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Improving reporting standards for polygenic scores in risk prediction studies

Author

Listed:
  • Hannah Wand

    (Stanford University School of Medicine
    Stanford Center for Inherited Cardiovascular Disease)

  • Samuel A. Lambert

    (University of Cambridge
    Baker Heart and Diabetes Institute
    University of Cambridge
    Wellcome Genome Campus and University of Cambridge)

  • Cecelia Tamburro

    (National Human Genome Research Institute)

  • Michael A. Iacocca

    (Stanford University School of Medicine)

  • Jack W. O’Sullivan

    (Stanford University School of Medicine
    Stanford Center for Inherited Cardiovascular Disease)

  • Catherine Sillari

    (National Human Genome Research Institute)

  • Iftikhar J. Kullo

    (Mayo Clinic)

  • Robb Rowley

    (National Human Genome Research Institute)

  • Jacqueline S. Dron

    (Massachusetts General Hospital
    Western University)

  • Deanna Brockman

    (Massachusetts General Hospital)

  • Eric Venner

    (Baylor College of Medicine)

  • Mark I. McCarthy

    (Genentech
    Wellcome Centre for Human Genetics)

  • Antonis C. Antoniou

    (University of Cambridge)

  • Douglas F. Easton

    (University of Cambridge)

  • Robert A. Hegele

    (Western University)

  • Amit V. Khera

    (Massachusetts General Hospital)

  • Nilanjan Chatterjee

    (Johns Hopkins Bloomberg School of Public Health
    Johns Hopkins School of Medicine)

  • Charles Kooperberg

    (Fred Hutchinson Cancer Research Center)

  • Karen Edwards

    (University of California)

  • Katherine Vlessis

    (Stanford University School of Medicine)

  • Kim Kinnear

    (Stanford University School of Medicine)

  • John N. Danesh

    (University of Cambridge
    Wellcome Genome Campus and University of Cambridge
    University of Cambridge and Cambridge University Hospitals)

  • Helen Parkinson

    (Wellcome Genome Campus and University of Cambridge
    European Bioinformatics Institute, Wellcome Genome Campus)

  • Erin M. Ramos

    (National Human Genome Research Institute)

  • Megan C. Roberts

    (UNC Eshelman School of Pharmacy)

  • Kelly E. Ormond

    (Stanford University School of Medicine
    Stanford University School of Medicine)

  • Muin J. Khoury

    (Centers for Disease Control and Prevention)

  • A. Cecile J. W. Janssens

    (Emory University)

  • Katrina A. B. Goddard

    (Kaiser Permanente Northwest
    Kaiser Permanente Northwest)

  • Peter Kraft

    (Harvard T.H. Chan School of Public Health)

  • Jaqueline A. L. MacArthur

    (European Bioinformatics Institute, Wellcome Genome Campus)

  • Michael Inouye

    (University of Cambridge
    Baker Heart and Diabetes Institute
    University of Cambridge
    Wellcome Genome Campus and University of Cambridge)

  • Genevieve L. Wojcik

    (Johns Hopkins Bloomberg School of Public Health)

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.

Suggested Citation

  • Hannah Wand & Samuel A. Lambert & Cecelia Tamburro & Michael A. Iacocca & Jack W. O’Sullivan & Catherine Sillari & Iftikhar J. Kullo & Robb Rowley & Jacqueline S. Dron & Deanna Brockman & Eric Venner , 2021. "Improving reporting standards for polygenic scores in risk prediction studies," Nature, Nature, vol. 591(7849), pages 211-219, March.
  • Handle: RePEc:nat:nature:v:591:y:2021:i:7849:d:10.1038_s41586-021-03243-6
    DOI: 10.1038/s41586-021-03243-6
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    Citations

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    Cited by:

    1. Jiacheng Miao & Hanmin Guo & Gefei Song & Zijie Zhao & Lin Hou & Qiongshi Lu, 2023. "Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Atlas Khan & Ning Shang & Jordan G. Nestor & Chunhua Weng & George Hripcsak & Peter C. Harris & Ali G. Gharavi & Krzysztof Kiryluk, 2023. "Polygenic risk alters the penetrance of monogenic kidney disease," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Carles Foguet & Yu Xu & Scott C. Ritchie & Samuel A. Lambert & Elodie Persyn & Artika P. Nath & Emma E. Davenport & David J. Roberts & Dirk S. Paul & Emanuele Angelantonio & John Danesh & Adam S. Butt, 2022. "Genetically personalised organ-specific metabolic models in health and disease," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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