Summary Statistics

Here we present a range of summary statistics related to the data which powers the dashboard (and some which doesn't), updated when the Catalog is updated. Note that these summary statistics and the figures themselves only use raw (wrangled) data from the Catalog. Currently:

  • There are a total of 7817 studies in the Catalog.
  • The earliest study in the Catalog is PubMedID 15761122 published on 2005-03-10 by Klein RJ et al.
  • The most recent study in the Catalog is PubMedID 42616615 published on 2026-08-19 by Garrett ME et al.
  • The accession with biggest sample is presently PubMedID 39620219 (N=5519491) by Liu X et al.
  • There are presently a total of 230057 unique study accessions.
  • There are presently a total of 158514 unique diseases and traits studied.
  • There are presently a total of 29643 unique EBI "Mapped Traits".
  • The total number of associations found is presently 1192604.
  • The average number of associations found is presently 5.18.
  • The mean P-Value for the strongest SNP risk allele is presently: 5.199e-07.
  • The number of associations reaching the 5e-8 significance threshold is presently: 1015568.
  • The journal to feature the most GWAS studies is presently: Nat Genet.
  • The total number of different journals publishing GWAS is presently: 930.
  • The most frequently studied (Non-European) disease or trait is presently: Type 2 diabetes.
  • The most commonly seen funder is presently Medical Research Council (UK), acknowledged by 1,981 publications covering 47,419 unique GWAS study accessions.
  • The most commonly seen cohort is presently UKB, appearing in 60,032 unique GWAS study accessions across 827 publications.
  • The funder and cohort filters currently index 1,175 canonical funders and 1,370 named cohorts.
  • Of 230,057 GWAS study accessions, 187,436 (81.47%) are linked to at least one named funder and 214,990 (93.45%) name at least one cohort.

Funding, Contact and Acknowledgements

This work is currently maintained and hosted by the Leverhulme Centre for Demographic Science, which is supported by the Leverhulme Trust and Nuffield College. We are also grateful to an ESRC Impact Acceleration Account award which helped fund the "by funder" analysis. Earlier versions of the manuscript were also partly funded by the European Research Council (grants 615603 and 835079), and The British Academy. This dashboard has been redesigned in consultation with Global Initiative, with special thanks there to Quentin Brunier, Alex Malowany, Jamie May, Lea Misseri, Gareth Nixon, Veatriki Ntova and Chris Sinclair. In addition, we are grateful for comments on the source code and dashboard more generally from Ian Knowles, Yi Liu, Molly Przeworski, Ben Domingue and Sam Trejo. The dashboard is currently primarily maintained by Charles Rahal, Mingyue Liu, and Daniel Valdenegro. We look forward to updating the GWAS Diversity Monitor with your suggestions via email (contact@gwasdiversitymonitor.com) or GitHub.

About

This interactive dashboard monitors the diversity of participants across all published Genome Wide Association Studies (GWAS), the primary technique used for genetic discovery. The objective of a GWAS is to identify statistical associations between a set of genetic variants across different individuals (Single Nucleotide Polymorphisms, or 'SNPs') with specific traits of interest. This monitor is an extension of our earlier project, 'The Scientometrics of Genome Wide Association Studies' published in Communications Biology in January 2019. As there, we leverage the magnificent dataset curated by the NHGRI-EBI Catalog (subject to their licensing information which can be found here). The dashboard itself is a combination of Python (Flask) and JavaScript (D3), designed to be used in modern web browsers for presentation. An earlier prototype appears here, with the full code base available for replication on GitHub. We are actively encouraging community-based suggestions and contributions. The backend code checks daily for updates to the NHGRI-EBI Catalog, writing to logs and refreshing the dataset which powers the dashboard as appropriate.

The dashboard's "by funder" and "by cohort" filters let users focus every chart on one or more funding organisations, named cohorts or both. Funder links come from canonicalised PubMed GrantList agencies, while cohort links come from the GWAS Catalog's cohort field after reviewed aliases are harmonised. Multiple choices within the same filter are combined as a union; using both filters retains only study accessions that match a selected funder and a selected cohort.

These filters use full rather than fractional counting: a publication or study accession linked to several funders or cohorts receives one complete credit in every relevant view, so entity totals overlap and should not be added together. Filter selections update the summary, charts and available years together. They also carry through to the downloadable data and detailed report, while the METRIC and STAGE controls continue to determine whether charts show studies or participants and discovery or replication records.

The dashboard is under perpetual development and review, but is currently comprised of two global widgets: METRIC (which toggles whether we are evaluating by number of studies or by number of participants), and STAGE (which determines whether we are considering the discovery or replication phase of research). Local widgets also toggle the 'EFO Parent Term', the 'Broader' ancestry category, and 'Year' (related to year of study). Due to the relatively small size of the dataset, not all widgets apply to each figure, and we describe our design choices below:

  • Summary Breakdown: Total GWAS participants diversity. Shown in the upper left panel, this displays summary statistics without any filtering (over time, ancestry, traits, or otherwise).
  • Bubble Plot: Ancestry over time by parent term. This graphic in the upper middle panel provides a granular overview of all GWAS in the Catalog, mapping onto EFO Parent terms. In addition to being affected by the STAGE global widget, it also allows a finer, more granular search term based on individual EFO Traits (and combinations thereof). Clicking on the individual bubbles provides detailed study information (including Unique Identifiers provided by the and links out to the relevant PubMed page.
  • Time Series Plot: Participants across all parent terms. This figure in the upper right panel displays how 'Broader Ancestry' varies over time across the two global widgets. Note that we do not divide by the ‘Parent Term’ widget here due to the fact that that least studied ancestry categories are relatively absent by this level of disaggregation. The tickbox for 'Include not recorded' provides a robustness check with respect to how we are mapping our 'Broader Ancestry' field.
  • Heatmap: Parent term by 'broader' ancestry. This figure in the bottom left panel displays maps studies across both STAGE and METRIC, broken down into each of the individual 'Broader Ancestry' and 'Parent Terms' across each of the years in the dataset. Hovering over the figure reveals numbers by each category.
  • Choropleth Map.Due to the sparsity of countries recruited from across all EFO Parent terms, this figure draws from the two global widgets, adjustable by Year only. Polygon data are available from here. Hovering over the figure provides the name of the country and number of participants.
  • Doughnut Chart. This figure displays the percent of 'Broader Ancestry' by 'Parent Term' across both the global widgets. A 'Show'/'Hide' toggle reveals an inset graph of the breakdown of associations discovered across all EFO Parent terms at the discovery stage alone.