Review

. 2021 Mar;591(7849):211-219.

doi: 10.1038/s41586-021-03243-6. Epub 2021 Mar 10.

Improving reporting standards for polygenic scores in risk prediction studies

Hannah Wand^#^{1

2}, Samuel A Lambert^#^{3

4

5

6

7}, Cecelia Tamburro⁸, Michael A Iacocca¹, Jack W O'Sullivan^{1

2}, Catherine Sillari⁸, Iftikhar J Kullo⁹, Robb Rowley⁸, Jacqueline S Dron^{10

11}, Deanna Brockman¹⁰, Eric Venner¹², Mark I McCarthy^{13

14}, Antonis C Antoniou¹⁵, Douglas F Easton¹⁵, Robert A Hegele¹¹, Amit V Khera¹⁰, Nilanjan Chatterjee^{16

17}, Charles Kooperberg¹⁸, Karen Edwards¹⁹, Katherine Vlessis²⁰, Kim Kinnear²⁰, John N Danesh^{5

6

21}, Helen Parkinson^{6

7}, Erin M Ramos⁸, Megan C Roberts²², Kelly E Ormond^{20

23}, Muin J Khoury²⁴, A Cecile J W Janssens²⁵, Katrina A B Goddard^{26

27}, Peter Kraft²⁸, Jaqueline A L MacArthur⁷, Michael Inouye^{3

4

5

6

21

29}, Genevieve L Wojcik³⁰

Affiliations

¹ Stanford University School of Medicine, Stanford, CA, USA.
² Stanford Center for Inherited Cardiovascular Disease, Stanford, CA, USA.
³ Cambridge Baker Systems Genomic Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁴ Cambridge Baker Systems Genomic Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁵ BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁶ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
⁷ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
⁸ National Human Genome Research Institute, Bethesda, MD, USA.
⁹ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
¹⁰ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹¹ Western University, London, Ontario, Canada.
¹² Baylor College of Medicine, Houston, TX, USA.
¹³ Department of Human Genetics, Genentech, South San Francisco, CA, USA.
¹⁴ Wellcome Centre for Human Genetics, Oxford, UK.
¹⁵ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁶ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
¹⁷ Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
¹⁸ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
¹⁹ Department of Epidemiology, University of California, Irvine, CA, USA.
²⁰ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
²¹ National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
²² Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, Chapel Hill, NC, USA.
²³ Stanford Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
²⁴ Centers for Disease Control and Prevention, Atlanta, GA, USA.
²⁵ Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
²⁶ Department of Translational and Applied Genomics, Kaiser Permanente Northwest, Portland, OR, USA.
²⁷ Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA.
²⁸ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²⁹ The Alan Turing Institute, London, UK.
³⁰ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. gwojcik1@jhu.edu.

^# Contributed equally.

PMID: 33692554
PMCID: PMC8609771
DOI: 10.1038/s41586-021-03243-6

Review

Improving reporting standards for polygenic scores in risk prediction studies

Hannah Wand et al. Nature. 2021 Mar.

. 2021 Mar;591(7849):211-219.

doi: 10.1038/s41586-021-03243-6. Epub 2021 Mar 10.

Authors

Affiliations

¹ Stanford University School of Medicine, Stanford, CA, USA.
² Stanford Center for Inherited Cardiovascular Disease, Stanford, CA, USA.
³ Cambridge Baker Systems Genomic Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁴ Cambridge Baker Systems Genomic Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁵ BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁶ Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
⁷ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
⁸ National Human Genome Research Institute, Bethesda, MD, USA.
⁹ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
¹⁰ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹¹ Western University, London, Ontario, Canada.
¹² Baylor College of Medicine, Houston, TX, USA.
¹³ Department of Human Genetics, Genentech, South San Francisco, CA, USA.
¹⁴ Wellcome Centre for Human Genetics, Oxford, UK.
¹⁵ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
¹⁶ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
¹⁷ Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
¹⁸ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
¹⁹ Department of Epidemiology, University of California, Irvine, CA, USA.
²⁰ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
²¹ National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
²² Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, Chapel Hill, NC, USA.
²³ Stanford Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, CA, USA.
²⁴ Centers for Disease Control and Prevention, Atlanta, GA, USA.
²⁵ Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
²⁶ Department of Translational and Applied Genomics, Kaiser Permanente Northwest, Portland, OR, USA.
²⁷ Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA.
²⁸ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²⁹ The Alan Turing Institute, London, UK.
³⁰ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. gwojcik1@jhu.edu.

^# Contributed equally.

PMID: 33692554
PMCID: PMC8609771
DOI: 10.1038/s41586-021-03243-6

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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Figures

**Figure 1:. Prototype of PRS development and validation process.**
The prototypical steps for PRS construction, risk model development, and validation of performance are displayed with select aspects of the PRS-RS guideline (labeled in **bold**). During PRS development, variants associated with an outcome of interest, typically identified from a GWAS, are combined as a weighted sum of allele counts. Methods for optimizing variant selection (PRS construction & estimation) are not shown. To predict the outcome of interest the PRS is added to a risk model and may be combined with non-genetic variables (e.g. age, sex, ancestry, clinical variables; collectively referred to as risk model variables). After fitting procedures to select the best risk model, this model is validated in an independent sample. The PRS distribution should be described, and the performance of the risk model demonstrated in terms of its discrimination, predictive ability, and calibration. Though not displayed in the figure, these same results should also be reported for the training sample for comparison to the validation sample. In both training and validation cohorts, the outcome of interest criteria, demographics, genotyping, and non-genetic variables should be reported (Table 1).

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Improving reporting standards for polygenic scores in risk prediction studies

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Improving reporting standards for polygenic scores in risk prediction studies

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