A Bayesian predictive model for imaging genetics with application to schizophrenia

Abstract

Imaging genetics has rapidly emerged as a promising approach for investigating the genetic determinants of brain mechanisms that underlie an individual’s behavior or psychiatric condition. In particular, for early detection and targeted treatment of schizophrenia, it is of high clinical relevance to identify genetic variants and imaging-based biomarkers that can be used as diagnostic markers, in addition to commonly used symptom-based assessments. By combining single-nucleotide polymorphism (SNP) arrays and functional magnetic resonance imaging (fMRI), we propose an integrative Bayesian risk prediction model that allows us to discriminate between individuals with schizophrenia and healthy controls, based on a sparse set of discriminatory regions of interest (ROIs) and SNPs. Inference on a regulatory network between SNPs and ROI intensities (ROI–SNP network) is used in a single modeling framework to inform the selection of the discriminatory ROIs and SNPs. We use simulation studies to assess the performance of our method and apply it to data collected from individuals with schizophrenia and healthy controls. We found our approach to outperform competing methods that do not link the ROI–SNP network to the selection of discriminatory markers.