title: Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test.
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.
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, 49931, United States of America.
authors.forename: Zhenchuan
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, 49931, United States of America.
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Sha
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, 49931, United States of America.
authors.forename: Shuanglin
authors.initials: S
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation; D010641:Phenotype