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15 March 2019 Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia
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Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies, but its underlying mechanism is still unclear. Recent advances have combined heterogeneous data including both medical images (e.g., fMRI) and genomic data (e.g., SNPs and DNA methylations), which give rise to a new perspective on SZ. In this paper, we aim to explore the associations between DNA methylations and various brain regions to shed light on the neuro-epigenetic interactions in the SZ disease. We proposed a joint Gaussian copula model, where we used the Gaussian copula model to address the data integration issue and the joint network estimation for different conditions (case-control study). Unlike previous studies using methods such as CCA or ICA, the proposed method not only can provide the neuro-epigenetic interactions but also the brain connectivity, and methylation selfinteractions all at the same time. The data we used were collected by the Mind Clinical Imaging Consortium (MCIC), which includes the fMRI image and the epigenetic information such as methylation levels. The data were from 183 subjects, among them 79 SZ patients and 104 healthy controls. We have identified several hub brain regions and hub DNA methylations of the SZ patients and have also detected 10 methylation-brain ROI interactions for SZ. Our analysis results are shown to be both statistically and biologically significant.
Conference Presentation
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Aiying Zhang, Vince D. Calhoun, and Yu-Ping Wang "Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540R (15 March 2019);

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