Paper Info
Title
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 apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients’ brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.
Year
DOI
Venue
2017
10.1016/j.neuroimage.2017.01.052
NeuroImage
Keywords
Field
DocType
Genetic variants,Longitudinal imaging phenotypes,Low-rank regression,Markov chain Monte Carlo,Spatiotemporal correlation
Markov chain Monte Carlo,Computer science,Regression analysis,Cognitive psychology,Artificial intelligence,Bayes' theorem,Linear regression,Random effects model,Pattern recognition,Multivariate statistics,Markov chain,Statistics,Bayesian probability
Journal
Volume
ISSN
Citations 
149
1053-8119
2
PageRank 
References 
Authors
0.43
12
5
Name
Order
Citations
PageRank
Zhao-Hua Lu16612.54
Zakaria Khondker220.43
Joseph G Ibrahim3212.97
Yue Wang4960143.63
Hongtu Zhu525230.21