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Subtle killers and sudden death: Genetic variants modulating ventricular fibrillation in the setting of myocardial infarction

Pazoki, R.

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Download date:04 Oct 2021 RahaRahaRaha PazokiPazokiPazoki GeneticGenetic Variants Variants ModulatingModulating VentricularVentricular FibrillationFibrillation inin thethe SettingSetting ofof MyocardialMyocardial InfarctionInfarction Subtle Killers Killers Killers Subtle Subtle & Sudden& Sudden Death Death

Raha Pazoki Subtle Killers and Sudden Death Subtle Killers and Sudden Death

Genetic Variants Modulating Ventricular Fibrillation in the Setting of Myocardial Infarction

Raha Pazoki The research described in this thesis was carried out in the department of Clinical Epidemiology, Biostatistics and Bioinformatics & The Heart Center of the Academic Medical Center, Amsterdam.

Financial support for printing this thesis was kindly provided by the university of Amsterdam.

ISBN: 978-94-6169-631-1

Layout and printing: Optima Grafische Communicatie, Rotterdam, The Netherlands

© R. Pazoki, 2015 All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronical or mechanical, including photocopy, recording, or otherwise, without the prior written permission from the author. Subtle Killers and Sudden Death: Genetic Variants Modulating Ventricular Fibrillation in the Setting of Myocardial Infarction

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor

aan de Universiteit van Amsterdam

op gezag van de Rector Magnificus

prof. dr. D.C. van den Boom

ten overstaan van een door het college voor promoties ingestelde

commissie, in het openbaar te verdedigen in de Agnietenkapel

op vrijdag 20 maart 2015, te 12:00 uur

door Raha Pazoki geboren te Bijar, Iran Promotiecommissie:

Promotoren: Prof. dr. C.R. Bezzina Prof. dr. A.A.M. Wilde Co-promotor: Dr. ir. M.W.T. Tanck

Overige leden: Dr. L. Crotti Prof. dr. O.H. Franco Dr. J.R. de Groot Prof. dr. J.W. Jukema Prof. dr. M.M.A.M. Mannens Prof. dr. A.H. Zwinderman

Faculteit der Geneeskunde

Financial support by the Dutch Heart Foundation for the publication of this thesis is gratefully acknowledged

The research described in this thesis was supported by grants of the Dutch Heart Foundation (DHF-2007B202, DHF-2001D019, and DHF-2003T302) To Abbas and Nick

And my parents, brother, and sister

CONTENTS

1. Outline of the thesis 9

2. Genetic basis of ventricular arrhythmias 17

3. Complex inheritance for susceptibility to sudden cardiac death 33

4. Genome-wide association study identifies a susceptibility locus at 21q21 55 for ventricular fibrillation in acute myocardial infarction Supplementary material

5. Multiple genomic approaches for identification of genetic modifiers of 73 ventricular fibrillation risk in the setting of acute myocardial infarction in the AGNES study

6. SNPs identified as modulators of ECG traits in the general population do 113 not markedly affect ECG traits during acute myocardial infarction nor ventricular fibrillation risk in this condition. Supplementary material

7. SNPs modulating inflammatory biomarkers as intermediate phenotype 131 of susceptibility to ventricular fibrillation in the setting of myocardial infarction

8. Summary and conclusions 149

Nederlandse samenvatting 157

PhD portfolio 163

Publications 165

Contributing authors 168

Curriculum Vitae 173

Acknowledgement 175

C hapter 1 Outline of the thesis

Outline of the thesis 11

Sudden cardiac death

Sudden cardiac death (SCD) is defined as a sudden unexpected death of cardiac origin occurring within an hour after onset of symptoms. SCD is a major contributor Chapter 1 to mortality in the general population, accounting for almost 20% of total mortality and 50% of cardiac mortality in industrialized countries 1,2. Although it may strike at all ages, it primarily occurs in adults. Here the most common arrhythmia causing SCD is ventricular fibrillation (VF) which most commonly arises in cardiac pathologies that are associated with coronary artery disease (CAD). Sequela of CAD which present with VF-predisposing substrate include first acute myocardial infarction (MI) and structural cardiac alterations such as myocardial dilatation and fibrosis (scar formation) in those with prior MI. Considering the size of the problem of SCD, substantial efforts have been made to develop risk-stratification methods aimed at identifying individuals at increased risk of SCD. Thus far, risk stratification has mostly revolved around clinical indicators for increased risk. For example, reduced left ventricular ejection fraction (LVEF) is useful to identify risk of SCD in patients with diagnosed ischemic heart disease. However, while LVEF as a clinical indicator of SCD risk has been widely implemented in clinical practice, and has been clearly successful, it is not universally applicable since 40-50% of SCDs occur in individuals without previously recognized heart disease 3-5. Clearly other means of risk stratification are required to predict risk of SCD in this large group of patients to prevent such catastrophic events. Among possible risk stratifies are genetic factors. This approach has become possible because of the current understanding of the genome and the development of new cost-effective methodologies for genetic testing at the molecular level.

Genetic factors predisposing to Sudden Cardiac Death

The search for genetic modifiers of SCD risk is supported by the consistent identification of family history of SCD as an independent risk factor of SCD in epidemiological studies. Evidence for existence of a heritable component for risk of SCD was first found in two studies published in the late 1990s 6,7. In the Paris Prospective I study 7 a long-term (> 20 years) cohort study of > 7,000 middle-aged French men, parental history of SCD was a predisposing factor for SCD. The relative risk of SCD increased by 1.8-fold if one parent had SCD, and by 9.4-fold if both parents had SCD 7. In a case–control study published by Friedlander and colleagues, a positive family history of early-onset SCD (aged < 65 years) was associated with a 2.7-fold increased risk of SCD after adjustment for other risk factors and family history of MI 6. Following the 12 Chapter 1

first evidence from these two initial studies, our group initiated the AGNES study A( r- rhythmia Genetics in the NEtherlandS Study) specifically to analyze risk factors for VF in patients with a first acute ST-segment elevation MI (STEMI). Our group identified SCD among first-degree relatives as an independent risk factor for VF (Odds ratio: 2.72) 8 by comparing STEMI patients who also had VF, with STEMI patients who did not have VF. This finding rationalized the search for genetic determinants of VF risk in the AGNES case-control set which formed the major aim of this thesis. By focusing on patients presenting with this specific phenotype (VF in the setting of STEMI), we aimed at enriching the homogeneity of pathways leading to VF across the patients studied in order to favor genetic locus identification. Establishment of the AGNES case-control set coincided with the advances in genetic technology and emergence of techniques that allowed for the large (genome-wide) assessment of genetic variants. The genetic architecture of risk of VF in the setting of a first acute MI is expected to be complex as assumed for other multi-factorial disorders affecting older individuals.

Aim of thesis and outline

The aim of this thesis is to identify genetic risk factor for the occurrence of VF in the setting of an acute STEMI. Studies were conducted primarily in the AGNES case- control set. The first section of the thesis consists of two reviewsChapters ( 2 and 3) that in- troduce the topic and review the literature pertaining to the genetic perspectives of SCD/VF. In Chapter 4 and Chapter 5 of the thesis, I employed an approach entailing genome-wide association study (GWAS) of common genetic variants for the identification of genetic loci predisposing to VF. The GWAS method, which was enabled by the mapping of genetic variation in the general population within the HapMap 9 and the 1000 Genomes projects 10, has in recent years been broadly applied to uncover genetic determinants of risk in a wide range of complex diseases and traits 11. GWAS has provided a large number of new loci associated with complex traits and diseases and has thus provided new biologic insight into their mechanisms 12. GWAS tests millions of common single nucleotide polymorphisms (SNPs; variants present in more than 5% of the population) to identify those that are associated with the disease or trait of interest. The GWAS conducted in the AGNES case-control set inChapter 4 constitutes the first published GWAS on the phenotype of SCD/VF. Chapter 5 describes our second GWAS effort in the AGNES study, conducted on an extended number of AGNES cases and controls. In this chapter, alongside GWAS, we undertook complementary strategies in which we harnessed available data on expression quantitative trait loci Outline of the thesis 13

(eQTL) in human heart and also conducted pathway-based analysis. The eQTL approach is based on the fact that most loci identified by GWAS reside in non-coding regions of the genome 13 and are therefore expected to exert their effect on the associated trait through regulation of gene expression 14. We therefore examined the effects Chapter 1 of SNPs (uncovered in AGNES GWAS) on the expression level of genes located within 1 Mb spanning the VF-associated lead-SNP, with the aim of identifying possible causal genes at the associating loci. In another approach in this chapter, we selected 911 independent candidate SNPs previously identified as eQTLs in human heart and tested them for association with VF. The aim here was to prioritize SNPs that are already recognized to be putatively functional by virtue of their effect on gene expression in human heart, and to investigate whether they may increase risk of VF. In Chapter 5, in an attempt to approach the exact biological mechanism, we also performed a pathway analysis to investigate whether the genes in the vicinity of SNPs found to associate with VF through GWAS in AGNES are enriched in any pre-existing biological pathway. In this chapter, we additionally extracted SNPs previously associated with SCD in published candidate gene studies and assessed their association with VF. Chapter 6 and 7 in this thesis are dedicated to gene discovery through intermediate phenotype approach. Phenotypes that are considered risk factors for SCD are often referred to as “intermediate phenotypes” of SCD and genetic modifiers of these phenotypes are thus expected to also determine the risk of SCD 15. The use of the intermediate phenotype approach in the dissection of genetics of VF may increase the chances of genetic locus discovery as it considers candidate SNPs in pathways that are biologically plausible for VF. An intermediate phenotype that is highly relevant for VF is heart rate and electrocardiographic (ECG) indices of conduction and repolarization. These quantifiable traits are heritable 15 and several studies have linked them to the risk of VF/SCD 16. In the last 8 years a number of large-scale GWAS studies have identified genetic modifiers of these parameters in large samples of the general population. In Chapter 6 of this thesis we investigated the role of candidate SNPs previously found to be associated with heart rate and ECG indices of cardiac conduction and repolarization and assessed them for possible modulatory effects on risk of VF in the setting of acute MI in the AGNES case-control set. Another phenotype that we regarded as an intermediate phenotype for VF was inflammation. Several lines of evidence from population based, case-control, and experimental studies support the hypothesis that a pro-inflammatory status may increase risk of arrhythmia and SCD. In Chapter 7 of this thesis, we described a second candidate SNP approach that we undertook by selecting SNPs previously found to associate with inflammatory biomarkers in the general population. The overarching aim of this thesis is thus the identification of new genetic loci that predispose to VF in the setting of acute MI. 14 Chapter 1

