Author + information
- Received April 5, 2016
- Revision received July 14, 2016
- Accepted July 14, 2016
- Published online September 27, 2016.
- Pim van der Harst, MD, PhDa,b,c,∗ (, )
- Jessica van Setten, PhDd,e,
- Niek Verweij, PhDa,
- Georg Vogler, PhDf,
- Lude Franke, PhDb,
- Matthew T. Maurano, PhDg,h,i,j,
- Xinchen Wang, BSck,
- Irene Mateo Leach, PhDa,
- Mark Eijgelsheim, MD, PhDl,m,
- Nona Sotoodehnia, MD, MPHn,
- Caroline Hayward, PhDo,
- Rossella Sorice, PhDp,
- Osorio Meirelles, PhDq,
- Leo-Pekka Lyytikäinen, MDr,s,
- Ozren Polašek, MD, PhDt,u,
- Toshiko Tanaka, PhDv,
- Dan E. Arking, PhDw,
- Sheila Ulivi, PhDx,
- Stella Trompet, PhDy,z,
- Martina Müller-Nurasyid, PhDaa,bb,cc,dd,
- Albert V. Smith, PhDee,ff,
- Marcus Dörr, MDgg,hh,
- Kathleen F. Kerr, PhDii,
- Jared W. Magnani, MD, MScjj,
- Fabiola Del Greco M., PhDkk,
- Weihua Zhang, PhDll,mm,
- Ilja M. Nolte, PhDnn,
- Claudia T. Silva, MScoo,pp,qq,
- Sandosh Padmanabhan, MD, PhDrr,
- Vinicius Tragante, PhDd,e,
- Tõnu Esko, PhDss,tt,
- Gonçalo R. Abecasis, PhDuu,
- Michiel E. Adriaens, PhDvv,ww,
- Karl Andersen, PhDff,xx,
- Phil Barnett, PhDyy,
- Joshua C. Bis, PhDzz,
- Rolf Bodmer, PhDf,
- Brendan M. Buckley, MD, PhDaaa,
- Harry Campbell, MDt,
- Megan V. Cannon, PhDa,
- Aravinda Chakravarti, PhDw,
- Lin Y. Chen, MD, MSbbb,
- Alessandro Delitala, PhDccc,
- Richard B. Devereux, MDddd,
- Pieter A. Doevendans, MD, PhDe,
- Anna F. Dominiczak, MDrr,
- Luigi Ferrucci, MD, PhDv,
- Ian Ford, PhDeee,
- Christian Gieger, PhDcc,fff,ggg,
- Tamara B. Harris, PhDhhh,
- Eric Haugeng,
- Matthias Heinig, PhDiii,jjj,kkk,
- Dena G. Hernandez, MSclll,
- Hans L. Hillege, MD, PhDa,
- Joel N. Hirschhorn, MD, PhDtt,mmm,nnn,
- Albert Hofman, MD, PhDl,m,
- Norbert Hubner, MDiii,ooo,
- Shih-Jen Hwang, PhDppp,
- Annamaria Iorio, MDqqq,
- Mika Kähönen, MD, PhDrrr,sss,
- Manolis Kellis, PhDttt,uuu,
- Ivana Kolcic, MD, PhDu,
- Ishminder K. Kooner, MD, PhDmm,
- Jaspal S. Kooner, MBBS, MDmm,vvv,
- Jan A. Kors, PhDwww,
- Edward G. Lakatta, MDxxx,
- Kasper Lage, PhDuuu,yyy,zzz,
- Lenore J. Launer, PhDhhh,
- Daniel Levy, MDaaaa,
- Alicia Lundby, PhDbbbb,cccc,
- Peter W. Macfarlane, DScdddd,
- Dalit May, MDeeee,
- Thomas Meitinger, MD, MScdd,ffff,gggg,
- Andres Metspalu, PhDss,
- Stefania Nappo, MScp,
- Silvia Naitza, PhDccc,
- Shane Neph, BSg,
- Alex S. Nord, PhDhhhh,iiii,
- Teresa Nutile, PhDp,
- Peter M. Okin, MDddd,
- Jesper V. Olsen, PhDbbbb,
- Ben A. Oostra, PhDoo,
- Josef M. Penninger, MDjjjj,
- Len A. Pennacchio, PhDhhhh,kkkk,
- Tune H. Pers, PhDtt,mmm,llll,mmmm,
- Siegfried Perz, MScfff,nnnn,
- Annette Peters, PhDdd,fff,
- Yigal M. Pinto, MD, PhDvv,
- Arne Pfeufer, MD, MSckk,oooo,
- Maria Grazia Pilia, PhDccc,
- Peter P. Pramstaller, MDkk,pppp,qqqq,
- Bram P. Prins, MScrrrr,
- Olli T. Raitakari, MD, PhDssss,tttt,
- Soumya Raychaudhuri, MD, PhDuuuu,vvvv,
- Ken M. Rice, PhDii,
- Elizabeth J. Rossin, MD, PhDzzz,wwww,
- Jerome I. Rotter, MDxxxx,
- Sebastian Schafer, PhDiii,ooo,yyyy,
- David Schlessinger, PhDq,
- Carsten O. Schmidt, PhDzzzz,
- Jobanpreet Sehmi, PhD, MDmm,vvv,
- Herman H.W. Silljé, PhDa,
- Gianfranco Sinagra, MDqqq,
- Moritz F. Sinner, MD, MPHaa,
- Kamil Slowikowski, BSaaaaa,
- Elsayed Z. Soliman, MD, MScbbbbb,
- Timothy D. Spector, MBBS, MD, MScccccc,
- Wilko Spiering, MD, PhDddddd,
- John A. Stamatoyannopoulos, MDg,
- Ronald P. Stolk, MD, PhDnn,
- Konstantin Strauch, PhDbb,cc,
- Sian-Tsung Tan, MDmm,vvv,
- Kirill V. Tarasov, MD, PhDxxx,
- Bosco Trinh, BScf,
- Andre G. Uitterlinden, PhDl,m,
- Malou van den Boogaard, MDyy,
- Cornelia M. van Duijn, PhDoo,
- Wiek H. van Gilst, PhDa,
- Jorma S. Viikari, MD, PhDeeeee,fffff,
- Peter M. Visscher, PhDggggg,hhhhh,
- Veronique Vitart, PhDo,
- Uwe Völker, PhDhh,iiiii,
- Melanie Waldenberger, PhD, MPHfff,ggg,
- Christian X. Weichenberger, PhDkk,
- Harm-Jan Westra, PhDmmm,jjjjj,kkkkk,
- Cisca Wijmenga, PhDb,
- Bruce H. Wolffenbuttel, MD, PhDlllll,
- Jian Yang, PhDggggg,
- Connie R. Bezzina, PhDvv,
- Patricia B. Munroe, PhDmmmmm,nnnnn,
- Harold Snieder, PhDnn,
- Alan F. Wright, MBChB, PhDo,
- Igor Rudan, MD, PhDt,
- Laurie A. Boyer, PhDk,
- Folkert W. Asselbergs, MD, PhDc,e,ooooo,
- Dirk J. van Veldhuisen, MD, PhDa,
- Bruno H. Stricker, MD, PhDl,m,
- Bruce M. Psaty, MD, PhDppppp,qqqqq,
- Marina Ciullo, PhDp,rrrrr,
- Serena Sanna, PhDccc,
- Terho Lehtimäki, MD, PhDr,s,
- James F. Wilson, DPhilo,t,
- Stefania Bandinelli, MScsssss,
- Alvaro Alonso, MD, PhDttttt,
- Paolo Gasparini, MDx,uuuuu,vvvvv,
- J. Wouter Jukema, MD, PhDy,wwwww,
- Stefan Kääb, MD, PhDaa,dd,
- Vilmundur Gudnason, MD, PhDee,ff,
- Stephan B. Felix, MDgg,hh,
- Susan R. Heckbert, MD, PhDqqqqq,xxxxx,
- Rudolf A. de Boer, MD, PhDa,
- Christopher Newton-Cheh, MD, MPHmmm,yyyyy,zzzzz,
- Andrew A. Hicks, PhDkk,
- John C. Chambers, MBBS, PhDll,mm,
- Yalda Jamshidi, PhDrrrr,
- Axel Visel, PhDhhhh,kkkk,aaaaaa,
- Vincent M. Christoffels, PhDyy,
- Aaron Isaacs, PhDoo,bbbbbb,
- Nilesh J. Samani, MDcccccc,dddddd and
- Paul I.W. de Bakker, PhDd,eeeeee
