View source for 2016-genetic-association-commands

__NOTITLE__

==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))==Imputation exercise==

 plink --file chr22_imputation_ex --noweb
 plink --file chr22_imputation_ex --maf 0.01 --mind 0.02 --geno 0.05 --hwe 0.001 --out qc_check --noweb
 plink --file chr22_imputation_ex --maf 0.01 --mind 0.02 --geno 0.05 --hwe 0.001 --recode --out chr22_clean1 --noweb
 plink --file chr22_clean1 --maf 0.01 --mind 0.02 --geno 0.05 --hwe 0.001 --out qc_check_2 --noweb
 plink --file chr22_clean1 --filter-cases --hwe 0.001 --recode --out chr22_cases_clean --noweb
 plink --file chr22_clean1 --filter-controls --recode --out chr22_controls_clean --noweb
 plink --file chr22_controls_clean --merge chr22_cases_clean.ped chr22_cases_clean.map --hwe 0.001 --recode --out chr22_all_clean --noweb
 plink --file chr22_all_clean --logistic --out chr22_all_clean_geno --noweb
 R
 mydata = read.table("chr22_all_clean_geno.assoc.logistic", header=T)
 names(mydata)
 plot(mydata$BP, -log10(mydata$P))
 smallp = mydata[which(mydata$P < 1E-4),]
 smallp
 smallp = smallp[order(smallp$BP),]
 smallp
 q()
 mach1 --hapmapFormat -d chr22_mach_merlin.map -p chr22_mach_merlin.ped --haps genotypes_chr22_CEU_r22_nr.b36_fwd.phase.gz --snps genotypes_chr22_CEU_r22_nr.b36_fwd_legend.txt.gz --greedy --rounds 100 --mle --mldetails --autoflip -o chr22_HIHII
 plink --dosage chr22_HIHII_dose_mach4plink.txt.gz Zin --fam chr22_imputation_ex.fam --map chr22_imputed_snps_positions.map --out chr22_HIHII_dosage --noweb
 R
 dosage = read.table("chr22_HIHII_dosage.assoc.dosage", header= T)
 names(dosage)
 plot(dosage$BP, -log10(dosage$P))
 dosagep = dosage[which(dosage$P < 5E-8),] dosagep = dosagep[order(dosagep$BP),]
 dosagep
 interest = dosage[which(dosage$SNP=='rs715586'),]
 interest
==PLINK_R==
Introduction

 plink --ped dbp.cc.ped --map dbp.map --map3 --missing --noweb
 plink --ped dbp.cc.ped --map dbp.map --map3 --mind 0.10 --geno 0.05 --recode --out cleaned --noweb
 plink --ped cleaned.ped --map cleaned.map --freq --out cleaned --noweb
 plink --ped cleaned.ped --map cleaned.map --hardy --out cleaned --noweb
 plink --ped cleaned.ped --map cleaned.map --out cleaned.R --recode --tab --noweb
 R
 city = c("Oslo", "Bergen", "Munich", "Berlin", "Rome", "Milan")
 population = c(0.58, 0.25, 1.3, 3.4, 2.7, 1.3)
 country = factor( c("Norway" , "Norway", "Germany", "Germany", "Italy", "Italy" ))
 capital = c(TRUE, FALSE, FALSE, TRUE, TRUE, FALSE)
 updated = 2009
 city
 population
 country
 capital
 c(city, city)
 c(population, updated)
 summary (city)
 summary (population)
 summary (country)
 summary (capital)
 is.numeric(city)
 is.character(city)
 is.factor(city)
 class (city)
 class (population)
 class (country)
 class (capital)
 length(city)
 names(population) = city
 population
 city [3]
 city [2:4]
 city[c(1,5:6)]
 population[3]
 population["Oslo"]
 population[c("Berlin","Rome")]
 population
 capital
 population[capital]
 population>=1.0
 population[population>=1.0]
 cities = data.frame (city=city, pop=population, country=country, capital=capital, stringsAsFactors=F)
 cities
 length(cities)
 dim(cities)
 is.data.frame(cities)
 is.list(cities)
 colnames(cities)
 rownames(cities)
 cities$city
 cities[,1]
 cities[2,]
 cities[2,3]
 cities$pop[3]
 cities[capital,]
 cities[cities$pop>=1.0,]
 ls()
 save(cities, city, country, file="myobjects.R")
 write.table(cities, file="cities.txt")
 sink("cities.output.txt")
 print(cities)
 sink()
 dir()
 rm(list=ls())
 ls()
 new.table = read.table ("cities.txt")
 ls()
 new.table
 load ("myobjects.R")
 ls()
 cities
 new.table
 q()

