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

ASSOCIATION STUDIES ARTICLE Discovery of novel heart rate-associated loci using the Exome Chip Marten E. van den Berg 1,† , Helen R. Warren 2,3,† , Claudia P. Cabrera 2,3 , Niek Verweij 4 , Borbala Mifsud 2,3 , Jeffrey Haessler 5 , Nathan A. Bihlmeyer 6 , Yi-Ping Fu 7 , Stefan Weiss 8,9 , Henry J. Lin 10,11 , Niels Grarup 12 , Ruifang Li-Gao 13 , Giorgio Pistis 14,15 , Nabi Shah 16,17 , Jennifer A. Brody 18 , Martina Mu¨ ller-Nurasyid 19,20,21 , Honghuang Lin 22 , Hao Mei 23 , Albert V. Smith 24,25 , Leo-Pekka Lyytik€ ainen 26 , Leanne M. Hall 27,28 , Jessica van Setten 29 , Stella Trompet 30,31 , Bram P. Prins 32,33 , Aaron Isaacs 34 , Farid Radmanesh 35,36 , Jonathan Marten 37 , Aiman Entwistle 2,3 , Jan A. Kors 1 , Claudia T. Silva 38,39,40 , Alvaro Alonso 41 , Joshua C. Bis 18 , Rudolf de Boer 4 , Hugoline G. de Haan 13 , Rene´e de Mutsert 13 , George Dedoussis 42 , Anna F. Dominiczak 43 , Alex S. F. Doney 16 , Patrick T. Ellinor 44,36 , Ruben N. Eppinga 4 , Stephan B. Felix 45 , Xiuqing Guo 10 , Yanick Hagemeijer 4 , Torben Hansen 12 , Tamara B. Harris 46 , Susan R. Heckbert 47,48 , Paul L. Huang 44 , Shih-Jen Hwang 49 , Mika K€ aho¨nen 50 , Jørgen K. Kanters 51 , Ivana Kolcic 52 , Lenore J. Launer 46 , Man Li 53 , Jie Yao 10 , Allan Linneberg 54,55,56 , Simin Liu 57 , Peter W. Macfarlane 58 , Massimo Mangino 59,60 , Andrew D. Morris 61 , Antonella Mulas 14 , Alison D. Murray 62 , Christopher P. Nelson 27,28 , Marco Orr u 63 , Sandosh Padmanabhan 43,64 , Annette Peters 65,20,66 , David J. Porteous 67 , Neil Poulter 68 , Bruce M. Psaty 69,70 , Lihong Qi 71 , Olli T. Raitakari 72 , Fernando Rivadeneira 73 , Carolina Roselli 74 , Igor Rudan 61 , Naveed Sattar 64 , Peter Sever 75 , Moritz F. Sinner 20,21 , Elsayed Z. Soliman 76 , Timothy D. Spector 59 , Alice V. Stanton 77 , Kathleen E. Stirrups 2,78 , Kent D. Taylor 79,80,81 , Martin D. Tobin 82 , Andre´ Uitterlinden 83 , Ilonca Vaartjes 84 , Arno W. Hoes 85 , Peter van der Meer 4 , Uwe Vo¨lker 8,9 , Melanie Waldenberger 61,65,20 , † The authors wish it to be known that in their opinion, the first two authors should be regarded as joint first authors because of equal contribution. Received: January 16, 2017. Revised: March 13, 2017. Accepted: March 18, 2017 V CThe Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 2346 Human Molecular Genetics, 2017, Vol. 26, No. 12 2346–2363 doi: 10.1093/hmg/ddx113 Advance Access Publication Date: 3 April 2017 Association Studies Article Zhijun Xie 22 , Magdalena Zoledziewska 14 , Andrew Tinker 2,3 , Ozren Polasek 52,61 , Jonathan Rosand 35,36 , Yalda Jamshidi 33 , Cornelia M. van Duijn 38 , Eleftheria Zeggini 32 , J. Wouter Jukema 30 , Folkert W. Asselbergs 29,86,87 , Nilesh J. Samani 27,28 , Terho Lehtim€ aki 88 , Vilmundur Gudnason 24,25 , James Wilson 89 , Steven A. Lubitz 44,36 , Stefan K€ a€ ab 21,20 , Nona Sotoodehnia 90 , Mark J. Caulfield 2,3 , Colin N. A. Palmer 16 , Serena Sanna 14 , Dennis O. Mook-Kanamori 13,91 , Panos Deloukas 2 , Oluf Pedersen 12 , Jerome I. Rotter 92 , Marcus Do¨rr 45 , Chris J. O’Donnell 93 , Caroline Hayward 37 , Dan E. Arking 94 , Charles Kooperberg 5 , Pim van der Harst 4 , Mark Eijgelsheim 95 , Bruno H. Stricker 95 and Patricia B. Munroe 2,3, * 1 Department of Medical Informatics Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, 3000CA, Rotterdam, the Netherlands, 2 Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK, 3 NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, EC1M 6BQ, UK, 4 University Medical Center Groningen, University of Groningen, Department of Cardiology, the Netherlands, 5 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, 6 Predoctoral Training Program in Human Genetics, McKusickNathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205, 7 Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA , 8 Interfaculty Institute for Genetics and Functional Genomics; University Medicine and Ernst-Moritz-Arndt-University Greifswald; Greifswald, 17475, Germany, 9 DZHK (German Centre for Cardiovascular Research); partner site Greifswald; Greifswald, 17475, Germany, 10 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502, USA, 11 Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA, 12 The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 13 Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands, 14 Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy , 15 Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA, 16 Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, DD1 9SY, UK, 17 Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, 22060, Pakistan, 18 Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA, 19 Institute of Genetic Epidemiology, Helmholtz Zentrum Mu¨ nchen - German Research Center for Environmental Health, Neuherberg, Germany, 20 DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany, 21 Department of Medicine I, University Hospital Munich, Ludwig-Maximilians-Universit€ at, Munich, Germany, 22 Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, 23 Department of Data Science, University of Mississippi Medical Center, Jackson, MI, USA, 24 Icelandic Heart Association, 201 Kopavogur, Iceland, 25 Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland, 26 Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Arvo, D339, P.O. Box 100, FI-33014 Tampere, Finland, 27 Department of Cardiovascular