Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults

Abstract

BackgroundCoronary 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.ObjectivesThis study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention.MethodsUsing 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.ResultsThe 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.ConclusionsThe 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.

Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status

“…Our study substantiates the findings by Inouye et al () and Khera et al () showing that GPRS can boost individual risk prediction of common diseases. Here, we come to the conclusion, that at least for prediction of CAD status there is no need to use a sledge‐hammer to crack the nut.…”

“…Therefore, a truly external validation in data generated with a different technology, at another time point, in a different population is required to evaluate the absolute classification performance of the models. Interestingly, this is the case also for other published GPRS by Khera et al () and Inouye et al ().…”

Genetic risk scores to predict the prognosis of chronic heart failure patients in Chinese Han

“…It has always been of clinical focus to foresee the risk of heart failure via genetic background characteristics. GRS was used to identify patients with Mendelian and complex disease patterns known as loci for HF risk factors, such as CAD, Cardiometabolic Disease, Blood Pressure and atrial fibrillation . Heart failure has similar or worse prognosis when compared to most cancers .…”

“…Genetic risk scores (GRS), which is the sum of risk genes in individuals has been beneficial to primary prevention. Genome‐wide association study (GWAS) studies have identified 73 gene mutation loci for coronary artery disease (CAD), and almost 100 genes were associated with cardiomyopathies (most of which are DCM and HCM), while only few previous studies such as studies on coronary artery disease, and cardiometabolic diseases in cardiovascular disease have shown the use of genomic information in risk prediction.…”

Widening the spectrum of genetic testing in familial hypercholesterolaemia: Will it translate into better patient and population outcomes?

“…[67][68][69] This approach has been shown to predict risk in patients with FH. 67 A different approach is to use a very large number of SNPs with a lower threshold of statistical significance (49 310 SNPs in one study 70 ; 1.7 million in another 71 ). This has also been shown to be valid in patients with familial hypercholesterolaemia.…”