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

Institution:
Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA.
Publication Date:
Jan-2014
Journal:
J Am Stat Assoc
Volume Number:
109
Issue Number:
507
Pages:
997-990
Citation:
J Am Stat Assoc. 2014;109(507):997-990.
PubMed ID:
25349462
PMCID:
PMC4208701
Keywords:
Generalized low rank regression, Genetic variant, High dimension, Imaging phenotype, Markov chain Monte Carlo, Penalized method
Appears in Collections:
NA-MIC
Sponsors:
UL1 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
Generated Citation:
Zhu H., Khondker Z., Lu Z., Ibrahim J.G. Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers. J Am Stat Assoc. 2014;109(507):997-990. PMID: 25349462. PMCID: PMC4208701.
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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).

Additional Material
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Zhu-JAmStatAssoc2014-fig4.jpg (165.191kB)