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Title: A Bayesian spatial model for imaging genetics Authors:  Yin Song - University of Victoria (Canada)
Shufei Ge - Simon Fraser University (Canada)
Jiguo Cao - Simon Fraser University (Canada)
Liangliang Wang - Simon Fraser University (Canada) [presenting]
Farouk Nathoo - University of Victoria (Canada)
Abstract: A Bayesian bivariate spatial group lasso model is developed for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. The 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 in the imaging phenotypes obtained from neighbouring regions on the same hemisphere of the brain. Second, we allow for correlation in the same phenotypes 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 the motivating application.