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.
authors.affiliation: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
authors.forename: Zhao-Hua
authors.initials: ZH
authors.lastname: Lu
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Zakaria
authors.initials: Z
authors.lastname: Khondker
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: hzhu5@mdanderson.org.
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging* / Q000235:genetics*; D001499:Bayes Theorem; D005819:Genetic Markers; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008390:Markov Chains; D009010:Monte Carlo Method; D059906:Neuroimaging / Q000379:methods*; D020641:Polymorphism, Single Nucleotide
chemical_list: D005819:Genetic Markers
keywords: Genetic variants; Longitudinal imaging phenotypes; Low-rank regression; Markov chain Monte Carlo; Spatiotemporal correlation