Journal of the American Statistical Association · 2014 · 80 citations · 30 references
Public HealthBiostatisticsGeneticsBayesian BiostatisticsBayesian Data AnalysisNeurogeneticsBayesian ComputationBayesian Generalized Linear ModelsBayesian InferenceBayesian StatisticsImaging-based PhenotypingMarkov Chain Monte CarloCovariance Matrix ApproximationFinite Sample EstimationGenetic Covariance MatrixHigh-dimensional Covariance EstimationHigh-dimensional RegressionLow-rank ApproximationResponse CovariatesStructural Brain Imaging
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).
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