Covariate selection for association screening in multiphenotype genetic studies

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DOI

  • Hugues Aschard, Institut Pasteur, Paris, Harvard School of Public Health
  • ,
  • Vincent Guillemot, Institut Pasteur, Paris
  • ,
  • Bjarni Vilhjalmsson
  • Chirag J. Patel, Harvard Medical School
  • ,
  • David Skurnik, Brigham and Women's Hospital, Boston, Massachusetts Technology and Analytics, Université Descartes, Sorbonne Paris Cité, Institut Necker-Enfants Malades
  • ,
  • Chun J. Ye, Institute of Human Genetics
  • ,
  • Brian Wolpin, Harvard Medical School
  • ,
  • Peter Kraft, Harvard School of Public Health
  • ,
  • Noah Zaitlen, University of California, San Francisco

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.

Original languageEnglish
JournalNature Genetics
Volume49
Issue12
Pages (from-to)1789-1795
Number of pages7
ISSN1061-4036
DOIs
Publication statusPublished - 1 Dec 2017

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