Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders

A Missense Variant in PLEC Increases Risk of Atrial Fibrillation

BACKGROUND

Genome-wide association studies (GWAS) have yielded variants at >30 loci that associate with atrial fibrillation (AF), including rare coding mutations in the sarcomere genes MYH6 and MYL4.

OBJECTIVES

The aim of this study was to search for novel AF associations and in doing so gain insights into the mechanisms whereby variants affect AF risk, using electrocardiogram (ECG) measurements.

METHODS

The authors performed a GWAS of 14,255 AF cases and 374,939 controls, using whole-genome sequence data from the Icelandic population, and tested novel signals in 2,002 non-Icelandic cases and 12,324 controls. They then tested the AF variants for effect on cardiac electrical function by using measurements in 289,297 ECGs from 62,974 individuals.

RESULTS

The authors discovered 2 novel AF variants, the intergenic variant rs72700114, between the genes LINC01142 and METTL11B (risk allele frequency = 8.1%; odds ratio [OR]: 1.26; p = 3.1 × 10-18), and the missense variant p.Gly4098Ser in PLEC (frequency = 1.2%; OR: 1.55; p = 8.0 × 10-10), encoding plectin, a cytoskeletal cross-linking protein that contributes to integrity of cardiac tissue. The authors also confirmed 29 reported variants. p.Gly4098Ser in PLEC significantly affects various ECG measurements in the absence of AF. Other AF variants have diverse effects on the conduction system, ranging from none to extensive.

CONCLUSIONS

The discovery of a missense variant in PLEC affecting AF combined with recent discoveries of variants in the sarcomere genes MYH6 and MYL4 points to an important role of myocardial structure in the pathogenesis of the disease. The diverse associations between AF variants and ECG measurements suggest fundamentally different categories of mechanisms contributing to the development of AF.

Discovery of novel heart rate-associated loci using the Exome Chip

Resting heart rate is a heritable trait, and an increase in heart rate is associated with increased mortality risk. Genome-wide association study analyses have found loci associated with resting heart rate, at the time of our study these loci explained 0.9% of the variation. This study aims to discover new genetic loci associated with heart rate from Exome Chip meta-analyses.Heart rate was measured from either elecrtrocardiograms or pulse recordings. We meta-analysed heart rate association results from 104 452 European-ancestry individuals from 30 cohorts, genotyped using the Exome Chip. Twenty-four variants were selected for follow-up in an independent dataset (UK Biobank, N = 134 251). Conditional and gene-based testing was undertaken, and variants were investigated with bioinformatics methods.We discovered five novel heart rate loci, and one new independent low-frequency non-synonymous variant in an established heart rate locus (KIAA1755). Lead variants in four of the novel loci are non-synonymous variants in the genes C10orf71, DALDR3, TESK2 and SEC31B. The variant at SEC31B is significantly associated with SEC31B expression in heart and tibial nerve tissue. Further candidate genes were detected from long-range regulatory chromatin interactions in heart tissue (SCD, SLF2 and MAPK8). We observed significant enrichment in DNase I hypersensitive sites in fetal heart and lung. Moreover, enrichment was seen for the first time in human neuronal progenitor cells (derived from embryonic stem cells) and fetal muscle samples by including our novel variants.Our findings advance the knowledge of the genetic architecture of heart rate, and indicate new candidate genes for follow-up functional studies.

The Rotterdam Study: 2018 update on objectives, design and main results

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1500 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Transient Notch Activation Induces Long-Term Gene Expression Changes Leading to Sick Sinus Syndrome in Mice

RATIONALE

Notch signaling programs cardiac conduction during development, and in the adult ventricle, injury-induced Notch reactivation initiates global transcriptional and epigenetic changes.

OBJECTIVE

To determine whether Notch reactivation may stably alter atrial ion channel gene expression and arrhythmia inducibility.

METHODS AND RESULTS

To model an injury response and determine the effects of Notch signaling on atrial electrophysiology, we transiently activate Notch signaling within adult myocardium using a doxycycline-inducible genetic system (inducible Notch intracellular domain [iNICD]). Significant heart rate slowing and frequent sinus pauses are observed in iNICD mice when compared with controls. iNICD mice have structurally normal atria and preserved sinus node architecture, but expression of key transcriptional regulators of sinus node and atrial conduction, including Nkx2-5 (NK2 homeobox 5), Tbx3, and Tbx5 are dysregulated. To determine whether the induced electrical changes are stable, we transiently activated Notch followed by a prolonged washout period and observed that, in addition to decreased heart rate, atrial conduction velocity is persistently slower than control. Consistent with conduction slowing, genes encoding molecular determinants of atrial conduction velocity, including Scn5a (Nav1.5) and Gja5 (connexin 40), are persistently downregulated long after a transient Notch pulse. Consistent with the reduction in Scn5a transcript, Notch induces global changes in the atrial action potential, including a reduced dV_m/dt_max. In addition, programmed electrical stimulation near the murine pulmonary vein demonstrates increased susceptibility to atrial arrhythmias in mice where Notch has been transiently activated. Taken together, these results suggest that transient Notch activation persistently alters ion channel gene expression and atrial electrophysiology and predisposes to an arrhythmogenic substrate.

CONCLUSIONS

Our data provide evidence that Notch signaling regulates transcription factor and ion channel gene expression within adult atrial myocardium. Notch reactivation induces electrical changes, resulting in sinus bradycardia, sinus pauses, and a susceptibility to atrial arrhythmias, which contribute to a phenotype resembling sick sinus syndrome.

Circulating microRNA-1a is a biomarker of Graves' disease patients with atrial fibrillation

PURPOSE

It has been increasingly suggested that specific microRNAs expression profiles in the circulation and atrial tissue are associated with the susceptibility to atrial fibrillation. Nonetheless, the role of circulating microRNAs in Graves' disease patients with atrial fibrillation has not yet been well described. The objective of the study was to identify the role of circulating microRNAs as specific biomarkers for the diagnosis of Graves' disease with atrial fibrillation.

METHODS

The expression profiles of eight serum microRNAs, which are found to be critical in the pathogenesis of atrial fibrillation, were determined in patients with Graves' disease with or without atrial fibrillation. MicroRNA expression analysis was performed by real-time PCR in normal control subjects (NC; n = 17), patients with Graves' disease without atrial fibrillation (GD; n = 29), patients with Graves' disease with atrial fibrillation (GD + AF; n = 14), and euthyroid patients with atrial fibrillation (AF; n = 22).

RESULTS

Three of the eight serum microRNAs,i.e., miR-1a, miR-26a, and miR-133, had significantly different expression profiles among the four groups. Spearman's correlation analysis showed that the relative expression level of miR-1a was positively correlated with free triiodothyronine (FT3) and free thyroxine (FT4), and negatively related to thyroid stimulating hormone. Spearman's correlations analysis also revealed that the level of miR-1a was negatively correlated with a critical echocardiographic parameter (left atrial diameter), which was dramatically increased in GD + AF group compared to GD group. Furthermore, the receiver-operating characteristic curve analysis indicated that, among the eight microRNAs, miR-1a had the largest area under the receiver-operating characteristic curves not only for discriminating between individuals with and without Graves' disease, but also for predicting the presence of atrial fibrillation in patients with Graves' disease.

CONCLUSIONS

Our findings showed that the levels of serum miR-1a were significantly decreased in GD + AF group compared with GD group, suggesting that serum miR-1a might serve as a novel biomarker for diagnosis of atrial fibrillation in patients with Graves' disease.

The growing world of small heat shock proteins: from structure to functions

Small heat shock proteins (sHSPs) are present in all kingdoms of life and play fundamental roles in cell biology. sHSPs are key components of the cellular protein quality control system, acting as the first line of defense against conditions that affect protein homeostasis and proteome stability, from bacteria to plants to humans. sHSPs have the ability to bind to a large subset of substrates and to maintain them in a state competent for refolding or clearance with the assistance of the HSP70 machinery. sHSPs participate in a number of biological processes, from the cell cycle, to cell differentiation, from adaptation to stressful conditions, to apoptosis, and, even, to the transformation of a cell into a malignant state. As a consequence, sHSP malfunction has been implicated in abnormal placental development and preterm deliveries, in the prognosis of several types of cancer, and in the development of neurological diseases. Moreover, mutations in the genes encoding several mammalian sHSPs result in neurological, muscular, or cardiac age-related diseases in humans. Loss of protein homeostasis due to protein aggregation is typical of many age-related neurodegenerative and neuromuscular diseases. In light of the role of sHSPs in the clearance of un/misfolded aggregation-prone substrates, pharmacological modulation of sHSP expression or function and rescue of defective sHSPs represent possible routes to alleviate or cure protein conformation diseases. Here, we report the latest news and views on sHSPs discussed by many of the world's experts in the sHSP field during a dedicated workshop organized in Italy (Bertinoro, CEUB, October 12-15, 2016).

Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74 Collapse

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MESH Headings

• Blood Pressure
• Cohort Studies
• Genetic Predisposition to Disease
• Genome-Wide Association Study
• Heart Diseases/genetics
• Heart Diseases/physiopathology
• Heart Rate
• Humans
• Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels/genetics
• Muscle Proteins/genetics
• Polymorphism, Single Nucleotide
• Potassium Channels/genetics
• Quantitative Trait Loci
• RGS Proteins/genetics
• Risk Factors
• White People/genetics

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Grants

• R01 HL120393 NHLBI NIH HHS
• U01 HL130114 NHLBI NIH HHS
• P01 GM099568 NIGMS NIH HHS
• UL1 TR000124 NCATS NIH HHS
• R01 HL105756 NHLBI NIH HHS
• S10 OD020069 NIH HHS
• K23 HL114724 NHLBI NIH HHS
• R01 HL116747 NHLBI NIH HHS
• P30 DK063491 NIDDK NIH HHS
• I01 BX002558 BLRD VA
• MC_UU_12019/1 Medical Research Council
• UL1 TR001881 NCATS NIH HHS
• P2C HD050924 NICHD NIH HHS

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Affiliation(s)

Ilja M. Nolte Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
M. Loretto Munoz Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Vinicius Tragante Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands
Azmeraw T. Amare Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands Department of Epidemiology, School of Medicine, University of Adelaide, Adelaide, South Australia 5005, Australia College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar 6000, Ethiopia
Rick Jansen Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Ahmad Vaez Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
Benedikt von der Heyde Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden
Christy L. Avery Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Joshua C. Bis Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA
Bram Dierckx Department of Child and Adolescent Psychiatry/Psychology, Department of Child and Adolescent Psychiatry, PO Box 2060, Rotterdam 3000 CB, The Netherlands The Generation R Study Group, Erasmus MC, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Jenny van Dongen Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands
Stephanie M. Gogarten Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Philippe Goyette Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8
Jussi Hernesniemi Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland Department of Cardiology, Heart Hospital, Tampere University Hospital, Tampere 33521, Finland
Ville Huikari Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland
Shih-Jen Hwang Framingham Heart Study, Framingham, Massachusetts 01702, USA Population Sciences Branch, National Heart, Lung and Blood Institute, Bethesda, Maryland 20892, USA
Deepali Jaju Department of Clinical Physiology, Sultan Qaboos University Hospital, Muscat—Al Khoudh 123, Sultanate of Oman
Kathleen F. Kerr Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Alexander Kluttig Institute of Medical Epidemiology, Biostatistics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale) 06097, Germany
Bouwe P. Krijthe Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Jitender Kumar Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden
Sander W. van der Laan Laboratory of Experimental Cardiology, Department of Heart and Lung, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
Leo-Pekka Lyytikäinen Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland
Adam X. Maihofer Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Arpi Minassian Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Peter J. van der Most Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Martina Müller-Nurasyid Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany Department of Medicine, University Hospital Munich, Ludwig-Maximilians-University, Munich 80539, Germany DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany
Michel Nivard Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Erika Salvi Department of Health Sciences, University of Milano, Milano 20122, Italy
James D. Stewart Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Julian F. Thayer Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, Ohio 43210, USA
Niek Verweij Department of Cardiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Andrew Wong MRC Unit for Lifelong Health and Ageing, University College London, 33 Bedford Place, London WC1B 5JU, UK
Delilah Zabaneh Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK University College London Genetics Institute, University College London, London WC1E 6BT, UK
Mohammad H. Zafarmand Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, Amsterdam 1105 AZ, The Netherlands Department of Obstetrics and Gynaecology, Academic Medical Centre, University of Amsterdam, Amsterdam 1105 AZ, The Netherlands
Abdel Abdellaoui Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Sulayma Albarwani Department of Physiology, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat Al-Khoudh 123, Sultanate of Oman
Christine Albert Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
Alvaro Alonso Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA
Foram Ashar McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
Juha Auvinen Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland Unit of Primary Health Care, Oulu University Hospital, Oulu 90220, Finland
Tomas Axelsson Department of Medical Sciences, Molecular Medicine, Uppsala University, Uppsala 75237, Sweden
Dewleen G. Baker Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Paul I. W. de Bakker Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
Matteo Barcella Department of Health Sciences, University of Milano, Milano 20122, Italy
Riad Bayoumi College of Medicine, Mohammed Bin Rashid University, PO Box 505055, Dubai Healthcare City, United Arab Emirates
Rob J. Bieringa Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Dorret Boomsma Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Gabrielle Boucher Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8
Annie R. Britton Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK
Ingrid Christophersen Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02114, USA Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Rud 1346, Norway
Andrea Dietrich Department of Child- and Adolescent Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
George B. Ehret Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA Cardiology, Department of Specialties of Internal Medicine, Geneva University Hospital, Geneva 1211, Switzerland
Patrick T. Ellinor Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02114, USA Cardiac Arrhythmia Service & Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Markku Eskola Department of Cardiology, Heart Hospital, Tampere University Hospital, Tampere 33521, Finland Department of Cardiology, University of Tampere School of Medicine, Tampere 33014, Finland
Janine F. Felix Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
John S. Floras University Health Network and Mount Sinai Hospital Division of Cardiology, Department of Medicine, University of Toronto, Ontario, Canada M5S Toronto General Research Institute, University Health Network, Toronto, Canada
Oscar H. Franco Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Peter Friberg Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
Maaike G. J. Gademan Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, Amsterdam 1105 AZ, The Netherlands
Mark A. Geyer Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA
Vilmantas Giedraitis Department of Public Health and Caring Sciences, Molecular Geriatrics, Uppsala University, Uppsala 75237, Sweden
Catharina A. Hartman Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Daiane Hemerich Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands CAPES Foundation, Ministry of Education of Brazil, Brasília DF 70040-020, Brazil
Albert Hofman Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Jouke-Jan Hottenga Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Heikki Huikuri Research Unit of Internal Medicine, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu 90220, Finland
Nina Hutri-Kähönen Department of Pediatrics, Tampere University Hospital, Tampere 33521, Finland Department of Pediatrics, University of Tampere School of Medicine, Tampere 33014, Finland
Xavier Jouven INSERM U970, Paris Descartes University, Paris 75006, France
Juhani Junttila Research Unit of Internal Medicine, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu 90220, Finland
Markus Juonala Department of Medicine, University of Turku, Turku 20520, Finland Division of Medicine, Turku University Hospital, Turku 20521, Finland
Antti M. Kiviniemi Research Unit of Internal Medicine, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu 90220, Finland
Jan A. Kors Department of Medical Informatics, Erasmus Medical Center, Rotterdam 3015 CE, The Netherlands
Meena Kumari Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK ISER, Essex University, Colchester, Essex CO4 3SQ, UK
Tatiana Kuznetsova Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven 3000, Belgium
Cathy C. Laurie Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Joop D. Lefrandt Department of Internal Medicine, Division of Vascular Medicine, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Yong Li Division of Genetic Epidemiology, Institute for Medical Biometry and Statistics, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg 79110, Germany
Yun Li Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Duanping Liao Division of Epidemiology, Department of Public Health Sciences, Penn State University College of Medicine, Hershey, Pennsylvania 17033, USA
Marian C. Limacher Division of Cardiovascular Medicine, University of Florida College of Medicine, Gainesville, Florida 32611, USA
Henry J. Lin Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California 90502, USA
Cecilia M. Lindgren Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford OX3 7BN, UK Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
Steven A. Lubitz Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02114, USA Cardiac Arrhythmia Service & Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
Anubha Mahajan Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
Barbara McKnight Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
Henriette Meyer zu Schwabedissen Biopharmacy, Department Pharmaceutical Sciences, University of Basel, Basel CH-4056, Switzerland
Yuri Milaneschi Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Nina Mononen Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland
Andrew P. Morris Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK
Mike A. Nalls Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA
Gerjan Navis Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Melanie Neijts Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Kjell Nikus Department of Cardiology, Heart Hospital, Tampere University Hospital, Tampere 33521, Finland Department of Cardiology, University of Tampere, School of Medicine, Tampere 33014, Finland
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Daniel T. O'Connor Department of Medicine, University of California, San Diego, San Diego, California 92093, USA
Johan Ormel Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Siegfried Perz Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
Annette Peters DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany German Center for Diabetes Research, Neuherberg 85764, Germany
Bruce M. Psaty Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA Departments of Epidemiology and Health Services, University of Washington, Seattle, Washington 98195, USA Group Health Research Institute, Group Health Cooperative, Seattle, Washington 98101, USA
Olli T. Raitakari Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland
Victoria B. Risbrough Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Moritz F. Sinner Department of Medicine, University Hospital Munich, Ludwig-Maximilians-University, Munich 80539, Germany DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany
David Siscovick The New York Academy of Medicine, New York, New York 10029, USA
Johannes H. Smit Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Nicholas L. Smith Group Health Research Institute, Group Health Cooperative, Seattle, Washington 98101, USA Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington 98195, USA Seattle Epidemiologic Research and Information Center, Veterans Affairs Office of Research and Development, Seattle, Washington 98108, USA
Elsayed Z. Soliman Epidemiological Cardiology Research Center (EPICARE), Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
Nona Sotoodehnia Cardiovascular Health Research Unit, Division of Cardiology, Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington 98101, USA
Jan A. Staessen Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven 3000, Belgium
Phyllis K. Stein Heart Rate Variability Lab, Washington University School of Medicine, St Louis, Missouri 63108, USA
Adrienne M. Stilp Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Katarzyna Stolarz-Skrzypek First Department of Cardiology, Interventional Electrocardiology and Hypertension, Jagiellonian University Medical College, Cracow 31-008, Poland
Konstantin Strauch Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich 81377, Germany
Johan Sundström Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala 751 85, Sweden
Cees A. Swenne Department of Cardiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands
Ann-Christine Syvänen Department of Medical Sciences, Molecular Medicine, Uppsala University, Uppsala 75237, Sweden
Jean-Claude Tardif Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8 Université de Montréal, Montreal, Quebec, Canada H3T IJ4
Kent D. Taylor Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90509, USA
Alexander Teumer Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
Timothy A. Thornton Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Lesley E. Tinker Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
André G. Uitterlinden Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands Department of Internal Medicine, Erasmus University Medical Center, Rotterdam 3015 CE, The Netherlands Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging NCHA), Leiden 2300 RC, The Netherlands
Jessica van Setten Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands
Andreas Voss Institute of Innovative Health Technologies—IGHT Jena Ernst-Abbe-Hochschule Jena, Jena 07745, Germany
Melanie Waldenberger Institute of Epidemiology II, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany Research Unit of Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg 85764, Germany
Kirk C. Wilhelmsen Departments of Genetics and Neurology University of North Carolina, Chapel Hill, North Carolina 27599, USA The Renaissance Computing Institute, Chapel Hill, North Carolina 27599, USA
Gonneke Willemsen Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands
Quenna Wong Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Zhu-Ming Zhang Epidemiological Cardiology Research Center (EPICARE), Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA Department of Epidemiology & Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
Alan B. Zonderman Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland 21224, USA
Daniele Cusi Institute of Biomedical Technologies, CNR—Italian National Research Council, Milan 20090, Italy KOS Genetic SRL, Bresso (Milano) 20091, Italy
Michele K. Evans Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland 21224, USA
Halina K. Greiser German Cancer Research Centre, Division of Cancer Epidemiology, Heidelberg 69210, Germany
Pim van der Harst Department of Cardiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Mohammad Hassan Department of Physiology, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat Al-Khoudh 123, Sultanate of Oman
Erik Ingelsson Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
Marjo-Riitta Järvelin Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland Unit of Primary Health Care, Oulu University Hospital, Oulu 90220, Finland Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St Mary’s campus, Imperial College London, London W2 1PG, UK Biocenter Oulu University of Oulu, Oulu 90014, Finland
Stefan Kääb Department of Medicine, University Hospital Munich, Ludwig-Maximilians-University, Munich 80539, Germany DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich 80336, Germany
Mika Kähönen Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland Department of Clinical Physiology, University of Tampere, School of Medicine, Tampere 33014, Finland
Mika Kivimaki Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK
Charles Kooperberg Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
Diana Kuh MRC Unit for Lifelong Health and Ageing, University College London, 33 Bedford Place, London WC1B 5JU, UK
Terho Lehtimäki Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014, Finland
Lars Lind Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala 751 85, Sweden
Caroline M. Nievergelt Department of Psychiatry, University of California, San Diego, San Diego, California 92093, USA Center for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, California 92161, USA
Chris J. O'Donnell Framingham Heart Study, Framingham, Massachusetts 01702, USA Population Sciences Branch, National Heart, Lung and Blood Institute, Bethesda, Maryland 20892, USA Cardiology Section, Boston Veteran’s Administration Healthcare, Boston, Maryland 02132, USA
Albertine J. Oldehinkel Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Brenda Penninx Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam 1081 BT, The Netherlands
Alexander P. Reiner Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Harriëtte Riese Interdisciplinary Center Psychopathology and Emotion regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Arie M. van Roon Department of Internal Medicine, Division of Vascular Medicine, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
John D. Rioux Montreal Heart Institute, Montreal, Quebec, Canada H1T 1C8 Université de Montréal, Montreal, Quebec, Canada H3T IJ4
Jerome I. Rotter Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90509, USA
Tamar Sofer Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Bruno H. Stricker Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands Inspectorate for Health Care, The Hague 2511 VX, The Netherlands
Henning Tiemeier Department of Child and Adolescent Psychiatry/Psychology, Department of Child and Adolescent Psychiatry, PO Box 2060, Rotterdam 3000 CB, The Netherlands Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, PO Box 2060, Rotterdam 3000 CB, The Netherlands
Tanja G. M. Vrijkotte Department of Public Health, Academic Medical Center (AMC), University of Amsterdam, Amsterdam 1105 AZ, The Netherlands
Folkert W. Asselbergs Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands Institute of Cardiovascular Science, University College London, 222 Euston Road, London NW1 2DA, UK Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht 3501 DG, The Netherlands
Bianca J. J. M. Brundel Department of Physiology, Institute for Cardiovascular Research, VU University Medical Center, De Boelelaan 1118, Amsterdam 1081 HV, The Netherlands
Susan R. Heckbert Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington 98104, USA Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington 98195, USA
Eric A. Whitsel Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, USA Department of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599, USA
Marcel den Hoed Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala 75237, Sweden Science for Life Laboratory, Uppsala University, Uppsala 75237, Sweden
Harold Snieder Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30001, Groningen 9700 RB, The Netherlands
Eco J. C. de Geus Department of Biological Psychology, Behavioral and Movement Sciences, VU University, Amsterdam 1081 BT, The Netherlands EMGO+ Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam 1081 HV, The Netherlands

