傻瓜式孟德尔随机化_孟德尔随机化结局是本地文件

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#install.packages("devtools")
#devtools::install_github("mrcieu/gwasglue", force = TRUE)
 
#if (!require("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
#BiocManager::install("VariantAnnotation")
 
#install.packages("remotes")
#remotes::install_github("MRCIEU/TwoSampleMR")
 
#install.packages("dplyr")
#install.packages("tidyr")
#install.packages("CMplot")
 
road<-getwd()
setwd(road)      #设置工作目录
 
#引用包
library(VariantAnnotation)
library(gwasglue)
library(dplyr)
library(tidyr)
library(CMplot)
library(TwoSampleMR)
 
exposureName="BMI"                         #图形中展示暴露数据的名称
outcomeName="Coronary heart disease"       #图形中展示结局数据的名称
 
inputFile="bbj-a-57.vcf.gz"     #暴露数据输入文件(根据下载暴露数据的文件名称进行修改)
outcomeFile="ukb-a-561.vcf.gz"   #结局数据输入文件(改成已下载的结局数据文件)
 
 
#读取输入文件, 并对输入文件进行格式转换
vcfRT1 <- readVcf(inputFile)
exposuredata=gwasvcf_to_TwoSampleMR(vcf=vcfRT1, type="exposure")
#读取结局数据的vcf文件,并对数据进行格式转换
vcfRT2 <- readVcf(outcomeFile)
outcomeData=gwasvcf_to_TwoSampleMR(vcf=vcfRT2, type="outcome")
#根据pvalue<5e-08对结果进行过滤
outTab<-subset(exposuredata, pval.exposure<5e-08)
write.csv(outTab, file="exposure.pvalue.csv", row.names=F)
 
#准备绘制暴露变量的曼哈顿图的数据
exposuredata=exposuredata[,c("SNP", "chr.exposure", "pos.exposure", "pval.exposure")]
colnames(exposuredata)=c("SNP","CHR","BP","pvalue")
 
#绘制线性的曼哈顿图
CMplot(exposuredata,  plot.type="m",
       LOG10=TRUE, threshold=5e-08, threshold.lwd=3, threshold.lty=1, signal.cex=0.2,
       chr.den.col=NULL, cex=0.2, bin.size=1e5, ylim=c(0,50),
       file="pdf", file.output=TRUE, width=15, height=9, verbose=TRUE)
 
#绘制圈图
CMplot(exposuredata,  plot.type="c",
       LOG10=TRUE, threshold=5e-08, threshold.lwd=3, threshold.lty=1, signal.cex=0.2,
       chr.den.col=NULL, cex=0.2, bin.size=1e5, ylim=c(0,100),
       file="pdf", file.output=TRUE, width=7, height=7, verbose=TRUE)
 
 
 
exposureFile="exposure.pvalue.csv"     #输入文件
#setwd(road)      #设置孟德尔结果数据保存的工作目录
 
#读取输入文件
exposure_dat<-read_exposure_data(filename=exposureFile,
                                 sep = ",",
                                 snp_col = "SNP",
                                 beta_col = "beta.exposure",
                                 se_col = "se.exposure",
                                 effect_allele_col = "effect_allele.exposure",
                                 other_allele_col = "other_allele.exposure",
                                 eaf_col = "eaf.exposure",
                                 samplesize_col = "samplesize.exposure",
                                 clump = F)
 
#去除连锁不平衡的SNP，一般标准为kb=10000,r2=0.001
exposure_dat_clumped <- clump_data(exposure_dat, clump_kb=10000, clump_r2=0.001)
write.csv(exposure_dat_clumped, file="exposure.LD.csv", row.names=F)
 
inputFile="exposure.LD.csv"      #输入文件
#setwd(road)      #设置工作目录
 
#读取输入文件
dat<-read.csv(inputFile, header=T, sep=",", check.names=F)
 
