title: Association Tests of Multiple Phenotypes: ATeMP.
abstract: Joint analysis of multiple phenotypes has gained growing attention in genome-wide association studies (GWASs), especially for the analysis of multiple intermediate phenotypes which measure the same underlying complex human disorder. One of the multivariate methods, MultiPhen (O' Reilly et al. 2012), employs the proportional odds model to regress a genotype on multiple phenotypes, hence ignoring the phenotypic distributions. Despite the flexibilities of MultiPhen, the properties and performance of MultiPhen are not well understood, especially when the phenotypic distributions are non-normal. In fact, it is well known in the statistical literature that the estimation is attenuated when the explanatory variables contain measurement errors. In this study, we first established an equivalence relationship between MultiPhen and the generalized Kendall tau association test, shedding light on why MultiPhen can perform well for joint association analysis of multiple phenotypes. Through the equivalence, we show that MultiPhen may lose power when the phenotypes are non-normal. To maintain the power, we propose two solutions (ATeMP-rn and ATeMP-or) to improve MultiPhen, and demonstrate their effectiveness through extensive simulation studies and a real case study from the Guangzhou Twin Eye Study.
authors.affiliation: Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, GD 510275, China; SYSU-CMU Shunde International Joint Research Institute, Shunde, GD 528300, China; Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou, GD 510275, China.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Guo
authors.affiliation: Peking University HSBC Business School, Shenzhen, GD 518055, China.
authors.forename: Yixi
authors.initials: Y
authors.lastname: Li
authors.affiliation: State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, GD 510080, China.
authors.forename: Xiaohu
authors.initials: X
authors.lastname: Ding
authors.affiliation: State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, GD 510080, China.
authors.forename: Mingguang
authors.initials: M
authors.lastname: He
authors.affiliation: Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, GD 510275, China; SYSU-CMU Shunde International Joint Research Institute, Shunde, GD 528300, China; Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou, GD 510275, China; Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, GD 510080, China.
authors.forename: Xueqin
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, GD 510275, China; Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520, United States of America; Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou, GD 510275, China.
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D008962:Models, Theoretical; D010641:Phenotype