A complex network approach reveals pivotal sub-structure of genes linked to Schizophrenia

Research on brain disorders with a strong genetic component and complex heritability, like schizophrenia and autism, has promoted the development of brain transcriptomics. This research field deals with the deep understanding of how gene-gene interactions impact on risk for heritable brain disorders. With this perspective, we developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters, also called community, of strongly interacting genes. The aim is to uncover a pivotal module of genes by gaining biological insight upon them. Our approach combined network topological properties, to highlight the presence of a pivotal community, matchted with information theory, to assess the informativeness of partitions. Shannon entropy of the complex networks based on average betweenness of the nodes is adopted for this purpose. We analyzed the publicly available BrainCloud dataset, containing post-mortem gene expression data and we focused on the Dopamine Receptor D2, encoded by the DRD2 gene. To parse the DRD2 community into sub-structure, we applied and compared four different community detection algorithms. A pivotal DRD2 module emerged for all procedures applied and it represented a considerable reduction, compared with the beginning network size. Dice index 80% for the detected community confirmed the stability of the results, in a wide range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified that the DRD2 was strongly connected to its neighborhood, stronger than any other randomly selected community and more than the Weighted Gene Coexpression Network Analysis (WGCNA) module, commonly considered the standard approach for these studies.