References

1. de Vreede-Swagemakers, J. J. et al. Out-of-hospital cardiac arrest in the 1990’s: a population- based study in the Maastricht area on incidence, characteristics and survival. J Am Coll Cardiol 30, 1500-1505 (1997). 2. Zipes, D. P. et al. ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death). J Am Coll Cardiol 48, e247-346, doi:10.1016/j.jacc.2006.07.010 (2006). 3. Chugh, S. S. Early identification of risk factors for sudden cardiac death. Nature reviews. Cardiology 7, 318-326, doi:10.1038/nrcardio.2010.52 (2010). 4. Chugh, S. S. et al. Current burden of sudden cardiac death: multiple source surveillance versus retrospective death certificate-based review in a large U.S. community. J Am Coll Cardiol 44, 1268-1275, doi:10.1016/j.jacc.2004.06.029 (2004). 5. Chugh, S. S. et al. Epidemiology of sudden cardiac death: clinical and research implications. Progress in cardiovascular diseases 51, 213-228, doi:10.1016/j.pcad.2008.06.003 (2008). 6. Friedlander, Y. et al. Sudden death and myocardial infarction in first degree relatives as predictors of primary cardiac arrest. Atherosclerosis 162, 211-216 (2002). 7. Jouven, X., Desnos, M., Guerot, C. & Ducimetiere, P. Predicting sudden death in the population: the Paris Prospective Study I. Circulation 99, 1978-1983 (1999). 8. Dekker, L. R. et al. Familial sudden death is an important risk factor for primary ventricular fibrillation: a case-control study in acute myocardial infarction patients. Circulation 114, 1140-1145, doi:10.1161/circulationaha.105.606145 (2006). 9. The International HapMap Project. Nature 426, 789-796, doi:10.1038/nature02168 (2003). 10. Siva, N. 1000 Genomes project. Nature biotechnology 26, 256, doi:10.1038/nbt0308-256b (2008). 11. Frazer, K. A., Murray, S. S., Schork, N. J. & Topol, E. J. Human genetic variation and its contribution to complex traits. Nature reviews. Genetics 10, 241-251, doi:10.1038/nrg2554 (2009). 12. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. American journal of human genetics 90, 7-24, doi:10.1016/j.ajhg.2011.11.029 (2012). 13. Cookson, W., Liang, L., Abecasis, G., Moffatt, M. & Lathrop, M. Mapping complex disease traits with global gene expression. Nature reviews. Genetics 10, 184-194, doi:10.1038/ nrg2537 (2009). 14. Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS genetics 6, e1000888, doi:10.1371/journal.pgen.1000888 (2010). 15. Kolder, I. C., Tanck, M. W. & Bezzina, C. R. Common genetic variation modulating cardiac ECG parameters and susceptibility to sudden cardiac death. Journal of molecular and cellular cardiology 52, 620-629, doi:10.1016/j.yjmcc.2011.12.014 (2012). 16. Straus, S. M. et al. Prolonged QTc interval and risk of sudden cardiac death in a population of older adults. J Am Coll Cardiol 47, 362-367, doi:10.1016/j.jacc.2005.08.067 (2006).

C hapter 2 Genetic basis of ventricular arrhythmias

Curr Cardiovasc Risk Rep. 2010 Nov;4(6):454-460.

Pazoki R, Wilde AA, Bezzina CR. 18 Chapter 2

Abstract

Sudden cardiac death (SCD) is a leading cause of total and cardiovascular mortality, and ventricular fibrillation is the underlying arrhythmia in the majority of cases. In the young, where the incidence of SCD is low, a great proportion of SCDs occur in the context of inherited disorders such as cardiomyopathy or primary electrical disease, where a monogenic hereditary component is a strong determinant of risk. Marked ad- vancement has been made over the past15 years in the understanding of the genetic basis of the primary electrical disorders, and this has had an enormous impact on the management of these patients. At older ages, the great majority of SCDs occur in the context of acute myocardial ischemia and infarction. Although epidemiologic studies have shown that heritable factors also determine risk in these cases, inheritance is likely complex and multifactorial, and progress in understanding the genetic and molecular mechanisms that determine susceptibility to these arrhythmias, affecting a greater proportion of the population, has been very limited. We review the most recent insights gained into the genetic basis of both the monogenic and the more complex ventricular arrhythmias. Genetic basis of ventricular arrhythmias 19

Introduction

Sudden cardiac death (SCD) accounts for 15% to 20% of all natural deaths in adults, and up to 50% of all cardiovascular deaths in the United States and Western Europe 1. Ventricular fibrillation is the most common underlying cause 2. Ventricular arrhythmias present with various symptoms including palpitations, chest pain, or syncope and may occur in various pathologic settings such as cardiomyopathies, congenital heart disease, inflammatory myocardial disease, as well as in the structurally normal Chapter 2 heart. SCD is rare among young individuals. In individuals younger than 40 years of age, incidence of sudden death is approximately 1.3 to 8.5 per 100,000 person-years, and the vast majority of cases are considered to be SCD 3,4. In this age group, a great proportion of SCDs occur in the context of potentially inherited disease such as cardiomyopathy and primary electrical disease 5,6, and a strong monogenic hereditary component is thought to determine risk of sudden death in most cases 7,8. In contrast, at older ages, the great majority of SCDs occur in the context of acute myocardial ischemia/infarction 9. Epidemiologic studies have shown that heritable factors also determine risk in these cases 10-12. We review recent insights gained into the genetics of ventricular arrhythmias. We provide a brief update on novel genes uncovered for the monogenic primary electrical disorders and highlight results of recent genome-wide association studies that have uncovered novel genetic loci that may contribute to risk of SCD in the general population.

Monogenic primary arrhythmia syndromes

Molecular genetic research into the monogenic arrhythmia syndromes over the past 15 years has had an enormous impact on the management of patients with these disorders 13. They are now known to be caused primarily by mutations in genes encoding ion channel subunits or their regulatory components. The increased understanding of the genetic basis of these disorders has provided great insight into many aspects of these disease entities, including their pathophysiology, and prognosis, and optimal treatment options for the specific molecular genetic subtypes. Perhaps the most significant impact has been on the management of patients with the congenital long QT syndrome (LQTS). This disorder is among the most prevalent of the monogenic arrhythmia syndromes and has facilitated large studies looking into genotype–phenotype relationships. Such studies have uncovered triggers specific to the different genetic subtypes, providing opportunities for lifestyle adjustments and an explana- tion as to why β-blockers might not be equally effective in all genetic subtypes (i.e. 20 Chapter 2

they are most effective in LQTS1 subtype and of high to moderate effectiveness in the LQTS2 subtype, in accordance with the adrenergic triggers in these subtypes, and of uncertain effectiveness in LQTS3 14,15. Knowledge of the genetic subtype may therefore be critical to the cardiologist in assessing optimal treatment strategies. The availability of a genetic test has also provided an opportunity for pre-symptomatic diagnosis of patients with these disorders. Active cascade screening within families is generally advised after the identification of an affected proband because it leads to timely pre-symptomatic treatment of relatives also carrying the mutation, which may be life-saving, whereas relatives not carrying the genetic defect can be reassured 16. However, genetic testing is not always straightforward, and a proper interpretation of the genetic test result is critical considering the implications of mutation identification. The occurrence of a mutation within a likely gene does not auto- matically imply genetic causation. Analysis for co-segregation with the disorder in an extended pedigree is often helpful in establishing causality of an identified mutation, but such pedigrees are not always available. Distinguishing pathogenic mutations from innocuous and clinically silent gene variants remains a major challenge in many instances. Mutation type (e.g. nonsense vs. missense), mutation location in channel subdomain (e.g. pore vs. transmembrane vs. linker), and ethnic-specific background rates have been shown to be critical factors in predicting the pathogenicity of novel mutations 17. For instance, in LQTS, a novel mutation (an unclassified variant) identified in the transmembrane regions of the SCN5A-encoded sodium channel is much more likely to be pathogenic than when located in an inter domain linker 17. Thus, genetic tests must be viewed as probabilistic tests to be interpreted along with other diagnostic tests. In the research setting, the identification of novel genes underlying the monogenic rhythm disorders may be rather challenging. Ideally for such studies, one has access to genetic material from multiple clinically affected individuals within an extended pedigree with the possibility of establishing genetic linkage to a particular chromosomal segment followed by the sequencing of candidate genes for identification of the causal defect. However, availability of an extended pedigree is rarely the case for these disorders in particular because individuals within a pedigree might have already died suddenly of arrhythmia before DNA collection is possible. Furthermore, as is the case for most Mendelian monogenic disorders, these disorders display reduced penetrance (not all mutation carriers have clinical signs of the disorder) and variable clinical expression 18,19, which further complicates the process of gene identification. One group of rhythm disorders for which it is exceptionally difficult to track the genetic substrate is idiopathic ventricular fibrillation (VF), defined as spontaneous VF in the absence of identifiable structural or electrical heart disease 20. The diagnosis of this disorder, which accounts for as many as 10% of sudden deaths, mainly in the young, Genetic basis of ventricular arrhythmias 21 cannot be made on the basis of electrocardiogram (ECG) abnormalities but can only be made after the occurrence of (aborted) SCD. Many affected patients die young, thus leaving only small number of patients and material available for analysis. Using an alternative approach, searching for shared ancestral haplotypes (chromosomal segments) among three distantly related pedigrees with the disorder originating from one region in the Netherlands, our group recently implicated the DPP6 gene in this disorder 21. DPP6 encodes dipeptidyl-peptidase 6, a putative subunit of the transient 22 outward current potassium-channel complex (ITo) . Although, expression of DPP6 Chapter 2 was increased in cardiac biopsies of carriers of the risk haplotype 21, more research is required to establish the pathophysiologic mechanism of DPP6-related idiopathic VF and how such a genetic defect is silent on ECG. Genetic testing is crucial in these patients because it is the only means of identifying those at risk of developing potentially fatal arrhythmia allowing for timely pre-symptomatic implantation of an implantable cardioverter defibrillator. A disorder that has attracted much attention in recent months is the Early Repo- larization Syndrome. For a long time, early repolarization, consisting of an elevation of the QRS-ST junction (J point), QRS notching or slurring (J wave), and a tall sym- metric T wave, was considered to be a benign feature 23. Three case-controls studies however recently demonstrated that a pattern of early repolarization in the inferior and/or lateral leads was more frequent in patients with idiopathic VF compared with controls 24-26. A community-based study in Finland also showed that an early repolarization pattern in the inferior leads of the ECG is associated with an increased risk of death from cardiac causes during very long-term follow-up in middle-aged individuals 27. So far, only one mutation in the KCNJ8 gene, encoding a subunit of the KATP channel, has been reported for the disorder in a single patient 28. It is expected that unraveling the genetic basis of this newly recognized disease entity will aid in the understanding of the mechanism for this ECG pattern and related arrhythmia.

Arrhythmias with a more complex inheritance pattern

In recent years, interest in the genetics of cardiac arrhythmias has shifted to include the search for those genetic factors influencing risk of SCD in the general adult population. In adults, the overwhelming majority (~80%) of SCDs is caused by the sequela of coronary artery disease, namely myocardial ischemia or acute myocardial infarction (MI) 9,29, where SCD is the first clinically identified expression of heart disease in up to one half of cases 30. In the late 1990s, two important studies advanced the concept that even in these more common arrhythmias, a genetic component also contributes to risk. In a population based case-control study, a family history of MI or SCD, after cor- 22 Chapter 2

rection for all common risk factors, was positively associated with the risk of SCD 10. In the Paris Prospective Study, selectively performed in men, among whom 118 cases of sudden death occurred, parental sudden death was found to be an independent risk factor for sudden death 11. A study from our group investigated this concept further in the Arrhythmia Genetics in the NEtherlands Study (AGNES), conducted specifically in patients with a first acute MI 12. In this study we demonstrated that familial sudden death occurred significantly more frequently among patients with a first MI complicated by VF (cases) compared with patients presenting with a first MI but without VF (controls) 12. However, in contrast to the significant advances made in the understanding of the genetics of the monogenic arrhythmia syndromes, progress in understanding the genetic and molecular mechanisms that determine susceptibility to these common arrhythmias, affecting a much greater proportion of the population, has been limited 31-33. An important reason for this slow progress is the fact that most victims die outside of the hospital, making it extremely difficult to include patients with appropriately consented DNA samples for genetic studies. In an effort to identify common genetic variation within the genome contributing to risk of VF during acute MI, our group recently conducted a genome-wide association study for VF in the AGNES population 34. By comparing the frequency of common single nucleotide polymorphisms (SNPs) spread throughout the 22 autosomes between MI patients with VF (cases) and MI patients without VF (controls) from the AGNES study, we identified a region on chromosome 21 in the vicinity of the CXADR gene associated with susceptibility to VF. CXADR encodes the Coxsackie and adenovirus receptor (CAR) protein, which has a long-recognized role as viral receptor in the pathogenesis of viral myocarditis and its sequela of dilated cardiomyopathy 35,36. Interestingly, the frequency of active Coxsackie B virus infection has been reported to be high in a group of MI patients who died suddenly 37. Two studies have reported a physiologic role for the receptor in localization of connexin 45 at the intercalated disks of the cardiomyocytes in the atrioventricular node, and a role in conduction of the cardiac impulse within this cardiac compartment 38,39. Thus, CXADR can be considered a very relevant candidate gene for the association detected at this locus. As a complementary approach to establishing direct bridges between genetic variation and arrhythmia susceptibility, researchers in the field have also undertaken a strategy whereby SNPs are first analyzed for effects on heart rate and other ECG indices of conduction and repolarization. This approach stems from the knowledge that these ECG measures constitute heritable traits 46,47 and that their extremes (too long or too short) influence risk of arrhythmia, both in the general population 48,49 as well in specific disease groups 50, which makes ECG indices potentially relevant intermediate phenotypes. Until now, the ECG parameter that is most extensively studied in this way has been the QT interval, reflecting ventricular repolarization. These studies have Genetic basis of ventricular arrhythmias 23

