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Coronary Artery Disease: HELP
Articles by Roby Joehanes
Based on 5 articles published since 2010
(Why 5 articles?)
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Between 2010 and 2020, Roby Joehanes wrote the following 5 articles about Coronary Artery Disease.
 
+ Citations + Abstracts
1 Article Blood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease. 2019

Agha, Golareh / Mendelson, Michael M / Ward-Caviness, Cavin K / Joehanes, Roby / Huan, TianXiao / Gondalia, Rahul / Salfati, Elias / Brody, Jennifer A / Fiorito, Giovanni / Bressler, Jan / Chen, Brian H / Ligthart, Symen / Guarrera, Simonetta / Colicino, Elena / Just, Allan C / Wahl, Simone / Gieger, Christian / Vandiver, Amy R / Tanaka, Toshiko / Hernandez, Dena G / Pilling, Luke C / Singleton, Andrew B / Sacerdote, Carlotta / Krogh, Vittorio / Panico, Salvatore / Tumino, Rosario / Li, Yun / Zhang, Guosheng / Stewart, James D / Floyd, James S / Wiggins, Kerri L / Rotter, Jerome I / Multhaup, Michael / Bakulski, Kelly / Horvath, Steven / Tsao, Philip S / Absher, Devin M / Vokonas, Pantel / Hirschhorn, Joel / Fallin, M Daniele / Liu, Chunyu / Bandinelli, Stefania / Boerwinkle, Eric / Dehghan, Abbas / Schwartz, Joel D / Psaty, Bruce M / Feinberg, Andrew P / Hou, Lifang / Ferrucci, Luigi / Sotoodehnia, Nona / Matullo, Giuseppe / Peters, Annette / Fornage, Myriam / Assimes, Themistocles L / Whitsel, Eric A / Levy, Daniel / Baccarelli, Andrea A. ·Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York (G.A., A.A.B.). · Population Sciences Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD (M.M.M., D.L., R.J.). · Framingham Heart Study, MA (M.M.M., D.L.). · Department of Cardiology, Boston Children's Hospital, MA (M.M.M.). · National Health and Environmental Effects Research Laboratory, Environmental Public Health Division, Chapel Hill, NC (C.K.W.C.). · Institute of Epidemiology II, Helmholtz Institute, Ingolstaedter Landstrasse 1, Neuherberg, Germany (C.K.W.C.). · Hebrew SeniorLife, Harvard Medical School, Boston, MA (R.J.). · The Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD (T.X.H.). · Department of Epidemiology (R.G.), University of North Carolina, Chapel Hill. · Department of Medicine, Stanford University School of Medicine, CA (E.S., P.S.T.). · Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle (J.A.B., J.S.F., K.L.W.). · Italian Institute for Genomic Medicine (IIGM/HuGeF) and Department of Medical Sciences, University of Turin, Italy (G.F., S.G., G.P.). · Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston (J.B.). · Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD (B.H.C., T.T.). · Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands (S.L.). · Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY (E.C., A.C.J.). · Research Unit Molecualr Epidemiology, Helmholtz Zentrum München, Germany (S.W., C.G.). · Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD (A.R.V., M.M.). · Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD (D.G.H., A.B.S.). · Epidemiology and Public Health Group, University of Exeter Medical School, United Kingdom (L.C.P.). · Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy (C.S.). · Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy (V.K.). · Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy (S.P.). · Cancer Registry And Histopathology Department, Civic- M.P. Arezzo2 Hospital, Asp Ragusa, Italy (R.T.). · Department of Genetics, Department of Biostatistics, Department of Computer Science (Y.L.), University of North Carolina, Chapel Hill. · Curriculum in Bioinformatics and Computational Biology, Department of Genetics, and Department of Statistics (G.Z.), University of North Carolina, Chapel Hill. · Carolina Population Center and Department of Epidemiology (J.D.S.), University of North Carolina, Chapel Hill. · The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, CA (J.I.R.). · Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor (K.B.). · Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles (S.H.). · HudsonAlpha institute of Biotechnology, Huntsville, AL (D.M.A.). · VA Normative Aging Study, VA Boston Healthcare System, Department of Medicine, Boston University School of Medicine, MA (P.V.). · Department of Medicine, Division of Endocrinology, Boston Children's Hospital, MA (J.H.). · Departments of Medicine and Pediatrics, Harvard Medical School, Boston, MA (J.H.). · Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (M.D.F.). · Department of Biostatistics, Boston University School of Public Health, MA (C.L.). · Azienda Sanitaria, USL Centro Firenze, Italy (S.B.). · Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston (E.B.). · Human Genome Sequencing Center, Baylor College of Medicine, TX (E.B.). · Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of 346 Public Health, Imperial College London, United Kingdom (A.D.). · Department of Epidemiology and Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA (J.D.S.). · Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle (B.M.P.). · Kaiser Permanente Washington Health Research Institute, Seattle (B.M.P.). · Departments of Medicine, Biomedical Engineering, and Mental Health, Johns Hopkins University, Baltimore, MD (A.P.F.). · Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL (L.H.). · Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD (L.F.). · Division of Cardiology, Departments of Medicine and Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle (N.S.). · Helmholtz Zentrum München, Institute of Epidemiology, Neuherberg, Germany; German Research Center for Cardiovascular Disease (DzHK e.V. - partner site Munich), Germany (A.P.). · Ludwig-Maximilians University, Institute for Biometry, Medical Information Science and Epidemiology, Munich, Germany (A.P.). · Brown Foundation Institute of Molecular Medicine McGovern Medical School, and Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston (M.F.). · Department of Medicine (Cardiovascular Medicine), and Department of Health Research & Policy, Stanford University School of Medicine, CA (T.L.A.). · Department of Epidemiology, Gillings School of Global Public Health, and Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill (E.A.W.). ·Circulation · Pubmed #31424985.

