NDLI: Imaging genetics via sparse canonical correlation analysis

Content Provider	IEEE Xplore Digital Library
Author	Chi, E.C. Allen, G.I. Hua Zhou Kohannim, O. Lange, K. Thompson, P.M.
Copyright Year	2013
Description	Author affiliation: Dept. of Stat., Rice Univ., Houston, TX, USA (Allen, G.I.) \|\| Sch. of Med., Lab. of Neuro Imaging, UCLA, Los Angeles, CA, USA (Kohannim, O.; Thompson, P.M.) \|\| Dept. of Stat., North Carolina State Univ., Raleigh, NC, USA (Hua Zhou) \|\| Sch. of Med., Dept. of Human Genetics, UCLA, Los Angeles, CA, USA (Chi, E.C.; Lange, K.)
Abstract	The collection of brain images from populations of subjects who have been genotyped with genome-wide scans makes it feasible to search for genetic effects on the brain. Even so, multivariate methods are sorely needed that can search both images and the genome for relationships, making use of the correlation structure of both datasets. Here we investigate the use of sparse canonical correlation analysis (CCA) to home in on sets of genetic variants that explain variance in a set of images. We extend recent work on penalized matrix decomposition to account for the correlations in both datasets. Such methods show promise in imaging genetics as they exploit the natural covariance in the datasets. They also avoid an astronomically heavy statistical correction for searching the whole genome and the entire image for promising associations.
Sponsorship	IEEE
Starting Page	740
Ending Page	743
File Size	369756
Page Count	4
File Format	PDF
ISBN	9781467364560
ISSN	19457928
e-ISBN	9781467364553
DOI	10.1109/ISBI.2013.6556581
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrices Correlation Genomics Bioinformatics Biomedical imaging lasso Diffusion tensor imaging Genome wide association Canonical correlation analysis sparsity
Content Type	Text
Resource Type	Article

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