GAAD: A Gene and Autoimmiune Disease Association Database.
10.1016/j.gpb.2018.05.001
- Author:
Guanting LU
1
;
Xiaowen HAO
2
;
Wei-Hua CHEN
3
;
Shijie MU
4
Author Information
1. Department of Blood Transfusion, Tangdu Hospital, Fourth Military Medical University, Xi'an 710032, China.
2. MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
3. MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China. Electronic address: weihuachen@hust.edu.cn.
4. Department of Blood Transfusion, Tangdu Hospital, Fourth Military Medical University, Xi'an 710032, China. Electronic address: musj1963@fmmu.edu.cn.
- Publication Type:Journal Article
- Keywords:
Autoimmune diseases;
Database;
Disease–gene association;
Text mining
- MeSH:
Autoimmune Diseases;
genetics;
Data Mining;
Databases, Factual;
Gene Regulatory Networks;
Genetic Association Studies;
Humans
- From:
Genomics, Proteomics & Bioinformatics
2018;16(4):252-261
- CountryChina
- Language:English
-
Abstract:
Autoimmune diseases (ADs) arise from an abnormal immune response of the body against substances and tissues normally present in the body. More than a hundred of ADs have been described in the literature so far. Although their etiology remains largely unclear, various types of ADs tend to share more associated genes with other types of ADs than with non-AD types. Here we present GAAD, a gene and AD association database. In GAAD, we collected 44,762 associations between 49 ADs and 4249 genes from public databases and MEDLINE documents. We manually verified the associations to ensure the quality and credibility. We reconstructed and recapitulated the relationships among ADs using their shared genes, which further validated the quality of our data. We also provided a list of significantly co-occurring gene pairs among ADs; with embedded tools, users can query gene co-occurrences and construct customized co-occurrence network with genes of interest. To make GAAD more straightforward to experimental biologists and medical scientists, we extracted additional information describing the associations through text mining, including the putative diagnostic value of the associations, type and position of gene polymorphisms, expression changes of implicated genes, as well as the phenotypical consequences, and grouped the associations accordingly. GAAD is freely available at http://gaad.medgenius.info.
