Improving reporting standards for polygenic scores in risk prediction studies

Wand, Hannah; Tamburro, Cecelia P.; Iacocca, Michael A.; O’Sullivan, Jack W; Sillari, Catherine H.; Kullo, Iftikhar J.; Rowley, Robb; Dron, Jacqueline S; Brockman, Deanna; McCarthy, Mark; Antoniou, Antonis C.; Easton, Douglas F.; Hegele, Robert A.; Khera, Amit; Chatterjee, Nilanjan; Kooperberg, Charles; Edwards, Karen L.; Vlessis, Katherine; Kinnear, Kim; Danesh, John; Parkinson, Helen; Ramos, Erin M.; Roberts, Megan C.; Ormond, Kelly E.; Khoury, Muin J.; Janssens, A. Cecile J.W.; Goddard, Katrina A.B.; Kraft, Peter; MacArthur, Jacqueline A. L.; Inouye, Michael; Wojcik, Genevieve L.

doi:10.1101/2020.04.23.20077099

Cited by 75 publications

(99 citation statements)

References 69 publications

(96 reference statements)

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“…Finally, it is important to acknowledge that commercial clinical testing of PGS has already begun [ 16 ▪▪ ]. Lipid PGS and ASCVD prediction are key first areas for commercialization, given the public health significance and anticipated actionability.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the Clinical Genomics Resource (ClinGen) consortium and European Bioinformatics Institute's Polygenic Score Catalog have created a joint statement (termed ‘PRS-RS’) on recommended reporting standards for PGS [ 16 ▪▪ ]. This effort builds on foundational reporting standards in genetic risk prediction [ 17 ] and multivariable prediction models for individual prognosis or diagnosis [ 18 ].…”

Section: Reporting Standards For Polygenic Scoresmentioning

confidence: 99%

“…The statistical measures of clinical validity are clearly important to report, as described by the reporting standards for score evaluation in PRS-RS [ 16 ▪▪ ]. However, all categories described in the reporting standard should be addressed to provide a full understanding of the clinical validity of a PGS.…”

Section: Reporting Standards For Polygenic Scoresmentioning

confidence: 99%

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The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case

Wand¹,

Knowles

Clarke³

2021

Current Opinion in Lipidology

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Purpose of review Polygenic scores (PGS) are used to quantify the genetic predisposition for heritable traits, with hypothesized utility for personalized risk assessments. Lipid PGS are primed for clinical translation, but evidence-based practice changes will require rigorous PGS standards to ensure reproducibility and generalizability. Here we review applicable reporting and technical standards for dyslipidemia PGS translation along phases of the ACCE (Analytical validity, Clinical validity, Clinical utility, Ethical considerations) framework for evaluating genetic tests. Recent findings New guidance suggests existing standards for study designs incorporating the ACCE framework are applicable to PGS and should be adopted. One recent example is the Clinical Genomics Resource (ClinGen) and Polygenic Score Catalog's PRS reporting standards, which define minimal requirements for describing rationale for score development, study population definitions and data parameters, risk model development and application, risk model evaluation, and translational considerations, such as generalizability beyond the target population studied. Summary Lipid PGS are likely to be integrated into clinical practice in the future. Clinicians will need to be prepared to determine if and when lipid PGS is useful and valid. This decision-making will depend on the quality of evidence for the clinical use of PGS. Establishing reporting standards for PGS will help facilitate data sharing and transparency for critical evaluation, ultimately benefiting the efficiency of evidence-based practice.

