Chi Eric C, Allen Genevera I, Zhou Hua, Kohannim Omid, Lange Kenneth, Thompson Paul M
Department of Human Genetics, UCLA School of Medicine, Los Angeles, CA, USA.
Department of Statistics, Rice University, Houston, TX, USA.
Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:740-743. doi: 10.1109/ISBI.2013.6556581.
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.
从已进行全基因组扫描基因分型的受试者群体中收集脑图像，使得寻找基因对大脑的影响成为可能。即便如此，仍然迫切需要多元方法，这种方法能够利用两个数据集的相关结构，同时在图像和基因组中搜索两者之间的关系。在此，我们研究使用稀疏典型相关分析（CCA）来找出能够解释一组图像中方差的基因变异集。我们扩展了近期关于惩罚矩阵分解的工作，以考虑两个数据集中的相关性。这类方法在影像遗传学中显示出前景，因为它们利用了数据集中的自然协方差。它们还避免了在全基因组和整个图像中搜索有前景的关联时进行极其繁重的统计校正。
