result2))
rm(result2)
result3 <- coloc.abf(dataset1=list(pvalues=X_200K$p1, type="quant", N=exp_N,snp=X_200K$snp), dataset2=list(pvalues=X_200K$p2, type="cc", s=final$ncase[i]/final$sample_size[i],N=final$sample_size[i],snp=X_200K$snp), MAF=X_200K$MAF1)
save(result2,file=paste0('merge_exp_out_3M_MAFexp_abf',nrow(X_3M),'.RData'))
print(paste0('3M abf:',result2))
rm(result2)
rm(X_1M,X_3M)
gc()
if(server=='hpc'){
try({ld<-ieugwasr::ld_matrix(
X_1M$snp,
# X_200K$snp,
plink_bin = genetics.binaRies::get_plink_binary(),
bfile = bfile
)
save(ld,file='merge_exp_out_1M_ld.RData')})
rm(ld)
}
gc()
}
dataset1<-list(pvaluefinals=X_1M$p1, type="quant", N=exp_N,snp=X_1M$snp)
dataset2<-list(pvalues=X_1M$p2, type="cc", s=final$ncase[i]/final$sample_size[i],N=final$sample_size[i],snp=X_1M$snp,MAF=X_1M$MAF1)
X<-list('dataset1'=dataset1,'dataset2'=dataset2)
library(gassocplot)
library(gwasglue)
temp<-coloc_to_gassocplot(X, bfile = bfile, plink_bin = genetics.binaRies::get_plink_binary())
temp <- coloc_to_gassocplot(result1)
#> Extracting LD matrix for 148 variants
#> Please look at vignettes for options on running this locally if you need to run many instances of this command.
#> Warning in ieugwasr::ld_matrix(markers[["marker"]], with_alleles = FALSE, : The following variants are not present in the LD reference panel
#> rs8110695
#> rs2738459
#> rs17001200
#> Found 145 variants in LD reference panel
gassocplot::stack_assoc_plot(temp$markers, temp$z, temp$corr, traits=temp$traits)
print(paste0('time spent:',' ',proc.time()-ptm))