Correcting “winner's curse” in odds ratios from genomewide association findings for major complex human diseases
Open accessAbstract
Genome‐wide association studies (GWAS) provide an important approach for identifying common genetic variants that predispose to human disease. However, odds ratio (OR) estimates for the reported findings from GWAS discovery data are typically affected by a bias away from the null sometimes referred to the “winner's curse”. Also standard confidence intervals (CIs) may have far from the desired coverage rates. We applied a bias reduction method to GWAS findings from several major complex human diseases, including breast cancer, colorectal cancer, lung cancer, prostate cancer, type I diabetes, and type II diabetes. We found the simple bias correction procedure allows one to estimate bias‐adjusted ORs that have substantial consistency with ORs from subsequent replication studies, and that corresponding selection‐adjusted CIs appear to help quantify the uncertainty of the findings. Selection‐adjusted ORs and CIs can provide a reliable summary of GWAS data, and can help to choose single nucleotide polymorphisms for subsequent validation studies. Genet. Epidemiol. 34:78–91, 2010. © 2009 Wiley‐Liss, Inc.
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The smaller odds ratios estimated from the replication data could be explained by the winner's curse and shrinkage to the mean.
“Moreover, the winner's curse and shrinkage to the mean (22,25,26) could explain the smaller odds ratios that we estimated from the replication data (Table 2).”
From A genome-wide scan for common alleles affecting risk for autism (2010) · cites this paper as [25]
In many crosses, the estimated effect size is inflated by the Beavis effect or winner's curse.
“In many cases as well, the effect size estimated in the cross will be an overestimate due to the Beavis effect/winner’s curse101,102”
From Rare and common variants: twenty arguments (2012) · cites this paper as [102]
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(2023) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2023.23.pr8
Review: Heterogeneity in precision oncology — R1/PR6
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Decision: Heterogeneity in precision oncology — R0/PR4
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Review: Heterogeneity in precision oncology — R0/PR2
(2023) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2023.23.pr2
Author comment: Heterogeneity in precision oncology — R1/PR5
Jassem, J. (2023) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2023.23.pr5
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Characterising the Stability of Polygenic Risk Scores: implications for risk stratificationPreprint
Ferreira, A., Lind, P. A., Moody, H., Hickie, I. B., Olsen, C. M., Whiteman, D. C., Law, M. H., Siskind, D. J., Martin, N. G., Medland, R. C., et al. (2026) · medRxiv · doi:10.64898/2026.05.17.26353273
- Authors
- Zhong, H., & Prentice, R. L.
- Affiliations
- Fred Hutch Cancer Center (US)
- Corresponding author
- Zhong, Hua
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2009-07-28
- Volume
- 34
- Issue
- 1
- Pages
- 78-91
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC2796696
- OpenAlex
- W1972929463
- Cited by
- 94
- References
- 30
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 13 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 3 · 2018: 2 · 2017: 8 · 2016: 3 · 2015: 4 · 2014: 8 · 2013: 10 · 2012: 8
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- U.S. Public Health Service (HHSN268200764314C)
- National Cancer Institute (CA53996, P01CA053996)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- winner's curse, genome-wide association study, confidence interval coverage, selection bias, complex diseases, single nucleotide polymorphisms, breast cancer, colorectal cancer, lung cancer, prostate cancer, type 1 diabetes, type 2 diabetes, replication study
- MeSH terms
- Breast Neoplasms, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Disease, Female, Humans, Lung Neoplasms, Male, Prostatic Neoplasms, Colorectal Neoplasms, Bias, Confidence Intervals, Odds Ratio, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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