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Dietary adaptation of FADS genes in Europe varied across time and geography

Fatty acid desaturase (FADS) genes encode rate-limiting enzymes for the biosynthesis of omega-6 and omega-3 long chain polyunsaturated fatty acids (LCPUFAs). This biosynthesis is essential for individuals subsisting on LCPUFAs-poor diets (e.g. plant-based). Positive selection on FADS genes has been reported in multiple populations, but its presence and pattern in Europeans remain elusive. Here, using ancient and modern DNA, we demonstrate that positive selection acted on the same FADS variants both before and after the advent of farming in Europe, but on opposite (i.e. alternative) alleles. Selection in recent farmers also varied geographically, with the strongest signal in Southern Europe. These varying selection patterns concur with anthropological evidence of varying diets, and with the association of farming-adaptive alleles with higher FADS1 expression and thus enhanced LCPUFAs biosynthesis. Genome-wide association studies reveal that farming-adaptive alleles not only increase LCPUFAs, but also affect other lipid levels and protect against several inflammatory diseases.

Altered long non-coding RNA expression profile in rabbit atria with atrial fibrillation: TCONS_00075467 modulates atrial electrical remodeling by sponging miR-328 to regulate CACNA1C

Electrical remodeling has been reported to play a major role in the initiation and maintenance of atrial fibrillation (AF). Long non-coding RNAs (lncRNAs) have been increasingly recognized as contributors to the pathology of heart diseases. However, the roles and mechanisms of lncRNAs in electrical remodeling during AF remain unknown. In this study, the lncRNA expression profiles of right atria were investigated in AF and non-AF rabbit models by using RNA sequencing technique and validated using quantitative real-time polymerase chain reaction (qRT-PCR). A total of 99,843 putative new lncRNAs were identified, in which 1220 differentially expressed transcripts exhibited >2-fold change. Bioinformatics analysis was conducted to predict the functions and interactions of the aberrantly expressed genes. On the basis of a series of filtering pipelines, one lncRNA, TCONS_00075467, was selected to explore its effects and mechanisms on electrical remodeling. The atrial effective refractory period was shortened in vivo and the L-type calcium current and action potential duration were decreased in vitro by silencing of TCONS_00075467 with lentiviruses. Besides, the expression of miRNA-328 was negatively correlated with TCONS_00075467. We further demonstrated that TCONS_00075467 could sponge miRNA-328 in vitro and in vivo to regulate the downstream protein coding gene CACNA1C. In addition, miRNA-328 could partly reverse the effects of TCONS_00075467 on electrical remodeling. In summary, dysregulated lncRNAs may play important roles in modulating electrical remodeling during AF. Our study may facilitate the mechanism studies of lncRNAs in AF pathogenesis and provide potential therapeutic targets for AF.

Whole‑exome sequencing identifies a novel mutation (R367G) in SCN5A to be associated with familial cardiac conduction disease

Cardiac conduction disease is a primary cause of sudden cardiac death. Sodium voltage‑gated channel‑α subunit 5 (SCN5A) mutations have been reported to underlie a variety of inherited arrhythmias. Numerous disease‑causing mutations of SCN5A have been identified in patients with ≥10 different conditions, including type 3 long‑QT syndrome and Brugada syndrome. The present study investigated a family with a history of arrhythmia, with the proband having a history of arrhythmia and syncope. Whole‑exome sequencing was applied in order to detect the disease‑causing mutation in this family, and Sanger sequencing was used to confirm the co‑segregation among the family members. A missense mutation (c.1099C>G/p.R367G) of SCN5A was identified in the family and was observed to be co‑segregated in all affected members of the family. The missense mutation results in a substitution of glycine for arginine, which may affect sodium transmembrane transport. The present study provides an accurate genetic test which may be used in individuals who exhibit no clinical symptoms.

Conditional eQTL analysis reveals allelic heterogeneity of gene expression

In recent years, multiple eQTL (expression quantitative trait loci) catalogs have become available that can help understand the functionality of complex trait-related single nucleotide polymorphisms (SNPs). In eQTL catalogs, gene expression is often strongly associated with multiple SNPs, which may reflect either one or multiple independent associations. Conditional eQTL analysis allows a distinction between dependent and independent eQTLs. We performed conditional eQTL analysis in 4,896 peripheral blood microarray gene expression samples. Our analysis showed that 35% of genes with a cis eQTL have at least two independent cis eQTLs; for several genes up to 13 independent cis eQTLs were identified. Also, 12% (671) of the independent cis eQTLs identified in conditional analyses were not significant in unconditional analyses. The number of GWAS catalog SNPs identified as eQTL in the conditional analyses increases with 24% as compared to unconditional analyses. We provide an online conditional cis eQTL mapping catalog for whole blood (https://eqtl.onderzoek.io/), which can be used to lookup eQTLs more accurately than in standard unconditional whole blood eQTL databases.

A genome-wide interaction analysis of tricyclic/tetracyclic antidepressants and RR and QT intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium

BACKGROUND

Increased heart rate and a prolonged QT interval are important risk factors for cardiovascular morbidity and mortality, and can be influenced by the use of various medications, including tricyclic/tetracyclic antidepressants (TCAs). We aim to identify genetic loci that modify the association between TCA use and RR and QT intervals.

METHODS AND RESULTS

We conducted race/ethnic-specific genome-wide interaction analyses (with HapMap phase II imputed reference panel imputation) of TCAs and resting RR and QT intervals in cohorts of European (n=45 706; n=1417 TCA users), African (n=10 235; n=296 TCA users) and Hispanic/Latino (n=13 808; n=147 TCA users) ancestry, adjusted for clinical covariates. Among the populations of European ancestry, two genome-wide significant loci were identified for RR interval: rs6737205 in BRE (β=56.3, pinteraction=3.9e-9) and rs9830388 in UBE2E2 (β=25.2, pinteraction=1.7e-8). In Hispanic/Latino cohorts, rs2291477 in TGFBR3 significantly modified the association between TCAs and QT intervals (β=9.3, pinteraction=2.55e-8). In the meta-analyses of the other ethnicities, these loci either were excluded from the meta-analyses (as part of quality control), or their effects did not reach the level of nominal statistical significance (pinteraction>0.05). No new variants were identified in these ethnicities. No additional loci were identified after inverse-variance-weighted meta-analysis of the three ancestries.

CONCLUSIONS

Among Europeans, TCA interactions with variants in BRE and UBE2E2 were identified in relation to RR intervals. Among Hispanic/Latinos, variants in TGFBR3 modified the relation between TCAs and QT intervals. Future studies are required to confirm our results.

Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study

Despite the recent explosive rise in number of genetic markers for complex disease traits identified in genome-wide association studies, there is still a large gap between the known heritability of these traits and the part explained by these markers. To gauge whether this 'heritability gap' is closing, we first identified genome-wide significant SNPs from the literature and performed replication analyses for 32 highly relevant traits from five broad disease areas in 13 436 subjects of the Lifelines Cohort. Next, we calculated the variance explained by multi-SNP genetic risk scores (GRSs) for each trait, and compared it to their broad- and narrow-sense heritabilities captured by all common SNPs. The majority of all previously-associated SNPs (median=75%) were significantly associated with their respective traits. All GRSs were significant, with unweighted GRSs generally explaining less phenotypic variance than weighted GRSs, for which the explained variance was highest for height (15.5%) and varied between 0.02 and 6.7% for the other traits. Broad-sense common-SNP heritability estimates were significant for all traits, with the additive effect of common SNPs explaining 48.9% of the variance for height and between 5.6 and 39.2% for the other traits. Dominance effects were uniformly small (0-1.5%) and not significant. On average, the variance explained by the weighted GRSs accounted for only 10.7% of the common-SNP heritability of the 32 traits. These results indicate that GRSs may not yet be ready for accurate personalized prediction of complex disease traits limiting widespread adoption in clinical practice.

Quantitative analysis of PKP2 and neighbouring genes in a patient with arrhythmogenic right ventricular cardiomyopathy caused by heterozygous PKP2 deletion

AIMS

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disease mainly caused by desmosome gene mutations. The genetic culprit, however, remains elusive in ∼50% of ARVC patients. One of the reasons for missing genetic abnormalities is the difficulty in detecting large deletions/duplications, which are called as copy number variation (CNV) by the Sanger sequencing method. This study aimed to identify CNVs in PKP2 and a part of other desmosome genes in ARVC patients.

METHODS AND RESULTS

The study cohort consisted of 71 ARVC probands who were diagnosed as definite or borderline cases based on 2010 Task Force Criteria. Among them, 32 (45%) carried at least one mutation in desmosome genes detected by the Sanger method. Using the multiplex ligation-dependent probe amplification method, we identified a male proband (1.4%) with a complete deletion of all PKP2 coding exons. He was 31 years old and showed exercise-induced sustained ventricular tachycardia with superior axis and left bundle-branch block pattern. His cardiac magnetic resonance imaging and computed tomography showed right ventricular dilatation and reduced ejection fraction. His 12-lead electrocardiogram showed T-wave inversion in V1-V3, and late potentials were positive, indicating definite ARVC. To confirm the precise location of the deletion, we performed relative quantitative PCR. We found complete deletion of both SYT10 and ALG10 located in 3' of PKP2; the total deletion size was at least 1.23 Mb.

CONCLUSION

Screening for CNVs in desmosome genes is useful to identify the genetic basis of disease in clinically suspected ARVC patients.

Age-dependent diastolic heart failure in an in vivo Drosophila model

While the signals and complexes that coordinate the heartbeat are well established, how the heart maintains its electromechanical rhythm over a lifetime remains an open question with significant implications to human health. Reasoning that this homeostatic challenge confronts all pulsatile organs, we developed a high resolution imaging and analysis toolset for measuring cardiac function in intact, unanesthetized Drosophila melanogaster. We demonstrate that, as in humans, normal aging primarily manifests as defects in relaxation (diastole) while preserving contractile performance. Using this approach, we discovered that a pair of two-pore potassium channel (K2P) subunits, largely dispensable early in life, are necessary for terminating contraction (systole) in aged animals, where their loss culminates in fibrillatory cardiac arrest. As the pumping function of its heart is acutely dispensable for survival, Drosophila represents a uniquely accessible model for understanding the signaling networks maintaining cardiac performance during normal aging.

