A systematic framework for assessing the clinical impact of polygenic risk scores
Scott KulmiD, Andrew R. MardersteiniD, Jason G. MezeyiD, Olivier ElementoiD
Abstract
Risk prediction models provide empirical recommendations that ultimately aim to deliver optimal patient outcomes. Genetic information, in the form of a polygenic risk score (PRS), may be included in these models to significantly increase their accuracy. Several analyses of PRS accuracy have been completed, nearly all focus on only a few diseases and report limited statistics. This narrow approach has limited our ability to assess as a whole whether PRSs can provide actionable disease predictions. This investigation aims to address this uncertainty by comprehensively analyzing 23 diseases within the UK Biobank. Our results show that including the PRS to a base model containing age, sex and the top ten genetic principal components significantly improves prediction accuracy, as measured by ROC curves, in a majority 21 of 23 diseases and reclassifies on average 68% of the individuals in the top 5% risk group. For heart failure, breast cancer, prostate cancer and gout, decision curve analyses using the 5% risk threshold determined that including the PRS in the base model would correctly identity at least 60 more individuals who develop the disease for every 1000 individuals screened, without making any incorrect predictions. Analyses that included disease-specific risk factors, such as Body-Mass Index, and consider time of disease onset found similar PRS benefits. The improved prediction accuracy was translated to 10 instances in which medications/supplements and 94 instances in which lifestyle modifications lead to significantly greater reduction in disease risk for individuals in the top PRS quintile compared to the bottom PRS quintile. Finally we provide guidance for tailored, future PRS generation by comprehensively ranking methods that generate PRS weights and identifying genome wide association study characteristics that influence PRS predictions. The unification of significantly enhanced disease predictions, novel risk mitigation opportunities and improved methodological clarity indicate that PRSs carry far greater clinical impact than previously known.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 23
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Polygenic risk score prediction accuracy convergence
Léo Henches, Jihye Kim, … Hugues Aschard·Human Genetics and Genomics AdvancesPeer-reviewed·May 14, 2025
- Clinical history shapes the predictive value of polygenic risk
Dmitrii Usoltsev, Иван Игоревич Молотков, … Mykyta Artomov·medRxiv·Sep 27, 2026
- Software as a Service for the Genomic Prediction of Complex Diseases.
Alessandro Bolli, Paolo Di Domenico, Giordano Bottà·bioRxiv·Sep 27, 2019
- Large-scale evaluation of proteomic and polygenic risk scores reveals complementary contributions to incident disease prediction
Jakob Woerner, Thomas M. Westbrook, … Dokyoon Kim·medRxiv·Jul 10, 2025
- Evaluation of phenotyping errors on polygenic risk score predictions
Ruowang Li, Jiayi Tong, … Jason H. Moore·bioRxiv·Aug 4, 2019
- Performance of polygenic risk scores in screening, prediction, and risk stratification
Aroon Dinesh Hingorani, Jasmine Gratton, … NJ Wald·medRxiv·Feb 18, 2022