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NatureGenetic & Genomic MedicinePeer-reviewed journal article

Improving reporting standards for polygenic scores in risk prediction studies

Hannah WandiD, Samuel A. LambertiD, Cecelia P. Tamburro, Michael A. IacoccaiD, Jack William O’SullivaniD, Catherine H. Sillari, Iftikhar J. KulloiD, Robb K. RowleyiD, Jacqueline S. DroniD, Deanna BrockmaniD, Eric VenneriD, Mark I. McCarthyiD, Antonis C. AntoniouiD, Douglas F. EastoniD, Robert Alexander HegeleiD, Amit V. KheraiD, Nilanjan ChatterjeeiD, Charles L KooperbergiD, Karen L. EdwardsiD, Katherine Vlessis, Kim Kinnear, John DaneshiD, Helen ParkinsoniD, Erin M. RamosiD, Megan Clarke RobertsiD, , Genevieve L. WojcikiD

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Abstract

Polygenic risk scores (PRS), often aggregating the results from genome-wide association studies, can bridge the gap between the initial variant discovery efforts and disease risk estimation for clinical applications. However, there is remarkable heterogeneity in the reporting of these risk scores due to a lack of adherence to reporting standards and no accepted standards suited for the current state of PRS development and application. This lack of adherence and best practices hinders the translation of PRS into clinical care. The ClinGen Complex Disease Working Group, in a collaboration with the Polygenic Score (PGS) Catalog, have developed a novel PRS Reporting Statement (PRS-RS), updating previous standards to the current state of the field and to enable downstream utility. Drawing upon experts in epidemiology, statistics, disease-specific applications, implementation, and policy, this 23-item reporting framework defines the minimal information needed to interpret and evaluate a PRS, especially with respect to any downstream clinical applications. Items span detailed descriptions of the study population (recruitment method, key demographics, inclusion/exclusion criteria, and phenotype definition), statistical methods for both PRS development and validation, and considerations for potential limitations of the published risk score and downstream clinical utility. Additionally, emphasis has been placed on data availability and transparency to facilitate reproducibility and benchmarking against other PRS, such as deposition in the publicly available PGS Catalog (www.PGScatalog.org). By providing these criteria in a structured format that builds upon existing standards and ontologies, the use of this framework in publishing PRS will facilitate translation of PRS into clinical care and progress towards defining best practices. SummaryIn recent years, polygenic risk scores (PRS) have become an increasingly studied tool to capture the genome-wide liability underlying many human traits and diseases, hoping to better inform an individuals genetic risk. However, a lack of tailored reporting standards has hindered the translation of this important tool into clinical and public health practice with the heterogeneous underreporting of details necessary for benchmarking and reproducibility. To address this gap, the ClinGen Complex Disease Working Group and Polygenic Score (PGS) Catalog have collaborated to develop the 23-item Polygenic Risk Score Reporting Statement (PRS-RS). This framework provides the minimal information expected of authors to promote the validity, transparency, and reproducibility of PRS by requiring authors to detail the study population, statistical methods, and potential clinical utility of a published score. The widespread adoption of this framework will encourage rigorous methodological consideration and facilitate benchmarking to ensure high quality scores are translated into the clinic.

In-depth summary

Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 3 (24 Nov 2020) · CC BY 4.0

ClinGen and the PGS Catalog expand the GRIPS guideline into a 22-item Polygenic Risk Score Reporting Statement covering study populations, model construction, evaluation, and clinical interpretation.

Polygenic risk score studies often omit ancestry, predictive performance, and variant weights, which makes benchmarking, reproduction, and clinical translation harder. Few authors follow the 2011 GRIPS guideline, which predates current score-construction methods; PRS-RS updates it for modern PRS work and pairs reporting with depositing scores in the PGS Catalog.

Key results

  1. ShownA 22-item checklist, the Polygenic Risk Score Reporting Statement (PRS-RS), sets the minimum information authors should report so that polygenic risk scores can be interpreted, reproduced, and benchmarked.

    Table 1 lists 22 items grouped by pipeline stage (score background, study populations, model development, model parameters, evaluation, interpretation). The ClinGen Complex Disease Working Group and PGS Catalog built it jointly to update the 2011 GRIPS Statement. It covers development and validation studies that predict onset, prognosis, or treatment response. · Abstract; The ClinGen-PGS Catalog Polygenic Risk Score…; Using the PRS-RS and PGS Catalog to improve PRS…; Tables

  2. SuggestedA pilot review of published PRS studies found that reporting varied widely. Ancestry, predictive performance, and the details needed to rebuild scores were often missing or too vague.