Sciences, University of Leicester, Cardiovascular Research Centre, Glenfield Hospital, Leicester, LE3 9QP, UK, 28 NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK, 29 Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht, the Netherlands, 30 Department of Cardiology, Leiden University Medical Center, 2300 RC, Leiden, the Netherlands, 31 Department of Gerontology and Geriatrics, Leiden university Medical Center, Leiden, the Netherlands, 32 Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, United Kingdom, CB10 1SA, 33 Cardiogenetics Lab, Genetics and Molecular Cell Sciences Research Centre, Cardiovascular and Cell Sciences Institute, St George’s, 2347Human Molecular Genetics, 2017, Vol. 26, No. 12 | University of London, Cranmer Terrace, London, SW17 0RE, UK, 34 CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), Dept. of Biochemistry, Maastricht University, Universiteitssingel 60, 6229 ER Maastricht, NL, 35 Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, 36 Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, 37 MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK, 38 Genetic Epidemiology Unit, Dept. of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, NL, 39 Doctoral Program in Biomedical Sciences, Universidad del Rosario, Bogot a, Colombia, 40 GENIUROS Group, Genetics and Genomics Research Center CIGGUR, School of Medicine and Health Sciences, Universidad del Rosario, Bogot a, Colombia, 41 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, 42 Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece, 43 Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK, 44 Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, USA, 45 Department of Internal Medicine B - Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine; University Medicine Greifswald; Greifswald, 17475, Germany & DZHK (German Centre for Cardiovascular Research); partner site Greifswald; Greifswald, 17475, Germany, 46 Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, Maryland, 20892, USA, 47 Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA, 48 Group Health Research Institute, Group Health Cooperative, 1730 Minor Ave, Suite 1600, Seattle, WA, USA, 49 Population Sciences Branch, Division of Intramural Research, NHLBI, NIH, Bethesda MD, USA, 50 Department of Clinical Physiology, Tampere University Hospital and University of Tampere School of Medicine, Finn-Medi 1, 3th floor, P.O. Box 2000, FI-33521 Tampere, Finland, 51 Laboratory of Experimental Cardiology, University of Copenhagen, Copenhagen, Denmark, 52 Faculty of Medicine, University of Split, Split, Croatia, 53 Division of Nephrology & Hypertension, Internal Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84109, USA, 54 Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark, 55 Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark, 56 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 57 Brown University School of Public Health, Providence, Rhode Island 02912, USA, 58 Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK, 59 Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK, 60 NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London SE1 9RT, UK, 61 Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9AG, UK, 62 Aberdeen Biomedical Imaging Centre, Lilian Sutton Building, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK, 63 Unita Operativa Complessa di Cardiologia, Presidio Ospedaliero Oncologico Armando Businco Cagliari , Azienda Ospedaliera Brotzu Cagliari, Caglliari, Italy, 64 Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF GCRC, Glasgow G12 8TA, UK, 65 Institute of Epidemiology II, Helmholtz Zentrum Mu¨nchen - German Research Center for Environmental Health, Neuherberg, Germany, 66 German Center for Diabetes Research, Neuherberg, Germany, 67 Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK, 68 School of Public Health, Imperial College London, W2 1PG, UK, 69 Cardiovascular Health Research Unit, Department of Health Services, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA, 70 Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA, 71 University of California Davis, One Shields Ave Ms1c 145, Davis, CA 95616 USA, 72 Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, P.O. Box 52, FI-20521 Turku, Finland, 73 Human Genomics Facility Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, 3000CA, Rotterdam, the Netherlands, 74 Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA, 75 National Heart and Lung Institute, Imperial College London, W2 1PG, UK, 76 Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, 77 Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin 2, Ireland, 78 Department of Haematology, University of Cambridge, Cambridge, UK, 79 Institute for 2348 |Human Molecular Genetics, 2017, Vol. 26, No. 12 Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA, 80 Division of Genomic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA, 81 Departments of Pediatrics, Medicine, and Human Genetics, UCLA, Los Angeles, CA, USA, 82 Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK, 83 Human Genotyping Facility Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, 3000CA, Rotterdam, the Netherlands, 84 Julius Center for Health Sciences and Primary Care, University Medical Center, PO Box 85500, 3508 GA Utrecht, the Netherlands, 85 Research Unit of Molecular Epidemiology, Helmholtz Zentrum Mu¨ nchen - German Research Center for