Design of the coronary ARtery disease genome-wide replication and meta-Analysis (CARDIoGRAM) study: A genome-wide association meta-analysis involving more than 22 000 cases and 60 000 controls

Michael Preuss, Inke R. König, John R. Thompson, Jeanette Erdmann, Devin Absher, Themistocles L. Assimes, Stefan Blankenberg, Eric Boerwinkle, Li Chen, L. Adrienne Cupples, Alistair S. Hall, Eran Halperin, Christian Hengstenberg, Hilma Holm, Reijo Laaksonen, Mingyao Li, Winfried Marz, Ruth McPherson, Kiran Musunuru, Christopher P. NelsonMary Susan Burnett, Stephen E. Epstein, Christopher J. O'Donnell, Thomas Quertermous, Daniel J. Rader, Robert Roberts, Arne Schillert, Kari Stefansson, Alexandre F.R. Stewart, Gudmar Thorleifsson, Benjamin F. Voight, George A. Wells, Andreas Ziegler, Sekar Kathiresan, Muredach P. Reilly, Nilesh J. Samani, Heribert Schunkert

Research output: Contribution to journalArticlepeer-review

Abstract

Background-Recent genome-wide association studies (GWAS) of myocardial infarction (MI) and other forms of coronary artery disease (CAD) have led to the discovery of at least 13 genetic loci. In addition to the effect size, power to detect associations is largely driven by sample size. Therefore, to maximize the chance of finding novel susceptibility loci for CAD and MI, the Coronary ARtery DIsease Genome-wide Replication And Meta-analysis (CARDIoGRAM) consortium was formed. Methods and Results-CARDIoGRAM combines data from all published and several unpublished GWAS in individuals with European ancestry; includes >22 000 cases with CAD, MI, or both and >60 000 controls; and unifies samples from the Atherosclerotic Disease VAscular functioN and genetiC Epidemiology study, CADomics, Cohorts for Heart and Aging Research in Genomic Epidemiology, deCODE, the German Myocardial Infarction Family Studies I, II, and III, Ludwigshafen Risk and Cardiovascular Heath Study/AtheroRemo, MedStar, Myocardial Infarction Genetics Consortium, Ottawa Heart Genomics Study, PennCath, and the Wellcome Trust Case Control Consortium. Genotyping was carried out on Affymetrix or Illumina platforms followed by imputation of genotypes in most studies. On average, 2.2 million single nucleotide polymorphisms were generated per study. The results from each study are combined using meta-analysis. As proof of principle, we meta-analyzed risk variants at 9p21 and found that rs1333049 confers a 29% increase in risk for MI per copy (P=2×10-20). Conclusion-CARDIoGRAM is poised to contribute to our understanding of the role of common genetic variation on risk for CAD and MI.

Original languageEnglish
Pages (from-to)475-483
Number of pages9
JournalCirculation: Cardiovascular Genetics
Volume3
Issue number5
DOIs
StatePublished - Oct 2010

Keywords

  • Coronary artery disease
  • Genetics
  • Meta-analysis
  • Myocardial infarction

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