PATZ1 down-regulates FADS1 by binding to rs174557 and is opposed by SP1/SREBP1c

The FADS1 and FADS2 genes in the FADS cluster encode the rate-limiting enzymes in the synthesis of long-chain polyunsaturated fatty acids (LC-PUFAs). Genetic variation in this region has been associated with a large number of diseases and traits many of them correlated to differences in metabolism of PUFAs. However, the causative variants leading to these associations have not been identified. Here we find that the multiallelic rs174557 located in an AluYe5 element in intron 1 of FADS1 is functional and lies within a PATZ1 binding site. The derived allele of rs174557, which is the common variant in most populations, diminishes binding of PATZ1, a transcription factor conferring allele-specific downregulation of FADS1. The PATZ1 binding site overlaps with a SP1 site. The competitive binding between the suppressive PATZ1 and the activating complex of SP1 and SREBP1c determines the enhancer activity of this region, which regulates expression of FADS1.

Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants

Background

The Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from approximately 24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to of the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a large genome-wide array.

Methods

GS:SFHS was analysed using genome-wide association studies (GWAS) to test the effects of a large spectrum of variants, imputed using the Haplotype Research Consortium (HRC) dataset, on medically relevant traits measured directly or obtained from EHRs. The HRC dataset is the largest available haplotype reference panel for imputation of variants in populations of European ancestry and allows investigation of variants with low minor allele frequencies within the entire GS:SFHS genotyped cohort.

Results

Genome-wide associations were run on 20,032 individuals using both genotyped and HRC imputed data. We present results for a range of well-studied quantitative traits obtained from clinic visits and for serum urate measures obtained from data linkage to EHRs collected by the Scottish National Health Service. Results replicated known associations and additionally reveal novel findings, mainly with rare variants, validating the use of the HRC imputation panel. For example, we identified two new associations with fasting glucose at variants near to Y_RNA and WDR4 and four new associations with heart rate at SNPs within CSMD1 and ASPH, upstream of HTR1F and between PROKR2 and GPCPD1. All were driven by rare variants (minor allele frequencies in the range of 0.08–1%). Proof of principle for use of EHRs was verification of the highly significant association of urate levels with the well-established urate transporter SLC2A9.

Conclusions

GS:SFHS provides genetic data on over 20,000 participants alongside a range of phenotypes as well as linkage to National Health Service laboratory and clinical records. We have shown that the combination of deeper genotype imputation and extended phenotype availability make GS:SFHS an attractive resource to carry out association studies to gain insight into the genetic architecture of complex traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0414-4) contains supplementary material, which is available to authorized users.

The UK Biobank Cardio-metabolic Traits Consortium Blood Pressure Working Group, Warren HR, Evangelou E, Cabrera CP, Gao H, Ren M, Mifsud B, Ntalla I, Surendran P, Liu C, Cook JP, Kraja AT, Drenos F, Loh M, Verweij N, Marten J, Karaman I, Segura Lepe MP, O’Reilly PF, Knight J, Snieder H, Kato N, He J, Tai ES, Said MA, Porteous D, Alver M, Poulter N, Farrall M, Gansevoort RT, Padmanabhan S, Mägi R, Stanton A, Connell J, Bakker SJL, Metspalu A, Shields DC, Thom S, Brown M, Sever P, Esko T, Hayward C, van der Harst P, Saleheen D, Chowdhury R, Chambers JC, Chasman DI, Chakravarti A, Newton-Cheh C, Lindgren CM, Levy D, Kooner JS, Keavney B, Tomaszewski M, Samani NJ, Howson JMM, Tobin MD, Munroe PB, Ehret GB, Wain LV, Barnes MR, Tzoulaki I, Caulfield MJ, Elliott P, collaboration with The International Consortium of Blood Pressure#, (ICBP) 1000G Analyses, The CHD Exome+ Consortium, The ExomeBP Consortium, The T2D-GENES Consortium, The GoT2DGenes Consortium, The Cohorts for Heart and Ageing Research in Genome Epidemiology (CHARGE) BP Exome Consortium, The International Genomics of Blood Pressure (iGEN-BP) Consortium. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk

Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide. We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK Biobank participants of European ancestry with independent replication in other cohorts, and robust validation of 107 independent loci. We also identify new independent variants at 11 previously reported blood pressure loci. In combination with results from a range of in silico functional analyses and wet bench experiments, our findings highlight new biological pathways for blood pressure regulation enriched for genes expressed in vascular tissues and identify potential therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure-raising genetic variants on future cardiovascular disease risk.

Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER) programme

Background

Electronic health records (EHRs), when linked across primary and secondary care and curated for research use, have the potential to improve our understanding of care quality and outcomes.

Objective

To evaluate new opportunities arising from linked EHRs for improving quality of care and outcomes for patients at risk of or with coronary disease across the patient journey.

Design

Epidemiological cohort, health informatics, health economics and ethnographic approaches were used.

Setting

230 NHS hospitals and 226 general practices in England and Wales.

Participants

Up to 2 million initially healthy adults, 100,000 people with stable coronary artery disease (SCAD) and up to 300,000 patients with acute coronary syndrome.

Main outcome measures

Quality of care, fatal and non-fatal cardiovascular disease (CVD) events.

Data platform and methods

We created a novel research platform [ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER)] based on linkage of four major sources of EHR data in primary care and national registries. We carried out 33 complementary studies within the CALIBER framework. We developed a web-based clinical decision support system (CDSS) in hospital chest pain clinics. We established a novel consented prognostic clinical cohort of SCAD patients.

Results

CALIBER was successfully established as a valid research platform based on linked EHR data in nearly 2 million adults with > 600 EHR phenotypes implemented on the web portal (seehttps://caliberresearch.org/portal). Despite national guidance, key opportunities for investigation and treatment were missed across the patient journey, resulting in a worse prognosis for patients in the UK compared with patients in health systems in other countries. Our novel, contemporary, high-resolution studies showed heterogeneous associations for CVD risk factors across CVDs. The CDSS did not alter the decision-making behaviour of clinicians in chest pain clinics. Prognostic models using real-world data validly discriminated risk of death and events, and were used in cost-effectiveness decision models.

Conclusions

Emerging ‘big data’ opportunities arising from the linkage of records at different stages of a patient’s journey are vital to the generation of actionable insights into the diagnosis, risk stratification and cost-effective treatment of people at risk of, or with, CVD.

Future work

The vast majority of NHS data remain inaccessible to research and this hampers efforts to improve efficiency and quality of care and to drive innovation. We propose three priority directions for further research. First, there is an urgent need to ‘unlock’ more detailed data within hospitals for the scale of the UK’s 65 million population. Second, there is a need for scaled approaches to using EHRs to design and carry out trials, and interpret the implementation of trial results. Third, large-scale, disease agnostic genetic and biological collections linked to such EHRs are required in order to deliver precision medicine and to innovate discovery.

Study registration

CALIBER studies are registered as follows: study 2 – NCT01569139, study 4 – NCT02176174 and NCT01164371, study 5 – NCT01163513, studies 6 and 7 – NCT01804439, study 8 – NCT02285322, and studies 26–29 – NCT01162187. Optimising the Management of Angina is registered as Current Controlled Trials ISRCTN54381840.

Funding

The National Institute for Health Research (NIHR) Programme Grants for Applied Research programme (RP-PG-0407-10314) (all 33 studies) and additional funding from the Wellcome Trust (study 1), Medical Research Council Partnership grant (study 3), Servier (study 16), NIHR Research Methods Fellowship funding (study 19) and NIHR Research for Patient Benefit (study 33).

Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality

Resting heart rate is a heritable trait correlated with life span. Little is known about the genetic contribution to resting heart rate and its relationship with mortality. We performed a genome-wide association discovery and replication analysis starting with 19.9 million genetic variants and studying up to 265,046 individuals to identify 64 loci associated with resting heart rate (P < 5 × 10-8); 46 of these were novel. We then used the genetic variants identified to study the association between resting heart rate and all-cause mortality. We observed that a genetically predicted resting heart rate increase of 5 beats per minute was associated with a 20% increase in mortality risk (hazard ratio 1.20, 95% confidence interval 1.11-1.28, P = 8.20 × 10-7) translating to a reduction in life expectancy of 2.9 years for males and 2.6 years for females. Our findings provide evidence for shared genetic predictors of resting heart rate and all-cause mortality.

Lifetime regular exercise affects the incident of different arrhythmias and improves organismal health in aging female Drosophila melanogaster

We used Drosophila melanogaster as an animal model system to study the impact of exercise training initiated early in life on cardiac function using a well-established model of inherent myogenic properties of the heart and discussed the changes on myosin, a myocardial contractile protein. We also explored the effect of early physical exercise on organismal aging by analyzing the wake-sleep pattern using a Drosophila activity monitor system. We found that a variety of arrhythmias are part of the heart spectrum in old flies after a lifetime of physical exercise as evidenced by reducing the incidence of fibrillations and increasing the occurrence of bradycardias. Maintenance of myocardial myosin levels may be an underlying contributor to these exercise-induced improvements in cardiac function at an advanced age. Moreover, we found that exercise training resulted in improved sleep quality by ameliorating age-related sleep inefficiency, fragmentation and sleep consolidation.

