A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

Area of Science:

• Cardiovascular Genetics
• Genomic Epidemiology
• Precision Medicine

Background:

• Identifying individuals at high risk for coronary artery disease (CAD) before symptom onset is crucial for public health.
• Genome-wide polygenic scores have been developed to stratify CAD risk, acknowledging its significant inherited component.

Purpose of the Study:

• To develop and validate an improved polygenic score for CAD, named GPS_Mult, by integrating extensive genome-wide association data.
• To assess the performance of GPS_Mult in predicting prevalent and incident CAD across diverse ancestral populations.

Main Methods:

• Developed GPS_Mult using genome-wide association data from over 269,000 CAD cases and 1,178,000 controls across five ancestries, incorporating ten CAD risk factors.
• Validated GPS_Mult in UK Biobank participants (European ancestry) for prevalent and incident CAD.
• Externally validated GPS_Mult in multiethnic datasets including African, European, Hispanic, and South Asian ancestries.

Main Results:

• GPS_Mult strongly associated with prevalent CAD (OR 2.14 per SD) and incident CAD (HR 1.73 per SD) in European ancestry individuals.
• Identified 20.0% of the population with 3-fold increased CAD risk and 13.9% with 3-fold decreased risk.
• Demonstrated superior performance and stronger associations across all tested ancestries compared to previous CAD polygenic scores.

Conclusions:

• GPS_Mult represents a significant advancement in polygenic risk prediction for CAD.
• The score effectively identifies individuals at substantially elevated or reduced risk, enabling better risk stratification.
• The findings support a generalizable framework for integrating diverse, large-scale genetic data to enhance polygenic risk prediction for CAD and related traits.