title: Parameter Expanded Algorithms for Bayesian Latent Variable Modeling of Genetic Pleiotropy Data.
abstract: Motivated by genetic association studies of pleiotropy, we propose a Bayesian latent variable approach to jointly study multiple outcomes. The models studied here can incorporate both continuous and binary responses, and can account for serial and cluster correlations. We consider Bayesian estimation for the model parameters, and we develop a novel MCMC algorithm that builds upon hierarchical centering and parameter expansion techniques to efficiently sample from the posterior distribution. We evaluate the proposed method via extensive simulations and demonstrate its utility with an application to aa association study of various complication outcomes related to type 1 diabetes. This article has supplementary material online.
authors.affiliation: Department of Statistical Sciences, University of Toronto, lizhen@utstat.toronto.edu.
authors.forename: Lizhen
authors.initials: L
authors.lastname: Xu
authors.affiliation: Department of Statistical Sciences, University of Toronto, craiu@utstat.toronto.edu.
authors.forename: Radu V
authors.initials: RV
authors.lastname: Craiu
authors.affiliation: Department of Statistical Sciences and Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, sun@utstat.toronto.edu.
authors.forename: Lei
authors.initials: L
authors.lastname: Sun
authors.affiliation: Program in Genetics and Genomic Biology, Hospital for Sick Children, and Dalla Lana School of Public Health, University of Toronto, Toronto, andrew.paterson@utoronto.ca.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Paterson
keywords: Bayesian inference; Latent Variable; Marginal Data Augmentation; Markov chain Monte Carlo; Pleiotropy