Improving reporting standards for polygenic scores in risk prediction studies

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.

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).