Bayesian Latent Variable Modelling of Longitudinal Family Data for Genetic Pleiotropy Studies

• Activity Number: 223
• Type: Topic Contributed
• Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Radu Craiu*+ and Lizhen Xu and Lei Sun
• Companies: University of Toronto and Princess Margaret Hospital and University of Toronto
• Keywords: Bayesian inference ; Longitudinal data ; Markov chain Mont Carlo ; Pleiotropy ; Laten Variable Model

Abstract

Motivated by genetic association studies of pleiotropy, we propose here a Bayesian latent variable approach to jointly study multiple outcomes or phenotypes. The proposed method models both continuous and binary phenotypes, and it accounts for serial and familial correlations when longitudinal and pedigree data have been collected. We present a Bayesian estimation method for the model parameters, and we develop a novel MCMC algorithm that builds upon hierarchical centering and parameter expansion techniques to efficiently sample the posterior distribution. We discuss phenotype and model selection in the Bayesian setting, and we study the performance of two selection strategies based on Bayes factors and spike-and-slab priors.