Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.

TitleBayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers.
Publication TypeJournal Article
Year of Publication2014
AuthorsZhu, Hongtu, Zakaria Khondker, Zhaohua Lu, and Joseph G. Ibrahim
Corporate AuthorsAlzheimer's Disease Neuroimaging Initiative
JournalJ Am Stat Assoc
Volume109
Issue507
Pagination997-990
Date Published2014
ISSN0162-1459
Abstract

We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1,071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI).

Alternate JournalJ Am Stat Assoc
Original PublicationBayesian generalized low rank regression models for neuroimaging phenotypes and genetic markers.
PubMed ID25349462
PubMed Central IDPMC4208701
Grant ListUL1 TR001111 / TR / NCATS NIH HHS / United States
U24 AG021886 / AG / NIA NIH HHS / United States
U01 AG024904 / AG / NIA NIH HHS / United States
U54 EB005149 / EB / NIBIB NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 CA074015 / CA / NCI NIH HHS / United States
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