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
 R
 sexcheck = read.table("GWAS_sex_checking.sexcheck", header=T)
 names(sexcheck)
 sex_problem = sexcheck[which(sexcheck$STATUS=="PROBLEM"),]
 sex_problem
 q()
 plink --file GWAS_clean2 --genome --out duplicates --noweb
 R
 dups = read.table("duplicates.genome", header = T)
 problem_pairs = dups[which(dups$PI_HAT > 0.4),]
 problem_pairs
 problem_pairs = dups[which(dups$PI_HAT > 0.05),]
 myvars = c("FID1", "IID1", "FID2", "IID2", "PI_HAT")
 problem_pairs[myvars]
 q()
 plink --file GWAS_clean2 --remove IBS_excluded.txt --recode --out GWAS_clean3 --noweb
 plink --file GWAS_clean3 --het --noweb
 R
 Dataset &lt;- read.table("plink.het", header=TRUE, sep="", na.strings="NA", dec=".",
 strip.white=TRUE)
 mean(Dataset$F)
 sd(Dataset$F)
 jpeg("hist.jpeg", height=1000, width=1000)
 hist(scale(Dataset$F), xlim=c(-4,4))
 dev.off()
 q()
 plink --file GWAS_clean3 --pheno pheno.txt --pheno-name Aff --hardy --noweb
 R
 hardy = read.table("plink.hwe", header = T)
 names(hardy)
 hwe_prob = hardy[which(hardy$P < 0.0000009),]
 hwe_prob
 q()
 plink --file GWAS_clean3 --exclude HWE_out.txt --recode --out GWAS_clean4 --noweb

Multifactorial Part 1

 plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.add --logistic --noweb
 plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.add.ci --logistic --ci 0.95 --noweb
 plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.age.add --logistic --covar dbp.age.pheno --noweb
 plink --ped dbp.cc.ped --map dbp.map --map3 --out logreg.sex.add --logistic --sex --noweb
 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
 R
 load("dbp.R")
 ls()
 dbp[1:5,]
 result.snp12 = glm (affection ~ rs1112, family=binomial("logit"), data=dbp)
 print (result.snp12)
 print ( class (result.snp12) )
 print ( summary(result.snp12) )
 dev.geno = anova (result.snp12, test="Chi")
 lrt.pvalue = pchisq(dev.geno[dim(dev.geno)[1],"Deviance"], df=2, ncp=0, FALSE)
 print ( lrt.pvalue )
 print ( summary(result.snp12)$coefficients )
 snp.beta = summary(result.snp12)$coefficients[2:3,1]
 print ( snp.beta )
 print ( exp(snp.beta) )
 ci = confint (result.snp12)
 print (ci)
 print ( exp(ci) )
 snp.data = dbp[,c("affection", "rs1112")]
 summary(snp.data)
 snp.data[,"rs1112"]<-as.numeric(snp.data[,"rs1112"])-1<br />summary(snp.data)<br />result.all=glm(affection ~ rs1112, family=binomial("logit"), data=snp.data)<br />dev.all = anova(result.all, test="Chi")<br />print(dev.all)<br />snp.data = dbp[,c("affection","trait","sex","age","rs1112","rs1117")]<br />summary(snp.data)<br />snp.data[,"rs1112"]<-as.numeric(snp.data[,"rs1112"])-1<br />snp.data[,"rs1117"]<-as.numeric(snp.data[,"rs1117"])-1<br />result.adj = glm(affection ~ sex + rs1112, family=binomial("logit"), data=snp.data)<br />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)

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.inter = glm (affection ~ age * rs1112, family=binomial("logit"), data=snp.data)
 summary(result.inter)
 result.inter = glm (affection ~ rs1112 * rs1117, 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)<br />summary(modelchoice.result)

