Parameter Expanded Algorithms for Bayesian Latent Variable Modeling of Genetic Pleiotropy Data

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 an association study of various complication outcomes related to Type 1 diabetes. This article has supplementary material online.