Statistical methods for testing genetic pleiotropy

Daniel J Schaid, Xingwei Tong, Beth Larrabee, Richard B Kennedy, Gregory A. Poland, Jason P. Sinnwell

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)483-497
Number of pages15
JournalGenetics
Volume204
Issue number2
DOIs
StatePublished - Oct 1 2016

Keywords

  • Constrained model
  • Likelihood-ratio test
  • Multivariate analysis
  • Seemingly unrelated regression
  • Sequential testing

ASJC Scopus subject areas

  • Genetics

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  • Cite this

    Schaid, D. J., Tong, X., Larrabee, B., Kennedy, R. B., Poland, G. A., & Sinnwell, J. P. (2016). Statistical methods for testing genetic pleiotropy. Genetics, 204(2), 483-497. https://doi.org/10.1534/genetics.116.189308