Table 1 | Candidate genes identified in genome-wide association studies for heart rate and electrocardiographic indices of conduction (PR, QRS) and repolarization (QTc) QT interval PR interval QRS interval Heart rate ATP1B1 40 ARHGAP24 41,42 CAV1 41 MYH6 41 GINS3 43 CAV1/CAV2 41,42 CDKN1N 41 KCNE1 43 MEIS1 42 DKK1 41 KCNH2 40,43 MYH6 41 SCN10A 44 KCNJ2 40 NKX2-5 42 TBX5 41 Chapter 2 KCNQ1 40,43 SCN10A 41,42,44 LIG3-RFFL 43 SCN5A 42 LITAF 40,43 SOX5 42 NDRG4 40,43 TBX5-TBX3 41,42 NOS1AP 40,43,45 WNT1L 42 PLN 40,43 RNF207 40,43 SCN5A 40,43 Only candidate genes at loci displaying association at genome-wide significance (P < 5 × 10−8) are listed. uncovered numerous genetic loci and SNPs modulating this measure (Table 1), the most significant of which have consistently been SNPs in and around the NOS1AP gene, encoding the nitric oxide synthase 1 (neuronal) adaptor protein 40,43,45. Although the exact mechanism for the effect of SNPs inNOS1AP on ventricular repolarization is still unknown, one must realize that this gene was previously unlinked to cardiac electrophysiology, underscoring the power of the genome-wide association approach to highlight unknown pathways that could represent an important means to ultimately unravel mechanisms of disease and development of new therapies. Crotti et al. 51 and Tomás et al. 52 went on to test whether SNPs in NOS1AP could modulate disease expression (QTc and arrhythmia) in the congenital LQTS, where pronounced inter-individual variability in QTc-prolongation and occurrence of arrhythmia exists 19. The first study investigated South African families segregating a founder mutation in KCNQ1 (all affected individuals were carriers of the A341V mutation in KCNQ1) and demonstrated that carriers of risk alleles at NOS1AP SNP sites had longer QTc and an increased risk of cardiac arrest and SCD. Carrying out such studies in the setting of families with a founder mutation is attractive because it circumvents the inter-individual variability in disease manifestations that could otherwise arise as a consequence of the different primary genetic defects among study participants. In the second study Tomás et al. 52 who carried out association studies of NOS1AP SNPs in a large LQT patient cohort (901 patients from 520 families) with mutations in different genes, subsequently demonstrated that the effects of SNPs 24 Chapter 2

in NOS1AP are also detectable in a more diverse LQT patient cohort. These authors proposed that genotyping NOS1AP SNPs could in the future become useful in refining risk stratification in this disorder. However, the situation remains rather complex. One NOS1AP SNP (rs10494366) associated with risk of cardiac events in the LQTS study by Tomás et al. 52 was not associated with risk of SCD in community based populations 53. Furthermore, in the latter study, another SNP (rs12567209), which was not correlated with QT interval, was still associated with SCD, suggesting that there may be multiple functional elements within the NOS1AP region, some of which may modulate risk of arrhythmia independently from the repolarization process. Research is still required to resolve the underlying issue. Following successful identification of several loci impacting the QT interval, investigators also turned their attention to other ECG parameters, namely heart rate 41, PR-interval (a measure of the time required for the electrical impulse to travel from the sinus node, through the atria and atrioventricular node to the Purkinje fibers) 41,42,44, and QRS duration (a measure of the time required for depolarization of the ventricles) 41. These studies presented compelling evidence that SNPs in the SCN5A and SCN10A genes modulate cardiac conduction. This finding is not surprising with respect to SNPs in SCN5A, which encodes the major sodium channel in the heart (Nav1.5). Sodium ion influx through this channel mediates the rapid upstroke of the cardiac action potential and is therefore a critical mediator of cardiac conduction, and mutations in SCN5A cause cardiac conduction disease 54. On the other hand, the implication of the SCN10A gene (located within < 100 kb-pairs of SCN5A on chromosome 3) as a modulator of cardiac conduction was a very novel finding. Note that SCN10A variant presented to be independent of SCN5A variant. SCN10A encodes the sodium channel Nav1.8, expressed primarily in the peripheral sensory nervous system and to a lesser extent in the heart. It has been hypothesized that amino acid–altering SNPs in SCN10A might be responsible for the observed effect on cardiac conduction 42,44. The exact mechanism however is still unknown. The A-allele at SNP rs6795970, which is associated with slower conduction in the general population, appeared protective against risk of VF in the setting of acute MI in the AGNES study 44. These genome-wide association studies have also uncovered SNPs in or near genes encoding transcription factors involved in cardiac development, including NKX2-5 which encodes the cardiac-specific homeobox transcription factor Nkx2.5. Mutations in this gene were previously linked to atrial septal defect with conduction defects, tetralogy of Fallot, and high-degree atrioventricular block 55. Another locus identified as impacting on conduction is in the region of the TBX3 and TBX5 genes, which encode T-box–containing transcription factors important for cardiac conduction system formation in the developing heart 56,57. Mutations in TBX5 cause Holt-Oram syndrome, which includes atrial and ventricular septal defects, conduction disease, and occasion- Genetic basis of ventricular arrhythmias 25 ally atrial fibrillation 58, whereas mutations in TBX3 cause ulnar-mammary syndrome, with limb, mammary, tooth, genital, and cardiac abnormalities 59. Furthermore, as for genome-wide association studies for QT interval, genome-wide association studies for heart rate and conduction indices have also uncovered genes previously unlinked to cardiac electrical function (e.g. the association of ARHGAP24, which encodes a Rho-GTPase activating protein, with PR, and the association of MYH6, encoding the alpha heavy chain subunit of cardiac myosin, with heart rate and PR interval), thereby potentially illuminating novel pathways. Chapter 2 The wealth of information being generated by genome-wide association studies is likely to trigger researchers around the world into following new avenues for research into novel pathways that could be involved in cardiac electrical function. However, one should realize that much work still needs to be done before we fully understand the exact molecular mechanisms whereby the identified genetic loci contribute to inter-individual variability in the related trait and ultimately to whether they also impact on risk of arrhythmia. Perhaps the most crucial of these issues relates to the fact that although these studies have uncovered regions on chromosomes that are linked to the traits of interest, some of which harbor highly plausible candidate genes, in basically all instances mentioned above, unequivocal evidence for genes mediating the observed effects is lacking, let alone knowledge of which functional genetic variants underlie these effects. Another fact that needs mentioning is that the variants identified as modulators of ECG parameters, as expected in complex genetic traits, are associated with very small effect sizes. For instance, effect sizes for alleles impacting on QTc range from 1 to 3 ms per allele 40,43. Even in aggregate, the identified genetic variants still explain only a very small percentage of the variance in these traits. In a meta-analysis by Pfeufer et al. 40 including 16,678 individuals, association signals from 10 loci found to be associated with QTc-interval (at genome-wide statistical significance) in aggregate explained only 3.3% of the variance in QTc. In another meta-analysis performed by Newton- Cheh et al. 46 comprising 13,685 individuals, in aggregate, 5.4% to 6.5% of the variation in QT interval was explained by 14 independent variants at 10 loci. Larger and larger association studies, including more study subjects, will be required to provide unequivocal evidence for novel genetic associations and complementary strategies to uncover the genetic underpinnings of these complex traits are obviously necessary. For instance, it is generally hypothesized that other genetic variants, such as rare variants not detected in genome-wide association studies that likely have stronger influences on these ECG indices, may be present. 26 Chapter 2

Conclusions

The identification of the genetic defects underlying the monogenic arrhythmia syndromes is important because it provides insight into important aspects of these disease entities, including prognosis and optimal treatment options for the specific molecular genetic subtypes and allows for pre-symptomatic identification and treatment of patients at risk. Recent genome-wide association studies have generated remarkable insight into chromosomal regions and genes that impact on cardiac electrical activity. Such strategies have now started to be applied to the identification of genes impacting on arrhythmia risk in the general population. These studies are likely to provide insight into pathways determining risk of arrhythmia in the general population.

Acknowledgment

The authors are supported by research grants from the Netherlands Heart Founda- tion (2007B202), the Inter- University Cardiology Institute of the Netherlands (ICIN, 06102), the Center for Translational Molecular Medicine of the Netherlands (CTMM- COHFAR) and the Leducq Foundation (05CVD). Connie R. Bezzina is an Established Investigator of the Netherlands Heart Foundation (2005T024). Genetic basis of ventricular arrhythmias 27

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C hapter 3 Complex inheritance for susceptibility to sudden cardiac death

Curr Pharm Des. 2013;19(39):6864-72.

Raha Pazoki, Michael W.T. Tanck, Arthur A.M. Wilde, Connie R. Bezzina 34 Chapter 3

Abstract

Sudden cardiac death (SCD) from ventricular fibrillation during myocardial infarction is a leading cause of total and cardiovascular mortality. It has a multifactorial, complex nature and aggregates in families, implicating the involvement of heritable factors in the determination of risk. During the last few years, genome-wide association studies have uncovered common genetic variants modulating risk of SCD. We here review the current insight on genetic determinants of SCD in the community and describe the genome-wide association approaches undertaken thus far in uncovering genetic determinants of SCD risk. Complex inheritance for susceptibility to sudden cardiac death 35

Introduction

Sudden death (SD) is defined as natural, unexpected death within an hour of the onset of symptoms and can be due to cardiac or non-cardiac causes (e.g. stroke or aneurysm) 1. The annual incidence of SD in the general population (20-75 years of age) is 1 per 1000 individuals, accounting for 18.5% of total mortality 1,2. SD is a rare event in young individuals 3,4; the incidence is 1.3-8.5 per 100,000 individuals younger than 40 years of age 1,3. The major proportion of SD is considered to be sudden cardiac death (SCD). In Europe and North America, SCD accounts for 50-100 deaths each year per 100,000 individuals in the general population 5,6. The incidence of SCD is 1 in 100,000 individuals in the young population (age < 35-40) and 1 in 1,000 in the older segment Chapter 3 (age > 35-40) of the population 6. Overall mortality of patients with sudden cardiac arrest, even in regions of the world with an advanced first responder system and resuscitation methodology, is more than 95% 6,7 Survival to hospital discharge for those cases who present with witnessed ventricular fibrillation (VF) or ventricular tachycardia (VT) is around 29% 8. SCD accounts for a major proportion of all cardiovascular deaths (up to 50%) 9. In fact, SCD is the fatal manifestation of a group of cardiac arrhythmias including VT, VF or severe bradyarrhythmia 7. The most common electrophysiological disorder underlying SCD is VF (Figure 1), which subsequently and ultimately degenerates to asystole 10. Ventricular arrhythmia present with various symptoms including palpitations, chest pain, or syncope and can lead to SCD within a few minutes 7. The underlying cardiac disorder is often not diagnosed before the event and in 40-50% of all SCD cases no symptoms precede the death 7,8,11. SCD may occur in a broad range of pathological settings such as coronary artery disease (CAD), cardiomyopathy, congenital heart disease, inflammatory myocardial 3

Figure 1 | Three lead ECG (II, I and III) displaying acute ischemia-related VF. The patient concerned Figure 1 | Three-lead ECG (II, I and III) displaying acute ischemia-related VF. The patient con- has an acute inferior wall MI (ST elevation in leads II and III). An R-on-T extrasystole in the third ST-T segmentcerned hasgives an rise acute to immediate inferior wall VF . MI (ST elevation in leads II and III). An R-on-T extrasystole in the third ST-T segment gives rise to immediate VF. SCD may occur in a broad range of pathological settings such as coronary artery disease

(CAD), cardiomyopathy, congenital heart disease, inflammatory myocardial disease, or even, sudden unexplained death, in the structurally normal heart. However, the overwhelming majority (~80%) 11 of SCDs occur in older individuals and are largely caused by sequela of CAD, namely in the setting of acute or previous myocardial ischemia / infarction and heart failure 12. Acute myocardial infarction (MI) is the most important source for ventricular arrhythmias 13 including VF, VT, and ventricular premature depolarization.

