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Including known covariates can reduce power to detect genetic effects in case-control studies

Nature Genetics volume 44, pages 848–851 (2012)Cite this article

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Abstract

Genome-wide association studies (GWAS) search for associations between genetic variants and disease status, typically via logistic regression. Often there are covariates, such as sex or well-established major genetic factors, that are known to affect disease susceptibility and are independent of tested genotypes at the population level. We show theoretically and with data from recent GWAS on multiple sclerosis, psoriasis and ankylosing spondylitis that inclusion of known covariates can substantially reduce power for the identification of associated variants when the disease prevalence is lower than a few percent. Whether the inclusion of such covariates reduces or increases power to detect genetic effects depends on various factors, including the prevalence of the disease studied. When the disease is common (prevalence of >20%), the inclusion of covariates typically increases power, whereas, for rarer diseases, it can often decrease power to detect new genetic associations.

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Figure 1: Results with and without the covariate.

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Acknowledgements

We thank G. Nicholson for helpful comments. This work was funded by the Wellcome Trust, as part of the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z) and through the Wellcome Trust core grant for the Wellcome Trust Centre for Human Genetics (090532/Z/09/Z). P.D. was supported in part by a Wolfson Royal Society Merit Award and a Wellcome Trust Senior Investigator Award (095552/Z/11/Z). C.C.A.S. was supported in part by a Wellcome Trust Career Development Fellowship (097364/Z/11/Z).

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Authors and Affiliations

  1. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Matti Pirinen, Peter Donnelly & Chris C A Spencer

  2. Department of Statistics, University of Oxford, Oxford, UK

    Peter Donnelly

Authors
  1. Matti Pirinen
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  2. Peter Donnelly
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  3. Chris C A Spencer
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Contributions

M.P., P.D. and C.C.A.S. jointly designed the study and wrote the paper. M.P. derived the mathematical results and carried out the example analyses.

Corresponding authors

Correspondence to Matti Pirinen or Chris C A Spencer.

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The authors declare no competing financial interests.

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Supplementary Figures 1–7 and Supplementary Note (PDF 259 kb)

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Pirinen, M., Donnelly, P. & Spencer, C. Including known covariates can reduce power to detect genetic effects in case-control studies. Nat Genet 44, 848–851 (2012). https://doi.org/10.1038/ng.2346

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