Environmental Health, Neuherberg, Germany, 86 Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, the Netherlands, 87 Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK, 88 Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Arvo, D338, P.O. Box 100, FI33014 Tampere, Finland, 89 Physiology & Biophysics, University of Mississippi Medical Center, Jackson, MI, USA, 90 Cardiovascular Health Research Unit, Division of Cardiology, Departments of Medicine and Epidemiology, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA, 91 Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands, 92 The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA 90502, USA, 93 Boston Veteran’s Administration Healthcare, Boston MA, USA, 94 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205 and 95 Department of Epidemiology Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, 3000CA, Rotterdam, the Netherlands *To whom correspondence should be addressed at: Department of Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Tel: þ44 2078823586; Fax: þ442078823408; Email: [email protected] Abstract 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 metaanalyses. 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, N¼134 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. Introduction Increased resting heart rate (HR) is a known risk factor for cardiovascular morbidity and mortality (1–3), including stroke (4) and sudden cardiac death (5,6). Heart rate increased by 20 beats per minute (BPM) is associated with 30-50% higher mortality and appears to be independent of confounder factors (7). High HR increases myocardial oxygen consumption yet lessens oxygen delivery to myocardial tissue. It also increases arterial stiffness and risk of plaque rupture (8). Although HR can be influenced by many non-genetic factors (e.g. exercise, smoking and cardiovascular drugs), the heritability of resting HR is estimated to be 26–32% from family studies (9,10), and 55–63% from twin studies (11). Several meta-analyses of genome-wide association studies (GWASs) have been undertaken to detect genetic determinants of HR (12–16). There were 21 HR loci previously reported at the time of our study by den Hoed et al. (12) in a GWAS analysis of 180 000 individuals, predominantly of European ancestry. The study implicated 20 candidate genes from follow-up functional 2349Human Molecular Genetics, 2017, Vol. 26, No. 12 | studies in Danio rerio and Drosophila melanogaster models. Smaller GWAS analyses have also been performed in Icelandic and Norwegian populations (15), African Americans (13) and genetically isolated European populations (16). The variants discovered by GWAS are common, and are mostly in introns or intergenic regions. Together the previous loci from GWAS at the time of our study only explain a small percentage [0.9% of the variability in HR (12,17)]. To increase our knowledge of genetic determinants influencing HR and discover novel loci, especially rare or low frequency coding variants with larger effects, we meta-analysed data from 104 452 individuals of European-ancestry using the Exome Chip, from cohorts that participated in the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) EKG consortium. The Exome Chip permits a cost-efficient analysis of coding variants derived from sequencing of >12 000 individuals and includes many rare and low-frequency variants (18). We performed a validation experiment using independent replication samples from UK Biobank data, and bioinformatics investigations to gain an understanding of the new HR loci. Results Single-nucleotide variant analysis in individuals of European-ancestry In the discovery phase, association results of 235 677 single-nucleotide variants (SNVs) from 104 452 individuals were metaanalysed using a fixed-effects model (Supplementary Material, Fig. S1). Two analyses were performed. The first used RR-intervals (RR in milliseconds¼60 000/HR, in beats per minute, according to the inverse relationship between HR and RR). The second used the inverse-normalized residuals of the linear regression RR-interval adjusted for age þsex þbody mass index (BMI) as covariates (denoted as RR-INVN). An overview of the study design is provided in Figure 1. We observed a high correlation of effect sizes and P-values between the RR-interval and RR-INVN meta-analyses (r 2 ¼0.99 and 0.98, respectively; Supplementary Material, Fig. S2). Furthermore, the RR-interval was near-normally distributed, so inverse normalization was deemed unnecessary (Supplementary Material, Fig. S3). Beta-blockers are clinically known to lower HR, therefore the phenotype measurements of beta-blocker users may be under-estimated, and hence the inclusion of beta-blocker users in our analysis may potentially bias our analysis results. We therefore performed a sensitivity analysis by also metaanalysing a subgroup of cohorts that provided beta-blocker data (N¼48 347; 17 cohorts). Results including or excluding beta-blocker users were highly correlated (r 2 of the betas ¼0.97; r 2 of the P-values ¼0.74; Supplementary Material, Fig. S4), suggesting there is little or no bias from including beta-blocker users in the analysis. Therefore we report the meta-analysis results from the full dataset for the RR-interval, to maximize sample size and power. Replication and meta-analysis with the UK Biobank dataset To identify novel associated loci, we selected 12 