Several factors have been mentioned to be associated with SCD in the setting of CAD

(Figure 2). These factors include left ventricular ejection fraction (LVEF), left ventricular hypertrophy, prolonged QTc interval, Diabetes, and hypertension 7. Up until now, LVEF is the only factor that is used for risk stratification of SCD. Patients with LVEF of less than 30–35% are considered to be at high risk for SCD and eligible for prophylactic implantation of a cardiac defibrillator 7,14.

Complex Inheritance for Susceptibility to Sudden Cardiac Death | 35

36 Chapter 3

• HTN • LVEF <30–35% • Diabetes • LVH • Prolonged QT interval • Aortic stenosis Non-modifiable Modifiable factors factors

SCD

• Structural disorders of the heart (HCM, ARVC, etc.) Mendelian Complex • CVD/CAD/ MI • Primary electrical disorders Inheritance Inheritance • Family history of SCD (LQTs,BrS, SSS, etc.)

Figure 2 | Predisposing factors to sudden cardiac death. SCD, sudden cardiac death; LVH, left ventricular hypertrophy; LVEF, left ventricular ejection fraction; HTN, hypertension; HCM, hyper- throphic cardiomyopathy; ARVC, arrhythmogenic right ventricular cardiomyopathy; LQTS, long QT syndrome; BrS, Brugada syndrome; SSS, sick sinus syndrome; CVD, cardiovascular diseases; CAD, coronary artery disease; MI, myocardial infarction.

disease, or even, sudden unexplained death, in the structurally normal heart. How- ever, the overwhelming majority (~80%) 11 of SCDs occur in older individuals and are largely caused by sequela of CAD, namely in the setting of acute or previous myocardial ischemia / infarction and heart failure 12. Acute myocardial infarction (MI) is the most important source for ventricular arrhythmias 13 including VF, VT, and ventricular premature depolarization. Several factors have been mentioned to be associated with SCD in the setting of CAD (Figure 2). Th ese factors include left ventricular ejection fraction (LVEF), left ventricular hypertrophy, prolonged QTc interval, Diabetes, and hypertension 7. Up until now, LVEF is the only factor that is used for risk stratifi cation of SCD. Patients with LVEF of less than 30–35% are considered to be at high risk for SCD and eligible for prophylactic implantation of a cardiac defi brillator7,14 .

Mechanism of arrhythmia in the setting of acute myocardial ischemia

In animal models, ventricular arrhythmias mainly occur during the fi rst 30 minutes of the experimental ischemia and in two distinct periods 13. Th e early phase (phase 1a) occurs from 2 to 10 minutes after the coronary occlusion and the delayed phase (phase 1b) occurs between the minutes 12 and 30. Th ere is no information whether this bimodal distribution of ischemia-induced arrhythmias during the fi rst 30 min- Complex inheritance for susceptibility to sudden cardiac death 37 utes of occlusion also occurs in human, but it is considered likely to be with a retarded time course. It has been proposed that during phase 1a, two different mechanisms are involved in development of arrhythmias. A reentrant excitation causes VF and VT while a non-reentrant mechanism causes premature ventricular depolarizations which subsequently lead to generation of reentry 13. Different factors influence the induction of arrhythmias during the acute phase of myocardial ischemia. One such factor is the current flow across the ischemic and normal myocardial tissue (i.e. the ‘injury current’). The membrane potential is different for ischemic myocardium (less negative) compared with the normal cells. This intercellular electrical imbalance causes current to flow between the depolarized and polarized muscle fibers and induce abnormal activity 13. Another important factor which facilitates VF during acute regional ischemia is Chapter 3 the cardiac subendocardial Purkinje fibers 15. During the early phase of acute myocardial ischemia, the electrophysiological properties of the cardiac Purkinje fibers are changed 13. In patients with MI and rapid polymorphic ventricular tachycardia, premature depolarizations and arrhythmias could be suppressed by ablation of Purkinje regions 16. It is suggested that early or delayed afterdepolarizations, sub threshold depolarizations caused by injury currents and the stretch caused by loss of contractility and systolic bulging of the ischemic myocardium could initiate reentry in the Purkinje network 13. During acute myocardial ischemia, the mechanical stretch due to left ventricular malfunction reduces the maximal diastolic potential of the Purkinje fibers and therefore, abnormal activity is induced or accelerated. Stretch can additionally trigger early or delayed afterdepolarizations. The ischemia and injury in the anterior wall seems to play a more prominent role than in the inferior wall in producing stretch and initiation of arrhythmias. Ischemia in the anterior wall causes a greater excursion than ischemia in the inferior wall and thus produces more stretch in the fibers neighboring the ischemic border 13. Heart rate is another important parameter for arrhythmia induction during the acute phase of MI. Changes in heart rate precede the onset of arrhythmia and patients with increased heart rate tend to have higher risk to develop VF during myocardial ischemia. Sequentially, increased heart rate gives rise to severity of ischemic damage and the size of ischemic area 13. Lastly, reperfusion of blood into the ischemic area can trigger the so called reperfusion arrhythmias. The likelihood for reperfusion arrhythmias increases from 5 to 30 minutes after the occlusion in animal models and decreases after 30 minutes. This is due to the fact that arrhythmias must occur in viable cells. During a sudden reperfusion, a very rapid restoration of action potentials occurs in the ischemic myocardium. Electrolytes and metabolites wash out of the ischemic area and change electrophysiological properties of the normal cells close to the ischemic area. This remarkable 38 Chapter 3

inhomogeneity in the ischemic myocardium and the border increases the likelihood of reentry and subsequent fibrillation 13.

Genetic susceptibility to sudden cardiac death

A strong Mendelian hereditary component determines risk of SCD in young individuals 17,18. The genetic underpinnings of the Mendelian disorders associated with increased risk of SCD have been brought into focus during the last two decades leading to an enormous impact on patient care. This Mendelian form of SCD can be caused by underlying electrical (primary electrical disorders) or structural (cardiomyopathies) disturbances (Figure 2). The primary electrical disorders (including long QT syndrome, Brugada syndrome and sick sinus syndrome) are often associated with characteristic abnormal features on the electrocardiogram (ECG). Arrhythmias in these disorders are caused by abnormal intrinsic cardiac electrophysiological properties mainly due to mutations in genes encoding ion channel subunits 19. The most common of these disorders is probably the Long QT Syndrome associated with prolonged cardiomyocyte repolarization. Mutations in a total of 13 genes have so far been linked to this disorder. The genetic underpinnings of the Brugada syndrome on the other hand remain largely unknown. Mutations in the SCN5A gene account for about 20% of probands and while other genes have been implicated in the disorder, between them they account for less than 5% of cases 20. Structural alteration of the myocardium forms the substrate for arrhythmia in the cardiomyopathies, the most common of which is hypertrophic cardiomyopathy, in most cases caused by mutations in MYH7 gene, encoding the β-myosin heavy chain, and in MYBPC3 gene, encoding cardiac myosin-binding protein C (cMyBP‑C) 21. Another rare cardiomyopathy associated with SCD in the young and in athletes is arrhythmogenic right ventricular cardiomyopathy (ARVC). The pathological hallmark of ARVC consists of progressive loss of cardiac myocytes that are replaced by fibrofatty tissue, leading to electric insta- bility. Five of the 8 causative genes thus far associated with the disorder encode major components of the cardiac desmosomes 19. Epidemiological studies have demonstrated that heritable factors also determine variability in susceptibility to SCD in the old age groups (age > 35-40) 22-24 where CAD is the most common underlying pathology. In a population-based case-control study by Friedlander et al. 22, a family history of MI or SCD was associated with more than 50% increased risk of SCD (relative risk [RR] = 1.57). This risk estimation was independent of all common risk factors. A few years later, the same investigators re-analyzed their case-control data. This time, they differentiated between family history of MI and family history of SCD and demonstrated that a positive family history of early-onset SCD Complex inheritance for susceptibility to sudden cardiac death 39

(< 65 years of age) independent of parental history of MI contributed to a greater risk of SCD (RR = 2.7) 25. Similarly, in the Paris Prospective Study, selectively performed in men, among whom 118 cases of sudden death occurred, SCD of one parent was found to be an independent risk factor for sudden death (RR = 1.8). There was a remarkable increase in risk if both parents had a history of SCD (RR = 9.4) 23. Although these initial studies highlighted a heritable component in the determination of risk of SCD in the community, they did not differentiate between the different cardiac pathologies in which SCD occurred. Our group has investigated familial aggregation of SCD in the setting of acute myocardial ischemia in the Arrhythmia Genetics in the Netherlands Study (AGNES), conducted specifically in patients with a first acute ST- segment elevation MI 24. This case-control study demonstrated that patients with familial SCD were at higher risk of developing VF during a first acute MI Chapter 3 (odds ratio [OR] = 2.72) 24. Within the same year, a study from Finland uncovered similar findings. Kaikkonen et al. 26 showed that patients with SCD in the setting of a first acute MI were more likely to have a family history of SCD compared with survivors of acute MI (OR = 1.6) or compared with healthy controls (OR = 2.2). The presence of SCD in two or more first degree relatives increased risk of SCD remarkably compared with survivors of MI (OR = 3.3) or compared with healthy controls (OR = 11.3). These studies clearly demonstrate familial aggregation of SCD pointing to a heritable component in the determination of risk. Importantly, since these studies control for the presence of acute MI, they show that genetic susceptibility specific for the arrhythmic event exists and is unrelated to, for example, the genetic susceptibility to the hemodynamic consequences of the MI itself. However, despite this evidence, advances in uncovering genetic factors underlying susceptibility to arrhythmias in the setting of complex cardiac pathologies, affecting a much greater proportion of the population, has been very limited 27-29. To date only a few genetic variants modulating risk in this setting have been uncovered. We here review the current insight on genetic determinants of SCD in the community and mainly describe genome-wide association approaches undertaken thus far in uncovering the genetic determinants of SCD risk.