- aDepartment of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- bDepartment of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- cDurrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, the Netherlands
- dDepartment of Medical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands
- eDepartment of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands
- fDevelopment, Aging and Regeneration, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California
- gDepartment of Genome Sciences, University of Washington, Seattle, Washington
- hDepartment of Medicine, Division of Oncology, University of Washington, Seattle, Washington
- iDepartment of Pathology, New York University Langone Medical Center, New York, New York
- jInstitute for Systems Genetics, New York University Langone Medical Center, New York, New York
- kDepartment of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
- lDepartment of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- mDepartment of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
- nDivision of Cardiology, Cardiovascular Health Research Unit, University of Washington, Seattle, Washington
- oMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland
- pInstitute of Genetics and Biophysics A. Buzzati-Traverso, Naples, Italy
- qLaboratory of Genetics, National Institute on Aging, Baltimore, Maryland
- rDepartment of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- sDepartment of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
- tCentre for Global Health Research, The Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
- uDepartment of Public Health, Faculty of Medicine, University of Split, Split, Croatia
- vTranslational Gerontology Branch, National Institute on Aging, Baltimore, Maryland
- wCenter for Complex Disease Genomics, McKusick–Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- xInstitute for Maternal and Child Health, IRCCS “Burlo Garofolo,” Trieste, Italy
- yDepartment of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- zDepartment of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- aaDepartment of Medicine I, University Hospital Munich, Campus Grosshadern, Ludwig-Maximilians-University, Munich, Germany
- bbInstitute of Medical Informatics, Biometry and Epidemiology, Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- ccInstitute of Genetic Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- ddDZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- eeIcelandic Heart Association, Kópavogur, Iceland
- ffUniversity of Iceland, Reykjavik, Iceland
- ggDepartment of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- hhDZHK partner site, Greifswald, Germany
- iiDepartment of Biostatistics, University of Washington, Seattle, Washington
- jjSection of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- kkCenter for Biomedicine, European Academy of Bozen/Bolzano (EURAC), Bolzano, Italy (Affiliated Institute of the University of Lübeck, Lübeck, Germany)
- llDepartment of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- mmEaling Hospital NHS Trust, Middlesex, United Kingdom
- nnDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- ooGenetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- ppDoctoral Program in Biomedical Sciences, Universidad del Rosario, Bogotá, Colombia
- qqDepartment of Genetics (GENIUROS), Escuela de Medicina y Ciencias de la salud, Universidad del Rosario, Bogotá, Colombia
- rrInstitute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- ssEstonian Genome Center, University of Tartu, Tartu, Estonia
- ttDivision of Endocrinology and Center for Basic and Translational Obesity Research, Children's Hospital Boston, Boston, Massachusetts
- uuCenter for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
- vvDepartment of Experimental Cardiology, University of Amsterdam, Academic Medical Center, Amsterdam, the Netherlands
- wwMaastricht Centre for Systems Biology, Maastricht University, Maastricht, the Netherlands
- xxLandspitali University Hospital, Reykjavik, Iceland
- yyDepartment of Anatomy, Embryology and Physiology, University of Amsterdam, Academic Medical Center, Amsterdam, the Netherlands
- zzCardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
- aaaDepartment of Pharmacology and Therapeutics, University College Cork, Cork, Ireland
- bbbDepartment of Medicine, Cardiovascular Division, University of Minnesota, Minneapolis, Minnesota
- cccIstituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Cagliari, Italy
- dddDepartment of Medicine, Division of Cardiology, Weill Cornell Medicine, New York, New York
- eeeRobertson Center for Biostatistics, University of Glasgow, Glasgow, United Kingdom
- fffInstitute of Epidemiology II, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- gggResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- hhhLaboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
- iiiCardiovascular and Metabolic Diseases, Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany
- jjjDepartment of Computational Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
