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 (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. 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. Two disease networks also followed the same procedures. 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 by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.

Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy

“…The connections were found to be reduced by schizophrenia, as shown in Figure 4. This finding was consistent with studies using other imaging modalities (Bellani et al, 2010;Deng et al, 2017).…”

“…Conventionally, clinical diagnostic criteria are predominately based on the relative subjective approaches, for example, according to the diagnostic manuals (American Psychiatric Association, 1994 ). With the development of neuroimaging, a number of objective methods to identify schizophrenia patients have emerged, e.g., single photon emission computed tomography (SPECT; Gordon et al, 1994 ), diffusion tensor imaging (DTI; Ohtani et al, 2014 ), functional magnetic resonance imaging (fMRI; Weiss et al, 2004 ; Deng et al, 2017 ; Tréhout et al, 2017 ), and functional near infrared spectroscopy (fNIRS; Kubota et al, 2005 ; Rosenbaum et al, 2017 ).…”

Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

“…Those methods aim to discard redundant genes from expression data sets and keep only a smaller subset of relevant genes that effectively participate in sample classification. These relevant and non-redundant genes are often recognized as disease-related genes or gene-markers [5][6][7], and they have a significant impact on genetic studies. Existing research indicates that genetic markers are highly involved in different cancer pathways; hence they can be useful for diagnosing and assessing drug efficacy and toxicity.…”

Deep Principal Correlated Auto-Encoders With Application to Imaging and Genomics Data Integration

“…In this paper, two-modality data were generated, the datasets use fMRI data of 116 brain regions, which has a dataset of 183 examples, and every example includes more than forty thousand features, and use SNPs dataset of specific genes [5], [6]. For the purposes of correlation analysis validation between fMRI and SNPs data, various modified CCA algorithms and different deep networks were simultaneously applied on the datasets.…”