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Activity Number: 620 - Axles for Voxels: Recent Statistical Advances in Neuroimaging Data Analysis
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #330731 Presentation
Title: A Spatial Group Sparse Multi-Task Regression Model for Imaging Genetics
Author(s): Farouk Nathoo* and Yin Song and Shufei Ge and Liangliang Wang and Jiguo Cao
Companies: and University of Victoria and Simon Fraser University and Simon Fraser University and Simon Fraser University
Keywords: spatial model; imaging genetics; Bayesian group lasso; variational Bayes

Advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Greenlaw et al. (Bioinformatics, 2017) have recently developed a Bayesian group sparse multi-task regression model for imaging genetics based on a three-level Gaussian scale mixture formulation. The model makes certain simplifying assumptions on the covariance structure of the imaging phenotypes, and in this work we extend the model to allow for more generality. Specifically, our new model accommodates two forms of response correlation: (i) spatial correlation between imaging phenotypes on the same hemisphere of the brain, and (ii) bilateral correlation between related phenotypes on opposite hemispheres of the brain. A Bayesian group lasso prior is adopted for the regression parameters corresponding to each SNP across all regions of interest. We present and compare a fully Bayesian and a variational Bayes implementation of the model along with a comparison to the original non-spatial model in simulation studies and an application to data obtained from the Alzheimer's Disease Neuroimaging Initiative.

Authors who are presenting talks have a * after their name.

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