A Bayesian Group-Sparse Multi-Task Regression Model for Imaging Genomics

Advances in technology for brain imaging and genotyping have motivated studies examining the relationships between genetic variation and brain structure. Wang et al. (Bioinformatics, 2012) developed an approach for simultaneous regression parameter estimation and SNP selection based on penalized regression with a group l_{2,1}-norm penalty. The group-norm penalty formulation incorporates the biological group structures among SNPs induced from their genetic arrangement and enforces sparsity at the group level. Wang et al. do not provide standard errors or other inferential methodology for their parameter estimates. In this paper, we propose a corresponding Bayesian model that allows for full posterior inference for the regression parameters using Gibbs sampling. Properties of our method are investigated using simulation studies and the methodology is applied to a large dataset collected as part of the Alzheimer's Disease Neuroimaging Initiative.Non UBCUnreviewedAuthor affiliation: University of VictoriaFacult