title: Joint Analysis of Multiple Traits in Rare Variant Association Studies.
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, the majority of existing methods for the joint analysis of multiple traits test association between one common variant and multiple traits. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. Current statistical methods for rare variant association studies are for one single trait only. In this paper, we propose an adaptive weighting reverse regression (AWRR) method to test association between multiple traits and rare variants in a genomic region. AWRR is robust to the directions of effects of causal variants and is also robust to the directions of association of traits. Using extensive simulation studies, we compare the performance of AWRR with canonical correlation analysis (CCA), Single-TOW, and the weighted sum reverse regression (WSRR). Our results show that, in all of the simulation scenarios, AWRR is consistently more powerful than CCA. In most scenarios, AWRR is more powerful than Single-TOW and WSRR.
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
authors.forename: Zhenchuan
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
authors.forename: Xuexia
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Sha
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
authors.forename: Shuanglin
authors.initials: S
authors.lastname: Zhang
mesh_terms: D003198:Computer Simulation; D056726:Genetic Association Studies; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D012044:Regression Analysis
keywords: Rare variants; association studies; multiple correlated phenotypes; multiple traits; pleiotropy