Ke Hao1,2, Raili Ermel3, Katyayani Sukhavasi3, Haoxiang Cheng1, Lijiang Ma1,4, Ling Li5,6,7, Letizia Amadori1,8, Simon Koplev9, Oscar Franzén10, Valentina d'Escamard4, Nirupama Chandel4, Kathryn Wolhuter11,12, Nicole S Bryce11,12, Vamsidhar R M Venkata13, Clint L Miller14, Arno Ruusalepp3,15, Heribert Schunkert5,7, Johan L M Björkegren1,10, Jason C Kovacic4,11,16. 1. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY (K.H., H.C., L.M., L.A., J.L.M.B.). 2. Sema4, Stamford, CT (K.H.). 3. Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital, Estonia (R.E., K.S., A.R.). 4. Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, NY (L.M., V.d., N.C., J.C.K.). 5. Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.L., H.S.). 6. Center for Doctoral Studies in Informatics and its Applications, Department of Informatics, Technische Universität München, Munich, Germany (L.L.). 7. Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Munich Heart Alliance, Germany (L.L., H.S.). 8. Now with New York University Cardiovascular Research Center, Department of Medicine, Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine, New York University Langone Health, NY (L.A.). 9. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, United Kingdom (S.K). 10. Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden (O.F., J.L.M.B). 11. Victor Chang Cardiac Research Institute, Darlinghurst, Australia (K.W., N.S.B., J.C.K.). 12. University of New South Wales, Faculty of Medicine and Health, Sydney, Australia (K.W., N.S.B.). 13. Weill Cornell Medicine, Vellore Institute of Technology, Harrison, NJ (V.R.M.V.). 14. Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA (C.L.M.). 15. Department of Cardiology, Institute of Clinical Medicine, Tartu University (A.R.). 16. St Vincent's Clinical School, University of New South Wales, Sydney, Australia (J.C.K.).
Abstract
BACKGROUND: Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. METHODS: We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). RESULTS: We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. CONCLUSIONS: We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.
BACKGROUND: Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. METHODS: We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). RESULTS: We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. CONCLUSIONS: We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.
Authors: Kathryn J Moore; Simon Koplev; Edward A Fisher; Ira Tabas; Johan L M Björkegren; Amanda C Doran; Jason C Kovacic Journal: J Am Coll Cardiol Date: 2018-10-30 Impact factor: 24.094
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