ABSTRACT: BACKGROUND: DNA methylation is implicated in coronary heart disease (CHD), but current evidence is based on small, cross-sectional studies. We examined blood DNA methylation in relation to incident CHD across multiple prospective cohorts. METHODS: Nine population-based cohorts from the United States and Europe profiled epigenome-wide blood leukocyte DNA methylation using the Illumina Infinium 450k microarray, and prospectively ascertained CHD events including coronary insufficiency/unstable angina, recognized myocardial infarction, coronary revascularization, and coronary death. Cohorts conducted race-specific analyses adjusted for age, sex, smoking, education, body mass index, blood cell type proportions, and technical variables. We conducted fixed-effect meta-analyses across cohorts. RESULTS: Among 11 461 individuals (mean age 64 years, 67% women, 35% African American) free of CHD at baseline, 1895 developed CHD during a mean follow-up of 11.2 years. Methylation levels at 52 CpG (cytosine-phosphate-guanine) sites were associated with incident CHD or myocardial infarction (false discovery rate<0.05). These CpGs map to genes with key roles in calcium regulation (ATP2B2, CASR, GUCA1B, HPCAL1), and genes identified in genome- and epigenome-wide studies of serum calcium (CASR), serum calcium-related risk of CHD (CASR), coronary artery calcified plaque (PTPRN2), and kidney function (CDH23, HPCAL1), among others. Mendelian randomization analyses supported a causal effect of DNA methylation on incident CHD; these CpGs map to active regulatory regions proximal to long non-coding RNA transcripts. CONCLUSION: Methylation of blood-derived DNA is associated with risk of future CHD across diverse populations and may serve as an informative tool for gaining further insight on the development of CHD.

2 Article Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach. 2017

Mendelson, Michael M / Marioni, Riccardo E / Joehanes, Roby / Liu, Chunyu / Hedman, Åsa K / Aslibekyan, Stella / Demerath, Ellen W / Guan, Weihua / Zhi, Degui / Yao, Chen / Huan, Tianxiao / Willinger, Christine / Chen, Brian / Courchesne, Paul / Multhaup, Michael / Irvin, Marguerite R / Cohain, Ariella / Schadt, Eric E / Grove, Megan L / Bressler, Jan / North, Kari / Sundström, Johan / Gustafsson, Stefan / Shah, Sonia / McRae, Allan F / Harris, Sarah E / Gibson, Jude / Redmond, Paul / Corley, Janie / Murphy, Lee / Starr, John M / Kleinbrink, Erica / Lipovich, Leonard / Visscher, Peter M / Wray, Naomi R / Krauss, Ronald M / Fallin, Daniele / Feinberg, Andrew / Absher, Devin M / Fornage, Myriam / Pankow, James S / Lind, Lars / Fox, Caroline / Ingelsson, Erik / Arnett, Donna K / Boerwinkle, Eric / Liang, Liming / Levy, Daniel / Deary, Ian J. ·Framingham Heart Study, Framingham, Massachusetts, United States of America. · Boston University School of Medicine, Boston, Massachusetts, United States of America. · Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, United States of America. · Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America. · Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom. · Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom. · Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. · Hebrew SeniorLife, Harvard Medical School, Boston, Massachusetts, United States of America. · Department of Biostatistics, Boston University, Boston, Massachusetts, United States of America. · Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden. · Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America. · Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America. · Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America. · Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America. · Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America. · Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America. · Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America. · Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America. · Cardiovascular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden. · Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia. · Wellcome Trust Clinical Research Facility, Western General Hospital, University of Edinburgh, Edinburgh, United Kingdom. · Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom. · Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom. · Center for Molecular Medicine and Genetics and Department of Neurology, Wayne State University, Detroit, Michigan, United States of America. · Children's Hospital Oakland Research Institute, Oakland, California, United States of America. · HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, United States of America. · Brown Foundation Institute of Molecular Medicine, University of Texas, Houston, Texas, United States of America. · Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, United States of America. · College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America. · Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America. · Departments of Epidemiology and Biostatistics, School of Public Health, Harvard University, Boston, Massachusetts, United States of America. ·PLoS Med · Pubmed #28095459.

ABSTRACT: BACKGROUND: The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. METHODS AND FINDINGS: We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. CONCLUSIONS: We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases.

