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Database Commons - GWASdb

GWASdb

Citations: 82

z-index 13.67

Short name GWASdb
Full name GWASdb
Description GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences.
URL http://jjwanglab.org/gwasdb
Year founded 2011
Last update & version 2015-08-01    v2.0
Availability Free to all users
University/Institution hosted The University of Hong Kong
Address
City Hong Kong SAR
Province/State
Country/Region Hong Kong SAR China
Contact name Junwen Wang
Contact email junwen@uw.edu
Data type(s)
Major organism(s)
Keyword(s)
  • genetic variant
  • GWAS
Publication(s)
  • GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. [PMID: 26615194]

    Mulin Jun Li, Zipeng Liu, Panwen Wang, Maria P Wong, Matthew R Nelson, Jean-Pierre A Kocher, Meredith Yeager, Pak Chung Sham, Stephen J Chanock, Zhengyuan Xia, Junwen Wang
    Nucleic acids research 2016:44(D1)
    Citation (to be updated)

    Abstract: Genome-wide association studies (GWASs), now as a routine approach to study single-nucleotide polymorphism (SNP)-trait association, have uncovered over ten thousand significant trait/disease associated SNPs (TASs). Here, we updated GWASdb (GWASdb v2, http://jjwanglab.org/gwasdb) which provides comprehensive data curation and knowledge integration for GWAS TASs. These updates include: (i) Up to August 2015, we collected 2479 unique publications from PubMed and other resources; (ii) We further curated moderate SNP-trait associations (P-value < 1.0×10(-3)) from each original publication, and generated a total of 252 530 unique TASs in all GWASdb v2 collected studies; (iii) We manually mapped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms. For each ontology term, we also predicted the putative causal genes; (iv) We curated the detailed sub-populations and related sample size for each study; (v) Importantly, we performed extensive function annotation for each TAS by incorporating gene-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across multiple biological domains, evolutionary signals and disease-related annotation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogenetic studies involving 257 drugs in this update; (vii) Last, we improved the user interface of website. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  • GWASdb: a database for human genetic variants identified by genome-wide association studies. [PMID: 22139925]

    Mulin Jun Li, Panwen Wang, Xiaorong Liu, Ee Lyn Lim, Zhangyong Wang, Meredith Yeager, Maria P Wong, Pak Chung Sham, Stephen J Chanock, Junwen Wang
    Nucleic acids research 2012:40(Database issue)
    82 Citations (Google Scholar as of 2016-01-24)

    Abstract: Recent advances in genome-wide association studies (GWAS) have enabled us to identify thousands of genetic variants (GVs) that are associated with human diseases. As next-generation sequencing technologies become less expensive, more GVs will be discovered in the near future. Existing databases, such as NHGRI GWAS Catalog, collect GVs with only genome-wide level significance. However, many true disease susceptibility loci have relatively moderate P values and are not included in these databases. We have developed GWASdb that contains 20 times more data than the GWAS Catalog and includes less significant GVs (P < 1.0 × 10(-3)) manually curated from the literature. In addition, GWASdb provides comprehensive functional annotations for each GV, including genomic mapping information, regulatory effects (transcription factor binding sites, microRNA target sites and splicing sites), amino acid substitutions, evolution, gene expression and disease associations. Furthermore, GWASdb classifies these GVs according to diseases using Disease-Ontology Lite and Human Phenotype Ontology. It can conduct pathway enrichment and PPI network association analysis for these diseases. GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences. It is freely available at http://jjwanglab.org/gwasdb and will be updated frequently.