- kkkInstitute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- lllLaboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
- mmmMedical and Population Genetics Program, Broad Institute, Cambridge, Massachusetts
- nnnDepartment of Genetics, Harvard Medical School, Boston, Massachusetts
- oooDZHK partner site, Berlin, Germany
- pppPopulation Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- qqqCardiovascular Department and Postgraduate School of Cardiovascular Disease, University of Trieste, Trieste, Italy
- rrrDepartment of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- sssDepartment of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland
- tttComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts
- uuuBroad Institute, Cambridge, Massachusetts
- vvvNational Heart and Lung Institute, Imperial College London, London, United Kingdom
- wwwDepartment of Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands
- xxxLaboratory of Cardiovascular Science, National Institute on Aging, Baltimore, Maryland
- yyyDepartment of Surgery, Massachusetts General Hospital, Boston, Massachusetts
- zzzHarvard Medical School, Harvard University, Boston, Massachusetts
- aaaaCenter for Population Studies, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, Maryland
- bbbbNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- ccccDepartment of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- ddddElectrocardiology Section, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- eeeeDepartment of Family Medicine, Clalit Health Services, and The Hebrew University-Hadassah Medical School, Jerusalem, Israel
- ffffInstitute of Human Genetics, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- ggggInstitute of Human Genetics, Technische Universität München, Munich, Germany
- hhhhGenomics Division, Lawrence Berkeley National Laboratory, Berkeley, California
- iiiiCenter for Neuroscience, Departments of Neurobiology, Physiology, and Behavior and Psychiatry and Behavioral Sciences, University of California, Davis, California
- jjjjInstitute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria
- kkkkDOE Joint Genome Institute, Walnut Creek, California
- llllNovo Nordisk Foundation Centre for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- mmmmDepartment of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- nnnnInstitute for Biological and Medical Imaging, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- ooooDepartment of Bioinformatics and Systems Biology IBIS, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- ppppDepartment of Neurology, General Central Hospital, Bolzano, Italy
- qqqqDepartment of Neurology, University of Lübeck, Lübeck, Germany
- rrrrCardiogenetics Lab, Human Genetics Research Centre, St. George's University of London, London, United Kingdom
- ssssDepartment of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- ttttResearch Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- uuuuDivision of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- vvvvProgram in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- wwwwAnalytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts
- xxxxInstitute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics and Medicine, Harbor-UCLA Medical Center, Torrance, California
- yyyyNational Heart Research Institute Singapore, National Heart Centre Singapore, Singapore
- zzzzInstitute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- aaaaaBioinformatics and Integrative Genomics, Harvard University, Cambridge, Massachusetts
- bbbbbEpidemiological Cardiology Research Center, Wake Forest School of Medicine, Winston Salem, North Carolina
- cccccDepartment of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- dddddDepartment of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
- eeeeeDivision of Medicine, Turku University Hospital, Turku, Finland
- fffffDepartment of Medicine, University of Turku, Turku, Finland
- gggggQueensland Brain Institute, University of Queensland, St. Lucia, Australia
- hhhhhUniversity of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, Australia
- iiiiiDepartment of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- jjjjjDivisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- kkkkkPartners Center for Personalized Genetic Medicine, Boston, Massachusetts
- lllllDepartment of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- mmmmmClinical Pharmacology and Barts and The London Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- nnnnnNational Institute for Health Research Biomedical Research Unit, Barts and the London School of Medicine, Queen Mary University of London, London, United Kingdom
- oooooInstitute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
- pppppDepartments of Medicine, Epidemiology, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, Washington
- qqqqqGroup Health Research Institute, Group Health Cooperative, Seattle, Washington
- rrrrrIRCCS Neuromed, Isernia, Italy
- sssssGeriatric Unit, Azienda Sanitaria Firenze, Florence, Italy
- tttttDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
