Detailed Information on Publication Record
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.