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Variance component model to account for sample structure in genome-wide association studies

Nature Genetics volume 42, pages 348–354 (2010)Cite this article

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Abstract

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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Figure 1: Scatter plots of the first two principal components against latitude and longitude.
Figure 2: The genomic control parameters for ten traits change with the number of principal components used for adjustment.
Figure 3: Comparison of P value distributions across different methods with NFBC66 data.
Figure 4: Rank concordance comparison of strongly associated SNPs between different methods.
Figure 5: Distribution of the marker-specific inflation factors from NFBC66 data sets.

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Acknowledgements

We thank the NFBC66 team for access to phenotype and genotype data used in the analyses presented here. The genotype data were generated at the Broad Institute with support from National Heart, Lung, and Blood Institute grant 6R01HL087679-03. We thank D. Clayton for reading through the manuscript and for providing important suggestions. We acknowledge the WTCCC for allowing us to use their data set. H.M.K., N.A.Z., J.H.S. and E.E. are supported by National Science Foundation grants 0513612, 0731455 and 0729049, and National Institutes of Health (NIH) grants 1K25HL080079 and U01-DA024417. N.A.Z. is supported by the Microsoft Research Fellowship. H.M.K. is supported by the Samsung Scholarship, National Human Genome Research Institute grant HG00521401, National Institute for Mental Health grant NH084698 and GlaxoSmithKline. C.S. is partially supported by NIH grants GM053275-14, HL087679-01, P30 1MH083268, 5PL1NS062410-03, 5UL1DE019580-03 and 5RL1MH083268-03. N.B.F. and S.K.S. are supported by NIH grants HL087679-03, 5PL1NS062410-03, 5UL1DE019580-03 and 5RL1MH083268-03. This research was supported in part by the University of California, Los Angeles subcontract of contract N01-ES-45530 from the National Toxicology Program and National Institute of Environmental Health Sciences to Perlegen Sciences.

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  1. Hyun Min Kang and Jae Hoon Sul: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Hyun Min Kang

  2. Center for Computational Medicine and Bioinformatics, The University of Michigan Medical School, Ann Arbor, Michigan, USA

    Hyun Min Kang

  3. Computer Science Department, University of California, Los Angeles, California, USA

    Jae Hoon Sul & Eleazar Eskin

  4. Center for Neurobehavioral Genetics, University of California, Los Angeles, California, USA

    Susan K Service, Sit-yee Kong & Nelson B Freimer

  5. Department of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA

    Noah A Zaitlen

  6. Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA

    Chiara Sabatti

  7. Department of Human Genetics, University of California, Los Angeles, California, USA

    Eleazar Eskin

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Contributions

H.M.K., J.H.S., C.S. and E.E. designed the methods and experiments; H.M.K., J.H.S., S.K.S., S.-y.K., N.B.F., C.S. and E.E. jointly analyzed the NFBC66 data set; H.M.K., J.H.S., N.A.Z., C.S. and E.E. jointly analyzed the WTCCC data set; H.M.K., J.H.S., S.K.S., N.B.F., C.S. and E.E. wrote the manuscript; all authors contributed their critical reviews of the manuscript during its preparation.

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Correspondence to Chiara Sabatti or Eleazar Eskin.

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Kang, H., Sul, J., Service, S. et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42, 348–354 (2010). https://doi.org/10.1038/ng.548

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