- uuuuuUniversity of Trieste, Trieste, Italy
- vvvvvSidra Medical and Research Center, Doha, Qatar
- wwwwwNetherlands Heart Institute, Utrecht, the Netherlands
- xxxxxDepartment of Epidemiology, University of Washington, Seattle, Washington
- yyyyyCardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- zzzzzCenter for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- aaaaaaSchool of Natural Sciences, University of California, Merced, California
- bbbbbbCARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology, Department of Biochemistry, Maastricht University, Maastricht, the Netherlands
- ccccccDepartment of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Leicester, United Kingdom
- ddddddNational Institute for Health Research, Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
- eeeeeeDepartment of Epidemiology, University Medical Center Utrecht, Utrecht, the Netherlands
- ↵∗Reprint requests and correspondence:
Dr. Pim van der Harst, Department of Cardiology & Department of Genetics, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700RB Groningen, the Netherlands.
Background Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death.
Objectives This meta-analysis sought to gain insights into the genetic determinants of myocardial mass.
Methods We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment.
Results We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10−8. These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo.
Conclusions Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets.
The heart’s role is to provide adequate circulation of blood to meet the body's requirements of oxygen and nutrients. The QRS complex on the electrocardiogram (ECG) represents the most widely used measurement of cardiac depolarization, which causes the ventricular muscle to contract, resulting in pulsatile blood flow. The amplitude and duration of the QRS complex reflects conduction through the left ventricle and is well correlated with left ventricular mass as measured by echocardiography (1,2). ECG measurements of the QRS complex are important in clinical and pre-clinical cardiovascular diseases, such as cardiac hypertrophy, heart failure, and various cardiomyopathies; in addition, they can predict cardiovascular mortality (3–6).
Identification of specific genes influencing the QRS complex may thus enhance our understanding of the human heart and ultimately lead to the prevention of cardiovascular disease and death. To further our understanding of the genetic factors influencing the QRS complex, we carried out a large-scale genome-wide association study (GWAS) and replication study of 4 related and clinically used QRS traits: the Sokolow-Lyon, Cornell, and 12-lead-voltage duration products (12-leadsum), and QRS duration. We identified 52 loci that were subsequently interrogated using bioinformatics and experimental approaches to gain more insights into the biological mechanisms regulating cardiac mass and QRS parameters.
Additional details about the methods of our study can be found in the Online Appendix.
Our study design is summarized in Online Figure 1. Briefly, we combined summary statistics from 24 studies for up to 2,766,983 autosomal single nucleotide polymorphisms (SNPs) using an inverse-variance fixed-effects meta-analysis for each QRS trait. We performed replication testing for loci showing suggestive association (1 × 10−8 < p < 5 × 10−7) (Online Tables 1 and 2). The threshold for genome-wide significance was set at p < 1 × 10−8.
We performed an intersection between SNPs and regions of deoxyribonuclease hypersensitivity sites (DHS), covalently modified histones, and genomic features (ChromHMM) of cardiac tissues mapped by the National Institutes of Health Roadmap Epigenomics Program, as well as various cardiac transcription factor binding sites (GATA4, MEF2, SRF, TBX5, TBX3, GATA4, and Nkx2-5) measured by Chip-seq.
Single cell suspensions of human ventricular tissue were obtained by dissociation with Ultra Turrax T5 FU (IKA-Works, Breisgau, Germany), followed by dounce homogenization. 4C templates were mixed and sequenced simultaneously in 1 HiSeq 2000 lane (Illumina, Inc., San Diego, California). Enhancer candidate regions with major and minor alleles for rs6781009 were obtained by polymerase chain reaction from human control deoxyribonucleic acid (DNA) and cloned into the Hsp68-LacZ reporter vector. DNA was injected into the pronucleus of a fertilized Friend virus B-type strain egg, and approximately 200 injections/construct were performed. Embryos were harvested and stained with X-gal to detect LacZ activity.
H10 cells, grown in 12-well plates in Dulbecco's Modified Eagle's medium supplemented with 10% fetal calf serum and glutamine, were transfected using polyethylenimine 25 kDa (PEI) at a 1:3 ratio (DNA:PEI). Transfections were carried out at least 3 times and measured in triplicate. Luciferase measurements were performed using a Modulus Multimode Reader luminometer (Promega Corporation, Madison, Wisconsin).
