Improving reporting standards for polygenic scores in risk prediction studies

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, Mark I. McCarthy, Antonis C. Antoniou, Douglas F. Easton, Robert A. Hegele, Amit V. Khera, Nilanjan Chatterjee, Charles Kooperberg, Karen Edwards, Katherine Vlessis, Kim Kinnear, John N. Danesh, Helen Parkinson, Erin M. Ramos, Megan C. Roberts, Kelly E. Ormond, Muin J. Khoury, A. Cecile J. W. Janssens, Katrina A. B. Goddard, Peter Kraft, Jaqueline A. L. MacArthur, Michael Inouye and Genevieve L. Wojcik ()
Additional contact information
Hannah Wand: Stanford University School of Medicine
Samuel A. Lambert: 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
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
Deanna Brockman: Massachusetts General Hospital
Eric Venner: Baylor College of Medicine
Mark I. McCarthy: Genentech
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
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
Helen Parkinson: Wellcome Genome Campus and University of Cambridge
Erin M. Ramos: National Human Genome Research Institute
Megan C. Roberts: UNC Eshelman School of Pharmacy
Kelly E. Ormond: 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
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
Genevieve L. Wojcik: Johns Hopkins Bloomberg School of Public Health

Nature, 2021, vol. 591, issue 7849, 211-219

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41586-021-03243-6

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