Coronary artery disease loci identified in over 190,000 individuals implicate lipid metabolism and inflammation as key causal pathways; evidence for independent signals in many of the risk loci. S. Kanoni1, C. Willenborg2,3, M. Farrall4,5, T. L. Assimes6, J. R. Thompson7, E. Ingelsson8, D. Saleheen9-11, J. Erdmann2,3, M. P. Reilly12, R. Collins13, S. Kathiresan14,15, A. Hamsten16,17, U. Thorsteinsdottir18,19, J. S. Kooner20, J. Danesh10, C. N. A. Palmer21, R. Roberts22, H. Watkins4,5, H. Schunkert2,3, N. J. Samani23, P. Deloukas1 for the CARDIoGRAMplusC4D consortium 1) Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Cambridge, UK, Cambridgshire, United Kingdom; 2) Universität zu Lübeck, Medizinische Klinik II, Lübeck, Germany; 3) Deutsches Zentrum für Herz-Kreislauf-Forschung, Lübeck, Germany; 4) Wellcome Trust Centre for Human Genetics, University of Oxford, UK; 5) Cardiovascular Medicine, University of Oxford, Oxford UK; 6) Department of Medicine, Stanford University School of Medicine; 7) Department of Health Sciences, University of Leicester, UK; 8) Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden; 9) Center for Non-Communicable Diseases, Karachi, Pakistan; 10) Department of Public Health and Primary Care, University of Cambridge, UK; 11) Department of Medicine, University of Pennsylvania, USA; 12) Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA; 13) Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, UK; 14) Cardiology Division, Center for Human Genetic Research, and Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, USA; 15) Broad Institute of Harvard/MIT, Cambridge, USA; 16) Atherosclerosis Res Unit, Department of Medicine, Karolinska Institutet; 17) Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden; 18) deCODE genetics, Reykjavik, Iceland; 19) University of Iceland, Faculty of Medicine, Reykjavik, Iceland; 20) National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK; 21) Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK; 22) Institute for Molecular Medicine FIMM, University of Helsinki and Public Health Genomics Unit, Helsinki, Finland; 23) Department of Cardiovascular Sciences, University of Leicester & National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK.
Coronary artery disease (CAD) is the commonest cause of death in the world. We undertook a 2 stage meta-analysis and here we report the association analysis results in up to 63,746 CAD cases and 130,681 controls, identifying 16 loci reaching genome-wide significance for the first time. In total, 14 loci reached genome-wide significance, namely IL6R, APOB, VAMP5-VAMP8-GGCX, SLC22A4-SLC22A5, ZEB2-AC074093.1, GUCY1A3, KCNK5, LPL, PLG, TRIB1, ABCG5-ABCG8, FURIN-FES, FLT1 and AK097927 (in the young cases subgroup analysis only). Another 6 loci reaching p< 10-6 were further validated in 4 independent studies and 2 loci (EDNRA and HDAC9) reached genome-wide significance in a 3-stage combined meta-analysis. Our results are taking the number of such loci for CAD to 47, and a further 103 independent variants (r2< 0.2) strongly associated with CAD (false discovery rate 5%). Together with the genome-wide loci these variants explain approximately 10.6% of CAD heritability. In total 14 CAD loci (6 novel and 8 previously reported) harbor a gene for which a mouse knock out model has a relevant cardiovascular phenotype. Of the 47 genome-wide significant lead SNPs, 12 demonstrate a significant association with a lipid trait and 5 with blood pressure but none with diabetes. Network analysis with 233 candidate genes (extended set of associated loci at 10% FDR) generated 5 interaction networks comprising 85% of the putative CAD genes. The 4 most significant pathways mapping to these networks are linked to lipid metabolism and inflammation underscoring their causal role in the genetic aetiology of CAD. Furthermore, we implemented the GCTA tool to undertake fine mapping in 27 of the 47 CAD loci via an approximate conditional analysis method. Prior to the analysis, we validated the robustness of GCTA versus the exact conditional analysis in a subset of studies. We then used meta-analysis summary-level statistics from 27 Metabochip studies and a representative reference panel for linkage disequilibrium estimation. Our preliminary findings provide evidence of additional independent signals in at least half of the regions. To further investigate these findings we will perform a conditional analysis using our 2-stage meta-analysis results after performing imputation to 1,000 Genomes.
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