Changes

 ==ANNOVARData QC Plink==  table_annovar.pl#PLINK table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Gene.vcf -remove -nastring . -protocol refGene -operation g -vcfinput cat APOC3_Gene.vcf.hg19_multianno.txt table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Gene.vcf -remove -nastring . -protocol refGene,knownGene,ensGene -operation g,g,g -arg '-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing' -vcfinput awk -F'\t' '{print $1,$2,$6,$7,$8,$9,$10}' APOC3_Gene.vcf.hg19_multianno.txt table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Region.vcf -remove -nastring . -protocol phastConsElements46way -operation r -vcfinput table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Region.vcf -remove -nastring . -protocol gwasCatalog -operation r -vcfinput table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Filter.vcf -remove -nastring . -protocol gnomad_genome,gnomad_exome,popfreq_max_20150413,gme,avsnp150,dbnsfp33a,dbscsnv11,cadd13gt20,clinvar_20170905,gwava -operation f,f,f,f,f,f,f,f,f,f -vcfinput awk -F'\t' '{print $1,$2,$103,$104}' APOC3_Filter.vcf.hg19_multianno.txt awk -F'\t' '{print $1,$2,$6,$14}' APOC3_Filter.vcf.hg19_multianno.txt awk -F'\t' '{print $1,$2,$15,$16,$17,$18,$19,$20,$21,$22}' APOC3_Filter.vcf.hg19_multianno.txt awk -F'\t' '{print $1,$2,$36,$86,$70}' APOC3_Filter.vcf.hg19_multianno.txt awk -F'\t' '{print $1,$2,$99,$100}' APOC3_Filter.vcf.hg19_multianno.txt table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_ANN.vcf -remove -nastring . -protocol refGene,knownGene,ensGene,wgRna,targetScanS,phastConsElements46way,tfbsConsSites,gwasCatalog,gnomad_genome,gnomad_exome,popfreq_max_20150413,gme,avsnp150,dbnsfp33a,dbscsnv11,cadd13gt20,clinvar_20170905,gwava -operation g,g,g,r,r,r,r,r,f,f,f,f,f,f,f,f,f,f -arg '-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing',,,,,,,,,,,,,,, -vcfinput ==GeneABEL==  plink --file GWAS_clean4 --pheno pheno.phen --pheno-name Aff --transpose --recode --out gwa_gabel --noweb plink --file GWAS_clean4 --pheno pheno.phen --pheno-name systolic --transpose --recode --out gwa_gabel_qtl --noweb R library(GenABEL) convert.snp.tped(tped = "gwa_gabel_qtl.tped", tfam = "gwa_gabel_qtl.tfam", out = "gwa_gabel_qtl.raw", strand = "u") g.dat &lt;- load.gwaa.data(phen = "gwa_gabel_qtl.praw", gen = "gwa_gabel_qtl.raw", force = T) slotNames(g.dat) slotNames(g.dat@gtdata) colnames(g.dat@phdata) sample.size &lt;- g.dat@gtdata@nids snps.total &lt;- g.dat@gtdata@nsnps print(c(sample.size, snps.total)) summary(g.dat@phdata$disease) hist(g.dat@phdata$disease, main="Quantitative Phenotype data summary", xlab = "Systolic pressure", freq = F,breaks=20, col="gray") rug(g.dat@phdata$disease) test.snp &lt;- scan.glm('disease ~ CRSNP', family = gaussian(), data = g.dat) names(test.snp) alpha &lt;- 5e-8 test.snp$snpnames[test.snp$P1df < alpha] test.snp$P1df[test.snp$P1df < alpha] test.qt &lt;- qtscore(disease, data = g.dat, trait = "gaussian") slotNames(test.qt) names(test.qt@results) head(results(test.qt)) test.qt@lambda descriptives.scan(test.qt) row.names(results(test.qt))[results(test.qt)$P1df < alpha] results(test.qt)$P1df[results(test.qt)$P1df < alpha] results(test.qt)$Pc1df[results(test.qt)$Pc1df < alpha] obs &lt;- sort(results(test.qt)$P1df) ept &lt;- ppoints(obs) plot(-log10(ept), -log10(obs), main = "GWAS QQ plot, qtl", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)") abline(0, 1, col = "red") abline(h = 8, lty = 2) plot(test.qt, col = "black") test.qt.sex &lt;- qtscore(disease ~ sex, data = g.dat, trait = "gaussian") row.names(results(test.qt.sex))[results(test.qt)$P1df < alpha] summary(lm(disease ~ sex, data = g.dat)) convert.snp.tped(tped = "gwa_gabel.tped", tfam = "gwa_gabel.tfam", out = "gwa_gabel.raw", strand = "u") b.dat &lt;- load.gwaa.data(phen = "gwa_gabel.praw", gen = "gwa_gabel.raw", force = T) slotNames(b.dat) slotNames(b.dat@gtdata) colnames(b.dat@phdata) b.dat@gtdata@nids case.size &lt;- length(which(b.dat@phdata$disease == 1)) control.size &lt;- length(which(b.dat@phdata$disease == 0)) case.size control.size snpsb.total &lt;- b.dat@gtdata@nsnps testb.snp &lt;- scan.glm('disease ~ CRSNP', family = binomial(), data = b.dat) names(testb.snp) alpha &lt;- 5e-8 testb.snp$snpnames[testb.snp$P1df < alpha] testb.snp$P1df[testb.snp$P1df < alpha] testb.qt &lt;- qtscore(disease, data = b.dat, trait = "binomial") slotNames(testb.qt) descriptives.scan(testb.qt) row.names(results(testb.qt))[results(testb.qt)$P1df < alpha] results(testb.qt)$P1df[results(testb.qt)$P1df < alpha] results(testb.qt)$Pc1df[results(testb.qt)$Pc1df < alpha] gkin &lt;- ibs(g.dat, weight = "freq") gkin[1:10,1:10] cps.full &lt;- cmdscale(as.dist(.5 - gkin), eig = T, k = 10) names(cps.full) cps &lt;- cps.full$points plot(cps[,1], cps[,2], pch = g.dat@phdata$popn) legend("topright", c("TSI","MEX", "CEU"), pch = c(1,2,3)) colnames(cps)&lt;-c('C1','C2','C3','C4','C5','C6','C7','C8','C9','C10') gpc.dat &lt;- g.dat gpc.dat@phdata&lt;-cbind(g.dat@phdata, cps) test.pc.a &lt;- scan.glm('disease ~ CRSNP + C1 + C2 + C3 + C4 + C5', family=gaussian(), data = gpc.dat) test.pc.a$snpnames[test.pc.a$P1df < alpha] test.pc.a$P1df[test.pc.a$P1df < alpha] test.pc.b &lt;- qtscore(disease ~ C1 + C2 + C3 + C4 + C5, data = gpc.dat, trait = "gaussian") test.pc.b@lambda plot(cps.full$eig[1:10]/sum(cps.full$eig), axes = F, type = "b", xlab = "Components", ylim = c(0,0.05), ylab = "Proportion of Variations", main = "MDS analysis scree plot") axis(1, 1:10) axis(2) plot(cumsum(cps.full$eig[1:10])/sum(cps.full$eig), axes = F, type = "b", ylim = c(0,0.2), xlab = "Components", ylab = "Proportion of Variations", main = "MDS analysis cumulative plot") axis(1, 1:10) axis(2) row.names(results(test.qt))[results(test.qt)$Pc1df < alpha] results(test.qt)$Pc1df[results(test.qt)$Pc1df < alpha] test.qt@lambda obs &lt;- sort(results(test.qt)$chi2.1df) ept &lt;- sort(qchisq(ppoints(obs), df = 1)) plot(ept, obs, main = "Genomic control (lambda = slope of the dashed line)", xlab="Expected chisq, 1df", ylab="Observed chisq, 1df") abline(0, 1, col = "red") abline(0, test.qt@lambda[1], lty = 2) median(results(test.qt)$chi2.1df)/0.456 obs &lt;- sort(results(test.qt)$Pc1df) ept &lt;- ppoints(obs) plot(-log10(ept), -log10(obs), main = "GWAS QQ plot adj. via Genomic Control", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)") abline(0, 1, col = "red") abline(h = 8, lty = 2) adj.gkin = gkin diag(adj.gkin) = hom(g.dat)$Var test.eg &lt;- egscore(disease, data = g.dat, kin = adj.gkin, naxes = 2) descriptives.scan(test.eg) snp.eg &lt;- row.names(results(test.eg))[results(test.eg)$P1df < alpha] pvalue.eg &lt;- results(test.eg)$P1df[results(test.eg)$P1df < alpha] lambda.eg &lt;- test.eg@lambda snp.eg pvalue.eg lambda.eg for (k in 1:10){ test.tmp &lt;- egscore(disease, data = g.dat, kin = adj.gkin, naxes = k) print(test.tmp@lambda$estimate) } obs &lt;- sort(results(test.eg)$Pc1df) ept &lt;- ppoints(obs) plot(-log10(ept), -log10(obs), main = "GWAS QQ plot adj. w/ EIGENSTRAT", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)") abline(0, 1, col = "red") abline(h = 8, lty = 2) plot(test.qt, col = "black") add.plot(test.eg, col = "gray", pch = 3) legend("topright", c("Original plot","After correction w/ EIGENSTRAT"), pch = c(1,3))==GWAS Data QC==  plink --file GWAS --noweb plink --file GWAS --mind 0.10 --recode --out GWAS_clean_mind --noweb plink --file GWAS_clean_mind --maf 0.05 --recode --out MAF_greater_5 --noweb plink --file GWAS_clean_mind --exclude MAF_greater_5.map --recode --out MAF_less_5 --noweb plink --file MAF_greater_5 --geno 0.05 --recode --out MAF_greater_5_clean --noweb plink --file MAF_less_5 --geno 0.01 --recode --out MAF_less_5_clean --noweb plink --file MAF_greater_5_clean --merge MAF_less_5_clean.ped MAF_less_5_clean.map --recode --out GWAS_MAF_clean --noweb plink --file GWAS_MAF_clean --mind 0.03 --recode --out GWAS_clean2 --noweb plink --file GWAS_clean2 --check-sex --out GWAS_sex_checking --noweb #### in R- open R by simply typing R setwd("to_your_working_directory/")