3 Article Integromic analysis of genetic variation and gene expression identifies networks for cardiovascular disease phenotypes. 2015

Yao, Chen / Chen, Brian H / Joehanes, Roby / Otlu, Burcak / Zhang, Xiaoling / Liu, Chunyu / Huan, Tianxiao / Tastan, Oznur / Cupples, L Adrienne / Meigs, James B / Fox, Caroline S / Freedman, Jane E / Courchesne, Paul / O'Donnell, Christopher J / Munson, Peter J / Keles, Sunduz / Levy, Daniel. ·From the National Heart, Lung, and Blood Institute's Framingham Heart Study, National Institutes of Health, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., L.A.C., C.S.F., P.C., C.J.O'D., D.L.) · Population Sciences Branch, National Institutes of Health, National Heart, Lung, and Blood Institute, Bethesda, MD (C.Y., B.H.C., R.J., X.Z., C.L., T.H., P.C., D.L.) · Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD (R.J., P.J.M.) · Department of Computer Engineering, Middle East Technical University, Ankara, Turkey (B.O.) · Department of Computer Engineering, Bilkent University, Ankara, Turkey (O.T.) · Department of Biostatistics, Boston University School of Public Health, Boston, MA (L.A.C.) · Harvard Medical School, Boston, MA (J.B.M.) · Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA (C.S.F.) · Department of Medicine, University of Massachusetts Medical School, Worchester (J.E.F.) · Division of Cardiology, Massachusetts General Hospital, Boston, MA (C.J.O'D.) · and Departments of Statistics and of Biostatistics and Medical Informatics, University of Wisconsin-Madison (S.K.). ·Circulation · Pubmed #25533967.

ABSTRACT: BACKGROUND: Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies have reported numerous single nucleotide polymorphisms (SNPs) to be associated with CVD, the role of most of these variants in disease processes remains unknown. METHODS AND RESULTS: We built a CVD network using 1512 SNPs associated with 21 CVD traits in genome-wide association studies (at P≤5×10(-8)) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-expression quantitative trait loci (eQTLs; SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples in which the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in genome-wide association studies may exert their function by altering expression of eQTL genes (eg, LDLR and PCSK7), which in turn may promote interindividual variation in phenotypes. CONCLUSIONS: Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.

4 Article Gene expression signatures of coronary heart disease. 2013

Joehanes, Roby / Ying, Saixia / Huan, Tianxiao / Johnson, Andrew D / Raghavachari, Nalini / Wang, Richard / Liu, Poching / Woodhouse, Kimberly A / Sen, Shurjo K / Tanriverdi, Kahraman / Courchesne, Paul / Freedman, Jane E / O'Donnell, Christopher J / Levy, Daniel / Munson, Peter J. ·The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA. ·Arterioscler Thromb Vasc Biol · Pubmed #23539218.

ABSTRACT: OBJECTIVE: To identify transcriptomic biomarkers of coronary heart disease (CHD) in 188 cases with CHD and 188 age- and sex-matched controls who were participants in the Framingham Heart Study. APPROACH AND RESULTS: A total of 35 genes were differentially expressed in cases with CHD versus controls at false discovery rate<0.5, including GZMB, TMEM56, and GUK1. Cluster analysis revealed 3 gene clusters associated with CHD, 2 linked to increased erythrocyte production and a third to reduced natural killer and T cell activity in cases with CHD. Exon-level results corroborated and extended the gene-level results. Alternative splicing analysis suggested that GUK1 and 38 other genes were differentially spliced in cases with CHD versus controls. Gene Ontology analysis linked ubiquitination and T-cell-related pathways with CHD. CONCLUSIONS: Two bioinformatically defined groups of genes show consistent associations with CHD. Our findings are consistent with the hypotheses that hematopoesis is upregulated in CHD, possibly reflecting a compensatory mechanism, and that innate immune activity is disrupted in CHD or altered by its treatment. Transcriptomic signatures may be useful in identifying pathways associated with CHD and point toward novel therapeutic targets for its treatment and prevention.

5 Article A systems biology framework identifies molecular underpinnings of coronary heart disease. 2013

Huan, Tianxiao / Zhang, Bin / Wang, Zhi / Joehanes, Roby / Zhu, Jun / Johnson, Andrew D / Ying, Saixia / Munson, Peter J / Raghavachari, Nalini / Wang, Richard / Liu, Poching / Courchesne, Paul / Hwang, Shih-Jen / Assimes, Themistocles L / McPherson, Ruth / Samani, Nilesh J / Schunkert, Heribert / Anonymous2080754 / Meng, Qingying / Suver, Christine / O'Donnell, Christopher J / Derry, Jonathan / Yang, Xia / Levy, Daniel. ·From the National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA. ·Arterioscler Thromb Vasc Biol · Pubmed #23539213.

ABSTRACT: OBJECTIVE: Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. APPROACH AND RESULTS: We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression-associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver genes. Multitissue key drivers (SPIB and TNFRSF13C) and tissue-specific key drivers (eg, EBF1) were identified. CONCLUSIONS: Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.