Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention

Michael Inouye, Gad Abraham, Christopher P Nelson, Angela M Wood, Michael J Sweeting, Frank Dudbridge, Florence Y Lai, Stephen Kaptoge, Marta Brozynska, Tingting Wang, Shu Ye, Thomas R Webb, Martin K Rutter, Ioanna Tzoulaki, Riyaz S Patel, Ruth J F Loos, Bernard Keavney, Harry Hemingway, John Thompson, Hugh Watkins, Panos Deloukas, Emanuele Di Angelantonio, Adam S Butterworth, John Danesh, Nilesh J Samani, UK Biobank CardioMetabolic Consortium CHD Working Group, Michael Inouye, Gad Abraham, Christopher P Nelson, Angela M Wood, Michael J Sweeting, Frank Dudbridge, Florence Y Lai, Stephen Kaptoge, Marta Brozynska, Tingting Wang, Shu Ye, Thomas R Webb, Martin K Rutter, Ioanna Tzoulaki, Riyaz S Patel, Ruth J F Loos, Bernard Keavney, Harry Hemingway, John Thompson, Hugh Watkins, Panos Deloukas, Emanuele Di Angelantonio, Adam S Butterworth, John Danesh, Nilesh J Samani, UK Biobank CardioMetabolic Consortium CHD Working Group

Abstract

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.

Keywords: coronary artery disease; genomic risk prediction; primary prevention.

Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
Relative Performance of Individual Genomic Risk Scores for CAD Compared With the metaGRS In the UKB validation set (n = 482,629), (A) hazard ratios per SD of each score for all CAD (n = 22,242), censored at age 75 years, from Cox regression stratified by sex and adjusted for genotyping array (BiLEVE/UKB) and 10 genetic PCs. (B) Positive predictive value versus sensitivity for a logistic regression for each GRS, adjusted for sex, age, genotyping array (BiLEVE/UKB), and 10 genetic PCs. CAD = coronary artery disease; CI = confidence interval; GRS = genomic risk score(s); PCs = principal components.
Figure 2
Figure 2
Predictive Measures of CAD Using the metaGRS and Conventional Risk Factors (A) Positive predictive values versus sensitivity for the reference model (sex + age + array + 10 genetic PCs) and when adding the metaGRS to the model for all CAD in the UKB testing set. (B) C-index for sex-stratified age-as-time-scale Cox regression of incident CAD for conventional risk factors individually and in combination with the metaGRS, including genotyping array and 10 genetic PCs as covariates. APRC = area under the precision-recall curve; other abbreviations as in Figure 1.
Figure 3
Figure 3
Cumulative Risk of CAD by Quintiles of metaGRS in Men and Women Dotted lines represent 95% confidence intervals. For subgroup sample sizes, see Online Table 1. HR = hazard ratio; other abbreviations as in Figure 1.
Figure 4
Figure 4
Cumulative Risk of Incident CAD for Increasing Numbers of Conventional Risk Factors Stratified by metaGRS Quintile Dotted lines represent 95% CIs. GRS = genomic risk score; HR = hazard ratio; other abbreviations as in Figure 1.
Figure 5
Figure 5
Cumulative Risk of Incident CAD Within Individuals on Lipid-Lowering or BP-Lowering Medication at Assessment Dotted lines represent 95% CIs. BP = blood pressure. Abbreviations as in Figure 1.
Central Illustration
Central Illustration
Genomic Risk Score for Coronary Artery Disease The genomic score provides potential for risk screening early in life as well as complements conventional risk factors for coronary artery disease.

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Source: PubMed

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