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Accessibility

Rate of accessibility:
HTTP status codeDate requested
200 OK2018-11-16
200 OK2018-11-13
200 OK2018-11-09
200 OK2018-11-06
200 OK2018-11-02
200 OK2018-10-30
200 OK2018-10-26
200 OK2018-10-23
200 OK2018-10-19
200 OK2018-10-16
200 OK2018-10-12
200 OK2018-10-09
200 OK2018-10-05
200 OK2018-10-02
200 OK2018-09-28
200 OK2018-09-25
200 OK2018-09-21
200 OK2018-09-18
200 OK2018-09-14
200 OK2018-09-11
200 OK2018-09-07
200 OK2018-09-04
200 OK2018-08-31
200 OK2018-08-28
200 OK2018-08-24
200 OK2018-08-21
200 OK2018-08-17
200 OK2018-08-14
200 OK2018-08-10
200 OK2018-08-07
200 OK2018-08-03
200 OK2018-07-31
200 OK2018-07-27
200 OK2018-07-24
200 OK2018-07-20
200 OK2018-07-17
200 OK2018-07-13
200 OK2018-07-10
200 OK2018-07-06
200 OK2018-07-03
200 OK2018-06-29
200 OK2018-06-26
200 OK2018-06-22
200 OK2018-06-19
200 OK2018-06-15
200 OK2018-06-12
200 OK2018-06-08
200 OK2018-06-05
200 OK2018-06-01
200 OK2018-05-29
200 OK2018-05-25
200 OK2018-05-22
200 OK2018-05-18
200 OK2018-05-15
200 OK2018-05-11
200 OK2018-05-08
200 OK2018-05-04
200 OK2018-05-01
200 OK2018-04-27
200 OK2018-04-24
200 OK2018-04-20
200 OK2018-04-17
200 OK2018-04-13
200 OK2018-04-10
200 OK2018-04-06
200 OK2018-04-03
200 OK2018-02-27
200 OK2018-02-23
200 OK2018-02-20
200 OK2018-02-16
200 OK2018-02-13
200 OK2018-02-09
200 OK2018-02-06
200 OK2018-02-02
200 OK2018-01-30
200 OK2018-01-26
200 OK2018-01-23
200 OK2018-01-19
200 OK2018-01-16
200 OK2018-01-12
200 OK2018-01-09
200 OK2018-01-05
200 OK2018-01-02
200 OK2017-12-29
200 OK2017-12-26
200 OK2017-12-22
200 OK2017-12-19
200 OK2017-12-15
200 OK2017-12-12
200 OK2017-12-08
200 OK2017-12-05
200 OK2017-12-01
200 OK2017-11-28
200 OK2017-11-24
200 OK2017-11-21
200 OK2017-11-17
200 OK2017-11-14
200 OK2017-11-10
200 OK2017-11-07
200 OK2017-11-03
200 OK2017-10-31
200 OK2017-10-27
200 OK2017-10-24
200 OK2017-10-20
200 OK2017-10-17
200 OK2017-10-13
200 OK2017-10-10
200 OK2017-10-06
200 OK2017-10-03
200 OK2017-09-29
200 OK2017-09-26
200 OK2017-09-22
200 OK2017-09-19
200 OK2017-09-15
200 OK2017-09-12
200 OK2017-09-08
200 OK2017-09-05
200 OK2017-09-01
200 OK2017-08-29
200 OK2017-08-25
200 OK2017-08-22
200 OK2017-08-18
200 OK2017-08-15
200 OK2017-08-11
200 OK2017-08-08
200 OK2017-08-04
200 OK2017-08-01
200 OK2017-07-28
200 OK2017-07-25
200 OK2017-07-21
200 OK2017-07-18
200 OK2017-07-14
200 OK2017-07-04
200 OK2017-06-30
200 OK2017-06-27
200 OK2017-06-23
200 OK2017-06-20
200 OK2017-06-16
200 OK2017-06-13
200 OK2017-06-09
200 OK2017-06-06
200 OK2017-06-02
200 OK2017-05-30
200 OK2017-05-26
200 OK2017-05-23
200 OK2017-05-19
200 OK2017-05-16
200 OK2017-05-12
200 OK2017-05-09
200 OK2017-05-05
200 OK2017-05-02
200 OK2017-04-28
200 OK2017-04-25
200 OK2017-04-21
200 OK2017-04-18
200 OK2017-04-14
200 OK2017-04-11
200 OK2017-04-07
200 OK2017-04-04
200 OK2017-03-31
200 OK2017-03-28
200 OK2017-03-24
200 OK2017-03-21
200 OK2017-03-17
200 OK2017-03-14
200 OK2017-03-10
200 OK2017-03-07
200 OK2017-03-03
200 OK2017-02-28
200 OK2017-02-24
200 OK2017-02-21
200 OK2017-02-17
200 OK2017-02-14
200 OK2017-02-10
200 OK2017-02-07
200 OK2017-02-03
200 OK2017-01-31
200 OK2017-01-27
200 OK2017-01-24
200 OK2017-01-20
200 OK2017-01-17
200 OK2017-01-13
200 OK2017-01-10
200 OK2017-01-06
200 OK2017-01-03
200 OK2016-12-30
200 OK2016-12-27
200 OK2016-12-23
200 OK2016-12-20
200 OK2016-12-16
200 OK2016-12-13
200 OK2016-12-09
200 OK2016-12-06
200 OK2016-12-02
200 OK2016-11-29
200 OK2016-11-25
200 OK2016-11-22
200 OK2016-11-18
200 OK2016-11-15
200 OK2016-11-11
200 OK2016-11-08
200 OK2016-11-04
200 OK2016-11-01
200 OK2016-10-28
200 OK2016-10-25
200 OK2016-10-21
200 OK2016-10-18
200 OK2016-10-14
200 OK2016-10-11
200 OK2016-10-07
200 OK2016-10-04
200 OK2016-09-30
200 OK2016-09-27
200 OK2016-09-23
200 OK2016-09-20
200 OK2016-09-16
200 OK2016-09-13
200 OK2016-09-09
200 OK2016-09-06
200 OK2016-09-02
200 OK2016-08-30
200 OK2016-08-26
200 OK2016-08-23
200 OK2016-08-19
200 OK2016-08-16
200 OK2016-08-12
200 OK2016-08-09
200 OK2016-08-05
200 OK2016-08-02
200 OK2016-07-29
200 OK2016-07-26
200 OK2016-07-22
200 OK2016-07-19
200 OK2016-07-15
200 OK2016-07-12
200 OK2016-07-08
200 OK2016-07-05
200 OK2016-07-01
200 OK2016-06-28
200 OK2016-06-24
200 OK2016-06-21
200 OK2016-06-17
200 OK2016-06-14
200 OK2016-06-10
200 OK2016-06-07
200 OK2016-06-03
200 OK2016-05-31
200 OK2016-05-27
200 OK2016-05-24
200 OK2016-05-20
200 OK2016-05-17
200 OK2016-05-13
200 OK2016-05-10
200 OK2016-05-06
200 OK2016-05-03
200 OK2016-04-29
200 OK2016-04-26
200 OK2016-04-22
200 OK2016-04-19
200 OK2016-04-15
200 OK2016-04-12
200 OK2016-04-08
200 OK2016-04-05
200 OK2016-04-01
200 OK2016-03-29
200 OK2016-03-28
200 OK2016-03-25
200 OK2016-03-23
200 OK2016-03-21
200 OK2016-03-18
200 OK2016-03-16
200 OK2016-03-14
200 OK2016-03-11
200 OK2016-03-09
200 OK2016-03-07
200 OK2016-03-04
200 OK2016-03-02
200 OK2016-02-29
200 OK2016-02-26
200 OK2016-02-24
200 OK2016-02-22
200 OK2016-02-19
200 OK2016-02-17
200 OK2016-02-15
200 OK2016-02-14
200 OK2016-02-12
200 OK2016-02-10
200 OK2016-02-08
200 OK2016-02-07
200 OK2016-02-05
200 OK2016-02-03
200 OK2016-02-01
200 OK2016-01-31
200 OK2016-01-29
200 OK2016-01-27
200 OK2016-01-25
200 OK2016-01-24
200 OK2016-01-22
200 OK2016-01-20
200 OK2016-01-18
200 OK2016-01-17
200 OK2016-01-15
200 OK2016-01-13
200 OK2016-01-11
200 OK2016-01-10
200 OK2016-01-08
200 OK2016-01-06
200 OK2016-01-04

Tags

Disease DNA
Homo sapiens
genetic variant GWAS

Record metadata

  • Created on: 2015-06-20
  • Curated by:
    • Zhang Zhang [2016-05-08]
    • Lin Liu [2016-03-26]
    • Lin Liu [2016-01-03]
    • Lin Liu [2016-01-01]
    • Jian Sang [2015-12-05]
    • Jian Sang [2015-06-27]
Stats