Genomic prediction of coronary heart disease.

Gad Abraham,Sean G Byars,Aarno Palotie,Laxman Yetukuri,Emmi Tikkanen,Oneil G Bhalala,Veikko Salomaa,Eric J Sijbrands,Michael Inouye,Nilesh J Samani,Aki S Havulinna,Alysha M De Livera,Samuli Ripatti,Heribert Schunkert,Markus Perola

doi:10.1093/eurheartj/ehw450

Abstract

AimsGenetics 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 resultsWe 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.ConclusionsA 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.

Highlights

And accurate identification of individuals with increased risk of coronary heart disease (CHD) is critical for effective implementation of preventative lifestyle modifications and medical interventions, such as statin treatment.[1,2] To this end, risk scores such as the Framingham Risk Score (FRS)[3] and the American College of Cardiology/American Heart Association 2013 risk score (ACC/ AHA13),[1] based on clinical factors and lipid measurements, have been developed and are widely used
To leverage the maximum amount of information, we examined whether a genomic risk score (GRS) comprising a large number of single nucleotide polymorphisms (SNPs), including those with less than genome-wide significance, could produce clinically relevant predictive power for CHD risk
To construct an optimized GRS using the WTCCC and MIGenHarps datasets, we first generated a series of GRSs, starting with the 79 128 CARDIoGRAMplusC4D SNPs progressively lowering the r2 threshold for linkage disequilibrium (LD) to reduce the redundancy of predictive information and corresponding number of SNPs in the score

Summary

Introduction

And accurate identification of individuals with increased risk of coronary heart disease (CHD) is critical for effective implementation of preventative lifestyle modifications and medical interventions, such as statin treatment.[1,2] To this end, risk scores such as the Framingham Risk Score (FRS)[3] and the American College of Cardiology/American Heart Association 2013 risk score (ACC/ AHA13),[1] based on clinical factors and lipid measurements, have been developed and are widely used. The scores can identify individuals at very high risk, a large proportion of individuals developing CHD during the 10 years remain unidentified They do not provide sufficient discrimination at a younger age when implementation of preventative measures is likely to provide the greatest long-term benefit. Recent advances have shown that genomic prediction models that consider all available genetic variants can more efficiently stratify those at increased risk of complex disease.[20,21,22,23,24] To leverage the maximum amount of information, we examined whether a genomic risk score (GRS) comprising a large number of SNPs, including those with less than genome-wide significance, could produce clinically relevant predictive power for CHD risk

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Conclusion