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Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test

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  • Zhenchuan Wang
  • Qiuying Sha
  • Shuanglin Zhang

Abstract

The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods use all of the traits for testing the association between multiple traits and a single variant. However, those methods for association studies may lose power in the presence of a large number of noise traits. In this paper, we propose an “optimal” maximum heritability test (MHT-O) to test the association between multiple traits and a single variant. MHT-O includes a procedure of deleting traits that have weak or no association with the variant. Using extensive simulation studies, we compare the performance of MHT-O with MHT, Trait-based Association Test uses Extended Simes procedure (TATES), SUM_SCORE and MANOVA. Our results show that, in all of the simulation scenarios, MHT-O is either the most powerful test or comparable to the most powerful test among the five tests we compared.

Suggested Citation

  • Zhenchuan Wang & Qiuying Sha & Shuanglin Zhang, 2016. "Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    • Handle: RePEc:plo:pone00:0150975
    DOI: 10.1371/journal.pone.0150975
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