Genome-wide association studies (GWAS)

GWAS test millions of common variants (variants present in more than 5% of the population) across the genome to identify those variants that are significantly associated with complex diseases 30. Conducting genetic studies for SCD in large scale is extremely challenging. Most cases of cardiac arrest occur early after the onset of symptoms. The majority of such 40 Chapter 3

early onset SCD cases that do not undergo resuscitation or are not witnessed are consequently not included in the analysis 11. DNA of individuals with the SCD phenotype is difficult to obtain due to the natural course of the event. Moreover, ethical issues related to collection of DNA from SCD victims limit inclusion of patients. In addition, the lack of homogeneity of the underlying cardiac pathology (and consequently the mechanism of the attendant arrhythmia) complicates the interpretation of findings across different genetic studies. However, large samples of homogeneous SCD phenotype are challenging to collect for various reasons. The SD cases in the community must have a cardiac origin to be defined as SCD. However, that is not always the case and other underlying pathologies (e.g. stroke or aneurysm) may be the cause. In addition, SCD may not always be mediated by VF. Even a documented VF may not always be in the setting of a first MI. Previous MI or structural disturbances of the heart (cardiomyopathies) may also be associated with VF and ultimately SCD. Detailed clinical information regarding the event (including previous medical history, drug use, etc.) or autopsies are the only methods to obtain sufficient information concerning the underlying cardiac pathology in SCD. However, obtaining information about previous medical and medication history of SCD victims, which would provide information about the underlying cardiac substrate, is largely restricted and autopsy is conducted only sporadically 31. Up to now, three genome wide association studies on SCD and one on VF have investigated the role of common genetic variants in occurrence of these traits 32-35. Only two studies were able to reach the genome-wide significant threshold of P < 5 × 10−8 and replicate the findings 33,35. Table 1 summarizes genetic variants that have been found in the studies for SCD/VF. The first GWAS for VF in MI was conducted in the AGNES case-control set 35 aiming to identify single nucleotide polymorphisms (SNPs) associated with risk of VF in the setting of acute MI. The AGNES study comprises Dutch individuals with a first ST-segment elevation acute MI where survivors of VF (n = 457) are compared with patients without VF (n = 515). The high specification of the phenotype in the AGNES study increases the chance to detect genetic variants that are associated with VF rather than other arrhythmias through which SCD may occur. The control set is collected

Table 1 | Genetic variants that have been found associated with SCD SNP Location Nearest Risk allele Effect P-value SCD phenotype gene size rs2824292 Intergenic CXADR G 1.78 3.3 × 10−10 VF rs4665058 Intronic BAZ2B A 1.92 1.8 × 10−10 SD/SCD SNP, single nucleotide polymorphism; SCD, sudden cardiac death; VF, ventricular fibrillation; SD, sudden death Complex inheritance for susceptibility to sudden cardiac death 41 from the same patient population as the VF patients. This similarly-exposed control set increases the statistical power for detection of variants and reduces the chance of confounding. The AGNES study identified (chapter 4 of this thesis) a genetic variant at chromosome 21q21 (lead SNP rs2824292) in a non-coding region in the vicinity of the CXADR gene, which encodes the coxsackie and adenovirus receptor. The association was replicated in a similar population consisting of 146 out of hospital cardiac arrest cases (in the setting of acute MI) and 391 controls (i.e. MI without VF). CXADR encodes the coxsackievirus and adenovirus receptor (CAR) and is expressed in the heart (http://www.genecards.org). CAR is long known to play a role in mediating viral myocarditis and its sequelum dilated cardiomyopathy 36,37. Two studies have uncovered a physiologic role for CAR in localization of connexin 45 at Chapter 3 the intercalated disks of cardiomyocytes in the atrioventricular node, and a role in conduction of the cardiac impulse within this cardiac compartment 38,39. Whether CAR plays a role in ventricular conduction is yet unknown as is the mechanism whereby it could impact on risk of VF during acute MI. The second GWAS, conducted by Arking et al 33 consisted of a large meta-analysis of multiple genome-wide association studies for SD/SCD carried out in individuals of European ancestry. In the first stage, this study compared 1,283 SD/SCD cases and > 20,000 controls drawn from a combination of five case-control and population-based studies conducted in Europe and the U.S. Various underlying pathologies causing SD or SCD were included in this meta-analysis. The controls comprised both CAD controls and population-based controls. These investigators 33 identified a genetic variant at chromosome 2q24.2 (lead SNP rs4665058) in an intron in the BAZ2B gene, which encodes bromodomain adjacent zinc finger domain 2B and is expressed in the heart. TheBAZ2B gene was not previously known to play a role in cardiac arrhythmia or SCD. BAZ2B which was first identified and described in 2000 by Jones et al. belongs to the ZK783.4 subfamily in the BAZ gene family (bromodomain adjacent zinc finger). The function of the ZK783.4 subfamily has yet to be elucidated. The finding was described to be consistent in studies comparing SD/SCD cases with CAD controls (FinGesture and Oregon-SUDS) 33 ensuring that the risk associated with rs4665058 may be specific to the SCD phenotype rather than CAD. This association was replicated in 3119 SD/ SCD cases and 11,146 controls from 11 European ancestry populations which also included the AGNES case-control set. When the AGNES case-control set was considered separately however, the BAZ2B locus did not demonstrate an effect on risk of VF. Similarly, the CXADR signal detected in the AGNES GWAS was not detected in a small case-control set (90 patients with acute MI and VF and 167 MI non-VF controls) from Germany 40. These differences may be due to differences in phenotype definition and study design, statistical power or pure chance. 42 Chapter 3

A pilot GWAS with a very small sample size 32 investigated genetic determinants of SCD by comparing genetic differences of 89 cases with 520 healthy controls. The cases were defined as patients suffering from sudden cardiac arrest due to documented sustained VT or VF requiring cardioversion or defibrillator in presence (n = 36) or absence (n = 53) of acute myocardial ischemia 32. This study identified 14 non-replicated variants associated with SCD at the genome wide significant level (see table 2 from Aouizerat et al. 32). The most significant SNP (rs12429889;P -value = 5.28 × 10−20) was located at chromosome 13q22.1 in the vicinity of KLF12 gene, which encodes Kruppel- like factor 12. KLF12 is a transcriptional silencer of adaptor-related protein complex 2, alpha 1 subunit (AP2A1) which has been implicated in vesicle trafficking (OMIM: 601026; 607531). In the study by Aouizerat et al; the number of cases was very small and multiple SCD phenotypes were included. In addition, the study lacked replication of the results in other independent populations. In causal inference studies, it is important to keep in mind that the sentinel genetic variant itself (i.e. the SNP displaying the strongest genetic association) may not necessarily be the causal variant. SNPs display linkage disequilibrium (LD, the non-random association between alleles at two or more loci along a chromosomal segment) and are inherited as haplotype blocks. Thus any variant within such a haplotype block (displaying high LD with the sentinel SNP) could in principle be the causal variant 30. Common genetic variants are expected to modify the risk of SCD through different genetic mechanisms. However, the mechanism through which the identified variants are associated with SCD is in most cases not immediately clear. Oftentimes the sentinel variant does not commonly occur in strong LD with any missense or splice variants in the coding regions of genes. Variants identified in GWAS are often located in the non- protein coding regions of the genome. This suggests that the genetic variation affects a regulatory element which modulates disease susceptibility through effects on the level of expression of a gene; the regulated gene may be neighboring the association signal but may also be further away in the genome. In this regard, studies investigating the genetic regulation of gene expression, commonly referred to as expression quantitative trait locus (eQTL) analysis, in cardiac tissue as well as genome-wide identification of regulatory regions by ChIP-Seq (chromatin immunoprecipitation sequencing) 41 are likely to aid causal inference. In addition to the future possibilities for risk stratification, an important motiva- tion of carrying out genetic studies on SCD is to uncover novel molecular players that could illuminate previously unsuspected pathways involved in the determination of risk. Following GWAS, follow-on causal inference and functional studies in animal models resembling the disorder in humans are therefore necessary and important to reap the full benefit of GWAS efforts. This is a crucial step in order to translate the Complex inheritance for susceptibility to sudden cardiac death 43 emerging scientific knowledge to patient management and development of new or better targeted therapy for prevention of SCD.

Intermediate phenotypes in SCD

An alternative approach to GWAS of SCD is to identify genetic variants associated with phenotypes that are considered as risk factors for SCD 31. Such phenotypes are often referred to as “intermediate phenotypes”. It is thought that genetic variants that are associated with an intermediate phenotype should have a similar relation to the disease of interest if the intermediate phenotype is a causal factor for that disease 42. A proper intermediate phenotype should be quantifiable and should display Chapter 3 heritability in order to be useful in gene discovery. Heart rate and ECG indices of conduction and repolarization are considered important quantifiable intermediate phenotypes for risk of developing cardiac arrhythmias (Table 2; reviewed in reference 31). For instance, we have previously shown that ECG indices measured during the acute phase of a first MI were associated with risk of ensuing VF 43. Furthermore, a number of studies have demonstrated that heart rate, PR interval, QRS duration and

Table 2 | Established SNPs affecting ECG indices of heart rate, conduction and repolarization SNP Coded/Non GWAS Beta Minor Allele Genea Coded Allele (ms per copy of (Frequency) coded Allele) RR SNPs rs11154022 A/G 5.8 A (0.33) 8 kb from GJA1 rs12666989 C/G −7.0 C (0.20) UFSP1 rs17287293 G/A 8.6 G (0.14) SOX5-BCAT1 rs174547 C/T −6.2 C (0.31) FADS1 rs223116 A/G −7.4 A (0.24) THTPA-NGDN-ZFHX2-MYH7 rs2745967 G/A 5.4 G (0.38) CD34-PLXNA2 rs281868 G/A −6.3 A (0.49) SLC35F1 rs314370 C/T −7.6 C (0.20) SLC12A9 rs452036 A/G −7.8 A (0.39) MYH6 rs885389 A/G −0.17 A (0.37) GPR133 rs9398652 A/C −12.6 A (0.12) GJA1-HSF2 PR SNPs rs11047543 A/G −2.09 A (0.14) SOX5 rs11708996 C/G 3.04 C (0.16) SCN5A aAccording to the definition of the original publication where the association was first identified. 44 Chapter 3

Table 2 | Established SNPs affecting ECG indices of heart rate, conduction and repolarization (continued) SNP Coded/Non GWAS Beta Minor Allele Genea Coded Allele (ms per copy of (Frequency) coded Allele) rs11897119 C/T 1.36 C (0.38) MEIS1 rs1896312 C/T 1.95 C (0.29) TBX5-TBX3 rs251253 C/T −1.49 C (0.41) NKX2-5 rs3807989 A/G 2.29 A (0.42) CAV1-CAV2 rs3825214 G/A 7.35 G (0.20) TBX5 rs4944092 G/A −1.19 G (0.33) WNT11 rs6795970 A/G 14.81 A (0.39) SCN10A rs7660702 T/C 8.46 C (0.30) ARHGAP24 QTc SNPs rs10919071 A/G 1.78 G (0.11) ATP1B1 rs11970286 T/C 1.53 T (0.47) PLN rs12029454 A/G 0.17 A (0.14) NOS1AP rs12053903 C/T −0.07 C (0.33) SCN5A rs12143842 T/C 0.18 T (0.24) NOS1AP rs12210810 C/G 0.06 C (0.04) PLN rs12296050 T/C 1.62 T (0.18) KCNQ1 rs16857031 G/C 0.15 G (0.14) NOS1AP rs17779747 T/G −1.10 T (0.34) KCNJ2 rs1805128 A/G 0.05 T (0.05) KCNE1 rs2074238 T/C −0.45 T (0.05) KCNQ1 rs2074518 T/C −0.06 T (0.49) LIG3 rs2968863 T/C −1.37 T (0.26) KCNH2 rs37062 G/A −0.10 G (0.23) CNOT1 rs4657178 T/C 0.3 T (0.25) NOS1AP rs4725982 T/C 0.09 T (0.19) KCNH2 rs8049607 T/C 0.07 C (0.49) LITAF rs846111 C/C 0.10 C (0.29) RNF207 QRS SNPs rs10850409 A/G −0.49 A (0.25) TBX3 rs10865879 T/C 0.77 C (0.25) SCN5A-EXOG rs11153730 C/T 0.59 T (0.49) C6orf204-SLC35F1- PLN-BRD7P3 rs11708996 C/G 0.79 C (0.16) SCN5A rs11710077 T/A −0.84 T (0.18) SCN5A aAccording to the definition of the original publication where the association was first identified. Complex inheritance for susceptibility to sudden cardiac death 45

Table 2 | Established SNPs affecting ECG indices of heart rate, conduction and repolarization (continued) SNP Coded/Non GWAS Beta Minor Allele Genea Coded Allele (ms per copy of (Frequency) coded Allele) rs11848785 G/A −0.5 G (0.27) SIPA1L1 rs13165478 A/G −0.55 A (0.37) HAND1-SAP30L rs1321311 A/C 7.14 A (0.24) CDKN1A rs1362212 A/G 0.69 A (0.16) TBX20 rs17020136 C/T 0.51 C (0.21) HEATR5B-STRN rs1733724 A/G 0.49 A (0.27) DKK1 rs17391905 G/T −1.35 G (0.02) C1orf185-RNF11- CDKN2C-FAF1 Chapter 3 rs17608766 C/T 0.53 C (0.13) GOSR2 rs1886512 A/T −0.4 A (0.37) KLF12 rs2051211 G/A −0.44 G (0.26) EXOG rs2242285 A/G 0.37 A (0.43) LRIG1-SLC25A26 rs4074536 C/T −0.42 C (0.32) CASQ2 rs4687718 A/G −0.63 A (0.12) TKT-PRKCD-CACNA1D rs6795970 A/G 4.45 A (0.39) SCN10A rs7342028 T/G 0.48 T (0.25) VTI1A rs7562790 G/T 0.39 G (0.43) CRIM1 rs7784776 G/A 0.39 G (0.41) IGFBP3 rs883079 C/T 0.49 C (0.26) TBX5 rs9436640 G/T −0.59 G (0.48) NFIA rs9851724 C/T −0.66 C (0.35) SCN10A rs991014 T/C 0.42 T (0.43) SETBP1 rs9912468 G/C 0.39 G (0.40) PRKCA aAccording to the definition of the original publication where the association was first identified.

QTc interval are strongly heritable traits 44-46. This implies that a strong genetic component determines the variation in these traits. Following these findings, large-scale GWAS in large samples of the general population have identified numerous SNPs modulating heart rate, and ECG indices of conduction (PR interval, QRS duration) and repolarization (QTc interval) 47-53. SNPs identified for each of these traits have recently been summarized by Kolderet al. 31. As is typical for genetic variants with high population frequency, these variants explain a very small portion of the population variance in the respective parameter. This translates to an effect of less than one or two milliseconds in the respective ECG parameter. Even when these common variants with marginal effects on ECG traits are considered 46 Chapter 3

in aggregate, they still explain only a very small percentage of the variation in these traits. For instance, in a meta-analysis for identification of QTc-associating SNPs by Pfeufer et al. 51, SNPs at 10 different loci in aggregate explained only around 3% of the variance in this trait. So far, studies have largely shown that SNPs that were previously implicated in modulating ECG traits, do not exert an influence in determination of risk of SCD, both when considered individually or in aggregate 33,47. Arking et al. 33 tested 49 SNPs which are known to exert small effects on ECG traits for effects on modulation of SCD risk in the general population. Only one SNP (rs4687718) showed association with the SD/ SCD phenotype in a large population consisting of 1,283 SD/SCD cases and more than 20,000 controls of European ancestry. Noseworthy et al. 54 tested 15 common variants that were previously found to be associated with QTc interval in GWAS. While almost all of these variants showed association with QTc interval individually and in aggregate, these SNPs did not display modulatory effects on SCD risk in a population consisting of 116 SCD cases and 6808 controls. These variants in aggregate showed a nonlinear association with SCD. However, Lahtinen et al. 55 from the same group could not replicate this finding in a further larger meta-analysis. We recently tested common variants that were previously found to be associated with RR interval, PR interval, QRS duration, and QTc interval for effects on VF in the setting of a first acute MI in the AGNES case-control set 56. Our findings were in line with those of previous investigators as the SNPs did not display any marked effect on risk of VF. In those few studies where an individual effect of a SNP on risk of SCD was shown, the effect was at times counter-intuitive. For instance the QRS-shortening A-allele at rs4687718 33,50 is a SNP that has been found in GWAS for QRS duration and each A allele at rs4687718 was found to be associated with an increased risk of SCD (OR = 1.27) 33. In another study by Chambers et al. 47, the PR interval-prolonging allele at rs6795970 was associated with decreased risk of VF during acute MI. It is clear that more work is required to both validate these initial findings and eventually understand the underlying mechanism. Obviously, genetic variants identified thus far as modulators of ECG traits cannot be used for assessing genetic susceptibility to SCD. The findings so far highlights the need for further studies with large sample sizes aiming to uncover additional genetic variants, such as rare variants associated with larger effects, which would lead to a more-complete representation of the allelic architecture of these ECG differences in the general population. Complex inheritance for susceptibility to sudden cardiac death 47

Future perspective for the genetics of SCD

Understanding the genetic structure of complex traits as is cardiac arrhythmia is a major challenge 57. The studies that have investigated the role of common genetic variation in SCD risk have so far identified only few signals. Difficulty in collection of SCD cases and the proper characterization of the underlying cardiac pathology for strict definition of the phenotype represent major obstacles. Collaborations in large scale have been initiated in order to increase the sample size and consequently the statistical power to detect more variants; the current efforts to gain statistical power are at the expense of phenotypic heterogeneity. Careful downstream epidemiological studies as well as functional studies will be required to understand the exact cardiac pathology where the variants exert their effect. The research in this field gets even Chapter 3 more challenging as the search for genetic predisposition to complex phenotypes is shifting to encompass the role of rare variants in risk of SCD which consequently necessitates even larger sample sizes to ensure statistical power.

Summary

SCD accounts for up to half of all cardiovascular deaths. In the young it occurs primarily in the setting of the primary electrical disorders and the cardiomyopathies, disorders displaying Mendelian inheritance. Significant progress has been made in the last 15 years in unraveling genes underlying these disorders. However, most SCD cases occur due to VF in the setting of myocardial infarction/ischemia in the older segment of the population. During ischemia, multiple factors that can trigger arrhythmia include injury current, changes in electrophysiological properties of the cardiac Purkinje fibers, mechanical stretch due to left ventricular malfunction, changes in the heart rate and reperfusion of blood into the ischemic area. Although unequivocal evidence exists for a genetic component in the determination of risk for VF in the setting of ischemia/infarction, the underlying genetic factors remain largely unidentified. To date only a few genetic variants modulating VF risk in this setting have been uncovered. So far, GWAS have identified two loci close to the CXADR and within BAZ2B genes, respectively, as susceptibility loci for VF and SCD. The molecular function through which these loci are associated with SCD has yet to be elucidated. Intermediate phenotypes such as ECG traits have been used as a surrogate in order to identify genetic variants modulating risk of SCD. However, studies conducted thus far demonstrate that, such variants do not have a marked effect on susceptibility. Fu- 48 Chapter 3

ture large scale collaborative studies on large patient sets might provide the statistical power required to detect their effect.

Acknowledgement

The authors are supported by research grants from the Netherlands Heart Foundation (grants 2001D019, 2003T302 and 2007B202), the Leducq Foundation (grant 05-CVD) and the Netherlands Heart Institute. Dr. C.R. Bezzina is an Established Investigator of the Netherlands Heart Foundation (grant 2005T024). We thank Jonas S. de Jong for providing Figure 1. Complex inheritance for susceptibility to sudden cardiac death 49

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C hapter 4 Genome-wide association study identifi es a susceptibility locus at 21q21 for ventricular fi brillation in acute myocardial infarction

Nat Genet. 2010 Aug;42(8):688-91.

Connie R. Bezzina*, Raha Pazoki*, Abdennasser Bardai*, Roos F. Marsman*, Jonas S.S.G. de Jong*, Marieke T. Blom, Brendon P. Scicluna, J. Wouter Jukema, Navin R. Bindraban, Peter Lichtner, Arne Pfeufer, Nanette Bishopric, Dan M. Roden, Th omas Meitinger, Sumeet S. Chugh, Robert J. Myerburg, Xavier Jouven, Stefan Kääb, Lukas R.C. Dekker, Hanno L. Tan, Michael W.T. Tanck, Arthur A.M. Wilde

*Th ese authors contributed equally to this study. 56 Chapter 4

Abstract

Sudden cardiac death from ventricular fibrillation (VF) during acute myocardial infarction (MI) is a leading mode of death. We report the first genome-wide association study addressing this problem. Genome-wide association analysis in a set of 972 patients with a first acute MI, 515 of whom had VF and 457 did not, identified SNPs at 21q21, in moderate to complete linkage disequilibrium (LD), associated with VF. The most significant association was found for rs2824292 and rs2824293 in complete LD (Odds ratio = 1.78; 95% CI: 1.47, 2.13; P = 3.3 × 10−10). The association of rs2824292 with VF was replicated in an independent case-control set consisting of 146 out-of-hospital cardiac arrest patients with MI and VF and 391 MI survivor controls (Odds ratio = 1.49; 95% CI: 1.14, 1.95; P = 0.004). The closest gene is CXADR, encoding a viral receptor implicated in myocarditis and dilated cardiomyopathy, and which has recently been identified as a modulator of cardiac conduction. This locus has not previously been implicated in arrhythmia susceptibility. GWAS identifies a susceptibility locus at 21q21 for VF in MI 57

Introduction

Sudden cardiac death (SCD) accounts for 15-20% of all natural deaths in adults in the U.S. and Western Europe, and up to 50% of all cardiovascular deaths 1. Ventricular fibrillation (VF, uncoordinated contraction of the ventricles) is the most common underlying cardiac arrhythmia 2. It arises through multiple mechanisms, depending on the underlying cardiac pathology 3. In the last decade, considerable progress has been made in the understanding of the genetic, molecular and electrophysiological basis of SCD in the uncommon (monogenic) familial arrhythmia syndromes affecting the structurally normal heart and the rare inherited structural disorders associated with increased SCD risk 4,5. However, the overwhelming majority (~80%) of SCDs in adults are caused by the sequelae of coronary artery disease, namely myocardial ischemia or acute myocardial infarction (MI) 6,7. Here SCD is the first clinically-identified expression of heart disease in up to one half of cases 8. Progress in understanding genetic and molecular mechanisms that determine susceptibility to these common arrhythmias, affecting a much greater proportion of the population, has been limited 3,9,10. The identification of genetic variants associated with this phenotype is challenging due to Chapter 4 difficulties in ascertaining these patients. Epidemiological studies have indicated that sudden death in a family member is a risk factor, suggesting a genetic component in susceptibility 11,12. Recently, we also identified family history of sudden death as a strong risk factor for cardiac arrest during MI in the Arrhythmia Genetics in the NEtherlandS (AGNES) study, in which we compared patients with and without VF during the early phase of a first acute MI 13. Accordingly, in the present study, we sought to identify common genetic factors underlying susceptibility to VF during MI by conducting a genome-wide association study (GWAS) in the AGNES case-control set. The strategy of selecting this highly specific arrhythmia phenotype allowed us to avoid the problem of loss of statistical power inherent in analyzing cases or controls with multiple pathophysiologies. In particular, confining the case group to those with a first MI minimizes the inclusion of patients with extensive pre-existing myocardial scarring from previous MIs, in whom VF may also arise from other mechanisms such as scar-related reentry.

