Variance component model to account for sample structure in genome-wide association studies
Open accessAbstract
Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.
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When conditioned on the deletion, the significance of pQTLs from only the SIRPB1 CNV region was reduced dramatically (chr 20 position 1,546,911 variant pQTL mapping, Pprimary = 3.75 × 10-11, Pconditional = 0.41).
“When conditioned on the deletion, the significance of pQTLs from only the SIRPB1 CNV region was reduced dramatically (chr 20 position 1,546,911 variant pQTL mapping, Pprimary = 3.75 × 10–11, Pconditional = 0.41, regional pQTL mapping with EMMAX linear mixed-model30 and the occurrence of the CNV and the number of its copies used as an additional covariate).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [30]
Population stratification and cryptic relatedness were addressed during summary statistic generation using kinship-based linear mixed models and genetic principal components.
“Population stratification and cryptic relatedness were addressed during the generation of summary statistics by each local study through the use of kinship-based linear mixed models54 and genetic principal components55.”
From Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use (2019) · cites this paper as [54]
We also carried out an association analysis with EMMAX, which adjusts for population stratification by including a relationship matrix as a random effect; the results were similar to those obtained using conventional association analysis.
“We also carried out an association analysis with the program EMMAX [22], which adjusts for population stratification by including a relationship matrix as a random effect; the results (Figure S1) were similar to those obtained using conventional association analysis (Figure 3).”
From Genetic Architecture of Skin and Eye Color in an African-European Admixed Population (2013) · cites this paper as [22]
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- Authors
- Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S.-Y., Freimer, N. B., Sabatti, C., & Eskin, E.
- Affiliations
- University of Michigan (US)
- University of California, Los Angeles (US)
- Harvard University (US)
- Stanford Medicine (US)
- Corresponding author
- Sabatti, Chiara
- Eskin, Eleazar
- Venue
- Nature Genetics
- Published
- 2010-03-07
- Volume
- 42
- Issue
- 4
- Pages
- 348-354
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3092069
- OpenAlex
- W2088486634
- Cited by
- 2,817
- References
- 60
- Citations by year
- 2026: 147 · 2025: 212 · 2024: 218 · 2023: 226 · 2022: 243 · 2021: 274 · 2020: 202 · 2019: 200 · 2018: 200 · 2017: 185 · 2016: 165 · 2015: 142 · 2014: 130 · 2013: 124 · 2012: 85
- FWCI
- 48.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (N01-AG-1-2109, 5u01da024417-02, 6r01hl087679-03, 5rl1mh083268-05, NH084698, HHSN271201100005C, NHLBI HL117626)
- National Heart, Lung, and Blood Institute (HL087679-01, HL117626, K25HL080079, R01 HL087679, 6R01HL087679-03)
- National Institute of Mental Health (RL1-MH083268, P30 1MH083268, 5RL1MH083268-03)
- National Institute on Drug Abuse (U01-DA024417)
- National Institute of General Medical Sciences (R01GM053275, GM053275-14)
- National Institute of Neurological Disorders and Stroke (PL1NS062410)
- National Institute of Environmental Health Sciences (N01 ES045530)
- National Institute of Dental and Craniofacial Research (UL1DE019580)
- 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
- variance component models, sample structure, population stratification, genome-wide association study, EMMAX, genetic relatedness, computational efficiency, principal component analysis, genomic control, quantitative traits, common diseases, Northern Finland Birth Cohort, Wellcome Trust Case Control Consortium
- MeSH terms
- Humans, Models, Genetic, Software, Models, Statistical, Polymorphism, Single Nucleotide, Principal Component Analysis, Quantitative Trait Loci, Population Groups, Genome-Wide Association Study
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