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

Author

Listed:
  • Sjoerd van Alten

    (Vrije Universiteit Amsterdam)

  • Benjamin Domingue
  • Jessica Faul

    (University of Michigan)

  • Titus Galama

    (University of Southern California)

  • Andries Marees

    (Vrije Universiteit Amsterdam)

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.

Suggested Citation

  • Sjoerd van Alten & Benjamin Domingue & Jessica Faul & Titus Galama & Andries Marees, 2023. "GWAS 2.0: Correcting for Volunteer Bias in GWAS Uncovers Novel Genetic Variants and Increases Heritability Estimates," Working Papers 2023-022, Human Capital and Economic Opportunity Working Group.
    • Handle: RePEc:hka:wpaper:2023-022
    Note: HI, MIP
    as

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    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/van-Alten_Domingue_Faul_etal_2023_gwas-20-correct-volunteer-bias.pdf
    File Function: First version, June 12, 2023
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    More about this item

    Keywords

    selection bias; UK Biobank; inverse probability weighting;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health

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