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Sci Rep. 2017 Jul 27;7(1):6745. doi: 10.1038/s41598-017-05846-4.

An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder.

Author information

1
Department of Psychiatry, First Clinical Medical College/First Hospital of Shanxi Medical University, Taiyuan, 030000, China.
2
Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu Province, 214151, China.
3
School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
4
Unit on Statistical Genomics, National Institute of Mental Health, National Institutes of Health, Bethesda, 20852, USA.
5
American Informatics Consultant LLC, Rockville, Maryland, 20852, USA.
6
Department of Psychological & Brain Sciences, University of Delaware, Newark, DE, 19716, USA.
7
Department of Biology Products, Life Science Solutions, Elsevier Inc., Rockville, MD, 20852, USA.
8
Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu Province, 214151, China. wuwangguoqiang@126.com.
9
Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu Province, 214151, China. zhangfq@njmu.edu.cn.

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.

PMID:
28751646
PMCID:
PMC5532256
DOI:
10.1038/s41598-017-05846-4
[Indexed for MEDLINE]
Free PMC Article

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