Identification of candidate genes
We considered genes to be causal candidates on the basis of: 1) the nearest gene and any other gene located within 10 kb of the sentinel SNP; 2) genes containing coding variants in linkage disequilibrium (LD) with the ST-T wave SNPs at r2 > 0.8; 3) GRAIL (Gene Relationships Across Implicated Loci) analyses using the 2006 dataset to avoid confounding by subsequent GWAS discovery; and 4) genes with an expression quantitative trait locus (eQTL) analysis in cis using 4 independent sets of cardiac left ventricle and blood tissues. Ingenuity Pathway Analysis Knowledge Base March 2015 (Ingenuity Systems, Redwood City, California) was used to explore molecular pathways between proteins encoded by the 67 candidate genes from the 52 genome-wide significant loci.
We queried a D. melanogaster dataset containing a genome-wide phenotypic screen of cardiac-specific ribonucleic acid interference (RNAi) silencing of evolutionarily conserved genes under conditions of stress. We also queried the international database resource for the laboratory mouse (MGI [Mouse Genome Informatics]) and manually curated mammalian phenotype (MP) identifiers related to cardiac phenotypes. To illustrate that prioritized genes may play a critical role in heart development, we tested CG4743/SLC25A26, Fhos/FHOD3, Cka/STRN, NACα/NACA, EcR/NR1H, and Hand/HAND1 by performing heart-specific RNAi knockdown with the cardiac Hand4.2-Gal4 driver line.
We collected 43,278 raw Human Genome U133 Plus 2.0 Arrays (Affymetrix, Santa Clara, California) from the Gene Expression Omnibus (GEO) containing human gene expression data. A robust multichip average was used for normalization, and we subsequently conducted stringent quality control and processing of the data, which resulted in a tissue-expression matrix. After quality control, 37,427 samples remained, and we assigned 54,675 different probe sets to 19,997 different Ensembl genes used for human tissue expression profiling. To explore gene-expression of our candidate genes during cardiac differentiation, we performed ribonucleic acid sequencing using E14 Tg (Nkx2-5-EmGFP) mouse embryonic stem cells that were cultured in feeder-free conditions and subsequently differentiated.
Our choice of the statistical threshold (p < 1 × 10−8) for the GWAS was grounded on the guidelines derived from studies of the ENCODE (Encyclopedia of DNA Elements) regions which suggests that p < 5 × 10−8 is the appropriate threshold for genome-wide significance in Europeans, but was also designed to provide us with additional adjustment for the multiple phenotypes tested. This threshold is conservative, considering that our 4 QRS phenotypes are also inter-related: correlation coefficients between the phenotype pairs range from r = 0.22 to 0.80. Additional details on our statistical analysis can be found in the Online Appendix.
Characteristics of studies, participants, genotyping arrays, and imputation are summarized in Online Tables 1 and 2. Together, our studies comprised 60,255 individuals of European ancestry ascertained in North America and Europe, with maximum sample sizes as follows: Sokolow-Lyon (n = 54,993), Cornell (n = 58,862), 12-leadsum (n = 48,632), and QRS duration (n = 60,255). Across the genome, 52 independent loci, 32 of which are novel, reached genome-wide significance for association with 1 or more QRS phenotypes (Figure 1, Online Figure 2, Online Table 3, Online Appendix). At each locus, we defined a single “sentinel” SNP with the lowest p value against any of the 4 phenotypes; regional association plots for the 52 loci are shown in Online Figure 3. Among the 52 loci, 32 were associated with only 1 QRS phenotype, and 20 with at least 2 phenotypes (Online Figure 4). The total number of locus-phenotype associations at p < 10−8 was 79 (72 SNPs), of which 59 are novel (Online Table 3). Full lists of the sentinel SNPs and the SNPs associated with any phenotype at p < 10−6 are provided in Online Tables 4 and 5. All previously known QRS duration loci showed evidence for association (p < 10−6) (Online Table 6). Among the 32 novel loci, 8 demonstrated genome-wide significant association with Sokolow-Lyon, 9 with Cornell, 20 with 12-leadsum, and 9 with QRS duration (Online Table 5). Collectively, the total variance explained by the 52 sentinel SNPs for the QRS traits was between 2.7% (Sokolow-Lyon) and 5.0% (QRS duration) (Online Table 7). At some loci, we found evidence for multiple independent associations with QRS phenotypes at p < 10−8 in conditional analyses (7) (Online Table 8, Online Appendix). Among the 52 loci identified, 8 have been associated previously with PR (reflecting atrial and atrioventricular node function), 5 with QT duration (ventricular repolarization), and 2 with heart rate (sinus node function) (Online Table 6), indicating genetic overlap among the 4 cardiac measures studied. We further demonstrated that there was directional consistency of the association of common variants identified in this study with QRS phenotypes in other ethnic groups (Online Figure 1, Online Table 9, Online Appendix).
Functional annotation of the QRS associations
To better capture common sequence variants at the 52 loci, we queried the 1000 Genomes Project dataset (8), and identified 41 nonsynonymous SNPs in 17 genes that are in high LD (r2 > 0.8) with 12 of the sentinel SNPs (Online Table 10), representing an initial set of candidate variants that may have a functional effect on the QRS phenotypes through changes in protein structure and function.
To assess the potential role of gene expression regulation, we tested the 52 loci for enrichment of DHS (9). In an analysis across 349 diverse cell lines, cultured primary cells, and fetal tissues (10) mapped by the ENCODE project (11) and the National Institute of Health Roadmap Epigenomics Program (12), the majority (42 of 52) of sentinel SNPs were located in DHS. In human fetal heart tissue, we found that less than one-half (22 of 52) overlapped DHS, which still represents a ∼3.5-fold enrichment compared with the null expectation (p = 7.7 × 10−12) (Figure 2A). Further, the enrichment of genome-wide significant SNPs (p < 10−8) in DHS was strongest within the first 100 base pairs around the sentinel variants (Figure 2B). Additionally, there was a strong enrichment for histone marks and chromatin states (13) associated with active enhancers, promoters, and transcription in the human heart; by contrast, no enrichment was observed for transcriptionally repressive histone marks or states (Figures 2C and 2D, Online Figure 5). Strikingly, we observed increasing enrichment of activating histone marks at the identified QRS loci during the process of differentiating mouse embryonic stem cells into cardiomyocytes (Online Figure 6). Altogether, these findings are consistent with earlier observations of selective enrichment of trait-associated variants within DHS of specific cells of tissue types (10), and they point to a regulatory role of the QRS-associated loci during cardiac development.
We next surveyed our genome-wide significant SNPs in DHS for perturbation of transcription factor (TF) recognition sequences, because these sites can point directly to binding events (Online Appendix). Of the 22 sentinel SNPs in human fetal heart DHS, 11 are predicted to alter TF recognition sequences (Online Table 11). When considering all genome-wide significant SNPs (p < 10−8) as well as those in high LD (r2 > 0.8), 402 SNPs in the colocalizing DHS perturb transcription recognition sequences, including those of important cardiac and muscle developmental regulators like TBX, GATA-4, and MEF2. When we intersected the GWAS results with ChIP-seq data from mouse and human cardiac tissue (14–16), we found enrichment in enhancers marked by p300, sites bound by RNA polymerase II, and the transcription factors NKX2-5, GATA-4, TBX3, TBX5, and SRF (Figure 2E). A total of 9 of our 52 loci contained not only fetal heart DHS but also ChIP-seq-validated TF binding sites. SNPs overlapping TF binding sites were 5.65-fold enriched within DHS (p = 9.0 × 10−10) but not outside of the DHS (p = 0.20). The associations of the 52 sentinel SNPs with all tested functional elements are summarized in Figure 1. We validated several candidate regulatory regions identified earlier as heart enhancers in vivo. Activity of 4 exemplar novel human cardiac enhancers in embryonic transgenic mice stained for LacZ enhancer reporter activity are shown in Figure 3A. Recently, rs6801957 (Figure 1) in the SCN5A/SCN10A locus was reported to influence the activity of a regulatory element affecting SCN5A expression (16,17). Conditional analysis (Online Table 8) revealed that rs6781009 (at 180 kb from the sentinel) is an additional novel independent signal at this locus. Our follow-up in silico and experimental results (Figure 3) indicate the presence of in vivo heart enhancers in genome regions associated with QRS traits.
Identification of candidate genes
Across the 52 loci, 974 annotated genes are located within 1 megabase of all sentinel SNPs. Among these genes, we prioritized potential candidates using an established complementary strategy (18,19) by choosing: 1) genes nearest to the sentinel SNP and any other genes within 10 kb (56 genes) (Figure 1); 2) genes containing a nonsynonymous SNP in high LD (r2 > 0.8) with the sentinel SNP (11 genes) (Online Table 10); 3) protein-coding genes with cis-eQTL associated with sentinel SNP (14 genes) (Online Table 12); and 4) GRAIL analysis of the published data (20) (16 genes) (Online Table 13) with “cardiac,” “muscle,” and “heart” as the top 3 keywords describing the observed functional connections. In total, this strategy identified 67 candidate genes at the 52 loci (Figure 1). Pathway analysis confirmed that the list of 67 candidate genes is strongly enriched for genes known to be involved in cardiovascular and muscular system development and function (p = 1 × 10−56) (Online Tables 14 and 15). We have summarized the available functional annotations for all 67 candidates in Online Table 16, including established links from the Online Mendelian Inheritance in Man between candidate genes and familial cardiomyopathies (TNNT2, TTN, PLN, and MYBPC3) and cardiac arrhythmias (CASQ2). We also identified genes that are associated with atrial septal defects (TBX20) and more complex syndromes involving cardiac abnormalities such as the Schinzel-Giedion midface retraction syndrome (SETBP1) (21) and the ulnar-mammary syndrome (TBX3) (22).