    30 publications were surveyed across diverse disease domains, score categories, and populations, and 10 were annotated in detail against the final field definitions (Supplemental Table 3). No per-item frequencies are given. The authors cite earlier work: 77% of 2017 GWAS publications did not share summary statistics, and 4% of GWAS gave no relevant ancestry information. · Using the PRS-RS and PGS Catalog to improve PRS…

  3. ClaimedThe study-population and model-development items ask for recruitment design, age, sex, and genetic ancestry (including how ancestry was inferred). They also ask for a full description of variant selection, weighting, non-genetic predictors, and the criteria used to choose the final model.

    Ancestry should be described with a controlled vocabulary where possible, for example the NHGRI-EBI GWAS Catalog framework, and source GWAS should preferably be cited by GWAS Catalog identifiers (GCSTs). Hazard models should state their time scale or follow-up period, and relative risks should name their reference group, for training, validation, and subgroup analyses. · Reporting on study populations; Reporting on risk model development

  4. ClaimedThe evaluation and model-parameter items ask for testing in an external, non-overlapping sample, reporting score distribution, calibration, and discrimination, and giving estimates for every evaluated model. Variant alleles and weights should be public, preferably via the PGS Catalog.

    Named metrics: effect size, variance explained, reclassification indices, sensitivity, specificity, PPV, NPV, and AUROC, AUPRC or C-index for discrimination. Any differences in definitions or performance between training and validation sets must be explained. · Reporting on model parameters; Reporting on risk model evaluation

  5. ClaimedPRS-RS items are mapped to the analytical and clinical validity evidence required by the College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendments (CLIA), and to PGS Catalog metadata. This links reporting to regulatory review and to score curation.

    Supplemental Table 1 maps items to CAP/CLIA evidence. Supplemental Table 2 compares PGS Catalog metadata with PRS-RS, and Supplemental Table 4 shows where in a manuscript each item belongs. These tables are not reproduced in the provided text. · Reporting on interpretation; Using the PRS-RS and PGS Catalog to improve PRS…

Methods, data and reproducibility

Expert consensus from the ClinGen Complex Disease Working Group and PGS Catalog turned the GRIPS Statement into a checklist that follows a typical PRS development and validation pipeline. Field definitions were refined iteratively by trying them on 30 published studies, 10 of which were annotated in detail.

  • Pilot survey of published PRS studies chosen for diversity of disease domains, risk score categories, and populations · human · n = 30 publications
  • Subset of surveyed publications annotated in detail against final PRS-RS field definitions (Supplemental Table 3) · human · n = 10 publications
Methods & tools
Polygenic Risk Score Reporting Statement (PRS-RS), GRIPS (Genetic Risk Prediction Studies) Statement, PGS Catalog, NHGRI-EBI GWAS Catalog, CAP/CLIA analytical and clinical validity criteria
Reproducibility gaps
The Data Availability statement says N/A. The full Table 1 checklist and the pilot annotations (Supplemental Table 3) are in tables and supplements that are missing from the extracted text.
Limitations (6)
  • The scope is limited to clinical validity. It does not cover what is needed to show clinical or public health utility, such as randomized trials, health-economic analyses, or feasibility studies. · stated by the authors
  • Reported data may not carry over directly to clinical use. PRS case definitions (for example, CHD scores trained on definitions that exclude angina) and catalog ancestry terms can differ from those used in health systems, which limits generalizability. · stated by the authors
  • Because the guideline is built around typical PRS development steps, it may miss unusual cases. Additional frameworks are still needed for newer methods such as deep learning and for clinical utility and readiness. · stated by the authors
  • Commercial restrictions, including intellectual property limits on reporting or distributing scores or their underlying data, are outside the guideline's scope. · stated by the authors
  • The main text describes the pilot survey only qualitatively. It gives no per-item adherence rates and no explicit selection protocol, so it does not measure how much PRS reporting falls short. · noted in this summary
  • The paper does not test whether adopting PRS-RS improves reporting completeness or reproducibility. The checklist rests on expert opinion, and the consensus methods are left to Supplemental Note 2. · noted in this summary

Read this if you develop, validate, or peer-review polygenic risk score studies and need a checklist for reporting ancestry, model construction, evaluation metrics, and score sharing.

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