title: 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 components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia data set, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features.
authors.affiliation: MD Anderson Cancer Center, Rice University and University of New Mexico.
authors.forename: Francesco C
authors.initials: FC
authors.lastname: Stingo
authors.forename: Michele
authors.initials: M
authors.lastname: Guindani
authors.forename: Marina
authors.initials: M
authors.lastname: Vannucci
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
keywords: Bayesian Hierarchical Model; Imaging Genetics; Markov Random Field; Neuroimaging; Single-nucleotide polymorphism; Variable Selection; functional Magnetic Resonance Imaging