Computational protocol: Genome-Wide Association Study Identifies Pharmacogenomic Loci Linked with Specific Antihypertensive Drug Treatment and New-Onset Diabetes

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Protocol publication

[…] DNA samples were genotyped at the RIKEN Center for Integrative Medical Sciences (Yokohama, Japan) using the Illumina OmniExpressExome Beadchip. Subsequently, individual and SNP level QC procedures were performed using PLINK (v1.07). Minor allele frequency (MAF) and genotyping call rates were assessed. Concordance of genetic sex to pedigree sex was evaluated via X-chromosome heterozygosity. Cryptic relativeness or sample duplication was assessed through genome-wide identity-by-descent analysis. Potential sample contamination was tested using the inbreeding coefficient. SNPs or individuals were removed if any of the following criteria were met: genotyping call rate <95%, mismatch of genetic sex with pedigree sex, sample duplication, or potential sample contamination. A PCA was performed using EIGENSTRAT on a linkage-disequilibrium (LD) pruned set of high-quality SNPs that passed QC, and genetic continental ancestry was determined based on PCA clustering. PCA was then performed within each defined genetic ancestry group to identify PCs that best summarized genetic structure and ancestry clusters for each race group. Hardy-Weinberg Equilibrium (HWE) was assessed for each SNP, and deviations were flagged.Genome-wide imputation was conducted at the RIKEN Center for Integrative Medical Sciences using the 1000 Genomes phase I, release 3, multiethnic haplotype dataset as a reference (released April 30, 2012) for all study participants. SNPs that passed QC with MAF >0.01 in any of the INVEST race groups or 1000 Genome reference populations (EUR/AFR/AMR) were included for imputation. Called SNPs were aligned to the forward strand on the human genome reference Build 37 and oriented to the 1000 Genomes reference and alternate alleles. SNPs with alleles that did not match those in 1000 Genomes were removed. The oriented genotypes were then phased using SHAPEIT2 (v.778) and genotypes were imputed using IMPUTE2 (v.2.3.0). After genome-wide imputation, SNPs with a quality information metric <0.4, which demonstrated lower imputation certainty, were excluded. Additionally, imputed SNPs with MAF<0.03 in each race group were excluded. […]

Pipeline specifications

Software tools PLINK, SHAPEIT, IMPUTE
Applications Population genetic analysis, GWAS
Diseases Diabetes Mellitus