 GWAS Control Substructure

 plink --file GWAS_clean4 --genome --mds-plot 10 --noweb
 R
 mydata = read.table("mds_components.txt", header=T)
 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=1000, width=1000)
 plot(mydata$C1, mydata$C2 ,pch=mydata$pch)
 dev.off()
 q()
 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --logistic --adjust --out unadj --noweb
 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.mds --covar-name C1 --logistic --adjust --out C1 --noweb
 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.mds --covar-name C1-C2 --logistic --adjust --out C1-C2 --noweb
 R
 broadqq &lt;-function(pvals, title)
 {
 &nbsp;&nbsp;&nbsp;&nbsp;observed &lt;- sort(pvals)
 &nbsp;&nbsp;&nbsp;&nbsp;lobs &lt;- -(log10(observed))
 &nbsp;&nbsp;&nbsp;&nbsp;expected &lt;- c(1:length(observed))
 &nbsp;&nbsp;&nbsp;&nbsp;lexp &lt;- -(log10(expected / (length(expected)+1)))
 &nbsp;&nbsp;&nbsp;&nbsp;plot(c(0,7), c(0,7), col="red", lwd=3, type="l", xlab="Expected (-logP)", ylab="Observed (-logP)", xlim=c(0,max(lobs)), ylim=c(0,max(lobs)), las=1, xaxs="i", yaxs="i", bty="l", main = title)
 &nbsp;&nbsp;&nbsp;&nbsp;points(lexp, lobs, pch=23, cex=.4, bg="black") }
 jpeg("qqplot_compare.jpeg", height=1000, width=1000)
 par(mfrow=c(2,1))
 aff_unadj&lt;-read.table("unadj.assoc.logistic", header=TRUE)
 aff_unadj.add.p&lt;-aff_unadj[aff_unadj$TEST==c("ADD"),]$P
 broadqq(aff_unadj.add.p,"Some Trait Unadjusted")
 aff_C1C2&lt;-read.table("C1-C2.assoc.logistic", header=TRUE)
 aff_C1C2.add.p&lt;-aff_C1C2[aff_C1C2$TEST==c("ADD"),]$P
 broadqq(aff_C1C2.add.p, "Some Trait Adjusted")
 dev.off()
 gws_unadj = aff_unadj[which(aff_unadj$P < 0.0000001),]
 gws_unadj
 gws_adjusted = aff_C1C2[which(aff_C1C2$P < 0.0000001),] gws_adjusted
 q()

 Multiple Testing

 plink --ped dbp.cc.ped --map dbp.map --map3 --out multtest --assoc --adjust --noweb
 plink --ped dbp.cc.ped --map dbp.map --map3 --out multperm5000 --assoc --mperm 5000 --noweb
 plink --ped dbp.cc.ped --map dbp.map --map3 --out multperm100000 --assoc --mperm 100000 --noweb
 R
 load("p.values.R")
 ls()
 p.values
 library (multtest)
 adj.p.values = mt.rawp2adjp(p.values,c("Bonferroni","Holm","SidakSS","BH"))
 adj.p.values
 rownames(adj.p.values$adjp) = names(p.values[adj.p.values$index])
 adj.p.values$adjp

==PSEQ exercise==
Data analysis:

 pseq help
 pseq help all
 pseq myproj new-project --resources hg19
 pseq myproj load-vcf --vcf CEU.exon.2010_03.genotypes.hg19.vcf.gz YRI.exon.2010_03.genotypes.hg19.vcf.gz
 pseq myproj load-pheno --file phenotype.phe
 pseq myproj v-view | head
 pseq myproj i-view | head
 pseq myproj summary 
 pseq myproj var-summary
 pseq myproj ind-summary
 pseq myproj loc-summary
 pseq myproj ref-summary
 pseq myproj seq-summary
 pseq myproj file-summary
 pseq myproj meta-summary
 pseq myproj v-stats
 pseq myproj i-stats | head
 pseq myproj tag-file --id 1 --name CEU
 pseq myproj tag-file --id 2 --name YRI
 pseq myproj var-summary
 pseq myproj v-freq | head
 pseq myproj v-freq --mask file=CEU | head 
 pseq myproj v-freq --mask file=YRI | head
 pseq myproj v-view --mask any.filter.ex | head
 pseq myproj v-view --mask any.filter.ex | wc -l
 pseq myproj v-view --mask any.filter | wc -l
 pseq myproj var-set --group pass --mask any.filter.ex
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15 --mask include="DP>14" var=pass
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10 --mask geno=DP:ge:11 var=pass_DP15
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10_CEU --mask file=CEU var=pass_DP15_DPgeno10
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10_CEU_HWE --mask hwe=5.7e-7:1 var=pass_DP15_DPgeno10_CEU
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10_CEU_HWE_MAFgt05 --mask maf=0.05:0.5 var=pass_DP15_DPgeno10_CEU_HWE
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10_CEU_HWE_MAFlt01 --mask "mac=1 maf=0.01" var=pass_DP15_DPgeno10_CEU_HWE
 pseq myproj var-summary
 pseq myproj glm --phenotype BMI --covar SEX --mask var=pass_DP15_DPgeno10_CEU_HWE_MAFgt05 > SNV_CEU.result 
 head SNV_CEU.result
 cat SNV_CEU.result | awk '{if(FNR==1) print $0; if(NR>1) print $0 | "sort -k9 2>/dev/null"}' | grep -v "NA\s\+NA\s\+NA" | head
 pseq myproj assoc --tests fw vt --phenotype BMI
 pseq myproj assoc --tests skat --phenotype BMI --covar SEX --mask var=pass_DP15_DPgeno10_CEU_HWE_MAFlt01 loc.group=refseq > SKAT_CEU.result
 pseq myproj assoc --tests skat --phenotype BMI --covar SEX --mask include="DP>14" geno=DP:ge:11 file=CEU hwe=5.7e-7:1 "mac=1 maf=0.01" loc.group=refseq > SKAT_CEU.result
 head -20 SKAT_CEU.result
 cat SKAT_CEU.result | grep SKAT | grep -v "P=NA" | sort -k6 | head -15

