A Bayesian Spatial Model for Imaging Genetics

TL;DR

This paper introduces a Bayesian bivariate spatial model for analyzing the influence of genetic variation on brain structure, specifically applied to Alzheimer's disease imaging data, with improved performance over standard models.

Contribution

The paper presents a novel Bayesian spatial model for multivariate imaging genetics analysis, incorporating correlation structures and Bayesian FDR for SNP selection.

Findings

Model demonstrates superior performance over standard approaches.

Incorporates spatial correlation within and between brain hemispheres.

Successfully applied to ADNI data with 486 SNPs and MRI measures.

Abstract

We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model is motivated by an imaging genetics study of the Alzheimer's Disease Neuroimaging Initiative (ADNI), where the objective is to examine the association between images of volumetric and cortical thickness values summarizing the structure of the brain as measured by magnetic resonance imaging (MRI) and a set of 486 SNPs from 33 Alzheimer's Disease (AD) candidate genes obtained from 632 subjects. A bivariate spatial process model is developed to accommodate the correlation structures typically seen in structural brain imaging data. First, we allow for spatial correlation on a graph structure in the imaging phenotypes obtained from a neighbourhood matrix for measures on the same hemisphere of the brain. Second, we allow for correlation in the same measures obtained from different hemispheres (left/right) of the brain. We develop a mean-field variational Bayes algorithm and a Gibbs sampling algorithm to fit the model. We also incorporate Bayesian false discovery rate (FDR) procedures to select SNPs. We implement the methodology in a new release of the R package bgsmtr. We show that the new spatial model demonstrates superior performance over a standard model in our application. Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).