IMAGING GENETICS VIA SPARSE CANONICAL CORRELATION ANALYSIS.
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.
Department of Human Genetics, UCLA School of Medicine, Los Angeles, CA, USA.
Eric C
EC
Chi
Department of Statistics, Rice University, Houston, TX, USA.
Genevera I
GI
Allen
Department of Statistics, North Carolina State University, Raleigh, NC, USA.
Hua
H
Zhou
Imaging Genetics Center, Lab. of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
Omid
O
Kohannim
Department of Human Genetics, UCLA School of Medicine, Los Angeles, CA, USA.
Kenneth
K
Lange
Imaging Genetics Center, Lab. of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
Paul M
PM
Thompson
Canonical correlation analysis; Diffusion tensor imaging; Genome wide association; lasso; sparsity