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, Daniel RUECKERT and Giovanni MONTANA. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease. Neuroimage. Spojené státy americké: Elsevier, 2012, vol. 60, No 1, p. 700-716. ISSN 1053-8119. Available from: https://dx.doi.org/10.1016/j.neuroimage.2011.12.029.

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

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 30000 3. Medical and Health Sciences
• Country of publisher: Netherlands
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 6.252
• Organization unit: Faculty of Medicine
• Doi: http://dx.doi.org/10.1016/j.neuroimage.2011.12.029
• 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

Abstract

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.