Deep Hyper-Laplacian Regularized Self-representation Learning Based Structured Association Analysis for Brain Imaging Genetics

Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed for learning genotype-phenotype associations. Specifically, a deep neural network is used first to explore the nonlinear relationships among samples. Secondly, self-representation learning based on hyper-Laplacian regularization is utilized to reconstruct the original data, which can not only capture the local data structure but also explore the higher-order relationships among different subjects. In addition, the introduction of the structural regularization terms in the association analysis enables to discover chain relationships of SNPs and graphical relationships of imaging QTs, thus enhancing the biological significance of the method. Experimental results show that DHRSAA displays best canonical correlation coefficient and recognizes clearer canonical weight patterns on real neuroimaging genetics data, which suggests that the proposed DHRSAA achieves superior performance and identifies the disease-related biomarkers.