Alzheimers Disease Neuroimaging Initiative (Including: Vounou, Maria, Nichols, Thomas E. and Montana, Giovanni). (2010) Discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach. NeuroImage, Vol.53 (No.3). pp. 1147-1159. ISSN 1053-8119

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Abstract

There is growing interest in performing genome-wide searches for associations between genetic variants and brain imaging phenotypes. While much work has focused on single scalar valued summaries of brain phenotype, accounting for the richness of imaging data requires a brain-wide, genome-wide search. In particular, the standard approach based on mass-univariate linear modelling (MULM) does not account for the structured patterns of correlations present in each domain. In this work, we propose sparse reduced rank regression (sRRR), a strategy for multivariate modelling of high-dimensional imaging responses (measurements taken over regions of interest or individual voxels) and genetic covariates (single nucleotide polymorphisms or copy number variations), which enforces sparsity in the regression coefficients. Such sparsity constraints ensure that the model performs simultaneous genotype and phenotype selection. Using simulation procedures that accurately reflect realistic human genetic variation and imaging correlations, we present detailed evaluations of the sRRR method in comparison with the more traditional MULM approach. In all settings considered, sRRR has better power to detect deleterious genetic variants compared to MULM. Important issues concerning model selection and connections to existing latent variable models are also discussed. This work shows that sRRR offers a promising alternative for detecting brain-wide, genome-wide associations. Crown Copyright (c) 2010 Published by Elsevier Inc. All rights reserved.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science > Statistics Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Brain -- Imaging, Regression analysis, Phenotype
Journal or Publication Title:	NeuroImage
Publisher:	Elsevier
ISSN:	1053-8119
Date:	15 November 2010
Volume:	Vol.53
Number:	No.3
Number of Pages:	13
Page Range:	pp. 1147-1159
Identification Number:	10.1016/j.neuroimage.2010.07.002
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), GlaxoSmithKline Clinical Imaging Centre, National Institutes of Health (U.S.) (NIH), National Institute on Aging (NIA), National Institute of Biomedical Imaging and Bioengineering (U.S.) , Dana Foundation
Grant number:	U01 AG024904 (NIH), P30 AG010129 (NIH), K01 AG030514 (NIH)
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URI:	http://wrap.warwick.ac.uk/id/eprint/5172

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