View source for AdvGeneMap2018Commands

__NOTITLE__
__FORCETOC__



===Functional Annotation===
 table_annovar.pl
 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

===GenABEL===
 # Load files
 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
 sample.size &lt;- g.dat@gtdata@nids
 # number of SNPs
 snps.total &lt;- g.dat@gtdata@nsnps
 print(c(sample.size, snps.total)) 
 # Trait
 summary(g.dat@phdata$disease)
 hist(g.dat@phdata$disease, main="Quantitative Phenotype data summary", xlab = "Systolic pressure measure", freq = F,breaks=20, col="gray")
 rug(g.dat@phdata$disease) 
 ###
 # tests for association
 ###
 # GLM test
 test.snp &lt;- scan.glm('disease ~ CRSNP', family = gaussian(), data = g.dat)
 names(test.snp)
 alpha &lt;- 5e-8  
 test.snp$snpnames[test.snp$P1df < alpha]
 test.snp$P1df[test.snp$P1df < alpha]
 # Score test
 test.qt &lt;- qtscore(disease, data = g.dat, trait = "gaussian")
 slotNames(test.qt)
 names(test.qt@results)
 test.qt@lambda
 descriptives.scan(test.qt)
 rownames(results(test.qt))[results(test.qt)$P1df < alpha]
 results(test.qt)$P1df[results(test.qt)$P1df < alpha] 
 results(test.qt)$Pc1df[results(test.qt)$Pc1df < alpha]
 # QQ plot
 obs &lt;- sort(results(test.qt)$P1df) 
 ept &lt;- c(1:length(obs)) / (length(obs) + 1) 
 plot(-log10(ept), -log10(obs), main = "GWAS QQ plot, qtl", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)")
 abline(0, 1, col = "red")
 abline(h = 8, lty = 2)
 # Manhattan plot        
 plot(test.qt, col = "black")
 # Adding confounders
 test.qt.sex &lt;- qtscore(disease ~ sex, data = g.dat, trait = "gaussian")
 rownames(results(test.qt.sex))[results(test.qt)$P1df < alpha]
 summary(lm(disease ~ sex, data = g.dat))
 ###
 # MDS
 ###
 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(-0.16, 0.06, c("TSI","MEX", "CEU"), pch = c(1,2,3))  
 ###
 # Corrected test
 ###
 # Incorporating PCs as predictors
 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
 # scree plot
 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)
 # cumulative plot
 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)
 # Genomic control
 # Uncorrected GIF
 test.qt@lambda 
 # Corrected p-value
 row.names(results(test.qt))[results(test.qt)$Pc1df < alpha]
 results(test.qt)$Pc1df[results(test.qt)$Pc1df < alpha]
 # Check for inflation of statistic 
 obs &lt;- sort(results(test.qt)$chi2.1df)
 ept &lt;- sort(qchisq(1:length(obs) / (length(obs) + 1), df = 1)) 
 plot(ept, obs, main = "Genomic control (slope is the inflation factor)", xlab="Expected chisq, 1df", ylab="Observed chisq, 1df")
 abline(0, 1, col = "red")
 abline(0, test.qt@lambda[1], lty = 2)
 # Definition of GIF
 # Conventional definition
 median(results(test.qt)$chi2.1df)/0.456
 # GenABEL definition
 lm(obs~ept)$coef[2]
 # QQ plot
 obs &lt;- sort(results(test.qt)$Pc1df)
 ept &lt;- c(1:length(obs)) / (length(obs) + 1)
 plot(-log10(ept), -log10(obs), main = "GWAS QQ plot adj. via Genomic Control", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)")
 abline(0, 1, col = "red")
 abline(h = 8, lty = 2)
 # EIGENSTRAT
 adj.gkin = gkin
 diag(adj.gkin) = hom(g.dat)$Var
 # naxes = 3 is default value
 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)$P1df < alpha]
 lambda.eg &lt;- test.eg@lambda
 snp.eg 
 pvalue.eg
 lambda.eg
 # Change #PCs
 for (k in 1:10){ 
 test.tmp &lt;- egscore(disease, data = g.dat, kin = adj.gkin, naxes = k)
 print(test.tmp@lambda$estimate)
 }
 # QQ plot
 obs &lt;- sort(results(test.eg)$Pc1df)
 ept &lt;- c(1:length(obs)) / (length(obs) + 1) 
 qqplot(-log10(ept), -log10(obs), main = "GWAS QQ plot adj. w/ EIGENSTRAT", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)")
 abline(0, 1, col = "red")
 abline(h = 8, lty = 2)
 # Manhattan plot comparison
 plot(test.qt, col = "black")
 add.plot(test.eg, col = "gray", pch = 3)
 legend("topright", c("Original plot","After correction w/ EIGENSTRAT"), pch = c(1,3))  
 ###
 # Basic test, binary trait
 ###
 # load files to GenABEL
 convert.snp.tped(tped = "gwa_gabel.tped", tfam = "gwa_gabel.tfam", out = "gwa_gabel.raw", strand = "u")
 b.dat &lt;- load.gwaa.data(phen = "gwa_gabel.praw", gen = "gwa_gabel.raw", force = T)
 slotNames(b.dat)
 slotNames(b.dat@gtdata)
 colnames(b.dat@phdata)
 # sample size
 b.dat@gtdata@nids
 # number of cases and controls
 case.size &lt;- length(which(b.dat@phdata$disease == 1))
 control.size &lt;- length(which(b.dat@phdata$disease == 0))
 case.size 
 control.size 
 # number of SNPs
 snpsb.total &lt;- b.dat@gtdata@nsnps
 # GLM test
 testb.snp &lt;- scan.glm('disease ~ CRSNP', family = binomial(), data = b.dat)
 names(testb.snp)  
 alpha &lt;- 5e-8
 testb.snp$snpnames[testb.snp$P1df < alpha]
 testb.snp$P1df[testb.snp$P1df < alpha]
 # Score test
 testb.qt &lt;- qtscore(disease, data = b.dat, trait = "binomial")
 slotNames(testb.qt)
 descriptives.scan(testb.qt)
 row.names(results(testb.qt))[results(testb.qt)$P1df < alpha]
 results(testb.qt)$P1df[results(testb.qt)$P1df < alpha] 
 results(testb.qt)$Pc1df[results(testb.qt)$Pc1df < alpha]


