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Evaluating Individual Level Performance of Polygenic Risk Scores Using Early Onset High Genetic Risk Coronary Artery
Shengxin Liang1,2, Min Seo Kim2,3, Yang Sui2,3,4
1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Insights
Polygenic risk scores (PRSs) show poor individual prediction for coronary artery disease (CAD). A new benchmark cohort reveals PRSs capture less than a third of high-genetic-risk patients, highlighting the need for better clinical validation.
Area of Science:
- Cardiovascular Genetics
- Precision Medicine
- Genomic Risk Prediction
Background:
- Population-level metrics for polygenic risk scores (PRSs) obscure individual risk prediction accuracy.
- Clinical translation of PRSs is hindered by a lack of validation in diverse patient subgroups.
Purpose of the Study:
- Introduce a novel "benchmark" cohort to assess individual-level PRS performance.
- Evaluate the clinical readiness of 58 published coronary artery disease (CAD) PRSs.
- Compare PRS performance against established non-genetic biomarkers.
Main Methods:
- Defined a "benchmark" cohort of 1184 early-onset CAD patients (<55 years) with low clinical risk.
- Assessed the proportion of benchmark patients captured by 58 published CAD PRSs.
- Analyzed lipid profiles of benchmark patients not captured by any PRS.
Main Results:
- A significant disconnection exists between population-level PRS performance and individual-level accuracy.
- The proportion of benchmark patients captured by PRSs ranged from 10.8% to 33.1%.
- The top-performing PRS was twice as effective as lipoprotein(a) in identifying high-risk individuals; uncaptured patients had healthier lipid profiles.
Conclusions:
- The novel benchmark cohort provides a "ground truth" for evaluating PRS clinical utility.
- Current PRSs demonstrate limited ability to identify individuals with high genetic predisposition to early CAD.
- This framework is essential for validating the clinical readiness of PRSs.
Abstract:
Polygenic risk scores (PRSs) are typically validated using population-level metrics, masking variability in individual-level risk prediction and hindering clinical translation. To address this, we introduced a novel framework using a "benchmark" cohort (N=1184) of "unexpected coronary artery disease (CAD)": early-onset patients (<55 years) with a clinical profile-low 10-year risk, no diabetes or severe hypercholesterolemia-that excludes therapy indications. The occurrence of early CAD in these clinically low-risk individuals establishes a "ground truth" for high genetic risk. We evaluated 58 published CAD PRSs and demonstrated a disconnection between population-level performance and individual-level accuracy (proportion of benchmark patients captured). The proportion captured by 58 PRSs varied from 10.8% to 33.1%, and the top-performing score was 2-fold more effective at identifying the benchmark group than established non-genetic biomarkers, such as lipoprotein(a). Furthermore, benchmark patients never captured by any score exhibited significantly healthier lipid profiles. Our framework provides an essential method for validating clinical readiness of PRSs.
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