Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach

Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations

Associating Multi-modal Brain Imaging Phenotypes and Genetic Risk Factors via A Dirty Multi-task Learning Method

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics

Detecting genetic associations with brain imaging phenotypes in Alzheimer’s disease via a novel structured SCCA approach

Brain imaging genetics becomes an important research topic since it can reveal complex associations between genetic factors and the structures or functions of the human brain. Sparse canonical correlation analysis (SCCA) is a popular bi-multivariate association identification method. To mine the complex genetic basis of brain imaging phenotypes, there arise many SCCA methods with a variety of norms for incorporating different structures of interest. They often use the group lasso penalty, the fused lasso or the graph/network guided fused lasso ones. However, the group lasso methods have limited capability because of the incomplete or unavailable prior knowledge in real applications. The fused lasso and graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. In this paper, we introduce two new penalties to improve the fused lasso and the graph/network guided lasso penalties in structured sparse learning. We impose both penalties to the SCCA model and propose an optimization algorithm to solve it. The proposed SCCA method has a strong upper bound of grouping effects for both positively and negatively highly correlated variables. We show that, on both synthetic and real neuroimaging genetics data, the proposed SCCA method performs better than or equally to the conventional methods using fused lasso or graph/network guided fused lasso. In particular, the proposed method identifies higher canonical correlation coefficients and captures clearer canonical weight patterns, demonstrating its promising capability in revealing biologically meaningful imaging genetic associations