===GxG Interaction===

 ./plink --noweb --ped simcasecon.ped --map simcasecon.map --assoc
 ./plink --noweb --ped simcasecon.ped --map simcasecon.map --fast-epistasis
 ./plink --noweb --ped simcasecon.ped --map simcasecon.map --fast-epistasis --case-only
 ./plink --noweb --ped simcasecon.ped --map simcasecon.map --epistasis
 ./plink --noweb --ped simcasecon.ped --map simcasecon.map --recodeA --out recoded
 ./plink --noweb --ped simcasecon.ped --map simcasecon.map --make-bed --out cassiformat
 R
 # The following commands are in the R environment
 je &lt;-read.table("cassi.out", header=T)
 je
 library(ORMDR)
 recoded&lt;-read.table("recoded.raw", header=T)
 head(recoded)
 newdata&lt;-recoded[7:106]
 ormdrdata&lt;-cbind(newdata,recoded$PHENOTYPE-1)
 names(ormdrdata)[101]&lt;-"casestatus"
 head(ormdrdata)
 mdr1&lt;-mdr.c(ormdrdata, colresp=101, cs=1, combi=1, cv.fold = 10)
 mdr1$min.comb
 mdr2&lt;-mdr.c(ormdrdata, colresp=101, cs=1, combi=2, cv.fold = 10)
 mdr2$min.comb
 mdr3&lt;-mdr.c(ormdrdata, colresp=101, cs=1, combi=3, cv.fold = 10)
 mdr3$min.comb
 mdr1$test.erate
 mdr2$test.erate
 mdr3$test.erate
 mdr1mean&lt;-mean(mdr1$test.erate)
 mdr2mean&lt;-mean(mdr2$test.erate)
 mdr3mean&lt;-mean(mdr3$test.erate)
 mdr1mean
 mdr2mean
 mdr3mean
 mdr2$best.combi
 mdr2$min.comb
 mdr3$best.combi
 mdr3$min.comb
 logreg12&lt;-glm(casestatus ~ factor(snp1_2)*factor(snp2_1), family=binomial,
 data=ormdrdata)
 summary(logreg12)
 anova(logreg12)
 pchisq(701.68,4,lower.tail=F)
 pchisq(703.82,8,lower.tail=F)
 logreg345&lt;-glm(casestatus ~ factor(snp3_2)*factor(snp4_2)*factor(snp5_2),
 family=binomial, data=ormdrdata)
 summary(logreg345)
 anova(logreg345)
 pchisq(45.6,8,lower.tail=F)
 q()
 ### The following commands are in the linux shell
 ./BEAM3 beam3data.txt -o beam3results
 ./BEAM3 beam3data.txt -o beam3results -T 10