variants with P<110 5 that mapped outside the 21 HR loci reported in the previous GWAS (12) for follow-up in an independent dataset. Within each unknown locus, there were no potential secondary SNVs not in linkage disequilibrium (LD) with the lead SNV (r 2 <0.2) and meeting our look-up significance threshold (P<110 5 ). Hence only 12 new lead SNVs were carried forward. We also followed-up 12 potential secondary signals at 9 of the 21 previously reported HR loci (further details on selection criteria are provided in the Materials and Methods) (12). None of the selected variants was in LD (r 2 <0.2) with each other, or with the published SNVs. Thus, a total of 24 variants were taken forward into replication. The UK Biobank dataset provided results for the selected genetic variants (N¼134 251 individuals). Nine of the 12 previously unknown variants were validated based on exome-wide significance (P2.12 10 7 ) in the combined meta-analysis of CHARGE and UK Biobank data, and on Bonferroni-adjusted significance (P0.0042 for 12 tests) in the replication dataset alone, with concordant directions of effects taking into account the inverse relationship between the RRinterval from the discovery data and HR from the replication data (Table 1;Fig. 2). Indeed, all nine SNV associations were genome-wide significant in the combined meta-analysis (P<5.0 10 8 ). Four of our nine validated novel loci were reported in a UK Biobank study (17) that was published after completion of our study (Table 1B). Hence, we present results here for five unreported novel loci (Table 1A; Supplementary Material, Figs S5 and S6). Twelve of the 21 HR-associated SNVs from the previously reported GWAS (12) were covered on the Exome Chip, either directly or by a proxy SNV in high LD (r 2 >0.8). Our discovery metaanalysis showed strong support for the previous findings, with 11 of the 12 SNVs validated at Bonferroni-adjusted significance (P0.0042 for 12 tests), of which nine were validated at exomewide significance (P<210 7 ;Fig. 2). Only rs4140885 at the TFPI locus was not supported in our data (P¼0.10; Supplementary Material, Table S1). Independent secondary signals at known loci All 12 potential secondary signals at loci previously reported by den Hoed et al. (12) were genome-wide significant in the combined meta-analysis (Supplementary Material, Table S2) and are independent to the known SNPs according to LD (r 2 <0.2). We performed a conditional analysis using Genome-wide Complex Traits Analysis (GCTA) to formally identify secondary signals of association. Five of the 12 validated potential secondary SNVs (within CD46,CCDC141,SLC35F1,ACHE and KIAA1755 loci) were selected within the final GCTA model (Supplementary Material, Table S3). At four of the previously reported HR regions the secondary signals that we identified were confirmed to be statistically independent signals of association: CD46 (rs2745967), CCDC141 (rs10497529), SLC35F1 (rs12210810) and KIAA1755 (rs41282820) in addition to the known SNV, as both the published SNV and the new secondary SNV were present in the final GCTA model of jointly independent associated variants. Hence, we identified two distinct signals of association at each of these four known HR loci. However, the published SNV at the ACHE locus (rs13245899) is not covered on the Exome Chip, or by any proxies (Supplementary Material, Table S1), so the GCTA analysis does not include the known variant. As we are not able to condition on the unavailable published SNV and formally test association jointly with the known SNV, we are unable to statistically confirm the total number of independent signals at the ACHE locus. The secondary SNVs at CCDC141, ACHE and KIAA1755 are non-synonymous variants. Furthermore, the SNVs at CCDC141 2350 |Human Molecular Genetics, 2017, Vol. 26, No. 12 and KIAA1755 are low-frequency with minor allele frequencies (MAFs) of 3.6 and 1.7%, respectively. Secondary signals have also recently been observed at four of the five loci (CD46, CCDC141,SLC35F1 and ACHE) in UK Biobank data (17), since completion of our meta-analysis. At CD46, our secondary SNV (rs2745967) is in high LD (r 2 ¼0.78) with the secondary SNV (rs2745959) reported in UK Biobank, so likely to be the same signal. At CCDC141 our secondary variant is exactly the same SNV as from UK Biobank (rs10497529). Similarly, at SLC35F1, our secondary SNV (rs12210810) is in very high LD (r 2 ¼0.98), so is likely to be the same signal. Hence at these three known loci (CD46, CCDC141,SLC35F1), all existing data suggest there are two independent signals of association. At the ACHE locus, our secondary SNV (rs542137; 38 kb and r 2 <0.2 from the published SNV) is not in LD (r 2 <0.2) with the secondary SNV from UK Biobank (rs140367586; 659 kb and r 2 <0.2 from the published SNV). We are unable to clearly determine the number of distinct signals at the ACHE locus from our Exome Chip RR-interval discovery meta-analysis data, without the published SNV being covered on the Exome Chip. The low-frequency non-synonymous variant (rs41282820) at the known KIAA1755 locus is a new, secondary variant, with strong evidence of independent association, it does not overlap with other published findings. Variance explained Twelve of the 21 previously reported HR-associated SNVs (12) covered on the Exome Chip explain 1.14% of RR-interval variance (P¼3.96 10 10 ) within the 1958 Birth Cohort study (see Materials and Methods). The added contribution of the lead SNVs at our five unreported novel loci, combined with the 12 Figure 1. Schematic flow diagram of the study design. N, sample size; SKAT, SNV-set Kernel Association Test; P,P-value; LD, linkage disequilibrium; SNV, single nucleotide variant; GCTA, Genome-wide Complex Traits Analysis software; 1958BC, 1958 Birth Cohort; UKB, UK Biobank. 