Genomic prediction of coronary heart disease - PubMed (original) (raw)

Meta-Analysis

. 2016 Nov 14;37(43):3267-3278.

doi: 10.1093/eurheartj/ehw450. Epub 2016 Sep 21.

Aki S Havulinna 3, Oneil G Bhalala 1 2, Sean G Byars 1 2, Alysha M De Livera 1 2 4, Laxman Yetukuri 5, Emmi Tikkanen 5, Markus Perola 3 5, Heribert Schunkert 6 7, Eric J Sijbrands 8, Aarno Palotie 5 9 10 11, Nilesh J Samani 12 13, Veikko Salomaa 14, Samuli Ripatti 15 16 17, Michael Inouye 18 2 4

Affiliations

Meta-Analysis

Genomic prediction of coronary heart disease

Gad Abraham et al. Eur Heart J. 2016.

Abstract

Aims: Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores.

Methods and results: We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61-1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5-1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6-5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12-18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.

Conclusions: A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.

Keywords: Coronary heart disease; Framingham risk score; Genomic risk score; Myocardial infarction; Primary prevention.

© The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

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Figures

Figure 1

Figure 1

Study workflow. (A) The procedure for deriving the GRS of incident CHD. The analysis workflow for evaluating the GRS within (B) ARGOS, (C) FINRISK, and (D) FHS.

Figure 2

Figure 2

Difference in C-index (95% CI) for time to incident CHD event within 10 years, relative to the reference model in the FINRISK and FHS cohorts. Reference models used age as the time scale, stratified by sex (FINRISK: adjusted for cohort and geographic location; FHS: adjusted for cohort). Family history was not available for all of the FHS cohorts and thus not considered here. _P_-values are from the correlated jackknife test.

Figure 3

Figure 3

Kaplan-Meier cumulative risk of incident CHD event by genomic risk group for men and women in the FINRISK and FHS cohorts. Showing the cumulative risk in quintiles 0–20%, 40–60%, 80–100%. The vertical bars along the x-axis indicate the age at which each risk group attains a cumulative CHD risk of 10%. Dashed lines indicate 95% CI.

Figure 4

Figure 4

Kaplan-Meier curves for incident CHD event risk stratified by GRS quintiles and smoking status at baseline, for men and women in the FINRISK cohorts.

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