Chi, Eric C.Allen, Genevera I.Zhou, HuaKohannim, OmidLange, KennethThompson, Paul M.2014-10-082014-10-082013Chi, Eric C., Allen, Genevera I., Zhou, Hua, et al.. "Imaging genetics via sparse canonical correlation analysis." <i>2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI),</i> (2013) IEEE: 740-743. http://dx.doi.org/10.1109/ISBI.2013.6556581.https://hdl.handle.net/1911/77452The 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.engThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE.Journal articlediffusion tensor imaginggenome wide associationcanonical correlation analysissparsitylassohttp://dx.doi.org/10.1109/ISBI.2013.6556581