#计算F检验值
N=dat[1,"samplesize.exposure"]     #获取样品的数目
dat=transform(dat,R2=2*((beta.exposure)^2)*eaf.exposure*(1-eaf.exposure))     #计算R2
dat=transform(dat,F=(N-2)*R2/(1-R2))      #计算F检验值
 
#根据F值>10进行过滤, 删除弱工具变量
Ffilter=10        #F值过滤条件，
outTab=dat[dat$F>Ffilter,]
write.csv(dat, "exposure.F.csv", row.names=F)
 
library(MendelianRandomization)     #引用包
inputFile="exposure.F.csv"          #输入文件
#setwd(road)     #可设置专属文件的独有工作目录
 
#读取已经去除连锁不平衡、低F值的暴露因素文件
dat=read.csv(inputFile, header=T, sep=",", check.names=F)
 
#对SNP分组
snpId=dat$SNP
y=seq_along(snpId)
chunks <- split(snpId, ceiling(y/100))
 
#对分组进行循环,每次得到一个分组
outTab=data.frame()
for(i in names(chunks)){
  #混杂因素分析
  confounder=phenoscanner(
    snpquery = chunks[[i]],
    catalogue = "GWAS",
    pvalue = 1e-05,
    proxies = "None",
    r2 = 0.8,
    build = 37)
  outTab=rbind(outTab, confounder$results)
}
#输出SNP相关性状的表格
write.csv(outTab, "confounder.result.csv", row.names=F)
 
#输出去除混杂因素的结果
delSnp=c("rs13078960", "rs2030323")     #混杂SNP的名称(需修改)
dat=dat[!dat$SNP %in% delSnp,]
write.csv(dat, "exposure.confounder.csv", row.names=F)
 
exposureFile="exposure.confounder.csv"     #输入经各种过滤的暴露数据文件
 
#读取暴露数据
exposure_dat<-read_exposure_data(filename=exposureFile,
                                 sep = ",",
                                 snp_col = "SNP",
                                 beta_col = "beta.exposure",
                                 se_col = "se.exposure",
                                 effect_allele_col = "effect_allele.exposure",
                                 other_allele_col = "other_allele.exposure",
                                 eaf_col = "eaf.exposure",
                                 clump = F)
 
 
 
#从结局数据中提取工具变量
outcomeTab<-merge(exposure_dat, outcomeData, by.x="SNP", by.y="SNP")
write.csv(outcomeTab[,-(2:ncol(exposure_dat))], file="outcome.csv")
 
#读取整理好的结局数据
outcome_dat<-read_outcome_data(snps=exposure_dat$SNP,
                               filename="outcome.csv", sep = ",",
                               snp_col = "SNP",
                               beta_col = "beta.outcome",
                               se_col = "se.outcome",
                               effect_allele_col = "effect_allele.outcome",
                               other_allele_col = "other_allele.outcome",
                               pval_col = "pval.outcome",
                               eaf_col = "eaf.outcome")
 
#将暴露数据和结局数据合并
exposure_dat$exposure=exposureName
outcome_dat$outcome=outcomeName
dat2<-harmonise_data(exposure_dat=exposure_dat,
                    outcome_dat=outcome_dat)
 
#输出用于孟德尔随机化的工具变量
outTab=dat2[dat2$mr_keep=="TRUE",]
write.csv(outTab, file="table.SNP.csv", row.names=F)
 
#MR-PRESSO异常值检测(偏倚的SNP)
presso=run_mr_presso(dat2)
write.csv(presso[[1]]$`MR-PRESSO results`$`Outlier Test`, file="table.MR-PRESSO.csv")
 
#孟德尔随机化分析
mrResult=mr(dat)
#选择孟德尔随机化的方法
#mr_method_list()$obj
#mrResult=mr(dat, method_list=c("mr_ivw", "mr_egger_regression", "mr_weighted_median", "mr_simple_mode", "mr_weighted_mode"))
#对结果进行OR值的计算
mrTab=generate_odds_ratios(mrResult)
write.csv(mrTab, file="table.MRresult.csv", row.names=F)
 
