Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.

ABSTRACT: To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We a

SUBMITTER: Lu ZH 

PROVIDER: S-EPMC5368019 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature