Kernel method for detecting higher order interactions in multi-view data: An application to imaging, genetics, and epigenetics

Technological advances are enabling us to collect multiple types of data at an increasing depth and resolution while decreasing the labor needed to compile and analyze it. A central goal of multimodal data integration is to understand the interaction effects of different features. Understanding the complex interaction among multimodal datasets, however, is challenging. In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulitview data. Using a semiparametric method on a reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects linear model and derived a score-based variance component statistic that tests for higher order interactions between multi-view data. The proposed method offers an intangible framework for the identification of higher order interaction effects (e.g., three way interaction) between genetics, brain imaging, and epigenetic data. Extensive numerical simulation studies were first conducted to evaluate the performance of this method. Finally, this method was evaluated using data from the Mind Clinical Imaging Consortium (MCIC) including single nucleotide polymorphism (SNP) data, functional magnetic resonance imaging (fMRI) scans, and deoxyribonucleic acid (DNA) methylation data, respectfully, in schizophrenia patients and healthy controls. We treated each gene-derived SNPs, region of interest (ROI) and gene-derivedDNA methylation as a single testing unit, which are combined into triplets for evaluation. In addition, cardiovascular disease risk factors such as age, gender, and body mass index were assessed as covariates on hippocampal volume and compared between triplets. Our method identified 13-triplets (p-values ≤ 0.001) that included 6 gene-derived SNPs, 10 ROIs, and 6 gene-derived DNA methylations that correlated with changes in hippocampal volume, suggesting that these triplets may be important in explaining schizophrenia-related neurodegeneration. With strong evidence (p-values ≤ 0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) has the potential to distinguish schizophrenia patients from the healthy control variations. This novel method may shed light on other disease processes in the same manner, which may benefit from this type of multimodal analysis.