|Title||Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Lu, Zhao-Hua, Zakaria Khondker, Joseph G. Ibrahim, Yue Wang, and Hongtu Zhu|
|Corporate Authors||Alzheimer’s Disease Neuroimaging Initiative|
|Date Published||2017 Apr 01|
|Keywords||Algorithms, Alzheimer Disease, Bayes Theorem, Genetic Markers, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Markov Chains, Monte Carlo Method, Neuroimaging, Polymorphism, Single Nucleotide|
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.
|Original Publication||Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.|
|PubMed Central ID||PMC5368019|
|Grant List||U01 AG024904 / AG / NIA NIH HHS / United States |
R01 MH086633 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
/ / CIHR / Canada
R01 GM070335 / GM / NIGMS NIH HHS / United States
Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.