Schizophrenia genes discovery by mining the minimum spanning trees from multi-dimensional imaging genomic data integration

Schizophrenia (SCZ) disease ranks among the top 10 causes of disability in developed countries worldwide. Its onset is the combination result of genetic, biological and environmental factors. It is increasingly important but difficult to determine which genes are potential biomarkers for SCZ, owing to the complex nature of the pathophysiology of this disease. In our study, we integrated genomic, epigenomic and neuroimaging data to identify genetic biomarkers for schizophrenia. Important cross-correlated features were selected using multiple sparse canonical correlation analysis (smCCA) among single nucleotide polymorphism (SNP), DNA methylation and functional magnetic resonance imaging (fMRI) data. The features were then used to construct two state (health and case) gene-gene interaction networks for SNP or DNA methylation data. A network-based framework was proposed by comparing two different minimum spanning trees (MSTs), which were extracted from two fused state gene networks, respectively. We selected top 20 genes with significant changes of network features for schizophrenia. These genes were finally validated by disease association enrichment analysis, Gene Ontology (GO) enrichment analysis, pathway enrichment analysis and related literature reports. We also demonstrated the effectiveness of our framework through the comparison with other network-based discovery methods. Therefore, our proposed network-based approach can effectively discover biomarkers and resulting genes, promising better diagnosis and treatment of schizophrenia disease.