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

Zhenchuan Wang, Qiuying Sha and Shuanglin Zhang

PLOS ONE, 2016, vol. 11, issue 3, 1-12

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0150975

DOI: 10.1371/journal.pone.0150975

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