===Plink - Part 1 - Data QC===

 plink --file GWAS
 plink --file GWAS --mind 0.10 --recode --out GWAS_clean_mind
 plink --file GWAS_clean_mind --maf 0.05 --recode --out MAF_greater_5
 plink --file GWAS_clean_mind --exclude MAF_greater_5.map --recode --out MAF_less_5
 plink --file MAF_greater_5 --geno 0.05 --recode --out MAF_greater_5_clean
 plink --file MAF_less_5 --geno 0.01 --recode --out MAF_less_5_clean
 plink --file MAF_greater_5_clean --merge MAF_less_5_clean.ped MAF_less_5_clean.map --recode --out GWAS_MAF_clean
 plink --file GWAS_MAF_clean --mind 0.03 --recode --out GWAS_clean2
 plink --file GWAS_clean2 --check-sex --out GWAS_sex_checking
 #### in R - open R by simply typing R
 setwd("to_your_working_directory/")
 sexcheck &#61; read.table("GWAS_sex_checking.sexcheck", header&#61;T)
 names(sexcheck)
 sex_problem &#61; sexcheck[which(sexcheck$STATUS&#61;&#61;"PROBLEM"),]
 sex_problem
 q()
 ##################################
 plink --file GWAS_clean2 --genome --out duplicates
 #### in R
 setwd("to_your_working_directory/")
 dups &#61; read.table("duplicates.genome", header &#61; T)
 problem_pairs &#61; dups[which(dups$PI_HAT > 0.4),]
 problem_pairs
 problem_pairs &#61; dups[which(dups$PI_HAT > 0.05),]
 myvars &#61; c("FID1", "IID1", "FID2", "IID2", "PI_HAT")
 problem_pairs[myvars]
 q()
 ######
 plink --file GWAS_clean2 --remove IBS_excluded.txt --recode --out GWAS_clean3
 plink --file GWAS_clean3 --het
 ###### in R
 Dataset &lt;- read.table("plink.het", header&#61;TRUE, sep&#61;"", na.strings&#61;"NA", dec&#61;".", strip.white&#61;TRUE)
 mean(Dataset$F)
 sd(Dataset$F)
 jpeg("hist.jpeg", height&#61;1000, width&#61;1000)
 hist(scale(Dataset$F), xlim&#61;c(-4,4))
 dev.off()
 q()
 ######
 plink --file GWAS_clean3 --pheno pheno.txt --pheno-name Aff --hardy 
 ##### in R
 hardy &#61; read.table("plink.hwe", header &#61; T)
 names(hardy)
 hwe_prob &#61; hardy[which(hardy$P < 0.0000009),]
 hwe_prob
 q()
 ##########
 plink --file GWAS_clean3 --exclude HWE_out.txt --recode --out GWAS_clean4


===Plink - Part 2 - Controlling for Substructure===

 plink --file GWAS_clean4 --genome --cluster --mds-plot 10
 #### in R
 mydata &#61; read.table("mds_components.txt", header&#61;T)
 mydata$pch[mydata$Group&#61;&#61;1 ] &lt;-15
 mydata$pch[mydata$Group&#61;&#61;2 ] &lt;-16
 mydata$pch[mydata$Group&#61;&#61;3 ] &lt;-2
 jpeg("mds.jpeg", height&#61;500, width&#61;500)
 plot(mydata$C1, mydata$C2 ,pch&#61;mydata$pch)
 dev.off()
 q()
 ######
 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --logistic --adjust --out unadj
 plink --file GWAS_clean4 --genome --cluster --pca 10 header
 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.eigenvec --covar-name PC1 --logistic --adjust --out PC1
 plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.eigenvec --covar-name PC1-PC2 --logistic --adjust --out PC1-PC2
 #### in R
 broadqq &lt;-function(pvals, title)
 {
     observed &lt;- sort(pvals)
     lobs &lt;- -(log10(observed))
     expected &lt;- c(1:length(observed))
     lexp &lt;- -(log10(expected / (length(expected)+1)))
     plot(c(0,7), c(0,7), col&#61;"red", lwd&#61;3, type&#61;"l", xlab&#61;"Expected (-logP)", ylab&#61;"Observed (-logP)", xlim&#61;c(0,max(lobs)), ylim&#61;c(0,max(lobs)), las&#61;1, xaxs&#61;"i", yaxs&#61;"i", bty&#61;"l", main &#61; title)
     points(lexp, lobs, pch&#61;23, cex&#61;.4, bg&#61;"black") }
 jpeg("qqplot_compare.jpeg", height&#61;1000, width&#61;500)
 par(mfrow&#61;c(2,1))
 aff_unadj&lt;-read.table("unadj.assoc.logistic", header&#61;TRUE)
 aff_unadj.add.p&lt;-aff_unadj[aff_unadj$TEST&#61;&#61;c("ADD"),]$P
 broadqq(aff_unadj.add.p,"Some Trait Unadjusted")
 aff_C1C2&lt;-read.table("PC1-PC2.assoc.logistic", header&#61;TRUE)
 aff_C1C2.add.p&lt;-aff_C1C2[aff_C1C2$TEST&#61;&#61;c("ADD"),]$P
 broadqq(aff_C1C2.add.p, "Some Trait Adjusted for PC1 and PC2")
 dev.off()
 gws_unadj &#61; aff_unadj[which(aff_unadj$P &lt; 0.0000001),]
 gws_unadj
 gws_adjusted &#61; aff_C1C2[which(aff_C1C2$P &lt; 0.0000001),]
 gws_adjusted




