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Improving reporting standards for polygenic scores in risk prediction studies

Nature volume 591, pages 211–219 (2021)Cite this article

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Abstract

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

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Fig. 1: Prototype of PRS development and validation process.

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Acknowledgements

We acknowledge the input of the ClinGen Complex Disease Working Group members, including C. D. Bustamante, M. Meyer, F. Harrell, D. Kent, P. Visscher, T. Assimes, S. Plon and J. Berg. We also thank D. Durham for her editorial support and A. Paolucci for her administrative support in the preparation and submission of this manuscript. ClinGen is primarily funded by the National Human Genome Research Institute (NHGRI), through the following three grants: U41HG006834, U41HG009649 and U41HG009650. ClinGen also receives support for content curation from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), through the following three grants: U24HD093483, U24HD093486 and U24HD093487. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health (NIH). In addition, the views expressed in this article are those of the author(s) and not necessarily those of the National Health Service (NHS), the National Institute for Health Research (NIHR) or the Department of Health. Research reported in this publication was supported by the NHGRI under award number U41HG007823 (EBI-NHGRI GWAS Catalog, PGS Catalog). We also acknowledge funding from the European Molecular Biology Laboratory (EMBL). Individuals were funded from the following sources: M.I.M. was a Wellcome Investigator and an NIHR Senior Investigator with funding from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; U01-DK105535) and Wellcome (090532, 098381, 106130, 203141 and 212259); M.I. is supported by the Munz Chair of Cardiovascular Prediction and Prevention at the Baker Heart and Diabetes Institute; M.I., S.A.L. and J.N.D. were supported by core funding from: the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194 and RG/18/13/33946) and the NIHR (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust); S.A.L. is supported by a Canadian Institutes of Health Research postdoctoral fellowship (MFE-171279); and J.N.D. holds a British Heart Foundation Personal Chair and a NIH Research Senior Investigator Award. This work was also supported 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.

Author information

Author notes

  1. These authors contributed equally: Hannah Wand, Samuel A. Lambert

  2. These authors jointly supervised this work: Michael Inouye, Genevieve L. Wojcik

Authors and Affiliations

  1. Stanford University School of Medicine, Stanford, CA, USA

    Hannah Wand, Michael A. Iacocca & Jack W. O’Sullivan

  2. Stanford Center for Inherited Cardiovascular Disease, Stanford, CA, USA

    Hannah Wand & Jack W. O’Sullivan

  3. Cambridge Baker Systems Genomic Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Samuel A. Lambert & Michael Inouye

  4. Cambridge Baker Systems Genomic Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

    Samuel A. Lambert & Michael Inouye

  5. BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Samuel A. Lambert, John N. Danesh & Michael Inouye

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

    Samuel A. Lambert, John N. Danesh, Helen Parkinson & Michael Inouye

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

    Samuel A. Lambert, Helen Parkinson & Jaqueline A. L. MacArthur

  8. National Human Genome Research Institute, Bethesda, MD, USA

    Cecelia Tamburro, Catherine Sillari, Robb Rowley & Erin M. Ramos

  9. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

    Iftikhar J. Kullo

  10. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Jacqueline S. Dron, Deanna Brockman & Amit V. Khera

  11. Western University, London, Ontario, Canada

    Jacqueline S. Dron & Robert A. Hegele

  12. Baylor College of Medicine, Houston, TX, USA

    Eric Venner

  13. Department of Human Genetics, Genentech, South San Francisco, CA, USA

    Mark I. McCarthy

  14. Wellcome Centre for Human Genetics, Oxford, UK

    Mark I. McCarthy

  15. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Antonis C. Antoniou & Douglas F. Easton

  16. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Nilanjan Chatterjee

  17. Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Nilanjan Chatterjee

  18. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Charles Kooperberg

  19. Department of Epidemiology, University of California, Irvine, CA, USA

    Karen Edwards

  20. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Katherine Vlessis, Kim Kinnear & Kelly E. Ormond

  21. National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK

    John N. Danesh & Michael Inouye

  22. Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, Chapel Hill, NC, USA

    Megan C. Roberts

  23. Stanford Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA

    Kelly E. Ormond

  24. Centers for Disease Control and Prevention, Atlanta, GA, USA

    Muin J. Khoury

  25. Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA

    A. Cecile J. W. Janssens

  26. Department of Translational and Applied Genomics, Kaiser Permanente Northwest, Portland, OR, USA

    Katrina A. B. Goddard

  27. Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA

    Katrina A. B. Goddard

  28. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Peter Kraft

  29. The Alan Turing Institute, London, UK

    Michael Inouye

  30. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Genevieve L. Wojcik

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  1. Hannah Wand
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  2. Samuel A. Lambert
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Contributions

H.W., C.T., M.A.I., J.S.D., I.J.K., C.S., R.R., K.V., K.K., K.A.B.G., P.K. and G.L.W. conducted the literature review of reporting practices. H.W., S.A.L., C.T., M.A.I., J.W.O., I.J.K., R.R., D.B., E.V., M.I.M., A.C.A., D.F.E., R.A.H., A.V.K., N.C., C.K., K.E., E.M.R., M.C.R., K.E.O., M.J.K., A.C.J.W.J., K.A.B.G., P.K., J.A.L.M., M.I. and G.L.W. provided feedback on the reporting framework. H.W., S.A.L., C.T., M.A.I., J.W.O., J.S.D., I.J.K., A.V.K., J.N.D., H.P., E.M.R., M.C.R., A.C.J.W.J., K.A.B.G., P.K., J.A.L.M., M.I. and G.L.W. wrote and edited the manuscript text and items.

Corresponding author

Correspondence to Genevieve L. Wojcik.

Ethics declarations

Competing interests

M.I.M. is on the advisory panels for Pfizer, Novo Nordisk and Zoe Global; honoraria: Merck, Pfizer, Novo Nordisk and Eli Lilly; research funding: Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. As of June 2019, he is an employee of Genentech with stock and stock options in Roche. No other authors have competing interests to declare.

Additional information

Peer review information Nature thanks Guillaume Lettre, Paul O’Reilly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

This file contains Supplementary Notes, including additional Supplementary Methods detailing the development and revisions of the Polygenic Risk Score Reporting Statement (PRS-RS), as well as additional considerations for each of the reporting items.

Supplementary Table 1

Considerations for clinical translation. This table outlines considerations to inform downstream stakeholder perspectives regarding the translational spectrum of lab test development.

Supplementary Table 2

Mappings between the PGS Catalog and PRS-RS fields. As the PGS Catalog and PRS-RS do not completely align in their fields, we have provided a mapping between the two frameworks to assist in reporting of published PRS.

Supplementary Table 3

Example curations of 10 manuscripts using PRSRS. We have detailed information from ten PRS manuscripts using the fields outlined in PRS-RS as an example for the research community.

Supplementary Table 4

Introduction, Methods, Results and Discussion (IMRAD) Mappings for PRS-RS. Additionally, we have provided mappings between PRS-RS reporting items and a traditional IMRAD structure to aid authors in the reporting of items in manuscript preparation.

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Wand, H., Lambert, S.A., Tamburro, C. et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591, 211–219 (2021). https://doi.org/10.1038/s41586-021-03243-6

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