Differential gene regulatory pattern in the human brain from schizophrenia using transcriptomic-causal network
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RESEARCH ARTICLE
Open Access
Differential gene regulatory pattern in the human brain from schizophrenia using transcriptomic‑causal network Akram Yazdani1*, Raul Mendez‑Giraldez2, Azam Yazdani3, Michael R. Kosorok4 and Panos Roussos5,6* *Correspondence: [email protected]; panagiotis.roussos@mssm. edu 1 Department of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Genetic Medicine Building, CB#7361, Chapel Hill, NC 27599‑7264, USA 5 Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics and Friedman Brain Institute, Icahn School of Medicine At Mount Sinai, Hess CSM Building Floor 9 Room 107, 1470 Madison Ave, New York, NY 10029, USA Full list of author information is available at the end of the article
Abstract Background: Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the intercon‑ nectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis. Methods: Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among indi‑ vidual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication. Results: Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory fac‑ tors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network. Conclusions: Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time. Keywords: Bayesian causal network, Mendelian randomization, Data integration, Transcriptomic, Cis/trans-regulatory factors, Schizophrenia
Background Differences in gene expression are likely to underpin much of human diversity, including psychiatric disorders such as schizophrenia (SCZ). SCZ is a severe, complex, and heritable psychiatric disorder with a worldwide prevalence of 1%, characterized by © The Author(s) 2020. O
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