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- Title
Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease.
- Authors
Elliott, Joshua; Bodinier, Barbara; Bond, Tom A.; Chadeau-Hyam, Marc; Evangelou, Evangelos; Moons, Karel G. M.; Dehghan, Abbas; Muller, David C.; Elliott, Paul; Tzoulaki, Ioanna
- Abstract
<bold>Importance: </bold>The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain.<bold>Objective: </bold>To examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations.<bold>Design, Setting, and Participants: </bold>Observational study of UK Biobank participants enrolled from 2006 to 2010. A case-control sample of 15 947 prevalent CAD cases and equal number of age and sex frequency-matched controls was used to optimize the predictive performance of a polygenic risk score for CAD based on summary statistics from published genome-wide association studies. A separate cohort of 352 660 individuals (with follow-up to 2017) was used to evaluate the predictive accuracy of the polygenic risk score, pooled cohort equations, and both combined for incident CAD.<bold>Exposures: </bold>Polygenic risk score for CAD, pooled cohort equations, and both combined.<bold>Main Outcomes and Measures: </bold>CAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed.<bold>Results: </bold>In the cohort of 352 660 participants (mean age, 55.9 years; 205 297 women [58.2%]) used to evaluate the predictive accuracy of the examined models, there were 6272 incident CAD events over a median of 8 years of follow-up. CAD discrimination for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of 0.61 (95% CI, 0.60 to 0.62), 0.76 (95% CI, 0.75 to 0.77), and 0.78 (95% CI, 0.77 to 0.79), respectively. The change in C statistic between the latter 2 models was 0.02 (95% CI, 0.01 to 0.03). Calibration of the models showed overestimation of risk by pooled cohort equations, which was corrected after recalibration. Using a risk threshold of 7.5%, addition of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement of 4.4% (95% CI, 3.5% to 5.3%) for cases and -0.4% (95% CI, -0.5% to -0.4%) for noncases (overall net reclassification improvement, 4.0% [95% CI, 3.1% to 4.9%]).<bold>Conclusions and Relevance: </bold>The addition of a polygenic risk score for CAD to pooled cohort equations was associated with a statistically significant, yet modest, improvement in the predictive accuracy for incident CAD and improved risk stratification for only a small proportion of individuals. The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.
- Publication
JAMA: Journal of the American Medical Association, 2020, Vol 323, Issue 7, p636
- ISSN
0098-7484
- Publication type
journal article
- DOI
10.1001/jama.2019.22241