An Integrative Bayesian Modeling Approach to Imaging Genetics

• Activity Number: 25
• Type: Topic Contributed
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Michele Guindani*+ and Francesco Stingo and Marina Vannucci and Vince Calhoun
• Companies: and MD Anderson Cancer Center and Rice University and University of New Mexico
• Keywords: Bayesian Analysis ; fMRI data ; Imaging Genetics

Abstract

In this talk 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. This is joint work with Francesco C. Stingo, Marina Vannu