===RV-TDT===
 ### Variant Annotation
 vtools init rvtdt
 vtools import --format vcf data/data.vcf --build hg19
 vtools phenotype --from_file data/phen.txt
 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
 vtools export func_variant --format tped --samples 'phenotype is not null' &gt; vat_raw.tped
 # set marker name as chr_pos, needs to avoid duplicate name
 sort -k4 -n vat_raw.tped | awk 'BEGIN{OFS&#61;"\t";prev&#61;"None";copy&#61;1} {$2&#61;$1"_"$4; $3&#61;0; if($2&#61;&#61;prev) {$2&#61;$2"_"copy; copy&#61;copy+1} else {prev&#61;$2; copy&#61;1}; print $0}' &gt; vat_export.tped
 vtools phenotype --out family sample_name pid mid sex phenotype &gt; vat_export.tfam
 vtools use refGene-hg19_20130904
 vtools update func_variant --set 'maf&#61;0.001' # set the maf to be 0.001
 vtools select func_variant -o chr pos refGene.name2 maf --header &gt; vat_export.anno
 
 ### Phasing Trio
 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 &gt; linkage.ped cut -f2 recode12_noME.map | awk 'BEGIN{OFS&#61;"\t";} {print "M",$0}' | sed '1i\I\tid\nA\tDisease' &gt; linkage.dat
 java -Xmx10000m -jar java/linkage2beagle.jar linkage.dat linkage.ped &gt; pre_beagle.bgl
 python script/pre_phase.py -i pre_beagle.bgl -a pre_beagle_withMissing.bgl
 java -Xmx10000m -jar java/beagle.jar missing&#61;0 trios&#61;pre_beagle.bgl out&#61;bgl_phased verbose&#61;false redundant&#61;true
 gunzip bgl_phased.pre_beagle.bgl.phased.gz
 
 ### RV-TDT Analysis
 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 "running 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
 done



===Seqspark===
 hdfs dfs -put demo.vcf.bz2
 hdfs dfs -put demo.tsv
 seqspark annotation.conf
 seqspark qc.conf
 seqspark demo.conf

===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_nameCEU_totalGD10CEU_numGD10YRI_totalGD10YRI_numGD10 --header
 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
 vtools select variant "mut_type like ’non%’ or mut_type like ’stop%’ or region_type=’splicing’" -t v_funct 
 vtools show tables
 vtools show samples --limit 5
 vtools select variant --samples "RACE=1" -t CEU
 mkdir -p ceu
 cd ceu
 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
 vtools use refGene
 vtools show annotation refGene
 vtools associate -h
 vtools show tests
 vtools show test LinRegBurden
 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 -10
 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 -10
 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
 cd ..
 vtools select variant --samples "RACE=0" -t YRI
 mkdir -p yri
 cd yri
 vtools init yri --parent ../ --variants YRI --samples "RACE=0" --build hg19 vtools select variant "YRI_mafGD10&gt;=0.05" -t common_yri vtools select v_funct "YRI_mafGD10&lt;0.01" -t rare_yri
 vtools use refGene
 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; ME\ TA_RV_VT.asso.res
 cut -f1,3 META_RV_VT.asso.res | head