Technical Report | Published:

Covariate selection for association screening in multiphenotype genetic studies

Nature Genetics volume 49, pages 17891795 (2017) | Download Citation

Abstract

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

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Acknowledgements

H.A. and N.Z. were supported by NIH grant R03DE025665. H.A. was also supported by NIH grant R21HG007687, and N.Z. was also supported by NIH career development award K25HL121295 and NIH grant U01HG009080. C.J.P. was supported by NIH grant R00 ES023504.

Author information

Author notes

    • Peter Kraft
    •  & Noah Zaitlen

    These authors jointly directed this work.

Affiliations

  1. Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France.

    • Hugues Aschard
    •  & Vincent Guillemot
  2. Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.

    • Hugues Aschard
    •  & Peter Kraft
  3. Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

    • Hugues Aschard
    •  & Peter Kraft
  4. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.

    • Bjarni Vilhjalmsson
  5. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

    • Chirag J Patel
  6. Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • David Skurnik
  7. Massachusetts Technology and Analytics, Brookline, Massachusetts, USA.

    • David Skurnik
  8. Department of Microbiology, Necker Hospital, University Paris-Descartes, Paris, France

    • David Skurnik
  9. Institut Necker–Enfants Malades, INSERM U1151–Equipe 11, Paris, France.

    • David Skurnik
  10. Department of Epidemiology and Biostatistics, Institute of Human Genetics, San Francisco, California, USA.

    • Chun J Ye
  11. Center for Gastrointestinal Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.

    • Brian Wolpin
  12. Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.

    • Peter Kraft
  13. Department of Medicine, University of California, San Francisco, San Francisco, California, USA.

    • Noah Zaitlen

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Contributions

H.A. conceived the approach and performed all real-data analyses. H.A., N.Z., B.V., C.J.P., D.S., and P.K. contributed substantially to improving the approach and the study design. C.J.Y. contributed to the quality control and analysis of the gEUVADIS data. B.W. collected the metabolite data and contributed to quality control and analysis of the metabolite data. H.A. and N.Z. conceptualized and performed the simulation study. V.G. contributed to the simulation study. H.A. and N.Z. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Hugues Aschard.

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DOI

https://doi.org/10.1038/ng.3975