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J Am Coll Cardiol. 2018 Oct 16;72(16):1883-1893. doi: 10.1016/j.jacc.2018.07.079.

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

Author information

1
Cambridge 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. Electronic address: mi336@medschl.cam.ac.uk.
2
Cambridge 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. Electronic address: gad.abraham@baker.edu.au.
3
Department of Cardiovascular Sciences and NIHR Leicester Biomedical Centre, University of Leicester, Leicester, United Kingdom.
4
MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
5
MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
6
MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge, United Kingdom.
7
Cambridge 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.
8
Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; Manchester Diabetes Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.
9
Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece.
10
Institute of Cardiovascular Sciences, University College London, London, United Kingdom; Barts Heart Centre, St. Bartholomew's Hospital, London, United Kingdom.
11
Charles Bronfman Institute for Personalized Medicine, Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
12
Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom; Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.
13
The Farr Institute of Health Informatics Research and the National Institute for Health Research, Biomedical Research Centre, University College London, London, United Kingdom.
14
Department of Health Sciences, University of Leicester, Leicester, United Kingdom.
15
Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom; The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
16
William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
17
MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge, United Kingdom; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom.
18
Department of Cardiovascular Sciences and NIHR Leicester Biomedical Centre, University of Leicester, Leicester, United Kingdom. Electronic address: njs@leicester.ac.uk.

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

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