The effects of human population structure on large genetic association studies
Abstract
Large-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases. A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations. Here we examine approximately 15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies. The consequences of population structure on association outcomes increase markedly with sample size. For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored. We also examine one method for correcting for population structure (Genomic Control). Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power. The results of our analysis can guide the design of large-scale association studies.
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Undesired population structures can mimic the signal of associations and lead to false positives or missed real effects.
“To avoid the influence of undesired population structures that can mimic the signal of associations and lead to false positives or missed real effects (Marchini et al., 2004), we estimated the population structure of 239 sweet potato genotypes using the model-based approach as implemented in STRUCTURE.”
From Development and identification of SSR markers associated with starch properties and β-carotene content in the storage root of sweet potato (Ipomoea batatas L.) (2016) · cites this paper as Marchini et al., 2004
The population structure of 239 sweet potato genotypes was estimated using the model-based approach in STRUCTURE to avoid false positives from population structure.
“To avoid the influence of undesired population structures that can mimic the signal of associations and lead to false positives or missed real effects (Marchini et al., 2004), we estimated the population structure of 239 sweet potato genotypes using the model-based approach as implemented in STRUCTURE.”
From Development and identification of SSR markers associated with starch properties and β-carotene content in the storage root of sweet potato (Ipomoea batatas L.) (2016) · cites this paper as Marchini et al., 2004
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- Authors
- Marchini, J., Cardon, L. R., Phillips, M. S., & Donnelly, P.
- Affiliations
- University of Oxford (GB)
- Centre for Human Genetics (GB)
- McGill Genome Centre (CA)
- Corresponding author
- Donnelly, Peter
- Venue
- Nature Genetics
- Published
- 2004-03-28
- Volume
- 36
- Issue
- 5
- Pages
- 512-517
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W1986265706
- Cited by
- 869
- References
- 31
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 14 · 2023: 19 · 2022: 29 · 2021: 34 · 2020: 16 · 2019: 30 · 2018: 38 · 2017: 35 · 2016: 33 · 2015: 39 · 2014: 28 · 2013: 46 · 2012: 48
- FWCI
- 31.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- population structure, genetic association studies, single nucleotide polymorphisms, genomic control, false positives, statistical power, common diseases
- MeSH terms
- Genetic Markers, Genetics, Population, Humans, Models, Genetic, Genetic Variation, Linkage Disequilibrium, Quantitative Trait, Heritable, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide
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