An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder

• Main Authors: Yong Xu, Jun Wang, Shuquan Rao, McKenzie Ritter, Lydia C. Manor, Robert Backer, Hongbao Cao, Zaohuo Cheng, Sha Liu, Yansong Liu, Lin Tian, Kunlun Dong, Yin Yao Shugart, Guoqiang Wang, Fuquan Zhang
• Format: Article
• Language: English
• Published: Nature Publishing Group 2017-07-01
• Series: Scientific Reports
• Online Access: https://doi.org/10.1038/s41598-017-05846-4

Abstract Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.