title: Determining Which Phenotypes Underlie a Pleiotropic Signal.
abstract: Discovering pleiotropic loci is important to understand the biological basis of seemingly distinct phenotypes. Most methods for assessing pleiotropy only test for the overall association between genetic variants and multiple phenotypes. To determine which specific traits are pleiotropic, we evaluate via simulation and application three different strategies. The first is model selection techniques based on the inverse regression of genotype on phenotypes. The second is a subset-based meta analysis ASSET [Bhattacharjee et al., ], which provides an optimal subset of nonnull traits. And the third is a modified Benjamini-Hochberg (B-H) procedure of controlling the expected false discovery rate [Benjamini and Hochberg, ] in the framework of phenome-wide association study. From our simulations we see that an inverse regression-based approach MultiPhen [O'Reilly et al., ] is more powerful than ASSET for detecting overall pleiotropic association, except for when all the phenotypes are associated and have genetic effects in the same direction. For determining which specific traits are pleiotropic, the modified B-H procedure performs consistently better than the other two methods. The inverse regression-based selection methods perform competitively with the modified B-H procedure only when the phenotypes are weakly correlated. The efficiency of ASSET is observed to lie below and in between the efficiency of the other two methods when the traits are weakly and strongly correlated, respectively. In our application to a large GWAS, we find that the modified B-H procedure also performs well, indicating that this may be an optimal approach for determining the traits underlying a pleiotropic signal.
authors.affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, United States of America.
authors.forename: Arunabha
authors.initials: A
authors.lastname: Majumdar
authors.affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, United States of America.
authors.forename: Tanushree
authors.initials: T
authors.lastname: Haldar
authors.affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco, United States of America.
authors.forename: John S
authors.initials: JS
authors.lastname: Witte
mesh_terms: D000328:Adult; D000375:Aging / Q000235:genetics; D015331:Cohort Studies; D003198:Computer Simulation; D058685:Genetic Pleiotropy; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable; D012984:Software
keywords: multiple phenotypes; multivariate association; nonnull traits; pleiotropy; selection