GN-SCCA: GraphNet based Sparse Canonical Correlation Analysis for Brain Imaging Genetics

摘要

Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings.