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

ASSOCIATION STUDIES ARTICLE Twenty-eight genetic loci associated with ST-T-wave amplitudes of the electrocardiogram Niek Verweij1,11,23, Irene Mateo Leach1, Aaron Isaacs4,24, Dan E. Arking6, Joshua C. Bis7, Tune H. Pers10,11, Marten E. Van Den Berg5, Leo-Pekka Lyytikäinen12, Phil Barnett13, Xinchen Wang14, LifeLines Cohort Study, Elsayed Z. Soliman15, Cornelia M. Van Duijn4, Mika Kähönen16, Dirk J. Van Veldhuisen1, Jan A. Kors5, Olli T. Raitakari17,18, Claudia T. Silva4, Terho Lehtimäki12, Hans L. Hillege1,2, Joel N. Hirschhorn10,11,19, Laurie A. Boyer14, Wiek H. Van Gilst1, Alvaro Alonso20, Nona Sotoodehnia8,9, Mark Eijgelsheim5, Rudolf A. De Boer1, Paul I. W. De Bakker11,21, Lude Franke3and Pim Van Der Harst1,3,22,* 1 Department of Cardiology, 2 Trial Coordination Center and 3 Department of Genetics, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands, 4 Department of Epidemiology, Genetic Epidemiology Unit and 5 Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands, 6 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 7 Department of Medicine, Cardiovascular Health Research Unit, 8 Division of Cardiology, Department of Medicine and 9 Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA, 10 Division of Endocrinology, Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, USA, 11 Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 301 Binney Street, Cambridge, MA 02142, USA, 12 Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Tampere 33520, Finland, 13 Department of Anatomy, Embryologyand Physiology, Academic Medical Center, Universityof Amsterdam, 1105 AZ Amsterdam, The Netherlands, 14 Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA, 15 Division of Public Health Sciences, Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston Salem, NC, USA, 16 Department of Clinical Physiology, Tampere University Hospital and University of Tampere School of Medicine, Tampere 33521, Finland, 17 Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20520, Finland, 18 Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland, 19 Department of Genetics, Harvard Medical School, Boston, USA, 20 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA, 21 Department of Medical Genetics, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands, 22 Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, 3511 GC Utrecht, The Netherlands, Received: October 5, 2015. Revised: February 11, 2016. Accepted: February 17, 2016 © The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] doi: 10.1093/hmg/ddw058 Advance Access Publication Date: 8 March 2016 Association Studies Article Human Molecular Genetics, 2016, Vol. 25, No. 10 –2093 2103 2093 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from |Human Molecular Genetics, 2016, Vol. 25, No. 10 23 Cardiovascular Research Center and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts and 24 CARIM School of Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Maastricht, The Netherlands *To whom correspondence should be addressed at: Department of Cardiology, University Medical Center Groningen University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands. Tel: +31 503615340; Fax: +31 503614391; Email: [email protected] Abstract 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 ventriculararrhythmias and sudden cardiac death. We performed the first genome-wide association meta-analysis of ST-T-wave amplitudes in up to 37977 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 geneticvariation influencing cardiac repolarization and provide novel leadsfor future functional follow-up. Introduction Duration of cardiac repolarization has been previously studied by genome-wide association studies (GWAS) and led to the discovery of 35 associated loci (1). However, abnormalities of repolarizationare notlimitedtochangesinduration, butarealsocapturedby changes in amplitudes (2). Ventricular repolarization occurs in a geometricpatternacrosstheheart.Thisprocessentailsdifferences in timings of recovery across the ventricular wall and between regions of the left ventricle after ventricular depolarization. The cellular basis for the origins of the ST-segment and adjacent Twave (ST-Twave) amplitudes is stillunder debate, but is generally thought to arise as a consequence of electrical heterogeneities that exist within the ventricular myocardium (3). It is thought that the ST-T wave begins when the epicardialcells start torecover aheadofthe midwallandendocardialcells.Electricalcurrentsflow from the midmyocardial and endocardial regions toward the epicardium, resultingin a gradualrise in current, until the epicardium is fully repolarized and the currents have reached maximal intensity (T-top) (4). Continuous indices of T-wave amplitudes are not only relevant in patients with myocardial infarction (5), ischemic cardiomyopathy (6) and a malignant form of ER (7), but also in non-diseased population cohorts. In the general or healthy population, inversions ofthe ST-Twave are stronglyassociated with adverse outcome (8–10), but also minor depressions or elevations in ST-T-wave amplitudes are predictors of cardiovascular mortality (11–13)anddisease(10,14). Deviations of ST-T-wave amplitudes can be indicative of a variety of cardiac pathologies, including myocardial ischemia, ventricular hypertrophy, long-QT syndrome, ER and Brugada syndrome (2,8,12,15–17). For clinical use, phenotypes are dichotomized based on optimal sensitivity and specificity to predict worse outcome. However, there is no evidence that the underlying biology of ST-T wave is truly binary. Therefore, we hypothesized that common genetic variation involved in the biology of quantitative ST-T waves traits might provide additional biological insights into the (patho)physiological mechanisms of repolarization. GWAS have proven to be a powerful and unbiased tool to identify novel mechanisms and pathways. Here, we aim to identify key genetic loci associated with the heart’s repolarization phase during the ST-T wave of the electrocardiogram (ECG) to further advance our knowledge on biological factors regulating cardiac repolarization and thereby provide new gene targets for future study. Results Genome-wide discovery analysis and replication We performed genome-wide meta-analyses in 15 943 subjects of European descent on the ST-T wave (Supplementary Material, Table S1), with up to 2 316 136 directly genotyped and imputed autosomal single nucleotide polymorphisms (SNPs). Phenotypes for the ST-segment amplitudes and T-wave amplitudes were derived from leads used in the clinic for diagnosing Brugada syndrome (17)(Septal,V 1 and V 2 ) and early repolarization (ER) (16– 18) (lateral: I, aVL, V 5 ,V 6 and inferior: II, III, aVF). Aiming to capture additional information available on the ECG during cardiac repolarization, we also investigated the other lead recordings (Anterior: V 3 ,V 4 and lead aVR) that are presumed to have anatomical meaning (Supplementary Material, Table S2). This resulted in 10 phenotypes representing the ST-T-wave amplitude: 5 ST-segment amplitudes and 5 T-wave amplitudes (Fig. 1A). We estimated that studying 10 correlated ST-T wave phenotypes was equal to studying eight independent phenotypes (see the ‘Materials and Methods’section); therefore, the threshold of genome-wide significance was set to P<5×10 −8 /8 (P<6.25×10 −9 ). We considered a threshold of P<1×10 −6 to be suggestive of association and used this threshold to prioritize SNPs for replication. The genome-wide meta-analysis across the 10 ST-T-wave phenotypes identified a total of 36 loci to pass the suggestive threshold for association with at least one of the ST-T wave phenotypes. There was no evidence for inflation of test statistics in the final meta-analysis (Supplementary Material, Table S3) or significant heterogeneity. Of the 36 loci, there were multiple loci were associated with one or more 10 ST-T-wave phenotypes: totaling 95 genotype–phenotype associations (P<1 ×10 −6 , Supplementary Material, Table S4). Next, we performed replication testing of the 95 genotype– phenotype associations (P<1×10 −6 ) in 22 034 independent individuals derived from 7 independent cohorts (Supplementary Material, Table S5), identifying 71 genotype–phenotype associations in 28 independent loci to be replicated (P< 0.01) and become 2094 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from Human Molecular Genetics, 2016, Vol. 25, No. 10 | genome-wide significantly associated (Fig. 1B, Table 1and Supplementary Material, Table S4 and Fig. S1). The majority of loci that show multiple genotype–phenotype associations in the same locus consist of sentinel SNPs that are either the same SNP or in LD with each other, as indicated by the LD (r 2 ) column in Table 1. Conditional analysis within the discovery set revealed a secondary signal [rs9851710 conditioned on rs6801957: β(SE) per minor allele = 0.13 (0.0204), P= 2.38 × 10 −10 ]in the SCN5A/SCN10A locus to be associated with ST aVR amplitudes. This SNP has also been identified to be associated with other ST-T phenotypes (see Table 1), suggesting that there are 29 independent signals within the 28 loci. Some loci were predominantly associated with ST-segment amplitudes (locus 4, 5, 16, 26 and 28) while others were predominantly associated with T-wave amplitudes (locus 6, 18 and 29). The test statistics for every combination of genotype–phenotype association are summarized in Supplementary Material, Table S6. Other electrocardiographic traits Previous genome-wide associations have studied other ECG indices (1,19–23) such as QRS duration, PR interval, heart rate and QT interval. We intersected these previously identified loci with our ST-T-wave loci and found overlap (within 2 MB) in 13 loci. Lead SNPs from previous and current findings in 4 loci were in low LD (r 2 < 0.02), suggesting that 19 of the current loci are novel associations with the ECG (Supplementary Material, Table S7). Heritability estimates The 28 identified sentinel SNPs collectively explained between 1.6% (T-wave septal) and 5.1% (ST-segment aVR) of the observed phenotypic variance (Supplementary Material, Table S8). Familial heritability estimates in the Erasmus Rucphen Family (ERF) study varied between h 2 = 30% (T-wave inferior) and h 2 = 42% (ST septal) suggesting additional genetic variants and other mechanisms that remain to be discovered (Supplementary Material, Table S9). The variation in proportion of variance associated with covariates varied more widely; between r 2 = 0.09 (ST-segment inferior) and r 2 = 0.36 (ST-segment septal). Identification of candidate genes and pathway analyses In total, we prioritized 54 candidate genes in the 28 genetic loci (Table 1) that could play a causal role in ST-T-wave amplitudes based on several criteria, as described in the ‘Materials and Figure 1. (A) We conducted genome-wide analyses of ST-segment amplitudes at 80 ms after J-point and T-wave (top) amplitudes reflecting temporal patterns in the cardiac cycle during the repolarization phase. In total, 12 phenotypes were defined by taking the sum of the ST-T-wave amplitudes in the lateral (I, aVL, V 5 and V 6 ), inferior (II, III and aVF), septal (V 1 and V 2 ), anterior (V 3 and V 4 ) leads and lead aVR. These lead groups cover the combination of leads with presumed anatomical meaning of the heart, and those that are used in the clinic for diagnosing Brugada syndrome (17)andER(16–18). (B) Genome-wide association analyses of all ST-T wave traits identified 71 significant genotype–phenotype associations in 28 genetic loci (2 MB). The x-axis represents the chromosomal position for each SNP, which was assigned the lowest P-value across the 12 traits; the y-axis represents the −log10 of the P-value for association. Twenty-seven loci were significant for ST-T-wave amplitudes. (C) Four genes overlapping four loci are directly involved in the cardiac action potential; SNPs near SCN5A were associated with ST-segment and T-top amplitudes whereas the loci containing potassium channel-coding genes KCND3,KCNA7 and KCNB1 were primarily associated with ST-segment amplitudes. 2095 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from |Human Molecular Genetics, 2016, Vol. 25, No. 10 Table 1. All sentinel SNP associations per ST-T wave phenotype No. Region SNP Trait LD (r 2 ) with Sentinel SNP (†) in Locus EA (FRQ)/NEA β(SE) P meta NGene 1 1p36.33 rs260505 T.ant †A (0.49)/C 0.05 (0.01) 1.30E-11 37775 SKI n 2 1p36.12 rs2072944 T.sep 0.37 T (0.55)/C 0.05 (0.01) 1.70E-13 37821 LUZP1 n , KDM1A n , WNT4 e 2 1p36.12 rs2298632 ST.sep †T (0.47)/C −0.07 (0.01) 4.40E-18 37753 TCEA3 ne 3 1p31.3 rs2207792 T.aVR –A (0.66)/G −0.06 (0.01) 3.30E-13 37799 NFIA nd 3 1p31.3 rs2207792 T.inf †A (0.66)/G 0.07 (0.01) 3.00E-20 37810 NFIA nd 4 1p13.2 rs12145374 ST.ant –A (0.76)/C 0.11 (0.01) 5.90E-33 37753 KCND3 ng , FAM212B d 4 1p13.2 rs12145374 ST.sep –A (0.76)/C 0.07 (0.01) 1.20E-14 37753 KCND3 ng , FAM212B d 4 1p13.2 rs12145374 ST.aVR –A (0.76)/C −0.12 (0.01) 1.40E-31 37784 KCND3 ng , FAM212B d 4 1p13.2 rs12145374 ST.lat †A (0.76)/C 0.12 (0.01) 8.20E-34 37771 KCND3 ng , FAM212B d 4 1p13.2 rs12145374 ST.inf –A (0.76)/C 0.09 (0.01) 6.00E-18 37770 KCND3 ng , FAM212B d 5 1q22 rs10908505 ST.aVR †A (0.66)/T −0.05 (0.01) 1.70E-11 37784 MEF2D ngd 6 1q23.3 rs12567315 T.ant 0.42 A (0.19)/G −0.06 (0.01) 4.60E-11 37775 NOS1AP n 6 1q23.3 rs4657178 T.inf †T (0.24)/C −0.06 (0.01) 1.90E-11 37810 NOS1AP n 7 1q24.2 rs545833 ST.ant †T (0.26)/C −0.06 (0.01) 7.00E-18 37753 DPT n 7 1q24.2 rs511187 ST.inf 0.79 A (0.72)/G 0.06 (0.01) 2.60E-12 37770 DPT n 7 1q24.2 rs511187 ST.aVR 0.79 A (0.72)/G −0.07 (0.01) 3.10E-17 37784 DPT n 7 1q24.2 rs511187 ST.lat 0.79 A (0.72)/G 0.07 (0.01) 7.20E-18 37771 DPT n 8 2p15 rs7576036 ST.sep †T (0.54)/C 0.05 (0.01) 2.80E-10 37753 XPO1 n 9 2q33.1 rs1866666 T.aVR †T (0.49)/C −0.05 (0.01) 1.80E-11 37799 PLCL1 nced , MARS2 e , RFTN2 e , MOB4 d 10 3p25.1 rs4684185 T.aVR †T (0.35)/C −0.05 (0.01) 7.10E-10 37799 LSM3 n , TMEM43 d 11 3p22.2 rs7638909 ST.sep 0.02 T (0.76)/G −0.06 (0.01) 2.00E-11 37753 SCN5A nd , ACVR2B d 11 3p22.2 rs9851710* T.inf 0.03 A (0.66)/C 0.08 (0.01) 9.30E-19 37810 SCN10A n , SCN5A d , ACVR2B d 11 3p22.2 rs7633988 T.ant 0.02 A (0.71)/T 0.06 (0.01) 3.80E-11 37775 SCN10A n , SCN5A d , ACVR2B d 11 3p22.2 rs6783110 T.lat 0.94 A (0.42)/G 0.06 (0.01) 3.70E-12 30699 SCN10A nc , SCN5A d , ACVR2B d 11 3p22.2 rs4076737 T.aVR 0.93 T (0.57)/G 0.07 (0.01) 1.20E-20 37799 SCN10A nc , SCN5A d , ACVR2B d 11 3p22.2 rs4076737 ST.inf 0.93 T(0.57)/G −0.07 (0.01) 8.30E-20 37770 SCN10A nc , SCN5A d , ACVR2B d 11 3p22.2 rs6801957 ST.aVR †T (0.43)/C −0.08 (0.01) 1.10E-25 37784 SCN10A nc , SCN5A d , ACVR2B d 11 3p22.2 rs6801957 ST.lat –T (0.43)/C 0.06 (0.01) 8.50E-16 37771 SCN10A nc , SCN5A d , ACVR2B d 12 6p21.31 rs7756236 ST.ant †A (0.76)/G −0.06 (0.01) 1.30E-14 37753 CDKN1A ned 13 6q22.2 rs210966 ST.lat –C (0.28)/G 0.07 (0.01) 5.10E-16 37771 ROS1 n , VGLL2 d 13 6q22.2 rs210966 ST.aVR †C (0.28)/G −0.07 (0.01) 4.00E-18 37784 ROS1 n , VGLL2 d 13 6q22.2 rs172409 ST.inf 0.41 A (0.54)/C −0.05 (0.01) 6.40E-11 30708 ROS1 n , VGLL2 d 14 6q22.31 rs9388451 ST.ant †T (0.52)/C −0.06 (0.01) 2.00E-18 37753 HEY2 ned 14 6q22.31 rs9388451 T.ant –T (0.52)/C −0.05 (0.01) 3.60E-13 37775 HEY2 ned 15 8p23.1 rs1458942 ST.aVR †A (0.60)/G 0.06 (0.01) 1.10E-13 37784 TNKS nd , SGK223 d , XKR6 d , PPP1R3B d 16 8p23.1 rs7011924 ST.aVR †A (0.40)/G 0.07 (0.01) 1.30E-19 37784 DEFB136 n , NEIL2 e 16 8p23.1 rs7011924 ST.inf –A (0.40)/G −0.07 (0.01) 3.90E-19 37770 DEFB136 n , NEIL2 e 17 12p13.32 rs7953399 ST.sep 0.95 A (0.61)/G −0.04 (0.01) 2.10E-09 37753 GALNT8 n 17 12p13.32 rs2286582 ST.ant †T (0.40)/C 0.06 (0.01) 2.40E-16 37753 GALNT8 n 18 12p12.1 rs10842350 T.lat –A (0.54)/G −0.09 (0.01) 4.60E-37 37759 SOX5 ng 18 12p12.1 rs10842350 T.aVR †A (0.54)/G 0.09 (0.01) 1.30E-37 37799 SOX5 ng 19 12q24.21 rs10850409 T.sep †A (0.27)/G 0.07 (0.01) 4.30E-17 34881 TBX3 n 20 13q22.1 rs728926 T.lat †T (0.38)/C −0.07 (0.01) 2.00E-18 37759 KLF12 n 20 13q22.1 rs728926 T.aVR –T (0.38)/C 0.06 (0.01) 1.50E-16 37799 KLF12 n 21 15q26.3 rs7174918 ST.ant 0.74 T (0.29)/C −0.06 (0.01) 6.90E-16 37753 IGF1R nd 21 15q26.3 rs3803476 T.sep –A (0.37)/G −0.05 (0.01) 2.00E-14 37821 IGF1R nd 21 15q26.3 rs3803476 ST.sep †A (0.37)/G −0.07 (0.01) 1.80E-23 37753 IGF1R nd 22 16p13.3 rs7192150 T.aVR †A (0.40)/G 0.05 (0.01) 4.30E-13 37799 LMF1 ne , SOX8 gd 23 16p13.13 rs7191330 T.aVR 0.95 T (0.46)/C −0.08 (0.01) 3.10E-16 30731 LITAF n 23 16p13.13 rs735951 ST.ant –A (0.47)/G 0.06 (0.01) 9.50E-17 37753 LITAF n 23 16p13.13 rs735951 ST.aVR –A (0.47)/G −0.07 (0.01) 1.70E-18 37784 LITAF n 23 16p13.13 rs735951 ST.lat †A (0.47)/G 0.07 (0.01) 1.20E-18 37771 LITAF n 24 16q21 rs4784938 ST.lat 0.7 A (0.74)/G 0.08 (0.01) 4.80E-21 37771 GINS3 n 24 16q21 rs4784939 ST.ant –T (0.36)/C −0.09 (0.01) 4.80E-41 37753 GINS3 n 24 16q21 rs4784939 T.ant –T (0.36)/C −0.06 (0.01) 1.00E-17 37775 GINS3 n 24 16q21 rs4784939 T.sep –T (0.36)/C −0.08 (0.01) 4.10E-29 37821 GINS3 n 24 16q21 rs4784939 ST.sep †T (0.36)/C −0.11 (0.01) 3.00E-57 37753 GINS3 n 24 16q21 rs8057901 T.aVR 0.93 A (0.63)/G −0.06 (0.01) 4.90E-11 30731 NDRG4 n 24 16q21 rs8057901 ST.aVR 0.93 A (0.63)/G −0.08 (0.01) 1.60E-21 30713 NDRG4 n Table continues 2096 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from Human Molecular Genetics, 2016, Vol. 25, No. 10 | Methods’section. Thirty-four genes were prioritized based on the proximity criteria, 5 genes contained one or more nonsynonymous SNPs in high LD with a lead SNP (Supplementary Material, Table S10), 12 genes had a top-eQTL SNP in high LD with a lead SNP (Supplementary Material, Table S11), 65 genes were selected by the data-driven expression prioritized integration for complex trait (DEPICT) analysis of which 25 were within genome-wide significant loci (at false discovery rate ≤5%; Supplementary Material, Table S12) and 3 genes are based on literature mining using GRAIL (Supplementary Material, Table S13). The top five keywords retrieved from GRAIL were ‘muscle’, ‘channel’,‘transcription’,‘heart’and ‘channels’. The DEPICT method (24) was applied to subsequently offer insight into the biological pathways underlying changes in ST-Twave amplitudes of the ECG. DEPICT identified 56 reconstituted gene sets, which grouped into 9 distinct clusters. The cluster containing the most significantly enriched reconstituted gene set (‘VCL protein complex’,aprotein–protein interaction network centered on the Vinculin gene product, P=6.00×10 −6 ) represented various protein complexes likely related to Fascia Adherens, while the second most significant gene cluster ‘Abnormal Cardiovascular System Physiology’contains reconstituted gene set known to be important for cardiac repolarization (Fig. 2and Supplementary Material, Table S14). Role of regulatory DNA and 1000 genomes imputation ST-T-wave associated SNPs were 3-fold enriched in Dnase1 Hypersensitivity sites (DHSs) from human fetal heart (Fig. 2), which was significantly higher compared with 337 other cell types and tissues (P= 0.0004, Z-score statistics) in line with earlier observations (25,26). We then performed fine mapping of all 71 genome-wide significant genotype–phenotype associations (Table 1) in prevention of renal and vascular end-stage disease (Prevend) and Lifelines imputed with 1000 genomes (1000G), covering more of the known common variants in humans. We observed that 64 (of 71) associations were assigned to a different lead SNP, that 63 of these associations were more significant in the 1000G imputed data and effect estimates were higher for 58 of these associations (Supplementary Material, Table S16). To facilitate future functional experiments toward the identification of causal variants and their underlying biological mechanisms, we prioritized potential causal SNPs using the probabilistic framework of Probabilistic Annotation INTegratOR (PAINTOR) (27). PAINTOR determined the significance of each annotation (Fig. 2) and used the five most significant annotations (conservation scores, DHS of fetal heart, enhancers of the left ventricle and Tbx3 bound regions) to prioritize potential causal SNPs in the 28 loci (27). This yielded 315 SNPs in the 99% confidence set and 96 SNPs in the 95% confidence set (Supplementary Material, Table S17). Discussion Amplitudes of the ST segment and T wave are important traits that are associated with abnormal heart rhythm, conduction disturbances and ventricular arrhythmias. In this study, we performed the first GWAS of ST-T-wave amplitudes of the ECG in up to 37 977 individuals. Traits were defined according to the combination of ECG leads presumed to have anatomical meaning or used in the clinic for diagnosing Brugada syndrome and early repolarization (16–18). ST-T-wave traits were moderately heritable (h 2 =30–42%) while the proportion of variance associated with covariates (gender, body mass index and age) varied considerably (9–36%) strongly supporting the genetic background of these traits. We identified 28 genome-wide significant loci for ST-T-wave amplitudes and a set of 54 candidate genes. A recent GWAS on Brugada syndrome revealed the association of two loci that are shared by our ST-T-wave loci (28). One of these signals in the SCN5A/SCN10A locus is in complete LD with our sentinel SNP (rs10428132, r 2 = 0.96 with rs6801957) and rs9388451 near HEY2, is a ST-T-wave sentinel SNP; suggesting that Brugada syndrome susceptibility loci share a common genetic background with ST-T-wave traits. One of the strongest associated GWAS signals for all amplitudes of the ST segment was in the KCND3 gene (locus 4). KCND3 encodes the Kv4.3 α-subunit that conducts the cardiac fast transient outward K+ current (I To,f ). This current is prominent in Phase 1 of the action potential Table 1. Continued No. Region SNP Trait LD (r 2 ) with Sentinel SNP (†) in Locus EA (FRQ)/NEA β(SE) P meta NGene 24 16q21 rs8057901 ST.inf 0.93 A (0.63)/G 0.07 (0.01) 1.30E-16 30708 NDRG4 n 24 16q21 rs9940062 T.inf 0.94 T (0.68)/G 0.07 (0.01) 1.20E-10 30738 NDRG4 n 25 18q12.1 rs8083566 ST.lat –A (0.07)/C −0.09 (0.01) 4.30E-10 37771 CDH2 nd 25 18q12.1 rs8083566 ST.inf –A (0.07)/C −0.11 (0.02) 1.70E-12 37770 CDH2 nd 25 18q12.1 rs8083566 ST.aVR †A (0.07)/C 0.12 (0.01) 3.60E-15 37784 CDH2 nd 26 19q13.33 rs11673003 ST.lat †A (0.90)/G −0.09 (0.01) 5.10E-14 37771 KCNA7 nc , NTF4 n , GYS1 d , HRC d 27 20q11.22 rs6087666 ST.inf †A (0.20)/G −0.07 (0.01) 3.10E-15 37770 TRPC4AP ne , EDEM2 ce , MYH7B cd ,NCOA d 27 20q11.22 rs6088738 ST.aVR 0.55 A (0.21)/G 0.06 (0.01) 5.90E-13 37784 EDEM2 ne , PROCR e , MYH7B cd , NCOA d 28 20q13.13 rs11907908 ST.ant 0.83 T (0.07)/C −0.12 (0.01) 8.00E-18 37753 ZNFX1 (−AS1) n , STAU1 d 28 20q13.13 rs6019750 ST.lat 1 C (0.93)/G 0.13 (0.01) 4.30E-23 37771 KCNB1 n ,STAU1 d 28 20q13.13 rs2202261 ST.inf –A (0.93)/G 0.12 (0.01) 4.40E-18 37770 KCNB1 n ,STAU1 d 28 20q13.13 rs2202261 ST.aVR †A (0.93)/G −0.14 (0.01) 5.60E-26 37784 KCNB1 n ,STAU1 d There are 71 genome-wide significant genotype–phenotype associations clustered in 28 genetic loci (based on a 2 MB locus definition, see the ‘Materials and Methods’ section). Gene superscripts indicate the method of identification: n, nearest gene or nearby gene (within 10 kb of the SNP); g, Grail’d, Depict; e, eQTL; c, coding SNP (non-synonymous). rs9851710 denoted by an asterisk (*) indicates that this SNP is an independent, secondary, association for ST.aVR (rs9851710 conditioned on rs6801957: β(SE) per minor allele = 0.13 (0.0204), P= 2.38 × 10 −10 ). 2097 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from |Human Molecular Genetics, 2016, Vol. 25, No. 10 and contributes to the ‘notch’of the cardiomyocyte’saction potential (2). Mutations in KCND3 have been implicated with increased risk of sudden cardiac death (29,30), as well as mutations in SCN5A (2)andSCN10A (31). We specifically identified loci containing potassium channels KCND3 (locus 4), KCNB1 (locus 28) and KCNA7 (locus 26) to be predominantly associated with amplitudes of the ST segment and not with the T wave, suggesting that these potassium channels are activated during the ST Figure 2. (A) DEPICT identified 56 significantly enriched gene-sets relevant for ST-T-wave amplitudes. (B) SNPs were significantly more enriched in DHSs of fetal heart tissue (n= 12) compared with other tissue and cells (n= 337), across the full range of P-values of the discovery meta-analyses (genome wide), suggesting that functionality of regulatory DNA elements may underlie some of the associations. (Cand D) Next, we performed a meta-analysis of the 28 identified ST-T wave loci using 1000 Genomes imputed data, for this 1000G variants needed to be in LD r 2 > 0.1 with the HapMap sentinel SNP. Subsequent prioritization of potential causal annotations in these loci also suggested that regions of DHS in fetal heart are possibly underlying the associations as well as cardiac transcription factors, conserved regions (exonic), active and weak enhancers. While regions that are transcribed, tightly packed (heterochromatin) or function as promoters in the ventricles may be less important for the biological mechanisms of genetic variants that are associated with ST-T-wave amplitudes. Subtle difference are present between the ventricles and fetal heart which could suggest that promoters that overlap potential causal ST-T wave SNPs may be active in the fetal heart but repressed in the ventricles. Percentages between parentheses indicate the amount of SNPs in the 28 loci overlapping with the annotation. Conservation (GERP and 29 mammals), DHS of fetal heart, enhancers of the left ventricle and Tbx3 bound regions were used to prioritize potential causal SNPs in the 28 loci. 2098 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from Human Molecular Genetics, 2016, Vol. 25, No. 10 | segment of the repolarization phase and may have clinical relevance (16,32,33). Future studies are required to examine the potential role of the other ST-T-waveSNPs for increased arrhythmogenic risk and sudden death through an altered cardiac repolarization. In addition to HEY2, a transcriptional regulator of cardiac electrical function in the right ventricular outflow tract (28), we identified six loci containing genes with strong evidence of being directly involved in cardiac tissue development via transcriptional regulatory pathways. Locus 5 contains a myocyte enhancer factor-2 (MEF2D) important for cardiac muscle morphogenesis and heart looping (34,35). Loci 18 and 22 contain two transcription factors from the SOX family, SOX5 and SOX8. SOX5 is important for a functioning heart and involved in correct Wnt signaling (36). This locus has previously been associated with the PR interval and resting heart rate, but the identified variant in the current study is in low LD (r 2 = 0.01), suggesting multiple molecular-genetic mechanisms at this locus to influence heart function. Transcripts of SOX8 are concentrated in the subendothelial mesenchym of the whole outflow tract (37) in which N-Cadherin (38)(encodedbyCDH2, locus 25), the transcription factor Tbx3 (39)(TBX3, locus 19) and WNT4 (40) (specific for the endocardial endothelial cushion development, part of the Wnt pathway) in locus 2 also play essential developmental roles, among other cardiac cell lineages. Other compelling cardiac genes are: SKI, for which mutations (1p36 deletionand Sprintzen–Goldberg syndrome) are characterized by heart defects, brain abnormalities and muscle tone (hypotonia) in infancy (41); TMEM43 (locus 10) in which mutations cause arrhythmogenic right ventricular cardiomyopathy and Emery–Dreifuss muscular dystrophy; the sarcomeric gene MYH7B and miR499 (locus 27), which regulates a multitude of cardiac mRNA and microRNAs and promotes ventricular specification; and GALNT1, a glycosyltransferases that is required for normal heart valve development and cardiac function by regulating the extracellular matrix and altering conserved signaling pathways that regulate cell proliferation duringheart development (42). We also identified a number of less well-characterized genes, which should be explored in further research. Five of the 28 ST-T-wave sentinel SNPs were in LD with SNPs associated with QT duration (1), suggesting that ST-T-wave amplitudes provides additional information on cardiac repolarization. The known functions of the candidate genes and our pathway analyses suggest that the ST-T-wave amplitudes are influenced and regulated by a wide variety of molecular mechanisms, including ion-channels, structural proteins and cardiac transcription factors. However, the finding that protein complexes related to Fascia Adherens are most enriched in our pathway analyses hints that some of the biology underlying ST-T waves is less well captured in well-established pathways and better represented by data-driven (and not manually curated) pathways. Ventricular repolarization is a complex process. To capture this process, we chose to apply a selection of composite ECG parameters with the aim of gaining more insight into the biological processes underlying the ST-T wave. These surface ECG parameters are not specific enough to identify the exact anatomical region of the heart; e.g. the septal leads (V 1 and V 2 ) may also include aspects of the right ventricular wall activity. However, there are no data available of more precise measurements such as could be derived from more sophisticated surface ECG equipment or intra-cardiac ECG measurements. In summary, we present a large number of genome-wide significant loci robustly associated with cardiac repolarization parameters, some with compelling biological basis for their association and a numberof loci not previously implicated in cardiac function. The identified loci and selected genes have the potential to aid future studies that are focused on risk stratification or on molecular mechanisms underlying cardiac repolarization and diseases of cardiac repolarization; to facilitate these studies, we have made our results (including the genome-wide association) publicly available. Materials and Methods Study populations The discovery phase included participants of the Prevend study and the Lifelines study cohort. Both are community-based cohort studies from the northern part of the Netherlands. The replication phase included participants of the Atherosclerosis Risk in Communities study (ARIC), Cardiovascular Health Study (CHS), Young Finns Study (YFS), ERF study, Rotterdam study I, II and III (RS I, II and III). Detailed descriptions of each cohort are given in the supplementary material. Characteristics of participants are summarized in Supplementary Material, Tables S1 and S5. Detailed Information on genotyping methods, quality control of SNPs, imputation and statistical analysis for each cohort is summarized in Supplementary Material, Table S18. Phenotype modeling Summing correlated variables when expecting that genetic variants are associated to multiple of these variables will increase the power for detection; with genetic variant (Snp), and traits (Ampitude i ), consider the correlation: Corr(Snp, Ampitude 1 + Ampitude 2 )=Corr(Snp, Ampitude 1 )+Corr(Snp, Ampitude 2 )+2×Corr (Ampitude 1 , Ampitude 2 )>Corr(Snp, Ampitude 1 )+Corr(Snp, Ampitude 2 ), hence if Corr(Ampitude 1 , Ampitude 2 )>0, the traits Ampitude i measure the same latent variable (L). Therefore, phenotypes for the STsegment amplitudes 80 ms after J-point and T-wave amplitudes were defined by taking the sum of the lateral (I, aVL, V 5 and V 6 ), inferior (II, III and aVF), septal (V 1 and V 2 ), anterior (V 3 and V 4 )leads and lead aVR. Individuals were excluded for bundlebranch blockor QRS duration> 120 ms, atrial fibrillation, flutter, history of myocardial infarction or electronic pacemaker rhythm and when available, heart failure and ECG altering medication. Also participants with extreme measurements (more than ±4SD from mean) were excluded on a per phenotype basis. Statistical analyses To control for multiple testing of the 10 phenotypes while accounting for the correlation between them, we performed an eigenvalue decomposition of the correlation matrix (Supplementary Material, Table S2) of the phenotypes. The variance of the eigenvalues [Var (λ obs ) = 2.36] was used to estimate the effective number of independent phenotypes tested (43). Our findings indicate that studying 10-related ST-T-wave traits is equivalent to analysis of 8 independent phenotypes. We, therefore, adopt α a =5×10 −8 /8 = 6.25 × 10 −9 as threshold for declaring genomewide significance in order to correct for the effective number of independent phenotypes studied. Residuals of ST-T-wave amplitudes were calculated using general linear regression models to adjust for age, gender and body mass index and standardized to a mean of zero and a standard deviation of one. GWAS analyses in Prevend and Lifelines of 2 316 136 genotyped or imputed SNPs (Supplementary Material, Table S18) were performed on the standardized residuals using an additive genetic model in PLINK (v.1.07). Phenotypes were 2099 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from |Human Molecular Genetics, 2016, Vol. 25, No. 10 normally distributed (Supplementary Material, Fig. S2). Test statistics from each cohort were then corrected for their respective genomic control inflation factor to adjust for residual population sub-structure and meta-analyzed using the inverse-variance method. SNPs with minor allele frequency < 1% (weighted average across cohorts) were removed. Variants were considered to be independent if the pair-wise LD (r 2 )was<0.1andifthey were separated by at least 1 MB; this was defined a ‘locus’. We selected one sentinel SNP (the most-significantly associated SNP in a locus) for each genotype–phenotype combination. For conditional analyses, we repeated the primary association analysis for each trait while conditioning on the trait-specific genome-wide significant sentinel SNPs by adding the SNP genotypes as covariates. Association results for each study were again combined by inverse variance weighting. Replication of the significant genotype–phenotype associations (P<6.25×10 −9 ) and associations that did not exceed this threshold, but were suggestive (6.25 × 10 −9 <P<1 × 10 −6 ), was performed in the RS I, II and III, ERF, ARIC, CHS and YFS and combined using fixed-effects meta-analysis by inverse variance weighting (Supplementary Material, Table S18). The pre-specified statistical significance threshold for heterogeneity by Cochran’s Q(P het )wasP het < 0.0007 to account for multiple testing. Inverse variance weighting was used to determine meta-P-values for combined discovery and replication data. An association was considered replicated if the direction of effect was concordant with discovery, replication P< 0.01 and meta-P< 6.25 × 10 −9 . Heritability estimates were calculated in the ERF study using the ‘tdist’function in the SOLAR software, including gender, age and body mass index as covariates. Data-driven expression prioritized integration for complex traits DEPICT systematically identifies the most likely causal gene at a given associated locus, tests gene sets for enrichment in associated SNPs, and identifies tissues and cell types in which genes from associated loci are highly expressed [see Pers et al. (24) for a detailed description]. For this work, we ran DEPICT on 140 independently associated loci (association P<10 −5 ;PLINK parameters, ‘–clump-p1 1e-5 –clump-kb 500 –clump-r2 0.05’)resulting in 103 independent, autosomal DEPICT loci containing 363 genes (loci overlapping with the major histocompatibility complex region are by default excluded in DEPICT). We have extended the locus definition used in DEPICT (LD r 2 >0.5)with 100 kb at either side of the loci, because several genes that may be important for cardiac repolarization were outside the default DEPICT locus boundaries (e.g. SCN5A,TMEM43 and VGLL2). The gene set enrichment results for this slightly extended locus definition were similar to the results based on the default locus definition used in DEPICT (Supplementary Material, Table S19). Identification of candidate genes We prioritized candidate genes based on nearby genes: we considered the nearest gene and any other gene located within 10 kb of the sentinel SNP. Coding variants:foridentification of coding variants and LD calculations, we used the 1000G Project data set (March 2012 release) in the European populations. We considered genes that harbor non-synonymous SNPs in LD with the ST-T-wave SNPs at r 2 > 0.8. GRAIL analyses: we carried out a literature analysis by employing the GRAIL text-mining algorithm, a statistical tool that identifies subsets of genes with known functional interrelationships based on PubMed abstracts. We carried out the analysis using the 2006 data set to avoid confounding by subsequent GWAS discovery. The DEPICT method (see above), and expression QTL (eQTL) analyses in cis, we search for eQTLs (sentinel SNPs or SNPs in LD, r 2 >0.8,HapMapr27)in an eQTL data set that was compiled from the summary statistics of various studies and tissues (see Supplementary Material, Table S11). We only considered eQTLs that were in LD (r 2 > 0.8, HapMap r27) with the sentinel SNP and reached a P-value cut-off of at least P< 0.05/(28 loci × 17 eQTL studies). Imputation using 1000G Genome positions from the Prevend and Lifelines genotypes were converted from hg18 to hg19 using the UCSC LiftOver tool. Genome-wide genotype imputation was performed with SHAPEIT (v2) and IMPUTE2 (v2.3.0) using the complete 1000 Genomes v3, March 2012 haplotypes Phase I integrated variant set release as reference panel. Functional information We overlapped SNPs with data from the ENCODE project (44)and Roadmap Epigenomics Program (45), conservation across mammals, various cardiac transcription factor measured by ChIP-Seq and contiguous annotations of the human fetal heart,left ventricle and right ventricle as determined by ChromHMM (46)(Supplementary Material). Prioritization of potentially causal variants and enrichment of DNA elements For the prioritization of genetic variants for the future functional follow-up and insight of the underlying DNA elements that might be relevant for causal ST-T-wave amplitude variants, we employed the PAINTOR framework (27). In short, this method allows us to prioritize genetic variants in each of the 28 significant associated loci by an integrating strength of association and annotations of functional DNA elements to estimate the probability for each variant to be causal, but also investigate which annotations are potentially causal. Supplementary Material Supplementary Material is available at HMG online. Acknowledgements We thank Behrooz Alizadeh, Annemieke Boesjes, Marcel Bruinenberg, Noortje Festen, Ilja Nolte, Lude Franke, Mitra Valimohammadi for their help in creating the GWAS database, and Rob Bieringa, Joost Keers, René Oostergo, Rosalie Visser, Judith Vonk for their work related to data-collection and validation. The authors are grateful to the study participants, the staff from the LifeLines Cohort Study and Medical Biobank Northern Netherlands, and the participating general practitioners and pharmacists. LifeLines Scientific Protocol Preparation: Rudolf de Boer, Hans Hillege, Melanie van der Klauw, Gerjan Navis, Hans Ormel, Dirkje Postma, Judith Rosmalen, Joris Slaets, Ronald Stolk, Bruce Wolffenbuttel; LifeLines GWAS Working Group: Behrooz Alizadeh, Marike Boezen, Marcel Bruinenberg, Noortje Festen, Lude Franke, Pim van der Harst, Gerjan Navis, Dirkje Postma, Harold Snieder, Cisca Wijmenga and Bruce Wolffenbuttel. The authors acknowledge the services of the LifeLines Cohort Study, the contributing research centres delivering data to LifeLines and all the study participants. 2100 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from Human Molecular Genetics, 2016, Vol. 25, No. 10 | Conflict of Interest statement. None declared. Funding PREVEND: PREVEND genetics is supported by the Dutch Kidney Foundation (grant E033), the EU Project grant GENECURE (FP-6 LSHM CT 2006 037697), the National Institutes of Health (grant 2R01LM010098), The Netherlands Organization for Health Research and Development (NWO-Groot grant 175.010.2007.006, NWO VENI grant 916.761.70, ZonMw grant 90.700.441) and the Dutch Inter University Cardiology Institute Netherlands (ICIN). N.V. is supported by the Netherlands Heart Foundation (grant NHS2010B280). 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. L.F. is supported by the Netherlands Organization for Scientific Research (NWO VENI grant 916.10.135) and a Horizon Breakthrough grant from the Netherlands Genomics Initiative (grant 92519031. The research leading to these results has received funding from the European Community’s Health Seventh Framework Programme (FP7/ 2007-2013) under grant agreement 259867. RS I, II and III: Akzo Nobel, Alzheimer’s Association, Astra Pharmaceutial N.V., AstraZeneca, Bayer AG, Blinden-penning Foundation, Amsterdam, Brain Foundation of the Netherlands, Bristol-Myers Squibb, Center of Medical SystemsBiology (CMSB), Dutch Diabetes Research Foundation, Dutch Kidney Foundation, Dutch Arthritis Association, Elise Mathilde Foundation, Maarn, Erasmus Medical Center, Erasmus University Rotterdam, European Commission, Foundation for Helping the Blind, The Hague, Foundation for the Ophthalmic Diseased, Rotterdam, Foundation G. Ph. Verhagen, General Electric Healthcare, Glaxo Smith Kline, International Foundation Alzheimer’s Research, Inspectorate for Healthcare, Janivo Foundation, K.F. Hein Foundation, Merck Sharp and Dohme, Haarlem, Municipality of City of Rotterdam, National Epilepsy Fund, National Health Fundraising Foundation, National Institute on Aging, NIH, Bethesda, MD, USA, National Society for the Blind and Visually Impaired (LSBS), Netherlands Foundation for the Blind and Visually Handicapped, Netherlands Heart Foundation, Netherlands InstituteforHealth Sciences(Nihes),NetherlandsOphthalmicResearch Institute, Netherlands Organisation for Health Research and Development (ZonMw),NetherlandsOrganisation forScientificResearch (NWO), Netherlands Societyforthe Prevention ofBlindness, Netherlands Thrombosis Foundation, Novo Nordisk, Numico Research B.V., OOG Foundation, The Hague, N.V. Organon, Oxagen, Optimix Foundation, Amsterdam, Physicotherapeutic Institute, Prinses Beatrix Foundation, Procterand Gamble, ResearchInstitute for Diseases in the Elderly (RIDE), Rotterdam Foundation for Ophthalmic Research, Rotterdam Foundation for the Interests of the Blind, St Laurens Institute, Rotterdam, Topcon Europe B.V., Trust Fund Erasmus University Rotterdam, Unilever, Van Leeuwen Van Lignac Foundation, Rotterdam. ERF: The ERF study was supported by grants from the Netherlands Organization for ScientificResearch (NWO; Pioneer grant), Erasmus Medical Center, the Centre for Medical Systems Biology (CMSB) and the Netherlands Kidney Foundation. We are grateful to all patients and their relatives, general practitioners and neurologists for their contributions and to P. Veraart for her help in genealogy, Jeannette Vergeer for the supervision of the laboratory work and P. Snijders for his help in data collection. ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402 and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by grant number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. CHS: Cardiovascular Health Study: this CHS research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086 and NHLBI grants HL080295, HL087652, HL105756, HL103612 and HL120393 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org/. 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. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Young Finns Study has been financially supported by the Academy of Finland: grants 286284 (T.L.), 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 for T.L.); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation of Cardiovascular Research (T.L.); Finnish Cultural Foundation; Tampere Tuberculosis Foundation (T.L.); Emil Aaltonen Foundation (T.L.) and Yrjö Jahnsson Foundation (T.L.). Funding to pay the Open Access publication charges for this article was provided by the Marie Sklodowska-Curie GF grant (661395) from N. Verweij. References 1. Arking, D.E., Pulit, S.L., Crotti, L., van der Harst, P., Munroe, P.B., Koopmann, T.T., Sotoodehnia, N., Rossin, E.J., Morley, M., Wang, X. et al. (2014) Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization. Nat. Genet.,46, 826–836. 2. Nerbonne, J.M. and Kass, R.S. (2005) Molecular physiology of cardiac repolarization. Physiol. Rev.,85, 1205–1253. 3. Antzelevitch, C. (2006) Cellular basis for the repolarization waves of the ECG. Ann. N. Y. Acad. Sci.,1080, 268–281. 4. Goldberger, A.L. and Mirvis, D.M. (2011) In Bonow, R.O., Mann, D.L., Zipes, D.P. and Libby, P. (eds), Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. Elsevier Health Sciences, in press, US, Philadelphia, pp. 126–167. 5. Hasan, M.A., Abbott, D. and Baumert, M. (2013) Beat-to-beat QT interval variability and T-wave amplitude in patients with myocardial infarction. Physiol. Meas.,34, 1075–1083. 6. Al-Zaiti, S.S., Fallavollita, J.A., Canty, J.M. and Carey, M.G. (2015) The prognostic value of discordant T waves in lead aVR: a 2101 at Tampere University Library. Department of Health Sciences on October 19, 2016http://hmg.oxfordjournals.org/Downloaded from