Genome-wide Association-MDS and PCA Exercise
From Statistical Genetics Courses
Revision as of 18:22, 24 January 2019 by Serveradmin (Talk | contribs) (Created page with "__NOTITLE__ ==Genome-wide Association-MDS and PCA Exercise== plink --file GWAS_clean4 --genome --cluster --mds-plot 10 R mydata = read.table("mds_components.txt", header=...")
Genome-wide Association-MDS and PCA Exercise
plink --file GWAS_clean4 --genome --cluster --mds-plot 10 R mydata = read.table("mds_components.txt", header=T) mydata$pch[mydata$Group==1 ] <-15 mydata$pch[mydata$Group==2 ] <-16 mydata$pch[mydata$Group==3 ] <-2 jpeg("mds.jpeg", height=500, width=500) plot(mydata$C1, mydata$C2 ,pch=mydata$pch) dev.off() q() plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --logistic --adjust --out unadj plink --file GWAS_clean4 --genome --cluster --pca 10 header plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.eigenvec --covar-name PC1 --logistic --adjust --out PC1 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.eigenvec --covar-name PC1-PC2 --logistic --adjust --out PC1-PC2 R source("broadqq.R") gws_unadj = aff_unadj[which(aff_unadj$P < 0.0000001),] gws_unadj gws_adjusted = aff_C1C2[which(aff_C1C2$P < 0.0000001),] gws_adjusted q()