#异质性分析
heterTab=mr_heterogeneity(dat)
write.csv(heterTab, file="table.heterogeneity.csv", row.names=F)
 
#多效性检验
pleioTab=mr_pleiotropy_test(dat)
write.csv(pleioTab, file="table.pleiotropy.csv", row.names=F)
 
#绘制散点图
pdf(file="pic.scatter_plot.pdf", width=7.5, height=7)
mr_scatter_plot(mrResult, dat)
dev.off()
 
#森林图
res_single=mr_singlesnp(dat)      #得到每个工具变量对结局的影响
pdf(file="pic.forest.pdf", width=7, height=6.5)
mr_forest_plot(res_single)
dev.off()
 
#漏斗图
pdf(file="pic.funnel_plot.pdf", width=7, height=6.5)
mr_funnel_plot(singlesnp_results = res_single)
dev.off()
 
#留一法敏感性分析
pdf(file="pic.leaveoneout.pdf", width=7, height=6.5)
mr_leaveoneout_plot(leaveoneout_results = mr_leaveoneout(dat))
dev.off()
 
pFilter=1       #pvalue过滤条件
#setwd(road)     #设置工作目录
 
############定义森林图函数############
bioForest=function(inputFile=null, forestFile=null, forestCol=null){
  #读取输入文件
  rt=read.csv(inputFile, header=T, sep=",", check.names=F)
  row.names(rt)=rt$method
  rt=rt[rt$pval<pFilter,]
  method <- rownames(rt)
  or <- sprintf("%.3f",rt$"or")
  orLow  <- sprintf("%.3f",rt$"or_lci95")
  orHigh <- sprintf("%.3f",rt$"or_uci95")
  OR <- paste0(or,"(",orLow,"-",orHigh,")")
  pVal <- ifelse(rt$pval<0.001, "<0.001", sprintf("%.3f", rt$pval))
  
  #输出图形
  pdf(file=forestFile, width=7, height=4.6)
  n <- nrow(rt)
  nRow <- n+1
  ylim <- c(1,nRow)
  layout(matrix(c(1,2),nc=2),width=c(3.5,2))
  
  #绘制森林图左边的信息
  xlim = c(0,3)
  par(mar=c(4,2.5,2,1))
  plot(1,xlim=xlim,ylim=ylim,type="n",axes=F,xlab="",ylab="")
  text.cex=0.8
  text(0,n:1,method,adj=0,cex=text.cex)
  text(1.9,n:1,pVal,adj=1,cex=text.cex);text(1.9,n+1,'pvalue',cex=1,font=2,adj=1)
  text(3.1,n:1,OR,adj=1,cex=text.cex);text(2.7,n+1,'OR',cex=1,font=2,adj=1)
  
  #绘制右边的森林图
  par(mar=c(4,1,2,1),mgp=c(2,0.5,0))
  xlim = c(min(as.numeric(orLow)*0.975,as.numeric(orHigh)*0.975,0.9),max(as.numeric(orLow),as.numeric(orHigh))*1.025)
  plot(1,xlim=xlim,ylim=ylim,type="n",axes=F,ylab="",xaxs="i",xlab="OR")
  arrows(as.numeric(orLow),n:1,as.numeric(orHigh),n:1,angle=90,code=3,length=0.05,col="darkblue",lwd=3)
  abline(v=1, col="black", lty=2, lwd=2)
  boxcolor = ifelse(as.numeric(or)>1, forestCol, forestCol)
  points(as.numeric(or), n:1, pch = 15, col = boxcolor, cex=2)
  axis(1)
  dev.off()
}
 
#调用函数,绘制森林图
bioForest(inputFile="table.MRresult.csv", forestFile="forest.pdf", forestCol="red")

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