Date: 2013-11-01

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Motivated by genetic association studies of pleiotropy, we propose 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 discuss some of the model misspecification effects. Central to the analysis is 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, and we study the performance of two selection strategies based on Bayes factors and spike-and-slab priors.

Speaker

Radu Craiu is a Professor of Statistics at the University of Toronto, Toronto, ON.