dbBIP: a comprehensive bipolar disorder database for genetic research

Author:

Li Xiaoyan1,Ma Shunshuai1,Yan Wenhui1,Wu Yong2,Kong Hui1,Zhang Mingshan1,Luo Xiongjian34,Xia Junfeng1ORCID

Affiliation:

1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University , 111 Jiulong Road, Shushan District, Hefei, Anhui 230601, China

2. Affiliated Wuhan Mental Health Center, Tongji Medical College, Huazhong University of Science and Technology , 93 Youyi Road, Qiaokou District, Wuhan, Hubei 430030, China

3. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences , 32 Jiaochang East Road, Wuhua District, Kunming, Yunnan 650223, China

4. Kunming College of Life Science, University of Chinese Academy of Sciences , 19 Qingsong Road, Panlong District, Kunming, Yunnan 650204, China

Abstract

Abstract Bipolar disorder (BIP) is one of the most common hereditary psychiatric disorders worldwide. Elucidating the genetic basis of BIP will play a pivotal role in mechanistic delineation. Genome-wide association studies (GWAS) have successfully reported multiple susceptibility loci conferring BIP risk, thus providing insight into the effects of its underlying pathobiology. However, difficulties remain in the extrication of important and biologically relevant data from genetic discoveries related to psychiatric disorders such as BIP. There is an urgent need for an integrated and comprehensive online database with unified access to genetic and multi-omics data for in-depth data mining. Here, we developed the dbBIP, a database for BIP genetic research based on published data. The dbBIP consists of several modules, i.e.: (i) single nucleotide polymorphism (SNP) module, containing large-scale GWAS genetic summary statistics and functional annotation information relevant to risk variants; (ii) gene module, containing BIP-related candidate risk genes from various sources and (iii) analysis module, providing a simple and user-friendly interface to analyze one’s own data. We also conducted extensive analyses, including functional SNP annotation, integration (including summary-data-based Mendelian randomization and transcriptome-wide association studies), co-expression, gene expression, tissue expression, protein–protein interaction and brain expression quantitative trait loci analyses, thus shedding light on the genetic causes of BIP. Finally, we developed a graphical browser with powerful search tools to facilitate data navigation and access. The dbBIP provides a comprehensive resource for BIP genetic research as well as an integrated analysis platform for researchers and can be accessed online at http://dbbip.xialab.info. Database URL:  http://dbbip.xialab.info

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Information Systems

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