Development of the cardiac pacemaker

The sinoatrial node (SAN) is the dominant pacemaker of the heart. Abnormalities in SAN formation and function can cause sinus arrhythmia, including sick sinus syndrome and sudden death. A better understanding of genes and signaling pathways that regulate SAN development and function is essential to develop more effective treatment to sinus arrhythmia, including biological pacemakers. In this review, we briefly summarize the key processes of SAN morphogenesis during development, and focus on the transcriptional network that drives SAN development.

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ∼155,063 samples for independent replication. We identified 30 new blood pressure- or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.

GNB5 Mutations Cause an Autosomal-Recessive Multisystem Syndrome with Sinus Bradycardia and Cognitive Disability

GNB5 encodes the G protein β subunit 5 and is involved in inhibitory G protein signaling. Here, we report mutations in GNB5 that are associated with heart-rate disturbance, eye disease, intellectual disability, gastric problems, hypotonia, and seizures in nine individuals from six families. We observed an association between the nature of the variants and clinical severity; individuals with loss-of-function alleles had more severe symptoms, including substantial developmental delay, speech defects, severe hypotonia, pathological gastro-esophageal reflux, retinal disease, and sinus-node dysfunction, whereas related heterozygotes harboring missense variants presented with a clinically milder phenotype. Zebrafish gnb5 knockouts recapitulated the phenotypic spectrum of affected individuals, including cardiac, neurological, and ophthalmological abnormalities, supporting a direct role of GNB5 in the control of heart rate, hypotonia, and vision.

Association Between Heart Rate at Rest and Incident Atrial Fibrillation (from the Copenhagen Electrocardiographic Study)

Heart rate (HR) at rest is a well-known marker of cardiovascular morbidity and mortality. Results on the association between HR and incident atrial fibrillation (AF) have, however, been conflicting. Using digital electrocardiograms from 281,451 primary care patients, we aimed to describe the association between HR at rest and the hazards of incident AF. Secondary end points were death from all causes and pacemaker implantation. Data on drug use, co-morbidity, and outcomes were collected from nationwide administrative health care registries. During a median follow-up time of 8.4 years, 15,666 subjects were observed to develop AF, of which 1,631 were lone AF. A HR at rest from 30 to 51 beats/min was associated with an adjusted hazard ratio of 1.16 (95% CI 1.06 to 1.27) for AF compared with the reference group (66 to 72 beats/min). From 72 beats/min and upward, the hazard ratio of AF increased in a dose-response manner, reaching an adjusted hazard ratio of 1.36 (95% CI 1.26 to 1.46) for HR between 95 and 120 beats/min. Both for low and high HR, the associations were accentuated for the outcome lone AF (adjusted hazard ratios of 1.48, 95% CI 1.19 to 1.84 and 1.84, 95% CI 1.47 to 2.30 for HR between 30 to 51 and 95 to 120 beats/min, respectively). For death from all causes, the hazard increased almost linearly with increasing HR. A HR at rest from 30 to 51 beats/min was associated with an adjusted hazard ratio of 1.80 (95% CI 1.46 to 2.21) for pacemaker implantation. In conclusion, a U-shaped association was found between HR at rest and incident AF, and this association was strongest for the outcome lone AF.

The Risk of Atrial Fibrillation With Ivabradine Treatment: A Meta-analysis With Trial Sequential Analysis of More Than 40000 Patients

Recent trials reported that risk of atrial fibrillation (AF) is increased in patients using ivabradine compared with controls. We performed this meta-analysis to investigate the risk of AF association with ivabradine treatment on the basis of data obtained from randomized controlled trials (RCTs). We searched PubMed, EMBASE, Scopus, and the Cochrane Library for RCTs that comprised >100 patients. The incidence of AF was assessed. We obtained data from European Medicines Agency (EMA) scientific reports for the RCTs in which the incidence of AF was not reported. We used trial sequential analysis (TSA) to provide information on when we had reached firm evidence of new AF based on a 15% relative risk increase (RRI) in ivabradine treatment. Three RCTs and 1 EMA overall oral safety set (OOSS) pooled analysis (included 5 RCTs) were included in the meta-analysis (N = 40 437). The incidence of AF was 5.34% in patients using ivabradine and 4.56% in placebo. There was significantly higher incidence of AF (24% RRI) in the ivabradine group when compared with placebo before (RR: 1.24, 95% confidence interval: 1.08-1.42, P = 0.003, I 1980 = 53%) and after excluding OOSS (RR: 1.24, 95% confidence interval: 1.06-1.44, P = 0.008). In the TSA, the cumulative z-curve crossed both the traditional boundary (P = 0.05) and the trial sequential monitoring boundary, indicating firm evidence for ≥15% increase in ivabradine treatment when compared with placebo. Study results indicate that AF is more common in the ivabradine group (24% RRI) than in controls.

Genome-wide association studies and resting heart rate

Genome-wide association studies (GWASs) have revolutionized the search for genetic variants regulating resting heart rate. In the last 10years, GWASs have led to the identification of at least 21 novel heart rate loci. These discoveries have provided valuable insights into the mechanisms and pathways that regulate heart rate and link heart rate to cardiovascular morbidity and mortality. GWASs capture majority of genetic variation in a population sample by utilizing high-throughput genotyping chips measuring genotypes for up to several millions of SNPs across the genome in thousands of individuals. This allows the identification of the strongest heart rate associated signals at genome-wide level. While GWASs provide robust statistical evidence of the association of a given genetic locus with heart rate, they are only the starting point for detailed follow-up studies to locate the causal variants and genes and gain further insights into the biological mechanisms underlying the observed associations.

KCNN2 polymorphisms and cardiac tachyarrhythmias

Potassium calcium-activated channel subfamily N member 2 (KCNN2) encodes an integral membrane protein that forms small-conductance calcium-activated potassium (SK) channels. Recent studies in animal models show that SK channels are important in atrial and ventricular repolarization and arrhythmogenesis. However, the importance of SK channels in human arrhythmia remains unclear. The purpose of the present study was to test the association between genetic polymorphism of the SK2 channel and the occurrence of cardiac tachyarrhythmias in humans. We enrolled 327 Han Chinese, including 72 with clinically significant ventricular tachyarrhythmias (VTa) who had a history of aborted sudden cardiac death (SCD) or unexplained syncope, 98 with a history of atrial fibrillation (AF), and 144 normal controls. We genotyped 12 representative tag single nucleotide polymorphisms (SNPs) across a 141-kb genetic region containing the KCNN2 gene; these captured the full haplotype information. The rs13184658 and rs10076582 variants of KCNN2 were associated with VTa in both the additive and dominant models (odds ratio [OR] 2.89, 95% confidence interval [CI] = 1.505-5.545, P = 0.001; and OR 2.55, 95% CI = 1.428-4.566, P = 0.002, respectively). After adjustment for potential risk factors, the association remained significant. The population attributable risks of these 2 variants of VTa were 17.3% and 10.6%, respectively. One variant (rs13184658) showed weak but significant association with AF in a dominant model (OR 1.91, CI = 1.025-3.570], P = 0.042). There was a significant association between the KCNN2 variants and clinically significant VTa. These findings suggest an association between KCNN2 and VTa; it also appears that KCNN2 variants may be adjunctive markers for risk stratification in patients susceptible to SCD.

A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease

Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.

The formation and function of the cardiac conduction system

The cardiac conduction system (CCS) consists of distinctive components that initiate and conduct the electrical impulse required for the coordinated contraction of the cardiac chambers. CCS development involves complex regulatory networks that act in stage-, tissue- and dose-dependent manners, and recent findings indicate that the activity of these networks is sensitive to common genetic variants associated with cardiac arrhythmias. Here, we review how these findings have provided novel insights into the regulatory mechanisms and transcriptional networks underlying CCS formation and function.

Coding and non-coding variants in the SHOX2 gene in patients with early-onset atrial fibrillation

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia with a strong genetic component. Molecular pathways involving the homeodomain transcription factor Shox2 control the development and function of the cardiac conduction system in mouse and zebrafish. Here we report the analysis of human SHOX2 as a potential susceptibility gene for early-onset AF. To identify causal variants and define the underlying mechanisms, results from 378 patients with early-onset AF before the age of 60 years were analyzed and compared to 1870 controls or reference datasets. We identified two missense mutations (p.G81E, p.H283Q), that were predicted as damaging. Transactivation studies using SHOX2 targets and phenotypic rescue experiments in zebrafish demonstrated that the p.H283Q mutation severely affects SHOX2 pacemaker function. We also demonstrate an association between a 3'UTR variant c.*28T>C of SHOX2 and AF (p = 0.00515). Patients carrying this variant present significantly longer PR intervals. Mechanistically, this variant creates a functional binding site for hsa-miR-92b-5p. Circulating hsa-miR-92b-5p plasma levels were significantly altered in AF patients carrying the 3'UTR variant (p = 0.0095). Finally, we demonstrate significantly reduced SHOX2 expression levels in right atrial appendages of AF patients compared to patients with sinus rhythm. Together, these results suggest a genetic contribution of SHOX2 in early-onset AF.

Orthostatic Hypotension and Elevated Resting Heart Rate Predict Low-Energy Fractures in the Population: The Malmö Preventive Project

Background

Autonomic disorders of the cardiovascular system, such as orthostatic hypotension and elevated resting heart rate, predict mortality and cardiovascular events in the population. Low-energy-fractures constitute a substantial clinical problem that may represent an additional risk related to such autonomic dysfunction.

Aims

To test the association between orthostatic hypotension, resting heart rate and incidence of low-energy-fractures in the general population.

Methods and Results

Using multivariable-adjusted Cox regression models we investigated the association between orthostatic blood pressure response, resting heart rate and first incident low-energy-fracture in a population-based, middle-aged cohort of 33 000 individuals over 25 years follow-up.

The median follow-up time from baseline to first incident fracture among the subjects that experienced a low energy fracture was 15.0 years. A 10 mmHg orthostatic decrease in systolic blood pressure at baseline was associated with 5% increased risk of low-energy-fractures (95% confidence interval 1.01–1.10) during follow-up, whereas the resting heart rate predicted low-energy-fractures with an effect size of 8% increased risk per 10 beats-per-minute (1.05–1.12), independently of the orthostatic response. Subjects with a resting heart rate exceeding 68 beats-per-minute had 18% (1.10–1.26) increased risk of low-energy-fractures during follow-up compared with subjects with a resting heart rate below 68 beats-per-minute. When combining the orthostatic response and resting heart rate, there was a 30% risk increase (1.08–1.57) of low-energy-fractures between the extremes, i.e. between subjects in the fourth compared with the first quartiles of both resting heart rate and systolic blood pressure-decrease.

Conclusion

Orthostatic blood pressure decline and elevated resting heart rate independently predict low-energy fractures in a middle-aged population. These two measures of subclinical cardiovascular dysautonomia may herald increased risks many years in advance, even if symptoms may not be detectable. Although the effect sizes are moderate, the easily accessible clinical parameters of orthostatic blood pressure response and resting heart rate deserve consideration as new risk predictors to yield more accurate decisions on primary prevention of low-energy fractures.

[AFNET. A translational research network develops into an academic research organization]

"The whole is greater than the sum of its parts" (Aristotle).Atrial fibrillation (AF) is the most common sustained arrhythmia and affects 1-2 % of the population in developed countries, especially the elderly. We expect that the prevalence of AF will double in the next few decades. The last decades have seen important improvements in the management of atrial fibrillation, but many questions remain regarding the optimal diagnosis and management of the condition. The German Atrial Fibrillation NETwork (AFNET) was one of three cardiovascular competence networks in medicine funded by the German Ministry of Education and Research between 2003-2014. AFNET has contributed to the understanding of atrial fibrillation, and AFNET-led studies have led to improved clinical practices and practice guidelines in Germany and in Europe. This work has been expanded and is continuing in the AFNET association (AFNET e. V.). The AFNET association, founded in 2010 and continuing to this day, has developed into a small but fully formed academic research organisation that conducts investigator-initiated clinical trials as the responsible sponsor in Germany, Europe, and beyond. The AFNET association currently cooperates with EHRA (The European Heart Rhythm Association), ESC (The European Society of Cardiology) and DZHK (The German Centre for Cardiovascular Research) and receives funding from the European Union to generate evidence that can in the future lead to better prevention and management of AF.

Twenty-eight genetic loci associated with ST-T-wave amplitudes of the electrocardiogram

The ST-segment and adjacent T-wave (ST-T wave) amplitudes of the electrocardiogram are quantitative characteristics of cardiac repolarization. Repolarization abnormalities have been linked to ventricular arrhythmias and sudden cardiac death. We performed the first genome-wide association meta-analysis of ST-T-wave amplitudes in up to 37 977 individuals identifying 71 robust genotype–phenotype associations clustered within 28 independent loci. Fifty-four genes were prioritized as candidates underlying the phenotypes, including genes with established roles in the cardiac repolarization phase (SCN5A/SCN10A, KCND3, KCNB1, NOS1AP and HEY2) and others with as yet undefined cardiac function. These associations may provide insights in the spatiotemporal contribution of genetic variation influencing cardiac repolarization and provide novel leads for future functional follow-up.

Gene regulatory networks in atrial fibrillation

Atrial fibrillation (AF) is the most frequent arrhythmogenic syndrome in humans. With an estimate incidence of 1%-2% in the general population, AF raises up to almost 10%-12% in 80+ years. Thus, AF represents nowadays a highly prevalent medical problem generating a large economic burden. At the electrophysiological level, distinct mechanisms have been elucidated. Yet, despite its prevalence, the genetic and molecular culprits of this pandemic cardiac electrophysiological abnormality have remained largely obscure. Molecular genetics of AF familiar cases have demonstrated that single nucleotide mutations in distinct genes encoding for ion channels underlie the onset of AF, albeit such alterations only explain a minor subset of patients with AF. In recent years, analyses by means of genome-wide association studies have unraveled a more complex picture of the etiology of AF, pointing out to distinct cardiac-enriched transcription factors, as well as to other regulatory genes. Furthermore a new layer of regulatory mechanisms have emerged, i.e., post-transcriptional regulation mediated by non-coding RNA, which have been demonstrated to exert pivotal roles in cardiac electrophysiology. In this manuscript, we aim to provide a comprehensive review of the genetic regulatory networks that if impaired exert electrophysiological abnormalities that contribute to the onset, and subsequently, on self-perpetuation of AF.

Clock Genes Explain a Large Proportion of Phenotypic Variance in Systolic Blood Pressure and This Control Is Not Modified by Environmental Temperature

BACKGROUND

Diurnal variation in blood pressure (BP) is regulated, in part, by an endogenous circadian clock; however, few human studies have identified associations between clock genes and BP. Accounting for environmental temperature may be necessary to correct for seasonal bias.

METHODS

We examined whether environmental temperature on the day of participants' assessment was associated with BP, using adjusted linear regression models in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) (n = 819) and the Boston Puerto Rican Health Study (BPRHS) (n = 1,248) cohorts. We estimated phenotypic variance in BP by 18 clock genes and examined individual single-nucleotide polymorphism (SNP) associations with BP using an additive genetic model, with further consideration of environmental temperature.

RESULTS

In GOLDN, each additional 1 °C increase in environmental temperature was associated with 0.18 mm Hg lower systolic BP [SBP; β ± SE = -0.18 ± 0.05 mm Hg; P = 0.0001] and 0.10mm Hg lower diastolic BP [DBP; -0.10 ± 0.03 mm Hg; P = 0.001]. Similar results were seen in the BPRHS for SBP only. Clock genes explained a statistically significant proportion of the variance in SBP [V G/V P ± SE = 0.071 ± 0.03; P = 0.001] in GOLDN, but not in the BPRHS, and we did not observe associations between individual SNPs and BP. Environmental temperature did not influence the identified genetic associations.

CONCLUSIONS

We identified clock genes that explained a statistically significant proportion of the phenotypic variance in SBP, supporting the importance of the circadian pathway underlying cardiac physiology. Although temperature was associated with BP, it did not affect results with genetic markers in either study. Therefore, it does not appear that temperature measures are necessary for interpreting associations between clock genes and BP.

CLINICAL TRIAL REGISTRATION

Trials related to this study were registered at clinicaltrials.gov as NCT00083369 (Genetic and Environmental Determinants of Triglycerides) and NCT01231958 (Boston Puerto Rican Health Study).

