Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention
Michael Inouye1, Gad Abraham2, Christopher P Nelson3
1Cambridge Baker Systems Genomics Initiative, Melbourne, Victoria, Australia, and Cambridge, United Kingdom; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Department of Clinical Pathology and School of BioSciences, University of Melbourne, Parkville, Victoria, Australia; The Alan Turing Institute, London, United Kingdom.
Insights
A new genomic risk score (metaGRS) for coronary artery disease (CAD) significantly improves risk prediction. This tool can identify individuals at high risk for early intervention, complementing traditional screening methods.
Area of Science:
- Genetics
- Cardiology
- Preventive Medicine
Background:
- Coronary artery disease (CAD) is highly heritable with a polygenic basis.
- Previous genomic risk scores for CAD were limited by scope and sample size.
Purpose of the Study:
- To develop a comprehensive genomic risk score for CAD.
- To evaluate its potential as a primary prevention screening tool.
Main Methods:
- A meta-analytic approach combined genome-wide and targeted genetic association data.
- Developed a novel genomic risk score for CAD (metaGRS) using 1.7 million genetic variants.
- Externally validated metaGRS in the UK Biobank (22,242 cases, 460,387 noncases).
Main Results:
- metaGRS showed a hazard ratio of 1.71 per SD increase for CAD.
- Individuals in the top 20% of metaGRS had a 4.17-fold higher CAD risk compared to the bottom 20%.
- metaGRS outperformed 6 conventional risk factors in predicting incident CAD.
Conclusions:
- The developed genomic score advances genomic risk stratification for CAD.
- Highlights the potential of genomic screening for early life risk prediction and intervention.
Background:
Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes.
Objectives:
This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention.
Methods:
Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank.
Results:
The hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age.
Conclusions:
The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.
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