Polygenic Scoring for Detection of Ascending Thoracic Aortic Dilation

Area of Science:

• Cardiovascular Genetics
• Medical Informatics
• Population Health

Background:

• Ascending thoracic aortic dilation is a heritable condition influenced by various risk factors.
• Polygenic scores (PGS) are emerging tools for assessing complex disease risk.
• The utility of PGS in improving aortic diameter prediction across diverse populations remains unclear.

Purpose of the Study:

• To evaluate the effectiveness of incorporating a polygenic score (PGS) into existing clinical prediction models for ascending aortic diameter.
• To assess whether a PGS enhances risk prediction in a diverse biobank population.
• To compare the performance of PGS-augmented models against the established AORTA score.

Main Methods:

• Utilized data from 6235 participants in the Penn Medicine Biobank, including echocardiography, clinical data, and genome-wide genotype data.
• Employed linear regression models to integrate PGS weights from a UK Biobank genome-wide association study.
• Compared model performance using the AORTA score alone versus AORTA score combined with UK Biobank-derived PGS.

Main Results:

• The AORTA score explained 30.6% of aortic diameter variance; adding a UK Biobank-derived PGS improved this to 33.1%.
• A reweighted AORTA score with PGS achieved the highest variance explanation at 34.9%.
• Improvements were significant in individuals of European ancestry but minimal in those of African ancestry, with similar trends for predicting thoracic aortic dilation cases.

Conclusions:

• Integrating a UK Biobank-derived PGS with the AORTA score significantly improves thoracic aortic diameter prediction in individuals of European ancestry.
• The addition of PGS showed minimal benefit in individuals of African ancestry.
• The findings suggest that current PGS may exacerbate existing healthcare disparities if not carefully applied across diverse populations.