Quantifying and correcting for the winner's curse in genetic association studies
Open accessAbstract
Genetic association studies are a powerful tool to detect genetic variants that predispose to human disease. Once an associated variant is identified, investigators are also interested in estimating the effect of the identified variant on disease risk. Estimates of the genetic effect based on new association findings tend to be upwardly biased due to a phenomenon known as the “winner's curse.” Overestimation of genetic effect size in initial studies may cause follow‐up studies to be underpowered and so to fail. In this paper, we quantify the impact of the winner's curse on the allele frequency difference and odds ratio estimators for one‐ and two‐stage case‐control association studies. We then propose an ascertainment‐corrected maximum likelihood method to reduce the bias of these estimators. We show that overestimation of the genetic effect by the uncorrected estimator decreases as the power of the association study increases and that the ascertainment‐corrected method reduces absolute bias and mean square error unless power to detect association is high. Genet. Epidemiol. 33:453–462, 2009. © 2009 Wiley‐Liss, Inc.
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Many of the earlier childhood aggression candidate gene studies and GWASs were limited by insufficient sample sizes, lending itself to potential spurious relationship reportings and overestimation of effect sizes, a phenomenon known as the winner's curse.
“It should be noted that many of the earlier childhood aggression candidate gene studies and GWASs were limited by insufficient sample sizes, lending itself to potential spurious relationship reportings and overestimation of effect sizes, a phenomenon known as the winner’s curse [129, 130].”
From Genetics of child aggression, a systematic review (2024) · cites this paper as [129]
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Experimental — accuracy not yet measuredThe larger differential expression in earlier studies is consistent with Winner's Curse or false positives because those studies had much smaller sample sizes.
“because earlier studies tend to be far smaller in sample size, their larger differential expression is consistent with either the well-known “Winner’s Curse”41 or false positives that may occur in smaller samples.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [41]
Modeling can explain the difference between earlier studies and CommonMind Consortium results; larger differential expression in smaller earlier studies is consistent with Winner's Curse or false positives.
“Beyond case-control difference in allele frequency, our modeling can also explain the difference between earlier studies and CMC results (Supplementary Fig. 9); because earlier studies tend to be far smaller in sample size, their larger differential expression is consistent with either the well-known “Winner’s Curse”41 or false positives that may occur in smaller samples.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [41]
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- Authors
- Xiao, R., & Boehnke, M.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Boehnke, Michael
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2009-06-12
- Volume
- 33
- Issue
- 5
- Pages
- 453-462
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC2706290
- OpenAlex
- W2131935357
- Cited by
- 192
- References
- 21
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 14 · 2023: 14 · 2022: 8 · 2021: 11 · 2020: 10 · 2019: 12 · 2018: 8 · 2017: 8 · 2016: 5 · 2015: 8 · 2014: 8 · 2013: 12 · 2012: 17
- FWCI
- 11.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R56HG000376, HG-00376, R01HG000376)
- National Institute of Diabetes and Digestive and Kidney Diseases (U01DK062370, R56DK062370, DK62370, R01DK062370)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- winner's curse, genetic association studies, effect size estimation, bias correction, ascertainment correction, maximum likelihood estimation, odds ratio, allele frequency differences, case-control study, study power, follow-up study
- MeSH terms
- Algorithms, Alleles, Gene Frequency, Humans, Models, Theoretical, Phenotype, Genetic Variation, Reproducibility of Results, Models, Statistical, Genome, Human, Likelihood Functions, Odds Ratio, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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