################################## plink --file GWAS_clean2 --genome --out duplicates --noweb #### in R setwd("to_your_working_directory/")

###### plink --file GWAS_clean2 --remove IBS_excluded.txt --recode --out GWAS_clean3 --noweb plink --file GWAS_clean3 --het --noweb ###### in R Dataset &lt;<- read.table("plink.het", header=TRUE, sep="", na.strings="NA", dec=".", strip.white=TRUE)

###### plink --file GWAS_clean3 --pheno pheno.txt --pheno-name Aff --hardy --noweb ##### in R

########## plink --file GWAS_clean3 --exclude HWE_out.txt --recode --out GWAS_clean4 --noweb==GWAS Control Substructure== ############################################### ##### Part 2: controlling for substructure##### ############################################### plink --file GWAS_clean4 --genome --cluster --mds-plot 10 --noweb #### in R

mydata$pch[mydata$Group==1 ] &lt;<-15 mydata$pch[mydata$Group==2 ] &lt;<-16 mydata$pch[mydata$Group==3 ] &lt;<-2 jpeg("mds.jpeg", height=1000500, width=1000500)

###### plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --logistic --adjust --out unadj plink --nowebfile GWAS_clean4 --genome --cluster --pca 10 header plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.mds eigenvec --covar-name C1 PC1 --logistic --adjust --out C1 --nowebPC1 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.mds eigenvec --covar-name C1PC1-C2 PC2 --logistic --adjust --out C1PC1-C2 --nowebPC2 #### in R broadqq &lt;<-function(pvals, title)

&nbsp;&nbsp;&nbsp;&nbsp; observed &lt;<- sort(pvals) &nbsp;&nbsp;&nbsp;&nbsp; lobs &lt;<- -(log10(observed)) &nbsp;&nbsp;&nbsp;&nbsp; expected &lt;<- c(1:length(observed)) &nbsp;&nbsp;&nbsp;&nbsp; lexp &lt;<- -(log10(expected / (length(expected)+1))) &nbsp;&nbsp;&nbsp;&nbsp; plot(c(0,7), c(0,7), col="red", lwd=3, type="l", xlab="Expected (-logP)", ylab="Observed (-logP)", xlim=c(0,max(lobs)), ylim=c(0,max(lobs)), las=1, xaxs="i", yaxs="i", bty="l", main = title) &nbsp;&nbsp;&nbsp;&nbsp; points(lexp, lobs, pch=23, cex=.4, bg="black") } jpeg("qqplot_compare.jpeg", height=1000, width=1000500)

aff_unadj&lt;<-read.table("unadj.assoc.logistic", header=TRUE) aff_unadj.add.p&lt;<-aff_unadj[aff_unadj$TEST==c("ADD"),]$P

aff_C1C2&lt;<-read.table("C1PC1-C2PC2.assoc.logistic", header=TRUE) aff_C1C2.add.p&lt;<-aff_C1C2[aff_C1C2$TEST==c("ADD"),]$P broadqq(aff_C1C2.add.p, "Some Trait Adjustedfor PC1 and PC2")