Exercise&nbsp;analyzing YRI samples:

 pseq myproj var-set --group pass_DP15_DPgeno10_YRI --mask file=YRI var=pass_DP15_DPgeno10
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10_YRI_HWE --mask hwe=5.7e-7:1 var=pass_DP15_DPgeno10_YRI
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10_YRI_HWE_MAFgt05 --mask maf=0.05:0.5 var=pass_DP15_DPgeno10_YRI_HWE
 pseq myproj var-summary
 pseq myproj var-set --group pass_DP15_DPgeno10_YRI_HWE_MAFlt01 --mask "mac=1 maf=0.01" var=pass_DP15_DPgeno10_YRI_HWE
 pseq myproj var-summary
 pseq myproj glm --phenotype BMI --covar SEX --mask var=pass_DP15_DPgeno10_YRI_HWE_MAFgt05 > SNV_YRI.result
 head SNV_YRI.result
 cat SNV_YRI.result | awk '{if(FNR==1) print $0; if(NR>1) print $0 | "sort -k9 2>/dev/null"}' | grep -v "NA\s\+NA\s\+NA" | head
 pseq myproj assoc --tests skat --phenotype BMI --covar SEX --mask include="DP>14" geno=DP:ge:11 file=YRI hwe=5.7e-7:1 "mac=1 maf=0.01" loc.group=refseq > SKAT_YRI.result
 head -20 SKAT_YRI.result
 cat SKAT_YRI.result | grep SKAT | grep -v "P=NA" | sort -k6 | head -15

==RV-TDT exercise==
 vtools init rvtdt
 vtools import --format vcf data/data.vcf --build hg19
 vtools phenotype --from_file data/phen.txt
 # variant selection
 vtools execute ANNOVAR  geneanno
 vtools select variant "variant.region_type like '%splicing%'or variant.mut_type like 'nonsynonymous%' or variant.mut_type like 'frameshift%' or variant.mut_type like 'stop%'" -t func_variant
 # tped file
 vtools export func_variant --format tped --samples 'phenotype is not null'  > vat_raw.tped
 sort -k4 -n vat_raw.tped | awk 'BEGIN{OFS="\t";prev="None";copy=1} {$2=$1"_"$4; $3=0; if($2==prev) {$2=$2"_"copy; copy=copy+1} else {prev=$2; copy=1}; print $0}' > vat_export.tped
 # tfam file
 vtools phenotype --out family sample_name pid mid sex phenotype > vat_export.tfam
 # anno file
 vtools use refGene-hg19_20130904
 vtools update func_variant --set 'maf=0.001' 
 vtools select func_variant -o chr pos refGene.name2 maf --header > vat_export.anno
 # Mendelian error and recode
 plink --noweb --tfile vat_export --recode12 --me 1 1 --set-me-missing --out "recode12_noME"
 sort -n -k1 -k6 -k2 recode12_noME.ped | sed 's/ /\t/g' | cut -f1,3,4,5 --complement > linkage.ped
 cut -f2 recode12_noME.map | awk 'BEGIN{OFS="\t";} {print "M",$0}' | sed '1i\I\tid\nA\tDisease' > linkage.dat
 java -Xmx10000m -jar java/linkage2beagle.jar linkage.dat linkage.ped > pre_beagle.bgl
 python script/pre_phase.py -i pre_beagle.bgl -a pre_beagle_withMissing.bgl
 java -Xmx10000m -jar java/beagle.jar missing=0 trios=pre_beagle.bgl out=bgl_phased verbose=false redundant=true
 gunzip bgl_phased.pre_beagle.bgl.phased.gz
 python script/post_phase.py -a vat_export.anno -b bgl_phased.pre_beagle.bgl.phased -o genes/
 for g in `ls genes | grep tped | cut -d"." -f1 | head -20` 
 do 
     echo "runing rvTDT on gene "${g}
     rvTDT exercise_proj -G ./genes/${g}.tped -P ./data/rvtdt.phen -M ./genes/${g}.map --adapt 500 --alpha 0.00001 --permut 2000 --lower_cutoff 0 --upper_cutoff 100 -- minVariants 3 --maxMissRatio 1 
 done 
 # Answer
 vtools show tables
 ls genes/ | grep tped | wc
 cat exercise_proj_pval/*.pval | grep -v "^#" | sort -k2
 cat exercise_proj_pval/*.pval | grep -v "^#" | sort -k3
 # clean
 rm -r exercise_proj* genes/* bgl* linkage* recode12* pre_beagle* vat_export.*

