Polygenic risk scores in disease prediction
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Polygenic Risk Scores in Disease Prediction: Overview and Key Concepts
Polygenic risk scores (PRS) are tools that combine the effects of many genetic variants across the genome to estimate an individual's risk of developing common diseases. PRS are increasingly being considered as valuable additions to traditional risk prediction models, which typically include factors like age, sex, family history, and lifestyle behaviors 123.
Disease Risk Prediction and Clinical Utility of Polygenic Risk Scores
Improved Prediction for Common Diseases
PRS have shown predictive value for a range of diseases, including breast cancer, prostate cancer, coronary artery disease, obesity, type 1 and type 2 diabetes, and Alzheimer's disease 135. For example, in breast cancer, women in the highest 1% of PRS had over four times the risk of developing estrogen receptor-positive disease compared to those in the middle range, and PRS models were well-calibrated for risk prediction . Similarly, for coronary artery disease and type 2 diabetes, PRS improved risk prediction when added to existing clinical models 3710.
Enhanced Prediction for Early-Onset Disease
PRS are particularly effective in identifying individuals at risk for early-onset forms of diseases. For several conditions, the impact of a high PRS was more pronounced in early-onset cases, with risk prediction accuracy improving significantly when PRS were included in models 23. For example, the early-onset risk of heart failure associated with PRS was about twice as high as for late-onset cases .
Lifetime Risk and Disease Onset
Individuals with high PRS not only have a higher lifetime risk of disease but also tend to develop diseases earlier. Studies have shown that those with high PRS can have a 21% to 38% higher lifetime risk and experience disease onset 4 to 9 years earlier than those with average PRS .
Integration with Clinical Risk Tools and Electronic Health Records
When PRS are combined with traditional clinical risk factors, they consistently improve the accuracy of disease prediction models for conditions like coronary artery disease, atrial fibrillation, and type 2 diabetes 37. The integration of PRS with electronic health records (EHRs) in biobanks is creating new opportunities for personalized risk assessment, though challenges remain due to the complexity and variability of EHR data .
Challenges: Ancestry Bias and Implementation
Ancestry and Transferability
A major challenge for PRS is that their predictive accuracy can be biased and less reliable in populations that differ from those used to develop the scores. This is due to the underrepresentation of diverse populations in genetic studies and the design of genotyping tools, which can lead to health inequities if not addressed 19. Including more diverse populations in research and developing unbiased methods are critical steps for improving PRS utility across all groups .
Clinical Implementation and Future Directions
While PRS are commercially available and increasingly used to inform personalized healthcare decisions, their routine use in clinical practice is still being evaluated. Key questions remain about their health-economic value, best practices for implementation, and how to responsibly integrate PRS into clinical workflows 1410. Ongoing research is needed to define the most effective ways to use PRS for disease prevention, screening, and targeted interventions 46.
Conclusion
Polygenic risk scores represent a promising advance in disease risk prediction, offering improved accuracy—especially for early-onset disease—when combined with traditional risk factors. However, challenges such as ancestry bias and practical implementation must be addressed to ensure equitable and effective use in clinical care. As research continues, PRS are likely to play an increasingly important role in personalized medicine and disease prevention strategies 1234+6 MORE.
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