Abstract
Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon’s entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ≥80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder.
Highlights
Converging evidence suggests that risk for complex heritable diseases is associated with several interacting genes possibly merging in molecular modules or pathways [1], whose identification is key to shed light on the biology of brain diseases
We investigated brain-specific gene co-expression in a brain region crucially involved in schizophrenia, i.e., the dorsolateral prefrontal cortex to detect molecular pathways of risk genes
The DRD2 gene coding for the D2 dopamine receptor is an optimal candidate for investigating the genetic architecture of schizophrenia-related molecular pathways because of its genome-wide association with diagnosis of this brain disorder and for its well established role in its biological underpinnings [10]
Summary
Converging evidence suggests that risk for complex heritable diseases is associated with several interacting genes possibly merging in molecular modules or pathways [1], whose identification is key to shed light on the biology of brain diseases. The development and availability of an increasing number of precision techniques to quantify gene transcription challenges the field of molecular psychiatry In this context, gene co-expression network analysis addresses the need to formalize, include and manage all the information originating from genetic data [11]. A strength of WGCNA is that connections are graded, i.e., all genes are connected at variable degrees This procedure enhances the sensitivity to detect weak genetic links and takes into account the scale-free organization of known biological networks [15]. We aimed to demonstrate that the community found using our methodology was a pivotal gene community and it emerged consistently when we applied different community detection algorithms This community could represent a more accurate model of the coexpression interactions of the DRD2 gene relative to the WGCNA module we previously investigated
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