6533b858fe1ef96bd12b6d51

RESEARCH PRODUCT

Design of the Coronary ARtery DIsease Genome-Wide Replication And Meta-Analysis (CARDIoGRAM) Study

Stefan BlankenbergChristopher P. NelsonArne SchillertHeribert SchunkertKari StefanssonThomas QuertermousRuth McphersonBenjamin F. VoightBenjamin F. VoightThemistocles L. AssimesAlistair S. HallMary Susan BurnettChristopher J. O'donnellChristopher J. O'donnellL. Adrienne CupplesL. Adrienne CupplesChristian HengstenbergAndreas ZieglerMuredach P. ReillyEran HalperinEran HalperinDaniel J. RaderLi ChenGeorge A. WellsRobert RobertsEric BoerwinkleDevin AbsherStephen E. EpsteinGudmar ThorleifssonGudmar ThorleifssonSekar KathiresanSekar KathiresanReijo LaaksonenJeanette ErdmannHilma HolmMichael PreussWinfried MärzWinfried MärzWinfried MärzKiran MusunuruKiran MusunuruAlexandre F.r. StewartNilesh J. SamaniInke R. KönigJohn R. ThompsonMingyao Li

subject

AdultMaleGenotypeMultifunction cardiogramMyocardial InfarctionSingle-nucleotide polymorphismGenome-wide association studyCoronary Artery Disease030204 cardiovascular system & hematologyBioinformaticsPolymorphism Single NucleotideArticleCoronary artery disease03 medical and health sciences0302 clinical medicineGeneticsHumansMedicineGenetic Predisposition to DiseaseMyocardial infarctionGenetics (clinical)Aged030304 developmental biologyGenetic association0303 health sciencesbusiness.industryMiddle Agedmedicine.disease3. Good healthGenetic epidemiologyResearch DesignFemaleCardiology and Cardiovascular MedicinebusinessAlgorithmsImputation (genetics)Genome-Wide Association Study

description

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 Methods and Results— CARDIoGRAM combines data from all published and several unpublished GWAS in individuals with European ancestry; includes 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.

yearjournalcountryeditionlanguage
2010-10-01Circulation: Cardiovascular Genetics
https://doi.org/10.1161/circgenetics.109.899443