Bayesian variable selection using an informed reversible jump in imaging genetics: an application to schizophrenia

TL;DR

This paper introduces an informed reversible jump MCMC method for Bayesian variable selection in imaging genetics, improving schizophrenia risk prediction by integrating functional MRI and genetic data.

Contribution

It proposes a novel data-driven proposal mechanism for reversible jump MCMC tailored for Bayesian variable selection in imaging genetics.

Findings

Enhanced variable selection accuracy in schizophrenia prediction

Effective integration of MRI and genetic data for risk assessment

Improved computational efficiency of MCMC sampling

Abstract

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 or informed 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.