GWAS summary data ecosystem

Diagram

Background

Developed at the MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, this resource is a manually curated collection of complete GWAS summary datasets made available as open source files for download, or by querying a database of the complete data.

This project began as the underlying database for the MR-Base and LD Hub projects. These data now serve as an input source to a wider number of analytical tools that implement methods such as Mendelian randomization, fine mapping, colocalisation, GWAS visualisation etc. Please see the API page for a list of R and python packages that will connect to the data.

The database comprises mainly publicly available datasets, but also includes a number of private datesets whose access is controlled through OAuth2.0 authentication. Please contact us if you have datasets that you would like to add, whether public or private.


The GWAS VCF format

We have made all the public data available for download. We are using the GWAS VCF format to store the GWAS summary data to ensure alignment with the hg19 reference sequence, and to enable very fast querying. More information is available here: https://github.com/MRCIEU/gwas_vcf_spec.


Citing this resource

Please ensure that the original data are cited whenever it is used. If you access the data through the IEU GWAS database or use the QC'd GWAS VCF files please cite:

Hemani G, Zheng J, Elsworth B, Wade KH, Baird D, Haberland V, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC, The MR-Base Collaboration. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018;7:e34408. doi: 10.7554/eLife.34408


Credits

Many people have contributed to the IEU GWAS database project. Main parties are:

  • Ben Elsworth
  • Chris Zheng
  • Gibran Hemani
  • Jon Hallett
  • Matt Lyon
  • Peter Matthews
  • Philip Haycock
  • Tessa Alexander
  • Tom Gaunt
  • Valeriia Haberland
  • Yi Liu

Sincere thanks also go to the many GWAS consortia who have made the GWAS data that they generated publicly available, and many members of the IEU who have contributed to curating these data.

Dependencies

The IEU GWAS database project is built upon many important open source software projects.