Estimation of effect size distribution from genome-wide association studies and implications for future discoveries
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We report a set of tools to estimate the number of susceptibility loci and the distribution of their effect sizes for a trait on the basis of discoveries from existing genome-wide association studies (GWASs). We propose statistical power calculations for future GWASs using estimated distributions of effect sizes. Using reported GWAS findings for height, Crohn's disease and breast, prostate and colorectal (BPC) cancers, we determine that each of these traits is likely to harbor additional loci within the spectrum of low-penetrance common variants. These loci, which can be identified from sufficiently powerful GWASs, together could explain at least 15-20% of the known heritability of these traits. However, for BPC cancers, which have modest familial aggregation, our analysis suggests that risk models based on common variants alone will have modest discriminatory power (63.5% area under curve), even with new discoveries.
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If half a dozen common variants explain 10% of population risk, the remaining risk is due to many variants each explaining much less than 1% with genotype relative risks below 1.1.
“If half a dozen common variants explain 10% of risk in the population, the remainder is attributable to a myriad of variants that each explain considerably less than 1% and have genotype relative risks less than 1.112.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [12]
Sample sizes can be extrapolated from the range of variant effects in initial discovery samples.
“In fact, sample sizes can be extrapolated from the range of variant effects in initial discovery samples12”
From Rare and common variants: twenty arguments (2012) · cites this paper as [12]
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- Authors
- Park, J.-H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J., & Chatterjee, N.
- Affiliations
- National Institutes of Health (US)
- Fred Hutch Cancer Center (US)
- Corresponding author
- Chatterjee, Nilanjan
- Venue
- Nature Genetics
- Published
- 2010-06-20
- Volume
- 42
- Issue
- 7
- Pages
- 570-575
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC4615599
- OpenAlex
- W2138069158
- Cited by
- 678
- References
- 36
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 26 · 2023: 29 · 2022: 32 · 2021: 37 · 2020: 31 · 2019: 24 · 2018: 31 · 2017: 43 · 2016: 35 · 2015: 46 · 2014: 47 · 2013: 68 · 2012: 85
- FWCI
- 47.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Cancer Research UK
- National Institutes of Health (Z99 CA999999)
- National Cancer Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- effect size distribution, genome-wide association study, genetic susceptibility loci, statistical power, sample size, heritability, height, Crohn's disease, breast cancer, prostate cancer, colorectal cancer, risk prediction model, discriminatory power
- MeSH terms
- Algorithms, Body Height, Breast Neoplasms, Crohn Disease, Female, Gene Frequency, Genotype, Humans, Male, Models, Genetic, Prostatic Neoplasms, Colorectal Neoplasms, Statistics, Nonparametric, Penetrance, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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