4.4 Article

Statistical Methods for Testing Genetic Pleiotropy

Journal

GENETICS
Volume 204, Issue 2, Pages 483-497

Publisher

GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.116.189308

Keywords

constrained model; likelihood-ratio test; multivariate analysis; seemingly unrelated regression; sequential testing

Funding

  1. U.S. Public Health Service, National Institutes of Health (NIH) [GM065450]
  2. National Institute of Allergies and Infectious Diseases, NIH, Department of Health and Human Services [HHSN266200400025C (N01AI40065)]
  3. National Natural Science Foundation of China [11371062]
  4. Beijing Center for Mathematics and Information Interdisciplinary Sciences, China Zhongdian Project [11131002]
  5. Merck Research Laboratories

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Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that k+1 traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.

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