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Zeller T, Seiffert M, Müller C, et al. Genome-Wide Association Analysis for Severity of Coronary Artery Disease Using the Gensini Scoring System. Front Cardiovasc Med. 2017;4:57doi: 10.3389/fcvm.2017.00057.
Zeller, T., Seiffert, M., Müller, C., Scholz, M., Schäffer, A., Ojeda, F., Drexel, H., Mündlein, A., Kleber, M. E., März, W., Sinning, C., Brunner, F. J., Waldeyer, C., Keller, T., Saely, C. H., Sydow, K., Thiery, J., Teupser, D., Blankenberg, S., & Schnabel, R. (2017). Genome-Wide Association Analysis for Severity of Coronary Artery Disease Using the Gensini Scoring System. Frontiers in cardiovascular medicine, 457. https://doi.org/10.3389/fcvm.2017.00057
Zeller, Tanja, et al. "Genome-Wide Association Analysis for Severity of Coronary Artery Disease Using the Gensini Scoring System." Frontiers in cardiovascular medicine vol. 4 (2017): 57. doi: https://doi.org/10.3389/fcvm.2017.00057
Zeller T, Seiffert M, Müller C, Scholz M, Schäffer A, Ojeda F, Drexel H, Mündlein A, Kleber ME, März W, Sinning C, Brunner FJ, Waldeyer C, Keller T, Saely CH, Sydow K, Thiery J, Teupser D, Blankenberg S, Schnabel R. Genome-Wide Association Analysis for Severity of Coronary Artery Disease Using the Gensini Scoring System. Front Cardiovasc Med. 2017 Sep 20;4:57. doi: 10.3389/fcvm.2017.00057. eCollection 2017. PMID: 28979897; PMCID: PMC5611399.
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Front Cardiovasc Med. 2017 Sep 20;4:57. doi: 10.3389/fcvm.2017.00057. eCollection 2017.

Genome-Wide Association Analysis for Severity of Coronary Artery Disease Using the Gensini Scoring System.

Frontiers in cardiovascular medicine

Tanja Zeller, Moritz Seiffert, Christian Müller, Markus Scholz, Anna Schäffer, Francisco Ojeda, Heinz Drexel, Axel Mündlein, Marcus E Kleber, Winfried März, Christoph Sinning, Fabian J Brunner, Christoph Waldeyer, Till Keller, Christoph H Saely, Karsten Sydow, Joachim Thiery, Daniel Teupser, Stefan Blankenberg, Renate Schnabel

Affiliations

  1. Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.
  2. DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany.
  3. Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
  4. LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
  5. Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), Feldkirch, Austria.
  6. Private University of the Principality of Liechtenstein, Triesen, Liechtenstein.
  7. Drexel University College of Medicine, Philadelphia, PA, United States.
  8. Mannheim Medical Faculty, 5th Department of Medicine Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Heidelberg University, Mannheim, Germany.
  9. Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany.
  10. Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria.
  11. Kerckhoff Heart and Thorax Center, Department of Cardiology, Bad Nauheim, Germany.
  12. German Centre for Cardiovascular Research (DZHK), Partner Site RheinMain, Bad Nauheim, Germany.
  13. Department of Medicine and Cardiology, Academic Teaching Hospital Feldkirch, Feldkirch, Austria.
  14. Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany.
  15. Institute of Laboratory Medicine, University Hospital Munich (LMU) and Ludwig-Maximilian-University Munich, Munich, Germany.

PMID: 28979897 PMCID: PMC5611399 DOI: 10.3389/fcvm.2017.00057

Abstract

Coronary artery disease (CAD) has a complex etiology involving numerous environmental and genetic factors of disease risk. To date, the genetic 9p21 locus represents the most robust genetic finding for prevalent and incident CAD. However, limited information is available on the genetic background of the severity and distribution of CAD. CAD manifests itself as stable CAD or acute coronary syndrome. The Gensini score quantifies the extent CAD but requires coronary angiography. Here, we aimed to identify novel genetic variants associated with Gensini score severity and distribution of CAD. A two-stage approach including a discovery and a replication stage was used to assess genetic variants. In the discovery phase, a meta-analysis of genome-wide association data of 4,930 CAD-subjects assessed by the Gensini score was performed. Selected single nucleotide polymorphisms (SNPs) were replicated in 2,283 CAD-subjects by

Keywords: Gensini score; genetics; genome-wide association; polymorphism; severity of coronary artery disease

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