Changes

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 /> summary(result.all) 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)

gws_adjusted = aff_C1C2[which(aff_C1C2$P < 0.0000001),] gws_adjusted

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",$0}' | sed '1i\I\tid\nA\tDisease' > linkage.dat java -Xmx10000m -jar java/linkage2beagle.jar linkage.dat linkage.ped > pre_beagle.bgl python script/pre_phase.py -i pre_beagle.bgl -a pre_beagle_withMissing.bgl java -Xmx10000m -jar java/beagle.jar missing=0 trios=pre_beagle.bgl out=bgl_phased verbose=false redundant=true gunzip bgl_phased.pre_beagle.bgl.phased.gz python script/post_phase.py -a vat_export.anno -b bgl_phased.pre_beagle.bgl.phased -o genes/ for g in `ls rvtdt_exercise_data genes | grep tped | cut -d"." -f1| head -20` do echo "runing rvTDT on gene "${g} ./ rvTDT exercise_proj -G ./rvtdt_exercise_datagenes/${g}.tped \ -P ./rvtdt_exercise_datadata/rvtdt_exerciservtdt.phen \ -M ./rvtdt_exercise_datagenes/${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.*

done<br />spower show exercise2.SEQPowerDB LOGIT method power title ==Unphased==

unphased all.ped -window 2 -LD &gt;&gt; results.txt==VAT==