GSCAN dbGaP

Studies

Framingham

ID Mapping

The following snppit describes how the PLINK genotype file IDs (which use "sampid") were mapped to the dbGaP_Subject_ID contained in the available phenotype files. The snippet only contains one of Joyce Ling's phenotype files for illustration only.

This script and the results it produces can be found in /work/KellerLab/GSCAN/dbGaP/Framingham/ID_mapping.r

### ID mapping file from dbGaP_Subject_ID to SAMPID 
ID.map <- read.table(gzfile("/work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/RootStudyConsentSet_phs000007.Framingham.v28.p10.c1.HMB-IRB-MDS/PhenotypeFiles/phs000007.v28.pht001415.v16.p10.Framingham_Sample.MULTI.txt.gz"), header=T, sep="\t", stringsAsFactors=F)

### Genotype files
genotype.IDs <-  read.table("/work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/ChildStudyConsentSet_phs000342.Framingham.v16.p10.c1.HMB-IRB-MDS/GenotypeFiles/phg000006.v9.FHS_SHARe_Affy500K.genotype-calls-matrixfmt.c1/subject_level_PLINK_sets/FHS_SHARe_Affy500K_subjects_c1.fam", header=F)
names(genotype.IDs) <- c("famid", "SAMPID", "patid", "matid", "sex", "phenotype")

length(which(genotype.IDs$SAMPID %in% ID.map$SAMPID))
## 6954 

x <- merge(genotype.IDs, ID.map, by="SAMPID", all.x=T)
x <- x[,c(1:5,7)]

### One of Joyce's phenotype ID files (eventually all phenotypes for all available participants were merged in, but this is a good example.)
phenotypes <- read.table("OffC_Exam_1.txt", header=T, sep="\t")

xx <- merge(x, phenotypes, by="dbGaP_Subject_ID", all=TRUE)

xx <- xx[,c(2:ncol(xx),1)] ## Reorder so the dbGaP_Subject_ID is the last column 

write.table(xx, file="framingham_GSCAN_phenotypesCovariates.ped", quote=FALSE, sep="\t", row.names=F)

Phenotypes

(Joyce will update this section)

Genotypes

We used the Affy 500K genotypes found here: /work/KellerLab/GSCAN/dbGaP/Framingham/PhenoGenotypeFiles/ChildStudyConsentSet_phs000342.Framingham.v16.p10.c1.HMB-IRB-MDS/GenotypeFiles/phg000006.v9.FHS_SHARe_Affy500K.genotype-calls-matrixfmt.c1/subject_level_PLINK_sets/FHS_SHARe_Affy500K_subjects_c1.[bed|bim|fam]

ARIC

(Hannah/Joyce to update this section following Framingham as a guide)

MESA

(Hannah/Joyce to update following Framingham as a guide)

Phenotypes

Description of phenotypes can be found here: Media:MESA phenotypes - FINAL.pdf

eMERGE

(Hannah/Joyce to update following Framingham as a guide)

Phenotypes

Description of phenotypes can be found here: Media:EMERGE.pdf

Stroke

(Hannah/Joyce to update following Framingham as a guide)

Genotype Processing

Pre-Phasing QC

QC parameters that we chose: MAF > 0.01

SNP callrate > 0.95

Missingness per individual > 0.95

HWE = 0.05 / number of markers but greater than 5e-8

To update the strand builds: http://www.well.ox.ac.uk/~wrayner/strand/

## Check strands against latest 1000G: http://www.well.ox.ac.uk/~wrayner/tools/
#!/bin/bash
#SBATCH --qos=blanca-ibg
#SBATCH --mem=40gb
perl HRC-1000G-check-bim.pl -b ARIC_b37_filtered.bim -f ARIC_b37_filtered.frq -r  1000GP_Phase3_combined.legend -g -p EUR

## Phasing using shapeit
#!/bin/bash
#SBATCH --mem=20gb
#SBATCH --time=48:00:00
#SBATCH -o shapeit_aric_%j.out
#SBATCH -e shapeit_aric_%j.err
#SBATCH --qos blanca-ibgc1
#SBATCH --ntasks-per-node 48
#SBATCH -J shapeit_aric
shapeit -B ARIC_b37_filtered-updated-chr${1} -M /rc_scratch/meli7712/dbGAP/1000GP_Phase3/genetic_map_chr${1}_combined_b37.txt -O phased/ARIC_b37_filtered-updated-chr${1}.phased -T 48

## To convert the shapeit output into vcf
#!/bin/bash
#SBATCH --mem=20gb
#SBATCH --time=24:00:00
#SBATCH -o shapeit_mesa_%j.out
#SBATCH -e shapeit_mesa_%j.err
#SBATCH --qos janus
#SBATCH --ntasks-per-node 12
#SBATCH -J shapeit_mesa
shapeit -convert --input-haps mesa-chr${1}.phased --output-vcf mesa-chr${1}.phased.vcf -T 12

## Imputation
#!/bin/bash
#SBATCH --mem=30gb
#SBATCH --time=72:00:00
#SBATCH -o impute_mesa_%j.out
#SBATCH -e impute_mesa_%j.err
#SBATCH --qos blanca-ibgc1
#SBATCH --ntasks-per-node 48
#SBATCH -J impute_mesa

/work/KellerLab/Zhen/bin/Minimac3/bin/Minimac3 --haps mesa-chr${1}.phased.vcf --cpus 48 --refHaps /rc_scratch/meli7712/dbGAP/references/${1}.1000g.Phase3.v5.With.Parameter.Estimates.m3vcf.gz --chr ${1} --noPhoneHome --format GT,DS,GP --allTypedSites --prefix mesa-chr${1}.phased.imputed