title: USAT: A Unified Score-Based Association Test for Multiple Phenotype-Genotype Analysis.
abstract: Genome-wide association studies (GWASs) for complex diseases often collect data on multiple correlated endo-phenotypes. Multivariate analysis of these correlated phenotypes can improve the power to detect genetic variants. Multivariate analysis of variance (MANOVA) can perform such association analysis at a GWAS level, but the behavior of MANOVA under different trait models has not been carefully investigated. In this paper, we show that MANOVA is generally very powerful for detecting association but there are situations, such as when a genetic variant is associated with all the traits, where MANOVA may not have any detection power. In these situations, marginal model based methods, however, perform much better than multivariate methods. We investigate the behavior of MANOVA, both theoretically and using simulations, and derive the conditions where MANOVA loses power. Based on our findings, we propose a unified score-based test statistic USAT that can perform better than MANOVA in such situations and nearly as well as MANOVA elsewhere. Our proposed test reports an approximate asymptotic P-value for association and is computationally very efficient to implement at a GWAS level. We have studied through extensive simulations the performance of USAT, MANOVA, and other existing approaches and demonstrated the advantage of using the USAT approach to detect association between a genetic variant and multivariate phenotypes. We applied USAT to data from three correlated traits collected on 5, 816 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC, The ARIC Investigators []) Study and detected some interesting associations.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, United States of America.
authors.forename: Debashree
authors.initials: D
authors.lastname: Ray
authors.affiliation: Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minnesota, United States of America.
authors.forename: James S
authors.initials: JS
authors.lastname: Pankow
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, United States of America.
authors.forename: Saonli
authors.initials: S
authors.lastname: Basu
mesh_terms: D050197:Atherosclerosis / Q000235:genetics*; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015999:Multivariate Analysis; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide
keywords: GWAS; MANOVA; SSU; multiple correlated phenotypes; multivariate analysis; score-based test