plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.sexage.add --logistic --sex --covar dbp.age.pheno --noweb
plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.snp1112.add --logistic --condition rs1112 --noweb
plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.snp1117.add --logistic --condition rs1117--noweb
plink --ped dbp.qt.ped --map dbp.map --map3 --out linreg.sex.add --linear --sex --noweb
plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.sex.inter.add --logistic --sex --interaction --noweb
snp.data = dbp[,c("affection", "rs1112")]
summary(snp.data)
snp.data[,"rs1112"] <- as.numeric(snp.data[,"rs1112"]) - 1 summary(snp.data)
result.all = glm (affection ~ rs1112, family=binomial("logit"), data=snp.data)
dev.all = anova (result.all, test="Chi")
snp.data = dbp[,c("affection", "trait","sex", "age", "rs1112", "rs1117")]
summary(snp.data)
snp.data[,"rs1112"] <- as.numeric(snp.data[,"rs1112"]) - 1 snp.data[,"rs1117"] <- as.numeric(snp.data[,"rs1117"] ) - 1 result.adj = glm (affection ~ sex + rs1112 , family=binomial("logit"), data=snp.data)
summary(result.adj)
result.adj = glm (affection ~ age + rs1112 , family=binomial("logit"), data=snp.data)