ArticleNature communications2025
Correcting for volunteer bias in GWAS increases SNP effect sizes and heritability estimates.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Effects of TBK1 genetic polymorphisms and their interactions with hepatitis B virus mutations on the risk of hepatocellular carcinoma in Han Chinese population: a case-control study.International journal of surgery (London, England) · 2026Article
- Article
- Representativeness of the Danish Blood Donor Study relative to the general population: a cross-sectional assessment.Scientific reports · 2026Article
- Representation in genetic studies affects inference about genetic architecture.bioRxiv : the preprint server for biology · 2026Article
- Genetic underpinnings of chills from art and music.PLoS genetics · 2026Article
- Science and sensitivity in the study of not having sex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Genetics and Socioeconomic Status: Some Preliminary Evidence on Mechanisms.Journal of political economy microeconomics · 2025Article
- Unraveling the role of blood cell perturbation responses in lung cancer by Mendelian randomization.Discover oncology · 2025Article
- Correcting for volunteer bias in GWAS increases SNP effect sizes and heritability estimates.Nature communications · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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. Using inverse probability (IP) weights we estimate inverse probability weighted GWAS (WGWAS) to correct GWAS summary statistics in the UKB for volunteer bias. Our IP weights were estimated using UK Census data - the largest source of population-representative data - made representative of the UKB's sampling population. These weights have a substantial SNP-based heritability of 4.8% (s.e. 0.8%), suggesting they capture volunteer bias in GWAS. Across ten phenotypes, WGWAS yields larger SNP effect sizes, larger heritability estimates, and altered gene-set tissue expression, despite decreasing the effective sample size by 62% on average, compared to GWAS. The impact of volunteer bias on GWAS results varies by phenotype. Traits related to disease, health behaviors, and socioeconomic status are most affected. We recommend that GWAS consortia provide population weights for their data sets, or use population-representative samples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.