TY - JOUR
T1 - 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
AU - Preuss, Michael
AU - König, Inke R.
AU - Thompson, John R.
AU - Erdmann, Jeanette
AU - Absher, Devin
AU - Assimes, Themistocles L.
AU - Blankenberg, Stefan
AU - Boerwinkle, Eric
AU - Chen, Li
AU - Cupples, L. Adrienne
AU - Hall, Alistair S.
AU - Halperin, Eran
AU - Hengstenberg, Christian
AU - Holm, Hilma
AU - Laaksonen, Reijo
AU - Li, Mingyao
AU - Marz, Winfried
AU - McPherson, Ruth
AU - Musunuru, Kiran
AU - Nelson, Christopher P.
AU - Burnett, Mary Susan
AU - Epstein, Stephen E.
AU - O'Donnell, Christopher J.
AU - Quertermous, Thomas
AU - Rader, Daniel J.
AU - Roberts, Robert
AU - Schillert, Arne
AU - Stefansson, Kari
AU - Stewart, Alexandre F.R.
AU - Thorleifsson, Gudmar
AU - Voight, Benjamin F.
AU - Wells, George A.
AU - Ziegler, Andreas
AU - Kathiresan, Sekar
AU - Reilly, Muredach P.
AU - Samani, Nilesh J.
AU - Schunkert, Heribert
PY - 2010/10/1
Y1 - 2010/10/1
N2 - 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 Methods and Results-CARDIoGRAM combines data from all published and several unpublished GWAS in individuals with European ancestry; includes 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78649374846&partnerID=8YFLogxK
U2 - 10.1161/CIRCGENETICS.109.899443
DO - 10.1161/CIRCGENETICS.109.899443
M3 - Journal articles
C2 - 20923989
AN - SCOPUS:78649374846
SN - 1942-325X
VL - 3
SP - 475
EP - 483
JO - Circulation: Cardiovascular Genetics
JF - Circulation: Cardiovascular Genetics
IS - 5
ER -
