Efficient Bayesian variable selection with reversible jump MCMC in imaging genetics: an application to schizophrenia

From a practical perspective, proposals are one of the main bottleneck for any Markov chain Monte Carlo (MCMC) algorithm. This paper suggests a novel data driven proposal for reversible jump MCMC for Bayesian variable selection in the context of predictive risk assessment for schizophrenia based on imaging genetic data. Given functional Magnetic Resonance Image and Single Nucleotide Polymorphisms information of healthy and people diagnosed with schizophrenia, we use a Bayesian probit model to select discriminating variables for inferential purposes, while to estimate the predictive risk, the most promising models are combined using a Bayesian model averaging scheme.