Methods

Study samples All individuals studied were of self-declared Caucasian ancestry. Local ethics committees approved the study protocols, and all participants gave written informed consent. 58 Chapter 4

The AGNES sample: The AGNES case-control set consisted of patients with a first acute ST-segment elevation MI 13. AGNES cases had ECG-registered VF occurring before reperfusion therapy for an acute and first ST- segment elevation MI. AGNES controls were patients with a first acute ST- segment elevation MI but without VF. All were recruited at seven heart centers in the Netherlands from 2001-2008. We excluded patients with an actual non- ST- segment elevation MI, prior MI, congenital heart defects, known structural heart disease, severe co-morbidity, electrolyte disturbances, trauma at presentation, recent surgery, previous coronary artery bypass graft (CABG) and class I and III antiarrhythmic drug usage. Patients who developed VF during or after percutaneous coronary intervention (PCI) were not eligible. Furthermore, since early reperfusion limits the odds of developing VF, potential control patients undergoing PCI within 2 hours after onset of symptoms were not included. This time interval was based on the observation that > 90% of cases developed VF within 2 hours after onset of complaints. The ARREST‑MI sample: The ARREST‑MI sample was drawn from the “AmsteR- dam REsuscitation STudy” (ARREST‑11). ARREST‑11 is an ongoing prospective population-based study aimed at establishing the genetic, clinical and environmental determinants of SCD in the general population. Caucasian out-of-hospital cardiac arrest patients in the ARREST‑11 dataset presenting with MI at hospital admission and with ECG-documented VF were included in the replication analysis. The diagnosis of MI was established at hospital admission by a cardiologist based on ECG abnormalities and cardiac enzymes, and was retrieved retrospectively from hospital records. At the time of this study (August 2009), 146 such patients were retrieved and all were included in the replication study. The GENDER-MI sample: The GENDER-MI sample included patients with a history of MI drawn from the GENetic DEterminants of Restenosis study (GENDER) 14, a study aimed at investigating the association between genetic polymorphisms and clinical restenosis. The GENDER study comprises consecutive patients who underwent successful percutaneous transluminal coronary angioplasty (PTCA) for treatment of stable angina, non–ST-segment elevation acute coronary syndromes or silent ischemia in four referral centers for interventional cardiology in the Netherlands. The inclusion period lasted from March 1999 until June 2001 and in total, 3104 consecutive patients were included in this prospective, multicenter, follow-up study. Approximately 40% of subjects included in the GENDER study had a history of MI. A sample of 941 GENDER subjects was genotyped on the Illumina Human 610-Quad Beadchips 15. The GENDER- MI sample used in the current study consisted of 391 of these genotyped cases; the 391 were all those with a history of MI. Random sample of the Dutch Caucasian general population: The random sample of Dutch Caucasian general population constituted the white Dutch component of the GWAS identifies a susceptibility locus at 21q21 for VF in MI 59

“Surinamese in The Netherlands: Study on Ethnicity and Health” study, a cross-sectional study that aimed to assess the cardiovascular risk profile of three ethnic groups in The Netherlands: Creole, Hindustani, and individuals of European ancestry 16. It is based on a sample of 35–60 year-old, non-institutionalized people from south-east Amsterdam, The Netherlands. A random sample of 2000 Surinamese and approximately 1000 Dutch individuals of European ancestry were drawn from the Amsterdam population register between 2001 and 2003. People were classified as Dutch if they and both their parents were born in The Netherlands. The overall response rate was 61% among these Dutch individuals. Those who responded to the oral interview were invited for medical examination, at which blood for DNA extraction was drawn. The response rate at this stage among the Dutch individuals was 90%, resulting in a total of 508 subjects with DNA. DNA samples from these 508 subjects were used for genotyping in the current study.

Genotyping Genome-wide genotyping of SNPs on the 515 AGNES cases and 457 AGNES controls was done using the Illumina Human610-Quad v1 array. All except 3 DNA samples passed quality control criteria. Excluding sex chromosome and mitochondrial DNA Chapter 4 left 507,436 single nucleotide polymorphisms (SNPs). The analysis was performed with the BeadStudio software and the Genotyping module version 3.2.33 using the cluster file provided by Illumina (Human610-Quadv1_B.egt). The GenCall score cut-off was 0.15 and the average call rate was 99.45%. Multiple quality control measures were implemented. The estimated (genotyped) sex was compared with the phenotypic sex. Exclusion criteria included P-values for deviation from Hardy-Weinberg expectation < 10−4 (in controls), sample call rate < 0.95, and SNP call rate < 0.98. For GENDER-MI, genotypes at rs2824292, rs1353342 and rs12090554 were extracted from previously obtained genotypes (Illumina Human 610-Quad Beadchip) 15. Quality control criteria for genotypic data for the GENDER study were the same as those applied in the AG- NES study. For ARREST-MI and for the sample of the Dutch Caucasian population, genotypes at rs2824292, rs1353342 and rs12090554 were determined by means of a Taqman assay (Applied Biosystems).

Statistical analysis and imputation Differences in continuous phenotypic variables between cases and controls were tested using an independent t-test when data were normally distributed or a Mann- Whitney U test otherwise. Values are presented as means ± standard deviation (SD) or median and interquartile range (IQR), respectively. Differences in categorical variables were compared using a Fisher exact test and values are presented as numbers and percentages. Genotype imputation into the HapMap reference panel (release 22) was done using Markov chain Monte Carlo methods implemented in MACH1.0 17. 60 Chapter 4

An Rsq threshold of 0.3 is implemented in the program to identify and discard low- quality imputations. Each SNP was analyzed for association with VF using logistic regression assuming an additive genetic model with adjustment for age and sex, using the ProbABEL analysis package 18. SNPs with a minor allele frequency (MAF) > 0.05 were analyzed. The P-value for genome-wide significance was set at P < 5 × 10−8, corresponding to a target α (P-value) of 0.05 with a Bonferroni correction for 1 million independent tests 19. The P-values were corrected for the genomic control factor. A 2-sided P-value of P < 0.017 (Bonferroni-corrected P value for the three independent SNPs) was considered as significant in the replication study. In the multifactor analysis, we adjusted for age, sex, hypercholesterolemia, diabetes, ST‑segment deviation, maximal CK-MB, body mass index and family history of SCD.

Results

GWAS was performed in 515 AGNES cases (MI with VF) and 457 AGNES controls (MI without VF), all of Dutch Caucasian ethnicity. The clinical characteristics of AGNES cases and controls are presented in Table 1.

Table 1 | Baseline Characteristics of the AGNES case-control set Characteristic Na Total Cases Controls P-valueb (n = 972) (n = 515) (n = 457) Sex (male) c 783 (80.6) 412 (80.0) 371 (81.2) 0.68 Age at MI (years) d 56.4 ± 11 55.9±11.2 56.9±10.8 0.17 ST segment deviation (mm) e 735/379/356 17 (19) 22 (22) 14 (14) < 0.001 MI location c 962/505/457 Inferior MI 408 (42.4) 203 (40.2) 205 (44.9) 0.14 f Anterior MI 554(57.6) 302 (59.8) 252 (55.1) CK-MB (μg/L) e 786/359/427 215(299) 225.6(365) 211.4(258) 0.02 Family history of sudden death c 909/459/450 291 (32.0) 174 (37.9) 117 (26.0) < 0.001 Beta blocker usage c 936/482/454 85 (9.1) 50 (10.4) 35 (7.7) 0.17 Cardiovascular risk factors Current Smoking c 914/473/441 565 (61.8) 303 (64.1) 262 (59.4) 0.15 Diabetes Mellitus c 893/462/431 62 (6.94) 21 (4.6) 41 (9.5) 0.004 Hypertension c 294 (30.2) 149 (28.9) 145 (31.7) 0.36 Hypercholesterolemia c 297 (30.6) 127 (24.7) 170 (37.2) < 0.001 BMI (kg/m2) d 879/439/440 26.5 ± 3.9 26.1 ± 3.8 26.9 ± 4.0 0.004 MI, myocaridal infarction; CK-MB, creatine kinase-MB; aIn case of missing values, the sample sizes of the total, case and control sets (total, case, control) for whom information was available is given; bP-value for comparison of cases and controls for each item; cNumber (%); dmean ± s.d.; eMedian (interquartile range); fP-value for comparison of inferior and anterior MI between cases and controls. GWAS identifies a susceptibility locus at 21q21 for VF in MI 61

The genomic control factor was smallλ ( = 1.026) indicating that, as expected, overall inflation of genome-wide statistical results due to population stratification was minimal. There was a significant excess of SNPs associated with VF compared to the number expected under the null hypothesis of no association (Supplementary Figure 1). The distribution of P- values for the association of SNPs with VF is shown in Fig- ure 1 and SNPs displaying the strongest associations (chr. 21q21, chr. 9q21, chr. 1q25) are presented in Table 2 and Supplementary Table 1. Eight SNPs on chr. 1q21, all in moderate to complete LD with each other (r2 = 0.4– 1.0), surpassed our preset threshold for genome-wide significance of P < 5 × 10−8. Of these, the most significant association was found for rs2824292 and rs2824293 (338 bp apart, r2 = 1.0), situated 98 Kb from the gene CXADR (1.78 per G-allele; 95% CI: 1.47, 2.13; P = 3.3 × 10−10). After adjustment for the main association signal (rs2824292) a significant reduction in the association between VF and the other seven SNPs on chromosome 21 occurred, indicating that these SNPs do not represent independent secondary signals. The genotype frequencies for rs2824292 in AGNES cases and controls as well as Chapter 4 for rs1353342 (9q21) and rs12090554 (1q25) are presented in Supplementary Table 2, together with the genotype frequencies in a sample of the general Dutch Caucasian population. As expected, the frequencies of the risk and protective alleles for each SNP in the general population were between those of AGNES cases and AGNES controls. Since AGNES cases and controls differed in some characteristics, including risk factors for coronary artery disease and measures of infarct size (represented by CK- MB), we carried out multifactor analysis to assess whether rs2824292 was an independent predictor for VF. Multifactor analysis for the association of rs2824292 with VF adjusted for those factors that appeared different between AGNES cases and controls (Table 1) resulted in an odds ratio of 1.51 per G-allele (95% CI: 1.13, 2.01, P = 0.005), indicating that this SNP is an independent risk factor for VF. This was also the case for rs1353342 and rs12090554 (respectively, OR = 0.48, 95% CI: 0.30, 0.76, P = 0.002; OR = 0.58, 95% CI: 0.41, 0.82, P = 0.002). Nevertheless, future research will be required in order to provide more evidence of the independent effects of these SNPs. We next proceeded to validate the association of the leading SNPs at the 21q21, 9q21 and 1q25 loci in an independent set of cases and controls (Table 3). Our P-value threshold for this analysis was set to P < 0.017 (0.05/3). Cases for the replication study (ARREST-MI, n = 146; 80.1% male, average age 63.12±13.10 years) were Caucasian out-of-hospital cardiac arrest patients from the ARREST dataset presenting with MI at hospital admission and with ECG-documented VF. Controls for the replication analysis (GENDER-MI, n = 391; 79.5% male, average age 61.7±10.1 years) were MI survivors drawn from the GENDER study (see Methods). 4

VF and the other seven SNPs on chromosome 21 occurred, indicating that these

SNPs do not represent independent secondary signals.