==SEQPower==
 spower -h
 spower LOGIT -h
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.5 --method "CFisher --name CMC" -r 100 -j 4 -l 1 -o exercise
 spower show exercise.csv
 spower show exercise.csv power*
 spower show exercise.loci.csv
 spower show exercise.loci.csv maf
 spower show tests
 spower show test SKAT
 spower LOGIT Kryukov2009European1800.sfs --def_rare 0.01 --def_neutral -0.00001 0.00001 --moi A --proportion_detrimental 1 --proportion_protective 0 --OR_rare_detrimental 1.5 --OR_common_detrimental 1 --baseline_effect 0.01 --sample_size 1000 --p1 0.5 --limit 1 --alpha 0.05 --method "KBAC --name K1 --mafupper 0.01 --maflower 0 --alternative 1 --moi additive --permutations 1000 --adaptive 0.1" --replicates 1000 --jobs 4 -o exercise
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.2 --ORmax_rare_detrimental 3.0 --method CFisher -r 100 -j 4 -l 1 -o exercise
 spower show exercise.loci.csv effect*
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.2 --ORmax_rare_detrimental 3.0 --proportion_detrimental 0.8 --method CFisher -r 100 -j 4 -l 1 -o exercise
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.5 --missing_sites 0.05 --method CFisher -r 100 -j 4 -l 1 -o exercise 
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.5 --missing_low_maf 0.000125 --method CFisher -r 100 -j 4 -l 1 -o exercise 
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.5 --method "CFisher --alternative 1 --name CMC" "KBAC --permutations 1000 --alternative 1" "WSSRankTest --alternative 1 --name WSS" "VTtest --alternative 1 --permutations 1000" "SKAT disease" -r 100 -j 4 -l 1 -o exercise
 spower show exercise.csv method power
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.5 -j 1 -l 1 -o exercise
 spower LOGIT Kryukov2009European1800.sfs --power 0.8 --OR_rare_detrimental 1.5 -j 1 -l 1 -o exercise
 spower LNR Kryukov2009European1800.sfs --sample_size 1000 --meanshift_rare_detrimental 0.2 --method "CollapseQt --name CMC --alternative 2" -r 100 -j 4 -l 1 -o exercise
 spower LNR Kryukov2009European1800.sfs --sample_size 1000 --meanshift_rare_detrimental 0.2 --meanshiftmax_rare_detrimental 0.5 --method "CollapseQt --alternative 2" -r 100 -j 4 -l 1 -o exercise
 spower ELNR Kryukov2009European1800.sfs --sample_size 1000 --meanshift_rare_detrimental 0.2 --QT_thresholds 0.4 0.6 --method "CollapseQt --alternative 2" -r 100 -j 4 -l 1 -o exercise
 spower show exercise.csv sample* power<br />spower ELNR Kryukov2009European1800.sfs --sample_size 1000 --p1 0.5 --meanshift_rare_detrimental 0.5 --QT_thresholds 0.4 0.6 --method "CollapseQt --alternative 2" -r 100 -j 4 -l 1 -o exercise
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental 1.5 --method "GroupWrite ExerciseSimulation" -j 4 -o exercise -v1
 spower show exercise.SEQPowerDB
 spower show exercise.SEQPowerDB LOGIT
 spower show exercise.SEQPowerDB LOGIT method power title --condition "where power between 0.25 and 0.95"
 for i in 1 1.5 2 2.5 3 3.5 4; do
 spower LOGIT Kryukov2009European1800.sfs --sample_size 1000 --OR_rare_detrimental $i --method "CFisher --name CMC$i" --title FixedOR$i -r 100 -j 4 -l 1 -o exercise2
 done<br />spower show exercise2.SEQPowerDB LOGIT method power title

