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Eur Heart J. 2016 Nov 14;37(43):3267-3278. Epub 2016 Sep 21.

Genomic prediction of coronary heart disease.

Author information

1
Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria 3010, Australia.
2
Department of Pathology, The University of Melbourne, Parkville, Victoria 3010, Australia.
3
National Institute for Health and Welfare, Helsinki FI-00271, Finland.
4
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria 3010, Australia.
5
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki FI-00014, Finland.
6
Deutsches Herzzentrum München, and Technische Universität München, Munich 80636, Germany.
7
Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich 81377, Germany.
8
Department of Internal Medicine, Erasmus Medical Center, Rotterdam, CA 3000, The Netherlands.
9
Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
10
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.
11
Department of Psychiatry, Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
12
Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Rd, Leicester, LE3 9QP, United Kingdom minouye@unimelb.edu.au samuli.ripatti@helsinki.fi veikko.salomaa@thl.fi njs@leicester.ac.uk.
13
National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom.
14
National Institute for Health and Welfare, Helsinki FI-00271, Finland minouye@unimelb.edu.au samuli.ripatti@helsinki.fi veikko.salomaa@thl.fi njs@leicester.ac.uk.
15
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki FI-00014, Finland minouye@unimelb.edu.au samuli.ripatti@helsinki.fi veikko.salomaa@thl.fi njs@leicester.ac.uk.
16
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom.
17
Department of Public Health, University of Helsinki, Helsinki FI-00014, Finland.
18
Centre for Systems Genomics, School of BioSciences, The University of Melbourne, Parkville, Victoria 3010, Australia minouye@unimelb.edu.au samuli.ripatti@helsinki.fi veikko.salomaa@thl.fi njs@leicester.ac.uk.

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

PMID:
27655226
PMCID:
PMC5146693
DOI:
10.1093/eurheartj/ehw450
[Indexed for MEDLINE]
Free PMC Article

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