Insights from gene expression profiling and model organisms
We explored gene expression profiles of our candidate genes in data derived from 37,427 U133 Plus 2.0 arrays (Affymetrix) across 40 annotated tissues. We could reliably assign a probe for 63 of our 67 candidate genes. On average, expression levels for these transcripts were higher in cardiac-derived samples compared with other transcripts in the same sample (p = 9.8 × 10−6 for heart tissue; Wilcoxon test) (Online Figure 7) and also when compared to the same transcripts in other tissues (p = 0.005 after Bonferroni correction) (Online Figure 8). To further investigate the potential role of these candidate genes in cardiac development, we assessed temporal gene expression patterns during in vitro differentiation of mouse embryonic stem cells via mesoderm and cardiac precursor cells to cardiomyocytes. A total of 7% of genes are mainly expressed during the embryonic stem cell stage, 22% during the mesoderm stage, 7% in the cardiac precursor stage, and 64% in the cardiomyocyte stage. Compared with other genes, the candidate genes were more highly expressed in cardiomyocytes (p = 5.4 × 10−8; Wilcoxon test) (Online Figure 9). These results suggest that our candidate gene set was enriched for genes that were differentially expressed in cardiac tissue and increasingly expressed during cardiac development.
Next, we analyzed data from model organisms to explore the function of the selected candidate genes. From cardiac tissue-specific RNAi knockdown data collected in D. melanogaster, we found that the 67 candidate genes were 2.3-fold enriched for stress-induced cardiac death (9 genes; p = 1.84 × 10−2) (Online Figure 10). To illustrate that prioritized genes may play a critical role in heart development, we tested 4 (CG4743/SLC25A26, Fhos/FHOD3, Cka/STRN, and NACα/NACA) of these 9 genes with unknown cardiac function by performing heart-specific RNAi knockdown with the cardiac Hand4.2-Gal4 driver line. We also retested EcR/NR1H, which has multiple homologous genes in mammals, as well as Hand/HAND1, as this gene was only tested as a full-knockout in early development but not in the adult D. melanogaster heart using cardiac-specific knockdown. Adult hearts of Cka/STRN, NACα/NACA, and EcR/NR1H RNAi showed severe cardiac defects (Figure 4). Knockdown of Hand/HAND1 and Cka/STRN both had a reduced cardiac heart rate. We also expanded on gene-by-gene analysis and identified 6 further genes causing cardiac abnormalities (Online Appendix, Online Table 17). From the Mouse Genome Informatics database, knockout models were annotated for 45 orthologues of the 67 candidate genes, of which 18 (40%) revealed a cardiac phenotype (Online Table 16). This represents a 5.2-fold enrichment compared with randomly matched sets of 67 genes (p = 3.4 × 10−14) (Online Figure 10). Given the evolutionary conservation, the observed heart phenotypes in these model organisms suggest potentially important roles for the significant GWAS loci in electrical and contractile properties of the human heart.
Interestingly, the 11p11.2 locus harbors multiple candidate genes (Figure 1), including MYBPC3, ACP2, MADD, and NR1H3. MYBPC3 deficiency is well established to cause hypertrophic and dilated cardiomyopathies in both human and mouse models and, thus, represents a plausible candidate gene (Online Table 16). In addition to MYBPC3, eQTL and histone modification data also suggest a potential role for NR1H3 (Online Figure 11), as decreased expression of NR1H3 was associated with higher QRS voltages. However, NR1H3-deficient mice do not spontaneously develop a cardiac hypertrophic phenotype (MGI: 1352462). To study the potential cardiac effects of NR1H3, we created a transgenic mouse with cardiac-specific overexpression of NR1H3 under the control of the Myh6 promoter and found a diminished susceptibility to perturbations such as transverse aortic constriction and angiotensin II infusion that provoke cardiac hypertrophy (23). This observation is in line with protective effects due to treatment with T0901317, a synthetic NR1H3 agonist, in mice challenged with aortic constriction (24). These data highlight the importance of systematic approaches to identify causal genes beyond well-known candidates.
Insights from depict
As a complementary approach, we employed the newly developed computational tool DEPICT (Data-Driven Expression-Prioritized Integration for Complex Traits) (25) to analyze functional connections among associated loci (Online Appendix). Enrichment of expression in 209 particular tissues and cell types identified heart and heart ventricles as the most relevant tissue for our association findings (Figure 5A, Online Table 18) and identified 404 significantly (false discovery rate <5%) enriched gene sets (Online Table 19). Comparing the names of these sets with those of the remaining 14,057 gene sets showed an over-representation of the common key words “abnormal,” “muscle,” “heart,” “cardiac,” and “morphology” (Online Table 20). We investigated similarities among gene sets by clustering them on the basis of the correlation between scores for all genes (Online Appendix). Many of the resulting 43 meta-gene sets were correlated and relevant to cardiac biology (Figure 5B). As an example, we showed the correlation structure within the second most significant meta-gene set “dilated heart left ventricle” (Online Figure 12). When prioritizing genes on the basis of functional similarities among genes from different associated regions, DEPICT identified 35 genes (false discovery rate <5%) at 27 of the 52 loci (Figure 1, Online Table 21).
In this study, we performed a meta-analysis of GWAS in 73,518 individuals for 4 quantitative QRS phenotypes and identified 52 independent genetic loci influencing these traits with 79 locus-phenotype associations; the majority of these discoveries are novel. Our loci were colocalized with open chromatin, histone modification, and TF binding sites, specifically in cardiac tissue, and contain in vivo functional enhancers. We also provided direct evidence that rs6781009, located in a cardiac enhancer, interacts with the promoter of SCN5A to modify expression levels. On the basis of multiple criteria, we defined a core set of 67 candidate genes that we believe are likely to influence cardiac mass and function. We have provided several exemplar experiments to further support this hypothesis.