==Unphased==
 unphased.sh
 unphased mypeds.ped –marker 1 2 3 –missing –permutation 10
 unphased mypeds.ped –permuation 10 morepeds.ped
 unphased mypeds.ped –window 2 –reference 1 2
 unphased mypeds.ped –window 2 –reference 1 2 1 1
 unphased all.ped -window 2 -LD
 unphased all.ped -window 2 -LD &gt;&gt; results.txt==VAT==

 vtools -h
 vtools init VATDemo
 vtools import *.vcf.gz --var_info DP filter --geno_info DP_geno --build hg18 -j1
 vtools liftover hg19
 head phenotypes.csv
 vtools phenotype --from_file phenotypes.csv --delimiter ","
 vtools show project
 vtools show tables
 vtools show table variant
 vtools show samples
 vtools show genotypes
 vtools show fields
 vtools select variant --count
 vtools show genotypes &gt; GenotypeSummary.txt
 head GenotypeSummary.txt
 vtools output variant "max(DP)" "min(DP)" "avg(DP)" "stdev(DP)" "lower_quartile(DP)" "upper_quartile(DP)" --header
 vtools select variant "filter='PASS'" --count
 vtools select variant "filter='PASS'" -o "max(DP)" "min(DP)" "avg(DP)" "stdev(DP)" "lower_quartile(DP)" "upper_quartile(DP)" --header
 vtools update variant --from_stat 'total=#(GT)' 'num=#(alt)' 'het=#(het)' 'hom=#(hom)' 'other=#(other)' 'minDP=min(DP_geno)' 'maxDP=max(DP_geno)' 'meanDP=avg(DP_geno)' 'maf=maf()'
 vtools show fields
 vtools show table variant
 vtools update variant --from_stat 'totalGD10=#(GT)' 'numGD10=#(alt)' 'hetGD10=#(het)' 'homGD10=#(hom)' 'otherGD10=#(other)' 'mafGD10=maf()' --genotypes "DP_geno &gt; 10"
 vtools show fields
 vtools show table variant
 vtools output variant chr pos maf mafGD10 --header --limit 20
 vtools phenotype --set "RACE=0" --samples "filename like 'YRI%'"
 vtools phenotype --set "RACE=1" --samples "filename like 'CEU%'"
 vtools show samples --limit 10
 vtools update variant --from_stat 'CEU_mafGD10=maf()' --genotypes 'DP_geno&gt;10' --samples "RACE=1"
 vtools update variant --from_stat 'YRI_mafGD10=maf()' --genotypes 'DP_geno&gt;10' --samples "RACE=0"
 vtools output variant chr pos mafGD10 CEU_mafGD10 YRI_mafGD10 --header --limit 10
 vtools phenotype --from_stat 'CEU_totalGD10=#(GT)' 'CEU_numGD10=#(alt)' --genotypes 'DP_geno&gt;10' --samples "RACE=1"
 vtools phenotype --from_stat 'YRI_totalGD10=#(GT)' 'YRI_numGD10=#(alt)' --genotypes 'DP_geno&gt;10' --samples "RACE=0"
 vtools phenotype --output sample_name CEU_totalGD10 CEU_numGD10 YRI_totalGD10 YRI_numGD10 --header
 vtools select variant 'maf&gt;=0.01' -t variant_MAFge01 'Variants that have MAF &gt;= 0.01'
 vtools show tables
 vtools execute KING --var_table variant_MAFge01
 vtools_report plot_pheno_fields KING_MDS1 KING_MDS2 RACE --dot KING.mds.race.pdf --discrete_color Dark2
 vtools_report plot_pheno_fields KING_MDS1 KING_MDS2 panel --dot KING.mds.panel.pdf --discrete_color Dark2
 vtools execute ANNOVAR geneanno
 vtools output variant chr pos ref alt mut_type --limit 20 --header
 vtools_report trans_ratio variant -n num
 vtools_report trans_ratio variant -n numGD10
 vtools select variant "DP&lt;15" -t to_remove
 vtools show tables
