An Integrative Bayesian Modeling Approach to Imaging Genetics.

ABSTRACT: In this paper we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs' activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture compone

SUBMITTER: Stingo FC 

PROVIDER: S-EPMC3843531 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature