GWAS Central

NAR Molecular Biology Database Collection entry number 131
Hastings, R.K., Beck, T., Gollapudi, S., Free, R.C., Brookes, A.J.
Department of Genetics, University of Leicester, Leicester, United Kingdom

Database Description

Genome Wide Association Studies (GWAS) are an increasingly popular method used to investigate genetic diseases and traits. These studies produce comprehensive high-scale individual and aggregate level genetic data; generated by assaying hundreds of thousands of common Single Nucleotide Polymorphisms (SNPs) in hundreds of individuals in the search for genetic variants that have causative effects on a disease phenotype or trait.

The database resource GWAS Central (established in 2007, then named HGVbaseG2P [1]) is a comprehensive central collection of aggregate level genetic association data with a focus on advanced tools to integrate, search and compare summary-level data sets via genes, genome regions, phenotypes or traits.

The database now provides over 34 million p-values for over 1,000 studies, making it the largest online collection of summary-level association p-values from multiple GWAS. The web interface gives researchers easy access to advanced powerful text searches (keywords, phenotype ontologies, chromosomal regions, HGNC gene symbols) and graphical based data presentation methods for discovery, visualisation and comparison of data sets in multiple GWAS.

Recent Developments

GWAS Central collates data from a range of sources, including the published literature, collaborating databases such as the NHGRI GWAS Catalog [2], public outreach gathering, and direct submissions from collaborating investigators. Alongside the continually growing collection of GWAS data, recent database releases have seen the addition of the following features:

  • An interactive custom GWAS genome browser adapted from GBrowse ( allowing visual comparison of selected GWAS in relation to other genomic data, such as HapMap data (, gene based information and HGMD Professional ( variants in genomic and region based views, along with user defined custom tracks and uploads.

  • Facility to allow users to anonymously and privately upload their own GWAS p-values to compare and contrast alongside all other GWAS content. The uploaded data is automatically deleted once the session is complete and is only visible to the user who submitted it.

  • High-quality manually curated phenotype ontology annotations assigned using MeSH and the Human Phenotype Ontology [3]. GWAS phenotype data (diseases and traits) can be browsed using the graphical tree displays, and ontology terms and synonyms queried using the auto-complete text searches.

  • Provision of computational access to the datasets through the implementation of a GWAS BioMart, thus allowing users to mine and download data for use in their own analyses. A set of web services are also provided for common bulk data-access requests.

  • Providing GWAS assertions in the form of nanopublications [4] allowing key results from each publication such as individual markers, phenotypes and results (<10-5) in RDF to become semantically incorporated and linked to other semantic web resources. The resulting triple store of information can be queried using a SPARQL endpoint:

  • GWAS Central software is available as a virtual machine to allow external groups to run and control their own versions of the system. An example of this in practice is GWAS India.


We thank all current and previous staff of GWAS Central for the data collection, annotation, release management and software development. GWAS Central is funded by the the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement 200754, GEN2PHEN.


1. Thorisson GA, Lancaster O, Free RC, Hastings RK, Sarmah P, Dash D, Brahmachari SK, and Brookes AJ. 2009. HGVbaseG2P: a central genetic association database. Nucleic Acids Res 37: 797.

2. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 106(23):9362-9367.

3. Robinson PN, Mundlos S. 2010. The human phenotype ontology. Clin Genet. 77(6):525-34.

4. nanopub: a beginner's guide to data publishing. []

Go to the abstract in the NAR 2009 Database Issue.
Oxford University Press is not responsible for the content of external internet sites