Polygenic Risk Scores Poised to Reshape Precision Medicine Across Diseases
This comprehensive review outlines five clinical applications of polygenic risk scores: augmenting risk prediction, refining diagnosis, guiding treatment, improving trial efficiency, and advancing public health. The authors envision integrated risk models combining PRS with monogenic information, somatic DNA data, and multi-omic inputs. Key challenges to clinical implementation include cross-ancestry performance gaps, calibration standards, and seamless health system integration.
The original study
Polygenic Risk Scores in Human Disease.
- Authors
- Maamari DJ, Abou-Karam R, Fahed AC
- Journal
- Clinical chemistry
- Type
- Journal Article, Review
- PMID
- 39749511
Original abstract
BACKGROUND: Polygenic risk scores (PRS) are measures of genetic susceptibility to human health traits. With the advent of large data repositories combining genetic data and phenotypic information, PRS are providing valuable insights into the genetic architecture of complex diseases and are transforming the landscape of precision medicine. CONTENT: PRS have emerged as tools with clinical utility in human disease. Herein, details on how to develop PRS are provided, followed by 5 areas in which they can be used to improve human health: (a) augmenting risk prediction, (b) refining diagnosis, (c) guiding treatment choices, (d) making clinical trials more efficient, and (e) improving public health. Finally, some of the ongoing challenges to the clinical implementation of PRS are noted. SUMMARY: PRS can offer valuable information for providers and patients, including identifying risk of disease earlier in life and before the onset of clinical risk factors, guiding treatment decisions, improving public health outcomes, and making clinical trials more efficient. The future of genomic-informed risk assessments of disease is through integrated risk models that combine genetic factors including PRS, monogenic, and somatic DNA information with nongenetic risk factors such as clinical risk estimators and multiomic data. However, adopting PRS in a clinical setting at scale faces some challenges, including cross-ancestry performance, standardization and calibration of risk models, downstream clinical decision-making from risk information, and seamless integration into existing health systems.