Transcriptomic Causal Networks identified patterns of differential gene regulation in human brain from Schizophrenia cases versus controls

TL;DR

This study introduces a novel integrative method combining genetic, transcriptomic, and Hi-C data to uncover differential gene regulatory networks in the human brain associated with schizophrenia, revealing key modules and potential new genes.

Contribution

The paper presents a new approach to identify differential gene regulatory patterns in schizophrenia by constructing transcriptomic-causal networks integrating multiple data types.

Findings

Identified modules with many SCZ-associated genes

Unveiled differential regulatory patterns between cases and controls

Suggested new candidate genes related to schizophrenia

Abstract

Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, which work in a coordinated way. However, the vast majority of studies focus on the differential expression of one individual gene at a time. Here, we aim to provide insight into the underlying relationships of the genes expressed in the human brain in cases with schizophrenia (SCZ) and controls. We introduced a novel approach to identify differential gene regulatory patterns and identify a set of essential genes in the brain tissue. Our method integrates genetic, transcriptomic, and Hi-C data and generates a transcriptomic-causal network. Employing this approach for analysis of RNA-seq data from CommonMind Consortium, we identified differential regulatory patterns for SCZ cases and control groups to unveil the mechanisms that control the transcription of the genes in the human brain. Our analysis identified modules with a high number of SCZ-associated genes as well as assessing the relationship of the hubs with their down-stream genes in both, cases and controls. In addition, the results identified essential genes for brain function and suggested new genes putatively related to SCZ.