GWAS logistic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 beta 1> /dev/null ## plink 逻辑回归 root@PC1:/home/test# ls gwas_case_cont.map gwas_case_cont.ped plink.assoc.logistic plink.log root@PC1:/home/test# head plink.assoc.logistic CHR SNP BP A1 TEST NMISS BETA STAT P 1 snp1 3046 A ADD 288 0.06317 0.2228 0.8237 1 snp2 3092 T ADD 288 0.08104 0.2895 0.7722 1 snp3 3174 T ADD 288 -0.08676 -0.2947 0.7682 1 snp4 32399 T ADD 288 -0.03895 -0.1416 0.8874 1 snp5 32402 G ADD 288 -0.05129 -0.2792 0.7801 1 snp6 32406 G ADD 288 -0.07479 -0.411 0.6811 1 snp7 32443 C ADD 288 -0.1864 -0.8866 0.3753 1 snp8 32548 T ADD 288 -0.05275 -0.2156 0.8293 1 snp9 45044 A ADD 288 0.1644 0.6751 0.4996
002、R语言实现
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 ## 利用plink将基因型数据转换为数值型 root@PC1:/home/test# ls gwas_case_cont.map gwas_case_cont.ped plink.log plink.raw root@PC1:/home/test# head plink.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 1 1 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()
library(data.table)
dat <- fread("plink.raw", data.table = F)
dat[,6] <- dat[,6] - 1 ## 表型由 1、2改为0和1
dat <- dat[,-c(1,3:5)]
result <- data.frame()
for (i in 3:10) {
logis <- glm(dat[,2]~dat[,i], family = "binomial", data = dat) ## 逻辑回归
result <- rbind(result, summary(logis)$coefficients[2,])
}
names(result) <- names(summary(logis)$coefficients[2,])
result
标签：case,gwas,cont,plink,回归,GWAS,288,ADD,logistic 来源： https://www.cnblogs.com/liujiaxin2018/p/16534809.html