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GWAS 2.0: Correcting for Volunteer Bias in GWAS Uncovers Novel Genetic Variants and Increases Heritability Estimates

Sjoerd van Alten (), Benjamin Domingue, Jessica Faul, Titus Galama () and Andries Marees
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Sjoerd van Alten: Vrije Universiteit Amsterdam
Jessica Faul: University of Michigan
Titus Galama: University of Southern California
Andries Marees: Vrije Universiteit Amsterdam

No 2023-022, Working Papers from Human Capital and Economic Opportunity Working Group

Abstract: Selection bias in genome-wide association studies (GWASs) due to volunteer-based sampling (volunteer bias) is poorly understood. The UK Biobank (UKB), one of the largest and most widely used cohorts, is highly selected. We develop inverse probability weighted GWAS (WGWAS) to correct GWAS summary statistics in the UKB for volunteer bias. Across ten phenotypes, WGWAS decreases the effective sample size by 62% on average, compared to GWAS. WGWAS yields novel genome-wide significant associations, larger effect sizes and heritability estimates, and altered gene-set tissue expressions. The extent of volunteer bias’s impact on GWAS results varies by phenotype. Traits related to disease, health behaviors, and socioeconomic status were most affected. These findings suggest that volunteer bias in extant GWASs is substantial and call for a GWAS 2.0: a revisiting of GWAS, based on representative data sets, either through the development of inverse probability (IP) weights, or a greater focus on population-representative sampling.

Keywords: selection bias; UK Biobank; inverse probability weighting (search for similar items in EconPapers)
JEL-codes: C25 C83 H51 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-dem
Note: HI, MIP
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