nievergeltlab

R 

new_beta_se <- function(x ,phi=0.153)
{
 #Input should be a vector consisting of beta, p, maf. phi should be a value for the proportion of cases
 B=x[1]
 P=x[2]
 theta=x[3]
 
 B2=B/(phi*(1 - phi) + .5*(1-2*phi)*(1-2*theta)*B - (0.084 + 0.9*phi*(1 - 2*phi)*theta*(1-theta))/(phi*(1-phi))*B^2)

nievergeltlab

tar xvzf dnhs_qc_v2_mar7_2017.tgz --wildcards '*-qc.fam' --wildcards '*.header'

nievergeltlab

#Script to convert dosage data to GMMAT format. Requires also a list of usuable SNPs as input, but this list does not need to be filtered.

#Create a valid SNPlist
info=0.9
maf=0.01
zcat /home/maihofer/vets/qc/imputation/distribution/vets_eur_analysis_run2/daner_vets_eur_analysis_run2.gz | awk -v info=$info -v maf=$maf '{if (($8 > info) &&  ($6 > maf) && ($7 > maf) &&  ($6 < 1-maf) && ($7 < 1-maf) && ($11 != "NA")) print $2}' > european_valid_snps.snplist
echo "SNP" > snp.txt

cat snp.txt european_valid_snps.snplist | LC_ALL=C sort -k 1b,1 > european_valid_snps.sorted.snplist

nievergeltlab

#Merge data in PLINK, using merge mode 6 or 7
./plink --bfile YEHUDA --bmerge YEHUDA.bed YEHUDA.bim YEHUDA.fam --merge-mode 7 --out yehude-merge

R 
setwd('F:/rutgers_2')
dat <- read.table('yehude-merge.diff', header=T,nr=800000,stringsAsFactors=F)
library(plyr)
#Determine general amount of disagreement for each SNP. Ones that have especially high disagreement may be badly genotyped
quantile(table(dat$SNP))
#Plot it

nievergeltlab

#!/bin/bash
#PBS -V

while getopts l:d: option
do
  case "${option}"
    in
      l) snplocations=${OPTARG};;
      d) lzresults_list=${OPTARG};;   
    esac

nievergeltlab

setwd('C:/Users/adam/Desktop/roycepower')
library(plyr)

dat0 <- read.csv('me_power.csv', header=T)

dat <- dat0

#Fill in the blanks
for (i in 1:dim(dat)[1])
{

nievergeltlab

famfile <- read.table('/home/cnieverg/MEG2/qc/pts_meg2_mix_am.fam',header=F,stringsAsFactors=F)
names(famfile) <- c("FID","IID","F","M","Gender","Pheno")

mds <- read.table('/home/cnieverg/MEG2/qc/pca/pts_meg2_mix_am-qc-eu1_pca.menv.mds_cov',header=T,stringsAsFactors=F)

dat <- merge(famfile,mds,by=c("FID","IID"))


#Number of analyzed subjects of European ancestry
dim(dat)[1]

nievergeltlab

ls ../bgn | sed 's/.bed//g' | sed 's/.bim//g' | sed 's/.fam//g' | sort -u | awk '{print "../bgn/"$1".bed","../bgn/"$1".bim","../bgn/"$1".fam"}' > mergelist.txt

../plink --merge-list mergelist.txt --maf 0.00000001 --geno 0.02 --make-bed --out pgbd_nomono_goodgeno

nievergeltlab

pdf('h2_mf_perstudy.pdf',7,7)
 par(mar=c(5, 4, 4, 2) + 0.5)
 plot(dat$case,dat$SE,cex.axis=1.45,cex.lab=1.5,xlab="N Cases", ylab="Standard Error of SNP Based Heritability",main="",cex=1.5,pch=19)
 
 

nievergeltlab

LC_ALL=C sort -k1b,1 file1.txt > file1.txt.sorted
LC_ALL=C sort -k1b,1 file2.txt > file2.txt.sorted
LC_ALL=C join file1.txt.sorted file2.txt.sorted > joined.txt