Groupwise envelope models for imaging genetic analysis.
Motivated by searching for associations between genetic variants and brain imaging phenotypes, the aim of this article is to develop a groupwise envelope model for multivariate linear regression in order to establish the association between both multivariate responses and covariates. The groupwise envelope model allows for both distinct regression coefficients and distinct error structures for different groups. Statistically, the proposed envelope model can dramatically improve efficiency of tests and of estimation. Theoretical properties of the proposed model are established. Numerical experiments as well as the analysis of an imaging genetic data set obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study show the effectiveness of the model in efficient estimation. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.
Yeonhee
Y
Park
Department of Statistics, University of Florida, Gainesville, Florida 32611, U.S.A.
Zhihua
0000-0003-2656-1626
Z
Su
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.
Hongtu
H
Zhu
D000544:Alzheimer Disease; D001921:Brain / Q000000981:diagnostic imaging; D014644:Genetic Variation; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D059906:Neuroimaging / Q000706:statistics & numerical data*; D012044:Regression Analysis
Dimension reduction; Envelope model; Grassmann manifold; Reducing subspace