Groupwise envelope models for imaging genetic analysis

Yeonhee Park, Zhihua Su, Hongtu Zhu

Research output: Contribution to journalArticle

  • 1 Citations

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)
Pages1243-1253
Number of pages11
JournalBiometrics
Volume73
Issue number4
DOIs
StatePublished - Jan 1 2017

Fingerprint

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

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(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, Vol. 73, No. 4, 01.01.2017, p. 1243-1253.

Research output: Contribution to journalArticle

Park, Y, Su, Z & Zhu, H 2017, 'Groupwise envelope models for imaging genetic analysis' Biometrics, vol 73, no. 4, pp. 1243-1253. DOI: 10.1111/biom.12689
Park Y, Su Z, Zhu H. Groupwise envelope models for imaging genetic analysis. Biometrics. 2017 Jan 1;73(4):1243-1253. Available from, DOI: 10.1111/biom.12689
Park, Yeonhee ; Su, Zhihua ; Zhu, Hongtu. / Groupwise envelope models for imaging genetic analysis. In: Biometrics. 2017 ; Vol. 73, No. 4. pp. 1243-1253
@article{19f60cfa49804252a899dda8d28805d6,
title = "Groupwise envelope models for imaging genetic analysis",
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.",
keywords = "Dimension reduction, Envelope model, Grassmann manifold, Reducing subspace",
author = "Yeonhee Park and Zhihua Su and Hongtu Zhu",
year = "2017",
month = "1",
day = "1",
doi = "10.1111/biom.12689",
language = "English (US)",
volume = "73",
pages = "1243--1253",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "4",

}

TY - JOUR

T1 - Groupwise envelope models for imaging genetic analysis

AU - Park,Yeonhee

AU - Su,Zhihua

AU - Zhu,Hongtu

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

KW - Dimension reduction

KW - Envelope model

KW - Grassmann manifold

KW - Reducing subspace

UR - http://www.scopus.com/inward/record.url?scp=85016405493&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016405493&partnerID=8YFLogxK

U2 - 10.1111/biom.12689

DO - 10.1111/biom.12689

M3 - Article

VL - 73

SP - 1243

EP - 1253

JO - Biometrics

T2 - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 4

ER -