GWAS logistic + 隐性模型 回归分析
作者：互联网
001、plink
root@PC1:/home/test# ls gwas_case_cont.map gwas_case_cont.ped root@PC1:/home/test# plink --file gwas_case_cont --logistic recessive beta 1> /dev/null root@PC1:/home/test# ls gwas_case_cont.map gwas_case_cont.ped plink.assoc.logistic plink.log root@PC1:/home/test# head -n 5 plink.assoc.logistic CHR SNP BP A1 TEST NMISS BETA STAT P 1 snp1 3046 A REC 288 0.05716 0.08398 0.9331 1 snp2 3092 T REC 288 0.1674 0.249 0.8034 1 snp3 3174 T REC 288 -1.053 -0.7409 0.4588 1 snp4 32399 T REC 288 -0.778 -1.003 0.316
002、R
root@PC1:/home/test# ls gwas_case_cont.map gwas_case_cont.ped root@PC1:/home/test# plink --file gwas_case_cont --recode A 1> /dev/null root@PC1:/home/test# ls gwas_case_cont.map gwas_case_cont.ped plink.log plink.raw root@PC1:/home/test# sed 1d plink.raw | cut -d " " -f 7- | sed 's/1/0/g; s/2/1/g' > temp root@PC1:/home/test# head -n 1 plink.raw | cat - <(sed 1d plink.raw | cut -d " " -f 1-6 | paste -d " " - temp ) > plink2.raw root@PC1:/home/test# ls gwas_case_cont.map gwas_case_cont.ped plink2.raw plink.log plink.raw temp root@PC1:/home/test# head plink2.raw | cut -d " " -f 1-10 FID IID PAT MAT SEX PHENOTYPE snp1_A snp2_T snp3_T snp4_T A1 A1 0 0 1 1 0 0 0 0 A2 A2 0 0 1 1 0 0 0 0 A3 A3 0 0 1 1 0 0 0 0 A4 A4 0 0 1 1 0 0 0 0 A5 A5 0 0 1 1 0 0 0 0 A6 A6 0 0 1 1 0 0 0 0 A7 A7 0 0 1 1 0 0 0 0 A8 A8 0 0 1 1 0 0 0 0 A9 A9 0 0 1 1 0 0 0 0
dir()
dat <- fread("plink2.raw", header = T, data.table = F)
dat <- dat[,-c(1,3:5)]
dat[,2] <- dat[,2] - 1
result <- data.frame()
for (i in 3:10) {
logis <- glm(dat[,2]~dat[,i], family = "binomial", data = dat)
result <- rbind(result, c(exp(logis$coefficients[2]), summary(logis)$coefficients[2,]))
}
names(result) <- c("OR", names(summary(logis)$coefficients[2,]))
result
标签：case,gwas,cont,plink,GWAS,隐性,logistic,test,home 来源： https://www.cnblogs.com/liujiaxin2018/p/16536265.html