A Bayesian group sparse multi-task regression model for imaging genetics.

ABSTRACT:

Motivation

Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. have developed an approach for the analysis of imaging genomic studies using penalized multi-task regression with regularization based on a novel group l2,1-norm penalty which encourages structured sparsity at both the gene level and SNP level. While incorporating a number of useful features, the proposed method only furnishes a point estimate of the regression coefficients; techniques for conducting statistical inference are not provided. A new Bayesian method is proposed here to overcome this limitation.

Results

We develop a Bayesian hierarchical modeling formulation where the posterior

SUBMITTER: Greenlaw K 

PROVIDER: S-EPMC5870710 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature