  Modern attempts in providing predictive risk for complex disorders, such as
schizophrenia, integrate genetic and brain information in what is known as
imaging genetics. In this work, we propose inferential and predictive methods
to relate the presence of a complex disorder, schizophrenia, to genetic and
imaging features and predict its risk for new individuals. 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, while to estimate the predictive risk, the
most promising models are combined using a Bayesian model averaging scheme. For
these purposes, we propose an informed reversible jump Markov chain Monte
Carlo, named data driven or informed reversible jump, which is scalable to
high-dimension data when the number of covariates is much larger than the
sample size.

Applications;Computation;Methodology