Wang Zhenchuan, Sha Qiuying, Zhang Shuanglin
Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, 49931, United States of America.
PLoS One. 2016 Mar 7;11(3):e0150975. doi: 10.1371/journal.pone.0150975. eCollection 2016.
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.
多性状的联合分析近来颇受欢迎，因为它能够增强检测基因变异的统计效能，而且越来越多的证据表明基因多效性在复杂疾病中是一种普遍现象。目前，大多数现有方法使用所有性状来检验多性状与单个变异之间的关联性。然而，那些用于关联研究的方法在存在大量噪声性状的情况下可能会失去效能。在本文中，我们提出一种“最优”最大遗传力检验（MHT-O）来检验多性状与单个变异之间的关联性。MHT-O包括一个删除与该变异关联性弱或无关联的性状的过程。通过广泛的模拟研究，我们将MHT-O的性能与MHT、基于性状的扩展Simes程序关联检验（TATES）、SUM_SCORE和多变量方差分析（MANOVA）进行了比较。我们的结果表明，在所有模拟场景中，MHT-O要么是最具效能的检验，要么与我们比较的五项检验中最具效能的检验相当。
