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Testing for genetic associations in arbitrarily structured populations

Nature Genetics volume 47, pages 550–554 (2015)Cite this article

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Abstract

We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping data, such as those measured in genome-wide association studies (GWAS). We also derive a new set of methodologies, called a 'genotype-conditional association test' (GCAT), shown to provide accurate association tests in populations with complex structures, manifested in both the genetic and non-genetic contributions to the trait. We demonstrate the proposed method on a large simulation study and on the Northern Finland Birth Cohort study. In the Finland study, we identify several new significant loci that other methods do not detect. Our proposed framework provides a substantially different approach to the problem from existing methods, such as the linear mixed-model and principal-component approaches.

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Figure 1: Rationale for the proposed test of association.
Figure 2: Performance of the association testing methods.

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Acknowledgements

This research was supported in part by US National Institutes of Health grant R01 HG006448. The NFBC data were collected by the STAMPEED: Northern Finland Birth Cohort 1966 (NFBC1966) GWAS, made available through database of Genotypes and Phenotypes (dbGaP) study accession phs000276.v2.p1. A full list of contributors to the STAMPEED study can be found on its dbGaP web site.

Author information

Author notes

  1. Minsun Song

    Present address: Present address: Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health, US Department of Health and Human Services, Rockville, Maryland, USA.,

  2. Minsun Song and Wei Hao: These authors contributed equally to this work.

Authors and Affiliations

  1. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA

    Minsun Song, Wei Hao & John D Storey

  2. Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, USA

    John D Storey

  3. Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA

    John D Storey

Authors
  1. Minsun Song
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  2. Wei Hao
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  3. John D Storey
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Contributions

J.D.S. designed the study and wrote the manuscript. J.D.S. and M.S. developed statistical theory and methods. W.H., J.D.S. and M.S. designed the simulations. W.H. analyzed the data and developed the software.

Corresponding author

Correspondence to John D Storey.

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Competing interests

The authors declare no competing financial interests.

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Supplementary Text and Figures

Supplementary Note, Supplementary Figures 1–18 and Supplementary Tables 1 and 2. (PDF 14034 kb)

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Song, M., Hao, W. & Storey, J. Testing for genetic associations in arbitrarily structured populations. Nat Genet 47, 550–554 (2015). https://doi.org/10.1038/ng.3244

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