Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease

Abstract

It’s increasingly important but difficult to determine potential biomarkers of schizophrenia disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of complex diseases. Genomic, epigenomic and neuroimaging data were integrated by network fusion. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation and functional magnetic resonance imaging (fMRI) data to select Important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. And two disease networks were also the same. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases, which can be applied to the diagnosis of the complex diseases in future.