Genome-wide association study of perioperative myocardial infarction after coronary artery bypass surgery. Issue 5 (6th May 2015)

• Record Type: Journal Article
• Authors: Kertai, Miklos D; Li, Yi-Ju; Li, Yen-Wei; Ji, Yunqi; Alexander, John; Newman, Mark F; Smith, Peter K; Joseph, Diane; Mathew, Joseph P; Podgoreanu, Mihai V
• Is Part Of: BMJ open. Volume 5:Issue 5(2015)
• Publication Date: 2015-05-06
• Subjects: SURGERY -- GENETICS; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2014-006920
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: Identification of patient subpopulations susceptible to develop myocardial infarction (MI) or, conversely, those displaying either intrinsic cardioprotective phenotypes or highly responsive to protective interventions remain high-priority knowledge gaps. We sought to identify novel common genetic variants associated with perioperative MI in patients undergoing coronary artery bypass grafting using genome-wide association methodology. Setting: 107 secondary and tertiary cardiac surgery centres across the USA. Participants: We conducted a stage I genome-wide association study (GWAS) in 1433 ethnically diverse patients of both genders (112 cases/1321 controls) from the Genetics of Myocardial Adverse Outcomes and Graft Failure (GeneMAGIC) study, and a stage II analysis in an expanded population of 2055 patients (225 cases/1830 controls) combined from the GeneMAGIC and Duke Perioperative Genetics and Safety Outcomes (PEGASUS) studies. Patients undergoing primary non-emergent coronary bypass grafting were included. Primary and secondary outcome measures: The primary outcome variable was perioperative MI, defined as creatine kinase MB isoenzyme (CK-MB) values ≥10× upper limit of normal during the first postoperative day, and not attributable to preoperative MI. Secondary outcomes included postoperative CK-MB as a quantitative trait, or a dichotomised phenotype based on extreme quartiles of the CK-MB distribution. Results: Following quality control and adjustment for clinical covariates, we identified 521 single nucleotide polymorphisms in the stage I GWAS analysis. Among these, 8 common variants in 3 genes or intergenic regions met p<10 −5 in stage II. A secondary analysis using CK-MB as a quantitative trait (minimum p=1.26×10 −3 for rs609418), or a dichotomised phenotype based on extreme CK-MB values (minimum p=7.72×10 −6 for rs4834703) supported these findings. Pathway analysis revealed that genes harbouring top-scoring variants cluster in pathways of biological relevance to extracellular matrix remodelling, endoplasmic reticulum-to-Golgi transport and inflammation. Conclusions: Using a two-stage GWAS and pathway analysis, we identified and prioritised several potential susceptibility loci for perioperative MI.