BIG-S2 lab GNU Affero General Public License v3 Yes MD Anderson Cancer Center NITRC Bayesian longitudinal low-rank regression 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. 2017-1-02 L2R2-0.0.1 Bayesian longitudinal low-rank regression GNU Affero General Public License v3, Imaging Genomics http://www.nitrc.org/projects/l2r2/