J 2012

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

VOUNOU, Maria, Eva JANOUŠOVÁ, Robin WOLZ, Jason L STEIN, Paul M THOMPSON et. al.

Basic information

Original name

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

Authors

VOUNOU, Maria, Eva JANOUŠOVÁ, Robin WOLZ, Jason L STEIN, Paul M THOMPSON, Daniel RUECKERT and Giovanni MONTANA

Edition

Neuroimage, Spojené státy americké, Elsevier, 2012, 1053-8119

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30000 3. Medical and Health Sciences

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 6.252

Organization unit

Faculty of Medicine

UT WoS

000301218700072

Keywords in English

imaging genetics, genome-wide association, sparse reduce rank regression, sRRR, penalized multivariate model, Alzheimer's disease, mild cognitive impairment, variable selection

Tags

International impact, Reviewed
Změněno: 23/4/2014 15:22, Soňa Böhmová

Abstract

V originále

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer's disease (AD). Using a sample from the Alzheimer's Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data resampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.