 vtools remove variants to_remove -v0
 vtools show tables
 vtools remove genotypes "DP_geno&lt;10" -v0  <br />vtools select variant "mut_type like 'non%' or mut_type like 'stop%' or region_type='splicing'" -t v_funct  <br />vtools show tables  <br />vtools show samples --limit 5  <br />vtools select variant --samples "RACE=1" -t CEU  <br />mkdir -p ceu <br />cd ceu <br />vtools init ceu --parent ../ --variants CEU --samples "RACE=1" --build hg19
 vtools show project
 vtools select variant "CEU_mafGD10&gt;=0.05" -t common_ceu
 vtools select v_funct "CEU_mafGD10&lt;0.01" -t rare_ceu  <br />vtools use refGene  <br />vtools show annotation refGene  <br />vtools associate -h  <br />vtools show tests  <br />vtools show test LinRegBurden <br />vtools associate common_ceu BMI --covariate SEX -m "LinRegBurden --alternative 2" -j1 --to_db EA_CV &gt; EA_CV.asso.res
 grep -i error *.log
 less EA_CV.asso.res
 sort -g -k7 EA_CV.asso.res | head
 vtools show fields
 vtools associate rare_ceu BMI --covariate SEX -m "LinRegBurden --alternative 2" -g refGene.name2 -j1 --to_db EA_RV &gt; EA_RV.asso.res
 grep -i error *.log | tail -22
 less EA_RV.asso.res
 sort -g -k6 EA_RV.asso.res | head
 vtools associate rare_ceu BMI --covariate SEX -m "VariableThresholdsQt --alternative 2 -p 100000 --adaptive 0.0005" -g refGene.name2 -j1 --to_db EA_RV &gt; EA_RV_VT.asso.res
 grep -i error *.log | tail -22
 less EA_RV_VT.asso.res
 sort -g -k6 EA_RV_VT.asso.res | head
 vtools select rare_ceu "refGene.name2='ABCC1'" -o chr pos ref alt CEU_mafGD10 numGD10 mut_type --header
 vtools_report plot_association qq -o QQRV -b --label_top 2 -f 6 &lt; EA_RV.asso.res
 vtools_report plot_association manhattan -o MHRV -b --label_top 5 --color Dark2 --chrom_prefix None -f 6 &lt; EA_RV.asso.res <br />vtools associate rare_ceu BMI --covariate SEX KING_MDS1 KING_MDS2 -m "LinRegBurden --name RVMDS2 --alternative 2" -g refGene.name2 -j1 --to_db EA_RV &gt; EA_RV_MDS2.asso.res
 vtools_report plot_association qq -o QQRV_MDS2 -b --label_top 2 -f 6 &lt; EA_RV_MDS2.asso.res  <br />cd ..  <br />vtools select variant --samples "RACE=0" -t YRI <br />mkdir -p yri <br />cd yri <br />vtools init yri --parent ../ --variants YRI --samples "RACE=0" --build hg19 <br />vtools select variant "YRI_mafGD10&gt;=0.05" -t common_yri
 vtools select v_funct "YRI_mafGD10&lt;0.01" -t rare_yri  <br />vtools use refGene  <br />vtools associate common_yri BMI --covariate SEX -m "LinRegBurden --alternative 2" -j1 --to_db YA_CV &gt; YA_CV.asso.res
 vtools associate rare_yri BMI --covariate SEX -m "LinRegBurden --alternative 2" -g refGene.name2 -j1 --to_db YA_RV &gt; YA_RV.asso.res
 vtools associate rare_yri BMI --covariate SEX -m "VariableThresholdsQt --alternative 2 -p 100000 --adaptive 0.0005" -g refGene.name2 -j1 --to_db YA_RV &gt; YA_RV_VT.asso.res
 cd ..
 vtools_report meta_analysis ceu/EA_RV_VT.asso.res yri/YA_RV_VT.asso.res --beta 5 --pval 6 --se 7 -n 2 --link 1 &gt; META_RV_VT.asso.res
 cut -f1,3 META_RV_VT.asso.res | head