2351Human Molecular Genetics, 2017, Vol. 26, No. 12 | previously reported SNVs, increases the variance explained to 1.28% overall (P¼9.17 10 11 ). Comparison of results between European and non-European populations To investigate our data from non-European samples [9358 African Americans (AA), 1411 Hispanic (HIS) and 754 Chinese-Americans (CH); Supplementary Material, Table S4], we first extracted results for the 12 of the 21 previously reported HR-associated SNVs covered on the Exome Chip (12). In contrast to previous results for Europeans, only two known HR-SNVs showed evidence of association (P<0.05), at the GJA1 and MYH6 loci, in the AA population only. This is likely due to a lack of power from the smaller non-European sample sizes, considering the power was calculated to be only 48, 11.7 and 8.5% for AA, HIS and CH, respectively. Concordance in the direction of effects compared with Europeans was only significant for AA, with 92, 64 and 50% concordance, corresponding to P-values of 2.9 10 3 , 0.16 and 0.23 from binomial tests for AA, HIS and CH, Figure 2. Manhattan plot for the RR-interval discovery meta-analysis in European individuals. The Manhattan plot displays the results from the discovery meta-analysis of RR-intervals from N¼104,452 individuals of European ancestry (from 30 cohorts). On the X axis, P-values are expressed as log 10 (P) are plotted according to physical genomic locations by chromosome. The Y-axis is truncated to log 10 (P)¼20 with any variants with P <110 20 displayed on the log 10 (P)¼20 line. The nine novel variants validated from the combined meta-analysis with UK Biobank data are represented by squares. Variants in linkage disequilibrium (LD; r 2 >0.8) with published GWAS variants are highlighted with black circles (12). New secondary variants validated in our analysis are indicated as triangles. Locus names of the novel loci correspond to the nearest annotated gene, with 5p13.3 denoting an intergenic variant. The dashed line indicates a P-value threshold of 1 10 5 , corresponding to the lookup significance threshold and the continuous line indicates a P-value threshold of 2 10 7 , corresponding to exome-wide significance. Table 1. Heart rate-associated loci identified from Exome Chip analysis SNV Locus Chr:Pos EA EAF Ndiscovery BETA-RR (SE) Pdiscovery BETA-HR (SE) Preplication Pcombined (A) Five unreported novel loci rs17853159 a TESK2 1:45810865 A 0.07 104 452 6.03 (1.20) 5.02 10 7 0.31 (0.08) 9.55 10 5 4.09 10 10 rs3087866 a DALRD3 3:49054692 T 0.25 104 452 3.29 (0.72) 4.92 10 6 0.31 (0.05) 7.06 10 10 2.09 10 14 rs1635852 JAZF1 7:28189411 C 0.50 104 452 2.96 (0.62) 2.04 10 6 0.15 (0.04) 4.10 10 4 6.97 10 9 rs10857472 a C10orf71 10:50534599 A 0.45 104 452 2.97 (0.63) 2.11 10 6 0.16 (0.04) 1.49 10 4 2.21 10 9 rs3793706 a,b SEC31B 10:102269085 A 0.22 104 452 3.52 (0.75) 2.54 10 6 0.19 (0.05) 2.06 10 4 3.72 10 9 (B) Four loci validated in our study and also recently published in the UK Biobank study rs709209 a RNF207 1:6278414 G 0.35 104 452 3.30 (0.66) 4.94 10 7 0.27 (0.04) 2.14 10 9 5.44 10 15 rs6795970 a SCN10A 3:38766675 A 0.40 104 452 2.97 (0.64) 3.10 10 6 0.24 (0.04) 1.81 10 8 2.73 10 13 rs4282331 5p13.3 5:30881510 G 0.42 104 452 3.56 (0.63) 2.03 10 8 0.26 (0.04) 2.97 10 9 3.34 10 16 rs12004 a KDELR3 22:38877461 G 0.30 104 452 3.30 (0.68) 1.24 10 6 0.31 (0.05) 4.92 10 11 4.04 10 16 Due to the inverse relationship between R-R interval and HR the opposite beta directions do relate to concordant directions of effect between discovery and replication. SNV, single-nucleotide variant; Chr:Pos, Chromosome:Position based on HG build 19; EA, effect allele; EAF, effect allele frequency from the discovery data; BETA-RR, beta effect estimate of RR-interval (milliseconds) taken from the ExomeRR discovery data; SE, standard error of the effect estimate; N, sample size analysed per variant (provided for genotyped discovery data only, as replication data was imputed so N¼maximum Nfor all variants); BETA-HR, beta effect for heart rate (in beats per minute) taken from the UK Biobank replication data; P,P-value from either the discovery meta-analysis, the replication data, or the combined meta-analysis of discovery and replication data. Locus name indicates the nearest gene to the HR-associated SNV. a Indicates that the lead or a proxy SNV (r 2 >0.8) is a non-synonymous SNV. b Indicates if the lead SNV is predicted to be damaging. Mapping to more than 500 kb from either side of a previously reported HR-associated SNV. A novel locus is a genomic region with no SNVs in LD (r 2 <0.2) with HR-associated SNVs. 2352 |Human Molecular Genetics, 2017, Vol. 26, No. 12 respectively. The lack of support of previous findings from the under-powered non-European data led us to restrict our primary discovery meta-analysis to Europeans only. We also performed a look-up of the nine validated SNV associations in the non-European samples. Due to the lack of power, and different allele frequencies compared with Europeans, none of the SNVs had results with