We identified a number of loci containing genes that are directly or indirectly key to the function of cardiomyocytes and cardiac function (Central Illustration). TTN, MYBPC3, TNNT2, SYNPO2L, and MYH7B are essential components of the cardiac sarcomere; PLN, CTNNA3, PRKCA, CASQ2, and STRN are also examples of genes essential for cardiac myocyte function; whereas several key cardiac transcription factors are prominently involved in cardiac muscle and tissue development, such as MEF2D, HAND1, TBX20, TBX3, and NACA. The abundance of candidate genes known to be involved in cardiac muscle function strengthens the hypothesis that the easily obtainable QRS-voltage phenotypes of the ECG are effective in capturing unknown loci that harbor genes likely to play an important role in left ventricular mass, but these are currently not well understood. The colocalization of our genetic loci with regulatory DNA elements (e.g., enhancers, promoters, and transcription factor binding sites) that are active in cardiac tissues further supports the relevance of the genes within these loci. The current work was not designed to provide an explanation for association of each loci and each individual gene.
Clearly, future translational efforts should be undertaken to resolve the causal genes and exact molecular machinery resulting in the phenotype and should consider mapping the effect of genetic variants on these functional elements at each of the identified loci. Nevertheless, we have provided some exemplar preliminary elements to offer early insights into strategies that can be undertaken to follow-up our findings. For example, we performed a series of experiments to demonstrate in vivo effects of rs6781009 on expression. Dedicated experiments might also elucidate loci containing effects on multiple plausible genes. In 1 of our loci, we identified a very strong candidate gene (MYBPC3) that is well known to be involved in hypertrophic cardiomyopathies. However, using additional layers of information derived from gene expression and histone modifications, we also considered NR1H3 and were able to link overexpression of this gene to cardiac protection of hypertrophy. These examples fuel our expectation that the presented shortlist of SNP associations and the identified candidate genes provided in this work are valuable resources that will help to prioritize and guide future translational studies to further our knowledge on the (patho)physiology of cardiac hypertrophy.
As in all current GWAS, we have only studied a finite number (∼2.8 million) of markers on the genome. Additional fine-mapping studies might be required to narrow the signal of association even further and to identify the potential causal variants with higher accuracy. Also, additional exome-focused arrays or whole-genome sequencing might lead to a stronger signal within a locus or to multiple additional independent signals within a locus. To understand genetic mechanisms and to identify candidate genes, we have studied eQTLs. Although we studied the largest set of human cardiac eQTLs available to date, the absolute number of studied samples is relatively small compared to eQTL data available in easily accessible peripheral blood. Finally, our ECG indexes are generally considered to be markers of cardiac hypertrophy; they also might reflect electrical remodeling of the action potential and not mass per se. Nevertheless, the variables studied here harbor important prognostic information, independently from cardiac mass parameters as assessed by echocardiography (26). This further underscores the relevance of the trait studied and the importance of understanding its genetic determinants.
In this study, we identified 52 genomic loci, of which 32 are novel and associated with electrically active cardiac mass. We prioritized 67 candidate genes and showed their relevance in cardiac biology using bioinformatic approaches, and we performed in vitro and in vivo experiments, going beyond the classical GWAS approach. To facilitate and accelerate future studies aimed at a better understanding of cardiac hypertrophy, heart failure, and related diseases, we made our results of genome-wide associations publicly available.
COMPETENCY IN MEDICAL KNOWLEDGE: Numerous genetic loci have been identified that are associated with electrocardiographic markers of ventricular hypertrophy.
TRANSLATIONAL OUTLOOK: A better understanding of the genetic pathways underlying increased myocardial mass could be used to target therapeutic interventions that improve clinical outcomes for patients with hypertension, heart failure, and various forms of congenital heart disease.
A detailed list of acknowledgments is provided in the Online Appendix.
For an expanded Methods section as well as supplemental figures and tables, please see the online version of this article.
Dr. Abecasis has served on the scientific advisory board for Regeneron Genetics Center. Dr. Haugen’s current employer (Altius Institute) receives research funding from GlaxoSmithKline. Dr. Pennacchio is a salaried employee and owns stock in Metabiota. Dr. Stamatoyannopoulos is the director of a nonprofit research institute. Dr. Psaty has served on the data and safety monitoring board for a clinical trial funded by Zoll LifeCor; and has served on the steering committee of the Yale Open Data Access project funded by Johnson & Johnson. Dr. de Bakker is currently an employee of and owns equity in Vertex Pharmaceuticals. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Drs. van der Harst, van Setten, Verweij, Vogler, Franke, Maurano, Wang, Mateo Leach, Chambers, Jamshidi, Visel, Christoffels, Isaacs, Samani, and de Bakker contributed equally to this work.
- Abbreviations and Acronyms
- deoxyribonuclease hypersensitivity sites
- expression quantitative trait locus
- genome-wide association study
- linkage disequilibrium
- ribonucleic acid interference
- single nucleotide polymorphism
- transcription factor
- Received April 5, 2016.
- Revision received July 14, 2016.
- Accepted July 14, 2016.
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