A multi-task SCCA method for brain imaging genetics and its application in neurodegenerative diseases.

BACKGROUND AND OBJECTIVES: In brain imaging genetics, multi-task sparse canonical correlation analysis (MTSCCA) is effective to study the bi-multivariate associations between genetic variations such as single nucleotide polymorphisms (SNPs) and multi-modal imaging quantitative traits (QTs). However, most existing MTSCCA methods are neither supervised nor capable of distinguishing the shared patterns of multi-modal imaging QTs from the specific patterns. METHODS: A new diagnosis-guided MTSCCA (DDG-MTSCCA) with parameter decomposition and graph-guided pairwise group lasso penalty was proposed. Specifically, the multi-tasking modeling paradigm enables us to comprehensively identify risk genetic loci by jointly incorporating multi-modal imaging QTs. The regression sub-task was raised to guide the selection of diagnosis-related imaging QTs. To reveal the diverse genetic mechanisms, the parameter decomposition and different constraints were utilized to facilitate the identification of modality-consistent and -specific genotypic variations. Besides, a network constraint was added to find out meaningful brain networks. The proposed method was applied to synthetic data and two real neuroimaging data sets respectively from Alzheimer's disease neuroimaging initiative (ADNI) and Parkinson's progression marker initiative (PPMI) databases. RESULTS: Compared with the competitive methods, the proposed method exhibited higher or comparable canonical correlation coefficients (CCCs) and better feature selection results. In particular, in the simulation study, DDG-MTSCCA showed the best anti-noise ability and achieved the highest average hit rate, about 25% higher than MTSCCA. On the real data of Alzheimer's disease (AD) and Parkinson's disease (PD), our method obtained the highest average testing CCCs, about 40% ∼ 50% higher than MTSCCA. Especially, our method could select more comprehensive feature subsets, and the top five SNPs and imaging QTs were all disease-related. The ablation experimental results also demonstrated the significance of each component in the model, i.e., the diagnosis guidance, parameter decomposition, and network constraint. CONCLUSIONS: These results on simulated data, ADNI and PPMI cohorts suggested the effectiveness and generalizability of our method in identifying meaningful disease-related markers. DDG-MTSCCA could be a powerful tool in brain imaging genetics, worthy of in-depth study.