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") summary(result.all) print(dev.all) 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) summary(result.adj) result.adj = glm (affection ~ sex + age + rs1112, family=binomial("logit"), data=snp.data) summary(result.adj) result.adj = glm (affection ~ rs1117 + rs1112, family=binomial("logit"), data=snp.data) summary(result.adj) anova (result.adj, test="Chi") result.adj = glm (affection ~ rs1112 + rs1117, family=binomial("logit"), data=snp.data) summary(result.adj) anova (result.adj, test="Chi") result.adj = lm (trait ~ rs1112, data=snp.data) summary(result.adj) result.adj = lm (trait ~ sex + rs1112, data=snp.data) summary(result.adj) q()
Multifactorial Part 2
plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.sex.inter.add --logistic --sex --interaction --noweb
plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.snp1112.inter.add --logistic --condition rs1112 --interaction --noweb
R<br />load("dbp.R")<br />ls()<br />dbp[1:5,]<br />summary(dbp)
result.inter = glm (affection ~ sex * rs1112, family=binomial("logit"), data=snp.data)
summary(result.inter)
result.reg = glm (affection ~ sex + age + rs1112 + rs1117, family=binomial("logit"), data=snp.data)
summary(result.reg)
modelchoice.result<- step (result.reg) summary(modelchoice.result) q()
GWAS Control Substructure