Diagnosis status guided brain imaging genetics via integrated regression and sparse canonical correlation analysis

Brain imaging genetics use the imaging quantitative traits (QTs) as intermediate endophenotypes to identify the genetic basis of the brain structure, function and abnormality. The regression and canonical correlation analysis (CCA) coupled with sparsity regularization are widely used in imaging genetics. The regression only selects relevant features for pre-chctors. SCCA overcomes this but is unsupervised and thus could not make use of the diagnosis information. We propose a novel method integrating regression and SCCA together to construct a supervised sparse bi-multivariate learning model. The regression part plays a role of providing guidance for imaging QTs selection, and the SCCA part is focused on selecting relevant genetic markets and imaging QTs. We propose an efficient algorithm based on the alternative search method. Our method obtains better feature selection results than both regression and SCCA on both synthetic and real neuroimaging data. This demonstrates that our method is a promising bi-multivariate tool for brain imaging genetics.