GWAS as a Driver of Gene Discovery in Cardiometabolic Diseases

Cardiometabolic diseases represent a common complex disorder with a strong genetic component. Currently, genome-wide association studies (GWAS) have yielded some 755 single-nucleotide polymorphisms (SNPs) encompassing 366 independent loci that may help to decipher the molecular basis of cardiometabolic diseases. Going from a disease SNP to the underlying disease mechanisms is a huge challenge because the associated SNPs rarely disrupt protein function. Many disease SNPs are located in noncoding regions, and therefore attention is now focused on linking genetic SNP variation to effects on gene expression levels. By integrating genetic information with large-scale gene expression data, and with data from epigenetic roadmaps revealing gene regulatory regions, we expect to be able to identify candidate disease genes and the regulatory potential of disease SNPs.

Genome-wide rare copy number variations contribute to genetic risk for transposition of the great arteries

BACKGROUND

Transposition of the great arteries (TGA) is an uncommon but severe congenital heart malformation of unknown etiology. Rare copy number variations (CNVs) have been implicated in other, more common conotruncal heart defects like tetralogy of Fallot (TOF), but there are as yet no CNV studies dedicated to TGA.

METHODS

Using high-resolution genome-wide microarrays and rigorous methods, we investigated CNVs in a group of prospectively recruited adults with TGA (n=101) from a single center. We compared rare CNV burden to well-matched cohorts of controls and TOF cases, adjudicating rarity using 10,113 independent population-based controls and excluding all subjects with 22q11.2 deletions. We identified candidate genes for TGA based on rare CNVs that overlapped the same gene in unrelated individuals, and pre-existing evidence suggesting a role in cardiac development.

RESULTS

The TGA group was significantly enriched for large rare CNVs (2.3-fold increase, p=0.04) relative to controls, to a degree comparable with the TOF group. Extra-cardiac features were not reliable predictors of rare CNV burden. Smaller rare CNVs helped to narrow critical regions for conotruncal defects at chromosomes 10q26 and 13q13. Established and novel candidate susceptibility genes identified included ACKR3, IFT57, ITGB8, KL, NF1, NKX1-2, RERE, SLC8A1, SOX18, and ULK1.

CONCLUSIONS

These data demonstrate a genome-wide role for rare CNVs in genetic risk for TGA. The findings provide further support for a genetically-related spectrum of congenital heart disease that includes TGA and TOF.

The anatomical distribution of genetic associations

Deeper understanding of the anatomical intermediaries for disease and other complex genetic traits is essential to understanding mechanisms and developing new interventions. Existing ontology tools provide functional, curated annotations for many genes and can be used to develop mechanistic hypotheses; yet information about the spatial expression of genes may be equally useful in interpreting results and forming novel hypotheses for a trait. Therefore, we developed an approach for statistically testing the relationship between gene expression across the body and sets of candidate genes from across the genome. We validated this tool and tested its utility on three applications. First, we show that the expression of genes in associated loci from GWA studies implicates specific tissues for 57 out of 98 traits. Second, we tested the ability of the tool to identify novel relationships between gene expression and phenotypes. Specifically, we experimentally confirmed an underappreciated prediction highlighted by our tool: that white blood cell count--a quantitative trait of the immune system--is genetically modulated by genes expressed in the skin. Finally, using gene lists derived from exome sequencing data, we show that human genes under selective constraint are disproportionately expressed in nervous system tissues.

The Rotterdam Study: 2016 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over 1200 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Low resting heart rates are associated with new-onset atrial fibrillation in patients with vascular disease: results of the ONTARGET/TRANSCEND studies

BACKGROUND

Elevated systolic blood pressure (SBP) and high resting heart rate (HR) are associated with cardiovascular end-points. Although the association between atrial fibrillation (AF) and SBP is well established, the relation between AF and HR remains unclear.

METHODS

In patients from the ONTARGET and TRANSCEND studies with high cardiovascular disease risk (n = 27 064), new-onset AF was evaluated in relation to mean SBP, visit-to-visit variation in SBP (SBP-CV; i.e. SD/mean × 100%), mean HR and visit-to-visit variation in HR (HR-CV).

RESULTS

Low mean HR (P < 0.0001) and high SBP (P = 0.0021) were associated with incident AF. High SBP-CV (P = 0.031) and HR-CV (P < 0.0001) were also associated with incident AF. After adjustment for confounders, SBP and SBP-CV were no longer significantly associated with AF. The detrimental effect of low HR was particularly evident in subjects who were not receiving treatment with beta-blockers (P = 0.014 for interaction between beta-blocker use and mean HR). In addition to low HR, high HR-CV and high SBP had additive effects on incident AF.

CONCLUSIONS

Low mean HR (<60 beats min(-1) ) is independently associated with incident AF, and low HR-CV and high SBP further increase the incidence of new-onset AF in patients at high risk of cardiovascular disease.

Exome analysis of a family with Wolff-Parkinson-White syndrome identifies a novel disease locus

Wolff-Parkinson-White (WPW) syndrome is a common cause of supraventricular tachycardia that carries a risk of sudden cardiac death. To date, mutations in only one gene, PRKAG2, which encodes the 5'-AMP-activated protein kinase subunit γ-2, have been identified as causative for WPW. DNA samples from five members of a family with WPW were analyzed by exome sequencing. We applied recently designed prioritization strategies (VAAST/pedigree VAAST) coupled with an ontology-based algorithm (Phevor) that reduced the number of potentially damaging variants to 10: a variant in KCNE2 previously associated with Long QT syndrome was also identified. Of these 11 variants, only MYH6 p.E1885K segregated with the WPW phenotype in all affected individuals and was absent in 10 unaffected family members. This variant was predicted to be damaging by in silico methods and is not present in the 1,000 genome and NHLBI exome sequencing project databases. Screening of a replication cohort of 47 unrelated WPW patients did not identify other likely causative variants in PRKAG2 or MYH6. MYH6 variants have been identified in patients with atrial septal defects, cardiomyopathies, and sick sinus syndrome. Our data highlight the pleiotropic nature of phenotypes associated with defects in this gene.

Transcription factor ISL1 is essential for pacemaker development and function

The sinoatrial node (SAN) maintains a rhythmic heartbeat; therefore, a better understanding of factors that drive SAN development and function is crucial to generation of potential therapies, such as biological pacemakers, for sinus arrhythmias. Here, we determined that the LIM homeodomain transcription factor ISL1 plays a key role in survival, proliferation, and function of pacemaker cells throughout development. Analysis of several Isl1 mutant mouse lines, including animals harboring an SAN-specific Isl1 deletion, revealed that ISL1 within SAN is a requirement for early embryonic viability. RNA-sequencing (RNA-seq) analyses of FACS-purified cells from ISL1-deficient SANs revealed that a number of genes critical for SAN function, including those encoding transcription factors and ion channels, were downstream of ISL1. Chromatin immunoprecipitation assays performed with anti-ISL1 antibodies and chromatin extracts from FACS-purified SAN cells demonstrated that ISL1 directly binds genomic regions within several genes required for normal pacemaker function, including subunits of the L-type calcium channel, Ank2, and Tbx3. Other genes implicated in abnormal heart rhythm in humans were also direct ISL1 targets. Together, our results demonstrate that ISL1 regulates approximately one-third of SAN-specific genes, indicate that a combination of ISL1 and other SAN transcription factors could be utilized to generate pacemaker cells, and suggest ISL1 mutations may underlie sick sinus syndrome.

Long non-coding RNA expression profile in atrial fibrillation

To investigate the expression profiles of long non-coding RNAs (lncRNAs) in atrial fibrillation (AF), atrial tissues from 3 AF patients and 3 non-AF patients that were collected for lncRNA expression microarray analyses to explore the role of lncRNA in the pathogenesis of AF. Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify the main functions of the differentially expressed genes and AF-related pathways. A total of 219 lncRNAs was found to be differentially expressed between AFs and controls. Among them, 156 were upregulated and 63 were downregulated. Eight out of 10 dysregulated lncRNAs such as uc001eqh.1 were validated by quantitative real-time PCR. GO categories, pathway analyses, and interaction network showed a consistent result that differentially expressed genes contribute to the pathogenesis of AF. In conclusion, the findings of our study provide a perspective on lncRNA in AF and the foundation for further study of the biological functions of lncRNAs in AF.

Genome-wide association studies and contribution to cardiovascular physiology

The study of family pedigrees with rare monogenic cardiovascular disorders has revealed new molecular players in physiological processes. Genome-wide association studies of complex traits with a heritable component may afford a similar and potentially intellectually richer opportunity. In this review we focus on the interpretation of genetic associations and the issue of causality in relation to known and potentially new physiology. We mainly discuss cardiometabolic traits as it reflects our personal interests, but the issues pertain broadly in many other disciplines. We also describe some of the resources that are now available that may expedite follow up of genetic association signals into observations on causal mechanisms and pathophysiology.