Groupwise envelope models for imaging genetic analysis

Yeonhee Park, Zhihua Su, Hongtu Zhu

Research output: Research - peer-reviewArticle

Abstract

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.

LanguageEnglish (US)
JournalBiometrics
DOIs
StateAccepted/In press - 2017

Fingerprint

Envelope
Imaging
Model
Genetic Models
Neuroimaging
genetic techniques and protocols
image analysis
Imaging techniques
Alzheimer Disease
Alzheimer's Disease
Distinct
Alzheimer disease
Group Structure
Linear Models
Databases
Phenotype
Datasets
Multivariate Response
Efficient Estimation
Regression Coefficient

Keywords

  • Dimension reduction
  • Envelope model
  • Grassmann manifold
  • Reducing subspace

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Groupwise envelope models for imaging genetic analysis. / Park, Yeonhee; Su, Zhihua; Zhu, Hongtu.

In: Biometrics, 2017.

Research output: Research - peer-reviewArticle

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