P<0.05 within any ancestry (Supplementary Material, Fig. S6), and there was little concordance in effect directions: 56% and P¼0.246 for AA; 33% and P¼0.164 for HIS and CH. Gene-based tests Gene-based testing was performed to identify genes which may have multiple rare variant associations. None of the gene-based test results was significant, after excluding the single most significant low-frequency variant from the tests (Supplementary Material, Table S5). Look-up of UK Biobank HR-SNVs Since completion of our meta-analysis of Exome Chip genotypes, a genome-wide scan for HR has been completed in UK Biobank (17). This study published 46 new HR loci. Four of these novel loci were simultaneously discovered in our analyses (RNF207,SCN10A,5p13.3,KDELR3:Table 1B). Among the 42 remaining UK Biobank loci, only five of the lead SNVs were covered on the Exome Chip at r 2 0.8. Results from our exome RR European-ancestry meta-analyses show support for all five of these loci (P<0.01; Bonferroni-adjusted significance for five tests; Supplementary Material, Table S6). HR loci and association with other traits To provide insights into possible shared aetiologies or mechanisms of disease, we assessed association of our five unreported novel HR-SNVs (and their proxies, r 2 0.8) with other traits. Genome-wide significant phenotype–genotype associations were observed for three novel loci (Supplementary Material, Table S7). The SNV at the DLRD3 locus was associated with age of menarche. The SNV at the JAZF1 locus was highly pleiotropic, as shown by associations with several autoimmune disorders (systemic lupus erythematosus, Crohn’s disease and selective immunoglobulin A deficiency), height, type 2 diabetes and JAZF1 transcript levels in adipose tissue. The SNV at the SEC31B locus was associated with plasma palmitoleic acid levels and differential exon expression of SEC31B. Functional annotation of novel HR-SNVs and candidate genes Four of the five unreported novel HR-SNVs or their proxies (r 2 >0.8) are non-synonymous SNVs in TESK2,DALRD3,C10orf71 and SEC31B (Table 1A). The non-synonymous SNV in SEC31B (rs2295774, c.1096T>G, p.Ser332Ala) is in a conserved region of the protein, and is predicted to be damaging using three different algorithms in ANNOVAR (19). We also investigated whether the novel HR-associated SNVs or their proxies (r 2 >0.8) were associated with changes in expression levels of nearby genes (i.e. as expression quantitative trait loci, or eQTLs) in the Genotype-Tissue Expression database (GTEx) dataset (20). We observed a significant eQTL association at one novel HR locus (Supplementary Material, Table S8). Specifically, the HR increasing allele of the non-synonymous SNV at SEC31B was associated with increased levels of SEC31B in tibial nerves (P¼8.08 10 33 ), lung (P¼1.22 10 23 ), atrial appendage tissue (P¼4.56 10 11 ) and the left ventricle (P¼4.0 10 9 ), tissues which may be regarded as physiologically relevant for HR. We also observed HR loci to be significantly enriched for DNase I hypersensitive sites (DHSs; Fig. 3). We evaluated regions containing the five unreported novel HR loci and five independent secondary variants at previously reported HR loci (12) together with all 67 published HR-associated SNVs [21 loci reported from the original GWAS (12) plus 46 loci recently published from UK Biobank (17)]. Highest enrichment for DHSs in HR loci occurred within regions that are transcriptionally active in fetal heart tissue and fetal lung, as reported in the UK Biobank study. Moreover, for the first time we found significant enrichment for DHSs in human neuronal progenitor cells (derived from embryonic stem cells) and fetal muscle samples, with the inclusion of our novel loci. Pathway analyses We used Ingenuity pathway analyses to determine whether there was any increased enrichment in HR-associated pathways with the contribution of our five newly identified loci. We identified 16 significantly enriched pathways at P<110 4 . Most of these pathways are related to the cardiovascular system and involve, for example, supraventricular arrhythmias, dilated cardiomyopathy and HR (Supplementary Material, Table S9). Coding variants at HR loci The Exome Chip provides a unique opportunity to search for coding variants within known HR loci. Although GWAS analyses typically identify intron or intergenic variants, Exome Chip analysis may identify HR-associated coding variants, which would point to candidate causal genes. We considered all 67 published HR loci [21 previously reported GWAS loci (12) plus 46 recently published loci from UK Biobank (17)] and extracted all SNVs in high LD with the lead variants (r 2 0.8), tagging the same association signal, restricted to variants covered on the Exome Chip. We further filtered variants to obtain SNVs that reached exomewide significance for associations with RR-interval in our primary discovery meta-analysis, to ensure that variants have a highly significant association with the trait. Coding SNVs were identified, using the CHARGE Exome Chip annotation file. We only observed two such coding variants in two reported loci: CCDC141 and KIAA1755. The published CCDC141 coding variant was previously annotated as being non-synonymous (12), and is predicted to be damaging in our annotation (rs17362588; p.Arg935Trp). The coding SNV at KIAA1755 is the best proxy (r 2 1) for the published non-synonymous SNV (rs6127471) covered on the Exome Chip (Supplementary Material, Table S1). The original GWAS (12) had reported this signal as non-synonymous. Therefore, our Exome Chip analyses do not reveal any new evidence of likely causal coding variants at well-established HR loci. Regulatory variants at HR loci Our analyses of coding variants at all known HR loci indicated that the majority of HR-associated SNVs and the variants in high LD with them are non-coding. We thus investigated which variants could have a causal effect through regulatory 2353Human Molecular Genetics, 2017, Vol. 26, No. 12 | chromatin interactions, such as promoter–enhancer contacts. We considered all 67 published HR loci [21 previously reported GWAS loci (12) plus 46 recently published loci from UK Biobank (17)], and the five novel loci reported here. We found variants that potentially affect enhancer function using RegulomeDB (21) and found genes whose promoter regions form significant chromatin interaction with them from right ventricle Hi-C data (22). We found 64 potential target genes in 49 HR loci (4 new loci, 18 loci from the GWAS and 27 loci from the UK Biobank study; Supplementary Material, Table S10). Including these long-range interactors in the candidate causal genes list increased the significance of enrichment for many HR-related terms, such as arrhythmia and cardiac fibrillation in our IngenuityV RPathway Analysis (IPAV R; Supplementary Material, Table S11). For newly identified loci, the TESK2 promoter had a longrange interaction with the SNVs with highest regulatory potential in the locus, underlining it as a candidate. LOC441204, a gene of unknown function was found to interact with the JAZF1 locus. At the SEC31B locus, there were interactions with two genes, SCD and SLF2. At the C10orf71 locus, MAPK8 showed the most significant interaction. In the 21 loci from the previously published GWAS (12), we identified significant chromatin contacts for the regulatory SNVs of 18 loci. We found CALCRL,TTN,HTR2B,PLD1 and CHRM2 as strongest interactors at the TFPI,CCDC141,B3GNT7,FNDC3B and CHRM2 loci, respectively, out of these only CALCRL is in LD (r 2 >0.8) with the lead SNV. The previous study (12) functionally tested 31 candidate genes, they found 20 of them to have an HR phenotype in either Drosophila melanogaster or Danio rerio experiments. All five of the strongest interactor genes were amongst the 20 genes with an HR phenotype. Finally, we found 41 potential causal genes that have not been implicated by previous GWASs. A few of these genes have a cardiac function, including RAPGEF4 (18) and PIM1 (23), whereas some are involved in neuronal development and function, e.g. PBX3,NRNX3. These candidates open up new avenues that may aid our understanding of HR biology. Discussion Our meta-analysis of Exome Chip genotypes yielded five unreported novel HR loci, and one unreported independent new secondary signal, which was a low-frequency non-synonymous SNV at the previously reported KIAA1755 locus.Our data strongly supported the association of SNVs at 11 of the 12 previously reported GWAS loci that were covered on the Exome Chip. All lead SNVs at all validated novel loci are common (MAF 5%) and have similar effect sizes, which are smaller than the effect sizes for the majority of previously reported SNVs (Supplementary Material, Fig. S7). Our study did not yield any rare SNV associations with HR, indicating that much larger sample sizes will be required in future studies to have sufficient power to detect effects of any rare variants and assess their contributions to HR heritability. The same observation of the need of larger sample sizes applies to the analysis of HR loci identified within Europeans in other ancestries, where the lack of significance and concordance in the results from non-European populations is most likely due to a lack of power, as well as differences in the allele frequencies and LD patterns between Europeans and nonEuropeans. As the non-European samples were much smaller, we did not perform a comprehensive comparison across populations or a robust trans-ethnic meta-analysis. Annotation of novel HR-SNVs or their close proxies, eQTL analyses and long-range chromatin interactions in heart tissue reveal new potential causal candidate HR genes (Supplementary Material, Tables S10 and S12). At the SEC31B locus there is a predicted damaging non-synonymous variant in SEC31B, and SNVs at this locus are also significantly associated with SEC31B expression levels. Although its precise function is unknown, the SEC31B gene encodes SEC31 homolog B, a COPII coat complex component. SEC31B has been proposed to function in vesicle budding, and cargo export from the endoplasmic reticulum (24). The gene is ubiquitously expressed at low levels, but there are higher levels of expression in the cerebellum. There are 13 transcripts, and thus several predicted SEC31B proteins. The major isoform is 129 kDa, but the HR-associated nonsynonymous SNV maps to all SEC31B transcripts. There are no existing mouse models, and the predicted protein does not directly interact with other proteins or pathways currently Figure 3. Enrichment of HR-SNVs in DNase I hypersensitive sites of 299 tissue samples. The right panel shows the enrichment of the combined known and novel (all) HR-SNVs in DNase I hypersensitivity sites of 212 Roadmap Epigenome tissue samples (those with positive Z-scores). Enrichment is expressed as a Z-score compared with the distribution of 1000 matched background SNV sets. Significant enrichments are shown in red (Z-score 2.58, false discovery rate (FDR) <1.5%), enrichments below this threshold are shown in blue. The left panel shows the enrichment difference (DZscore¼Zscore all Zscore known ) for those tissue samples in which we found significant enrichment using all SNPs and that further show a positive change using all SNVs compared with only known SNVs, with increased enrichment hence due to the novel loci identified. 