Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.

ABSTRACT: We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We a

SUBMITTER: Zhu H 

PROVIDER: S-EPMC4208701 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature