Sequencing and imputation in GWAS: Cost‐effective strategies to increase power and genomic coverage across diverse populations. Issue 6 (9th June 2020)

• Record Type: Journal Article
• Authors: Quick, Corbin; Anugu, Pramod; Musani, Solomon; Weiss, Scott T.; Burchard, Esteban G.; White, Marquitta J.; Keys, Kevin L.; Cucca, Francesco; Sidore, Carlo; Boehnke, Michael; Fuchsberger, Christian
• Is Part Of: Genetic epidemiology. Volume 44:Issue 6(2020)
• Page Start: 537
• Page End: 549
• Publication Date: 2020-06-09
• Subjects: genotype imputation -- genotyping -- GWAS -- rare variants -- sequencing -- study design -- WGS; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22326
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: A key aim for current genome‐wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole‐genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost‐effective GWAS designs given current sequencing and array costs. For populations that are well‐represented in existing reference panels, we find that array genotyping alone is cost‐effective and well‐powered to detect common‐ and rare‐variant associations. For poorly represented populations, sequencing a subset of participants is often most cost‐effective, and can substantially increase imputation quality and GWAS power.