2354 |Human Molecular Genetics, 2017, Vol. 26, No. 12 Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, the Becket Foundation, and the Danish Diabetes Association. KORA: The KORA study was initiated and financed by the Helmholtz Zentrum Mu¨ nchen German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), LudwigMaximilians-Universit€ at, as part of LMUinnovativ. LifeLines: The LifeLines Cohort Study, and generation and management of GWAS genotype data for the LifeLines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. MESA: This research was supported by the Multi-Ethnic Study of Atherosclerosis (MESA) contracts HHSN2682015000031, N01HC-95159, N01-HC-95160, N01-HC-95161, N01HC-95162, N01HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01HC95167, N01-HC-95168, N01-HC-95169 and by grants UL1-TR000040, UL1-TR-001079, and UL1-RR-025005 from National Center Research Resources (NCRR). Funding for MESA Family was provided by National Institutes of Health grants R01-HL071205, R01-HL071051, R01-HL-071250, R01-HL-071251, R01-HL071252, R01-HL-071258, and R01-HL071259, and by UL1-RR025005 and UL1RR033176 from NCRR. Funding for MESA SHARe genotyping was provided by NHLBI Contract N02-HL-6-4278. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. NEO: The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. Dennis Mook-Kanamori is supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023). PROSPER: The PROSPER study was supported by an investigator initiated grant obtained from Bristol-Myers Squibb. Prof. Dr. J. W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Support for genotyping was provided by the seventh framework program of the European commission (grant 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). RS: The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The Exome Chip array data set was funded by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, from the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO)-sponsored Netherlands Consortium for Healthy Aging (NCHA; project nr. 050-060810); the Netherlands Organization for Scientific Research (NWO; project number 184021007) and by the Rainbow Project (RP10; Netherlands Exome Chip Project) of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRINL; www.bbmri.nl). SardiNIA: This research was supported by National Human Genome Research Institute grants HG005581, HG005552, HG006513, HG007022 and HG007089; by National Heart, Lung, and Blood Institute grant HL117626; by the Intramural Research Program of the US National Institutes of Health, National Institute on Aging, contracts N01-AG-1-2109 and HHSN271201100005C; by Sardinian Autonomous Region (L.R. 7/ 2009) grant cRP3-154 SHIP: SHIP (Study of Health in Pomerania) and SHIP-TREND both represent populationbased studies. SHIP is supported by the German Federal Ministry of Education and Research (Bundesministerium fu¨ r Bildung und Forschung (BMBF); grants 01ZZ9603, 01ZZ0103, and 01ZZ0403) and the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG); grant GR 1912/5-1). SHIP and SHIP-TREND are part of the Community Medicine Research net (CMR) of the Ernst-Moritz-Arndt University Greifswald (EMAU) which is funded by the BMBF as well as the Ministry for Education, Science and Culture and the Ministry of Labor, Equal Opportunities, and Social Affairs of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects that share data from SHIP. The EMAU is a member of the Center of Knowledge Interchange (CKI) program of the Siemens AG. SNP typing of SHIP and SHIP-TREND using the Illumina Infinium HumanExome BeadChip (version v1.0) was supported by the Federal Ministry of Education and Research (BMBF) grant 03Z1CN22. TwinsUK: This work was funded by a grant from the British Heart Foundation (PG/12/38/29615). The TwinsUK study was funded by the Wellcome Trust; European Community s Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London UHP: The Utrecht Health Project received grants from the Ministry of Health, Welfare and Sports (VWS), the University of Utrecht, the Province of Utrecht, the Dutch Organisation of Care. Research, the University Medical Centre of Utrecht, and the Dutch College of Healthcare Insurance Companies. The Exome Chip data were generated in a research project that was financially supported by Biobanking and Biomolecular resources Research Infrastructure (BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007). WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. YFS: The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Kuopio, Tampere and Turku University Hospital Medical Funds (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjo¨ Jahnsson Foundation; Signe and Ane 2361Human Molecular Genetics, 2017, Vol. 26, No. 12 | Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association. 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