Genomic prediction of coronary heart disease — ASN Events
  1. Schedule
  2. Poster Session 1
  3. Genomic prediction of coronary heart disease

Genomic prediction of coronary heart disease (#163)

Gad Abraham 1 2 , 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 , Eric J Sijbrands 7 , Aarno Palotie 5 8 9 10 , Nilesh J Samani 11 12 , Veikko Salomaa 3 , Samuli Ripatti 5 13 14 , Michael Inouye 1 2 5
  1. Centre for Systems Genomics, School of BioSciences, University of Melbourne, Parkville, VIC, Australia
  2. Department of Pathology, University of Melbourne, Parkville, VIC, Australia
  3. National Institute for Health and Welfare, Helsinki, Finland
  4. Centre for Epidemiology and Biostatistics, University of Melbourne, Parkville, VIC, Australia
  5. Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
  6. Deutsches Herzzentrum Munchen, Klinik fur Herz- und Kreislauferkrangkungen, Munich, Germany
  7. Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
  8. Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
  9. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
  10. Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
  11. Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
  12. National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK
  13. Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
  14. Department of Public Health, University of Helsinki, Helsinki, Finland

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=3,406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR=1.74, 95% 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 10y 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 12–18y 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.

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