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Enhancing the Polygenic Score Catalog with tools for score calculation and ancestry normalization

Nature Genetics (2024)Cite this article

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Polygenic scores (PGSs) have transformed human genetic research and have numerous potential clinical applications. Here we present a series of recent enhancements to the PGS Catalog and highlight the PGS Catalog Calculator, an open-source, scalable and portable pipeline for reproducibly calculating PGSs that democratizes equitable PGS applications.

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Fig. 1: Data growth and improvement of the PGS Catalog.
Fig. 2: The PGS Catalog Calculator for reproducible PGS calculation, with estimation and adjustment for genetic ancestry.

Data availability

Data in the PGS Catalog are accessible via our website (http://www.PGSCatalog.org), FTP server (https://ftp.ebi.ac.uk/pub/databases/spot/pgs/) and REST API (http://www.PGSCatalog.org/rest). PGS in the Catalog are distributed according to the European Bioinformatic Institute’s terms of use (https://www.ebi.ac.uk/about/terms-of-use) or specifically marked with open-access licenses supplied by the authors. The processed reference panels 1kGP and 1kGP+HGDP are available from our FTP server (https://ftp.ebi.ac.uk/pub/databases/spot/pgs/resources/). UK Biobank data can be accessed via application (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access); the data for this publication were accessed as part of projects 49978 and 78537.

Code availability

All codes for the PGS Catalog are hosted on GitHub under the PGS Catalog organization (https://github.com/PGScatalog/; see https://github.com/PGScatalog/pgsc_calc for calculator) and are released under an Apache v2 license.

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Acknowledgements

We thank all the authors of publications in the PGS Catalog for making their data available and indexable in our database, and all those who responded to our inquiries and requests for data. We also thank the users of the PGS Catalog Calculator, especially those that have contributed to bug reports, feature requests, or discussions. This work was supported by core funding from the British Heart Foundation (BHF) (grants RG/18/13/33946 and RG/F/23/110103); the National Human Genome Research Institute of the National Institutes of Health (grant 1U24HG012542-01 to H.P. and M.I.); the UK National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (grants BRC-1215–20014 and NIHR203312); the Cambridge BHF Centre of Research Excellence (grants RE/18/1/34212 and RE/18/1/34212]); a BHF Chair Award (CH/12/2/29428); the European Union’s Horizon 2020 research and innovation program (under grant agreement 101016775 INTERVENE to H.P., M.I. and B.W.); and by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health and Social Care. This work was carried out using computing resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/T022159/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). The authors wish to acknowledge the CSC–IT Center for Science, Finland, for providing computing resources. M.I. is supported by the Munz Chair of Cardiovascular Prediction and Prevention and the UK Economic and Social Research 878 Council (grant ES/T013192/1). H.P. is supported by European Molecular Biology Laboratory Core Funds.

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Author notes

  1. Rodrigo Cánovas

    Present address: The Australian E-Health Research Centre, CSIRO, Parkville, Victoria, Australia

  2. These authors contributed equally: Samuel A. Lambert, Benjamin Wingfield.

Authors and Affiliations

  1. Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Samuel A. Lambert, Joel T. Gibson, Florent Yvon, Scott C. Ritchie & Michael Inouye

  2. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Samuel A. Lambert, Joel T. Gibson, Florent Yvon, Scott C. Ritchie & Michael Inouye

  3. Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK

    Samuel A. Lambert, Joel T. Gibson, Florent Yvon, Scott C. Ritchie & Michael Inouye

  4. Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

    Samuel A. Lambert, Laurent Gil & Michael Inouye

  5. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Samuel A. Lambert, Benjamin Wingfield, Santhi Ramachandran, Shirin Saverimuttu, Emily Tinsley, Elizabeth Lewis, Aoife McMahon, Laura W. Harris & Helen Parkinson

  6. Wellcome Sanger Institute, Hinxton, UK

    Laurent Gil

  7. British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK

    Scott C. Ritchie & Michael Inouye

  8. Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

    Jingqin Wu, Rodrigo Cánovas & Michael Inouye

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  1. Samuel A. Lambert
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Corresponding authors

Correspondence to Samuel A. Lambert or Michael Inouye.

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Competing interests

M.I. is a trustee of the Public Health Genomics Foundation; is a member of the Scientific Advisory Board of Open Targets; and has research collaborations with AstraZeneca, Nightingale Health and Pfizer that are unrelated to this study. All other authors declare no competing interests.

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Nature Genetics thanks Niall Lennon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Notes 1–4, Supplementary Figures 1–6, and Supplementary Tables 1–2.

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Lambert, S.A., Wingfield, B., Gibson, J.T. et al. Enhancing the Polygenic Score Catalog with tools for score calculation and ancestry normalization. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01937-x

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