Clinical Scorecard: Translating Polygenic Risk Scores for Varied Population Groups

At a Glance

Key Highlights

• PRSs derived from predominantly European-ancestry datasets perform poorly in non-European populations.
• Performance disparities in PRSs are evident, with accuracy significantly lower in Hispanic/Latino, South Asian, East Asian, and African populations.
• Admixed populations require local ancestry-aware models for improved PRS performance.
• Recent methodological advances have improved predictive performance across diverse populations.
• Clinical integration of PRSs remains uneven, with CAD showing the most progress.

Guideline-Based Recommendations

Diagnosis

• Utilize multi-ancestry PRS models for risk assessment in diverse populations.

Management

• Implement ancestry-aware methods to enhance PRS accuracy in clinical settings.

Monitoring & Follow-up

• Continuously evaluate the calibration and robustness of PRS across different population subgroups.

Risks

• Be aware of the limitations of PRS in admixed and underrepresented populations due to genetic diversity.

Patient & Prescribing Data

Individuals from diverse ancestral backgrounds, particularly admixed populations.

PRS-informed risk assessment should be calibrated and interpretable for effective clinical use.

Clinical Best Practices

• Adopt multi-ancestry frameworks in PRS development.
• Focus on improving the predictive performance of PRSs in underrepresented populations.
• Ensure that PRS applications are contextually relevant and clinically useful.

Related Resources & Content

• Translating Polygenic Risk Scores for Varied Population Groups