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Section: Discussionmentioning

confidence: 99%

Section: Reporting Standards For Polygenic Scoresmentioning

confidence: 99%

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The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case

Wand¹,

Knowles

Clarke³

2021

Current Opinion in Lipidology

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“…Alternatively, the AUC/R 2 could be based upon resources such as The Polygenic Score (PGS) Catalog (Lambert et al, 2021), which collate polygenic score data and corresponding prediction metrics. To support this effort, future polygenic score risk prediction studies should follow suggested reporting standards (Wand et al, 2021). Thirdly, for continuous phenotypes, the approach is tailored to normally distributed phenotypes.…”

Section: Discussionmentioning

confidence: 99%

A Tool for Translating Polygenic Scores onto the Absolute Scale Using Summary Statistics

Pain

Austin

et al. 2021

Preprint

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Background: There is growing interest in the clinical application of polygenic scores as their predictive utility increases for a range of health-related phenotypes. However, providing polygenic score predictions on the absolute scale is an important step for their safe interpretation. Currently, polygenic scores can only be converted to the absolute scale when a validation sample is available, presenting a major limitation in the interpretability and clinical utility of polygenic scores. Methods: We have developed a method to convert polygenic scores to the absolute scale for binary and normally distributed phenotypes. This method uses summary statistics, requiring only the area-under-the-ROC curve (AUC) or variance explained (R2) by the polygenic score, and the prevalence of binary phenotypes, or mean and standard deviation of normally distributed phenotypes. Polygenic scores are converted using normal distribution theory. Given the AUC/R2 of polygenic scores may be unknown, we also evaluate two methods (AVENGEME, lassosum) for estimating these values from genome-wide association study (GWAS) summary statistics alone. We validate the absolute risk conversion and AUC/R2 estimation using data for eight binary and three continuous phenotypes in the UK Biobank sample. Results: When the AUC/R2 of the polygenic score is known, the observed and estimated absolute values were highly concordant. Across binary phenotypes, the mean absolute difference between the observed and estimated proportion of cases was 5%. For continuous phenotypes, the mean absolute difference between observed and estimated means was <0.3%. Estimates of AUC/R2 from the lassosum pseudovalidation method were most similar to the observed AUC/R2 values, though estimated values deviated substantially from the observed for autoimmune disorders. Conclusion: This study enables accurate interpretation of polygenic scores using only summary statistics, providing a useful tool for educational and clinical purposes. Furthermore, we have created interactive webtools implementing the conversion to the absolute scale for binary and normally distributed phenotypes (https://opain.github.io/GenoPred/PRS_to_Abs_tool.html). Several further barriers must be addressed before clinical implementation of polygenic scores, such as ensuring target individuals are well represented by the GWAS sample.

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“…There is widespread variability in PGS research, even with regard to nomenclature: they can be referred to as genetic or genomic scores, and as polygenic risk scores (PRS) or genomic risk scores (GRS) if they predict a discrete phenotype (such as a disease) 24 . There are also many approaches to derive PGS using individual level genotype data or GWAS summary statistics 25 .…”

Section: Main Textmentioning

confidence: 99%

The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation

Gil

Jupp

Ritchie

et al. 2020

Preprint

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Polygenic [risk] scores (PGS) can enhance prediction and understanding of common diseases and traits. However, the reproducibility of PGS and their subsequent applications in biological and clinical research have been hindered by several factors, including: inadequate and incomplete reporting of PGS development, heterogeneity in evaluation techniques, and inconsistent access to, and distribution of, the information necessary to calculate the scores themselves. To address this we present the PGS Catalog (www.PGSCatalog.org), an open resource for polygenic scores. The PGS Catalog currently contains 192 published PGS from 78 publications for 86 diverse traits, including diabetes, cardiovascular diseases, neurological disorders, cancers, as well as traits like BMI and blood lipids. Each PGS is annotated with metadata required for reproducibility as well as accurate application in independent studies. Using the PGS Catalog, we demonstrate that multiple PGS can be systematically evaluated to generate comparable performance metrics. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with an open platform for polygenic score research and translation.

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Improving reporting standards for polygenic scores in risk prediction studies

Cited by 75 publications

References 69 publications

The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case

The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case

A Tool for Translating Polygenic Scores onto the Absolute Scale Using Summary Statistics

The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation

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