Integrative Bayesian Modeling Approach to Imaging Genetics

• Published in: Journal of the American Statistical Association Vol. 108; no. 503; pp. 876 - 891
• Main Authors: Stingo, Francesco C, Guindani, Michele, Vannucci, Marina, Calhoun, Vince D
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Group 01.09.2013; Taylor & Francis Group, LLC; Taylor & Francis Ltd
• Subjects: Applications and Case Studies; Bayesian analysis; Bayesian hierarchical model; Brain; data collection; Functional magnetic resonance imaging; Genetics; image analysis; magnetic resonance imaging; Markov random field; Medical imaging; Neuroimaging; patients; Polymorphism; Schizophrenia; Single-nucleotide polymorphism; Statistics; Variable selection; Neuroimaging; Variable Selection; Bayesian Hierarchical Model; Markov Random Field; Imaging Genetics; Single-nucleotide polymorphism; functional Magnetic Resonance Imaging
• ISSN: 1537-274X; 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2013.804409

In this article 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 imaging (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 dataset, 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.