The genotype frequencies for rs2824292 in AGNES cases and controls as well as

for rs1353342 (9q21) and rs12090554 (1q25) are presented in

Supplementary Table 2, together with the genotype frequencies in a sample of

the general Dutch Caucasian population. As expected, the frequencies of the risk

and protective alleles for each SNP in the general population were between those 62 Chapter 4 of AGNES cases and AGNES controls. A

P=3.34 10

Chromosome 21 position (kb) Figure 1 | ResultsFigure of 1 genome-wide | Results of genome-wideassociation analysis association in the analysis AGNES incase-control the AGNES set. case-contr (A) Over-ol set. (A) Overview of the genome-wide association scan in the AGNES case-control population. P-values corrected view of the genome-wide association scan in the AGNES case-control population. P-values corrected for age, sex and inflation factor are shown for each SNP tested. Within each chromosome, the results are for age, sex andplott inflationed left to factorright f rareom shown the p-terminal for each end.SNP Thetested. horizo Withinntal eachred line chromosome, indicates the the p rresultseset th reshold for are plotted left to right from the p-terminal end. The-8 horizontal red line indicates the preset threshold genome-wide significance,−8 P < 5 × 10 . (B) Locus-specific association map, generated from genotyped as for genome-widewell assignificance, imputed SN P Ps,< 5 ×ce 10nter. ed(B) at Locus-specific rs2824293, lo cassociationated at 338bp map, from generated rs2824292 from and geno in -complete LD with it. typed as wellImput as imputeded SNP SNPs,s are incentered gray; rs2824293 at rs2824293, is depict locateded in at blue; 338bp SN fromPs in rs2824292 red have rand2 ≥ 0.8in com with- rs2824293; SNPs plete LD within it. or Imputedange have SNPs r2 = are 0.5-0.8; in gray; those rs2824293 in yell owis depicted have r2 = in 0.2-0.5; blue; SNPs those in inred whi havete have r2 ≥ 0.8 r2 with< 0.2 with the leading rs2824293; SNPsSNP. in Superimposed orange have r 2on = 0.5-0.8; the plot those are g inene yellow locations have (green) r2 = 0.2-0.5; and recombinationthose in white haverates r(blue).2 < 0.2 Chromosome 20 with the leadingpositions SNP. Superimposedare based on HapMap on the plotrelease are 22gene build locations 36.2. Figure (green) 1B and was recombination prepared using rates SNAP . (blue). Chromosome positions are based on HapMap release 22 build 36.2. Figure 1B was prepared using SNAP 20. GWAS identifies a locus at 21q21 as a susceptibility locus for VF in acute MI | 65

As observed in the AGNES case-control set, the risk G-allele of SNP rs2824292 at chr. 21q21 was more abundant while the protective A-allele was less abundant in ARREST-MI cases compared to GENDER-MI (Supplementary Table 2). The odds ratio per G-allele in this case-control set was 1.49 per risk allele (95% CI: 1.14, 1.95: P = 0.004), comparable to that found in the AGNES discovery set. SNP rs12090554 at chr. 1q25 showed a trend for association with a direction of effect similar to that found in the AGNES discovery set (OR = 0.70 per copy of A-allele, 95% CI: 0.49, 1.01, GWAS identifies a susceptibility locus at 21q21 for VF in MI 63 e Genes within an e CXADR, BTG3 CXADR, BTG3 Closest Genes HMCN1, IVNS1ABP PCSK5, RFK, GCNT1 d −7 −7 −10 −10 10 10 10 10 × × × × 7.9 3.3 P -value 3.3 3.3 Only rs2824293 was imputed, the rest were a c OR(95% CI) 0.58(0.47,0.72) 1.78(1.47,2.13) 1.78(1.47,2.13) 0.46(0.34,0.63) Chapter 4 -values P -values are corrected for genomic control factor; 28 39 39 14 Controls The d 7 19 53 53 Cases Minor Allele FrequencyMinor 23 47 47 23 Total A/C A/G G/A G/A Mi/Ma b Odds ratios are adjusted for age and sex; c 17709385 17709047 78064589 Position 183818971 1q25 9q21 21q21 21q21 Position Based on Build 36.2; b Chromosome a SNP rs2824293 rs2824292 rs1353342 rs12090554 genotyped directly; Table Table 2 | Leading single nucleotide polymorphisms (SNPs) at the three loci most strongly associated with ventricular control set fibrillation in theAGNES case- Chr, chromosome; Chr, Mi/Ma, minor/major allele; OR, odds ratio per copy of minor allele; CI, confidence interval; area of 1 Mb centered at the SNP are listed. are the SNP at centered of 1 Mb area 64 Chapter 4

Table 3 | Replication results for the three strongest AGNES loci SNP Odds Ratio Per Copy of P-value Minor Allele (95% CI)a rs2824292 1.49 (1.14-1.95) 0.004 rs1353342 0.84 (0.53-1.34) 0.46 rs12090554 0.70 (0.49-1.01) 0.057 ARREST-MI cases, n = 146; GENDER-MI controls, n = 391. aOdds ratios are adjusted for age and sex.

P = 0.057). On the other hand, rs1353342 on chr. 9q21 was not associated with VF in the ARREST-MI versus GENDER-MI analysis (OR = 0.84, 95% CI: 0.53, 1.34, P = 0.46).

Discussion

The SNPs most strongly associated with VF in this study appear to be located in an intergenic area which lacks LD to SNPs within genes (Supplementary Figure 2). These SNPs are not necessarily the functional variants for the observed effect but may serve as proxies for the actual functional variant(s). Thus, these SNPs, or functional SNPs in LD with them, are likely to modulate VF risk by exerting long-range effects on gene expression 21. Inspection of 1 megabase spanning rs2824292 identifies 3 genes in the region, all located within the 0.5 megabase downstream of this SNP. The gene closest to rs2824292, located 98 Kb away, is CXADR encoding the Coxsackievirus and adenovirus receptor (CAR) protein, a type I transmembrane protein of the tight junction 22,23. CAR has a long-recognized role as viral receptor in the pathogenesis of viral myocarditis and its sequela of dilated cardiomyopathy 24,25. Notably, active coxsackie B virus infection has been reported at a high frequency in a group of individuals with MI who died suddenly 26. More recently, two studies have reported a physiological role for the receptor in localization of connexin 45 at the intercalated disks of the atrio- ven- tricular node cardiomyocytes, and its role in conduction of the cardiac impulse within this cardiac compartment 27,28. Thus, CXADR is a candidate gene for the association reported here. Two other genes are located within 0.5 megabases of rs2824292. BTG3, located 179 Kb from rs2824292, encodes B-cell translocation gene 3, a member of the PC3/BTG/TOB family of growth inhibitory genes 29. Its mRNA expression level is relatively high in testis, lung, and ovary but low in heart 29. C21orf91, located 374 Kb from rs2824292, encodes the human ortholog of the chicken EURL (early undifferentiated retina and lens) gene, known to be expressed in the developing chick retina and lens and suggested to play a role in the development of these structures 30,31. GWAS identifies a susceptibility locus at 21q21 for VF in MI 65

The AGNES study did not include patients dying prior to hospital arrival and it is likely that some of these cases died from VF arising immediately or very soon after initiation of chest pain, before arrival of medical help. Thus, we cannot assert that the genetic association identified in this study also holds for the group of patients who die immediately after onset of symptoms. Nevertheless, an initial comparison of patients presenting with VF occurring within 5 minutes of onset of chest pain (n = 119) versus those presenting with VF occurring after more than 30 minutes of onset of chest pain (n = 204) uncovered no significant difference in distribution of genotypes for rs2824292 (Supplementary Table 3). Further studies, such as exploration of expression of these genes in human heart as a function of genotype at the rs2824292 SNP (or other linked SNPs), as well as functional studies, are next steps. SNPs in the NOS1AP locus, associated with variability in QTc duration in the general population 32-34 have recently been implicated as predictors of SCD risk in candidate SNP analyses 35,36. In our GWAS, however, there was no association between SCD risk and SNPs at this locus. Predicting and preventing SCD remains a major challenge for which new strategies are needed, and understanding the underlying molecular mechanisms represents a crucial step in this process. Al- Chapter 4 though much of the focus on genetic influences on SCD risk in common diseases has been on variants that directly or indirectly influence ion channel function, the results of this study are hypothesis-generating and support a concept of a novel avenue for research into alternative and/or complementary biological pathways that may be implicated in VF.

Acknowledgements

This study was supported by research grants from the Netherlands Heart Foundation (grants 2001D019, 2003T302 and 2007B202), the Leducq Foundation (grant 05-CVD) and the Interuniversity Cardiology Institute of the Netherlands (project 27). Dr. C.R. Bezzina is an Established Investigator of the Netherlands Heart Foundation (grant 2005T024). Dr. H.L. Tan is supported by the Netherlands Organization for Scientific Research (NWO, grant ZonMW Vici 918.86.616) and the Medicines Evaluation Board Netherlands (CBG). Dr. A. Bardai was supported by the Netherlands Organization for Scientific Research (NWO, grant Mozaiek 017.003.084). Prof. A.H. Zwinderman and E.P.A. van Iperen provided the genotype data of the GENDER study. We thank Leander Beekman for technical support. 66 Chapter 4

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Supplemental Tables

Supplementary Table 1 | Most significant associations between single nucleotide polymorphisms (SNPs) and ventricular fibrillation in the AGNES case-control set SNP Chr. Positiona Location Minor/ MAF b Imputed Rsqc OR P-Valuee Major (yes/no) (95% CI)d Allele rs2824293 21 17709385 Intergenic G/A 47 yes 1.00 1.78 (1.47,2.13) 3.3 × 10−10 rs2824292 21 17709047 Intergenic G/A 47 no 1.00 1.78 (1.47,2.13) 3.4 × 10−10 rs208910 21 17708105 Intergenic C/T 47 yes 0.99 1.77 (1.48,2.13) 4.4 × 10−10 rs12483129 21 17711037 Intergenic C/T 49 yes 0.96 0.58 (0.48,0.70) 6 × 10−9 rs9978331 21 17714804 Intergenic G/A 48 yes 0.97 0.59 (0.49,0.70) 1.3 × 10−8 rs2824294 21 17709789 Intergenic T/C 48 no 1.00 0.60 (0.50,0.72) 3.6 × 10−8 rs13050588 21 17721619 Intergenic T/C 37 yes 0.96 1.69 (1.40,2.04) 4.8 × 10−8 rs4818351 21 17720626 Intergenic C/T 37 yes 0.96 1.69 (1.40,2.05) 4.9 × 10−8 rs1493100 21 17713763 Intergenic G/A 37 yes 0.97 1.69 (1.39,2.04) 6.1 × 10−08 rs8130544 21 17708212 Intergenic C/T 36 no 1.00 1.63 (1.35,1.97) 3.2 × 10−7 rs2824290 21 17707528 Intergenic A/G 64 yes 0.99 0.61 (0.51,0.74) 3.3 × 10−07

rs2824289 21 17706394 Intergenic A/T 36 yes 0.99 1.63 (1.35,1.97) 3.3 × 10−7 Chapter 4 rs1353342 9 78064589 Intergenic A/C 11 no 1.00 0.46 (0.34,0.63) 3.3 × 10−07 rs208918 21 17719150 Intergenic T/A 49 yes 0.97 1.59 (1.33,1.92) 3.6 × 10−7 rs2824288 21 17702254 Intergenic G/C 36 yes 0.98 1.61 (1.33,1.96) 4.7 × 10−7 rs208926 21 17722200 Intergenic C/A 48 no 1.00 1.56 (1.31,1.88) 5.1 × 10−7 rs12090554 1 183818971 Intergenic A/G 23 no 1.00 0.58 (0.47,0.72) 7.9 × 10−7 Chr, Chromosome; MAF, minor allele frequency; OR, Odds Ratio Per Copy of Minor Allele; aBased on Build 36.2; bAllele Frequency was based on the total AGNES sample; cRsq estimates the squared correlation between imputed and true genotypes; dOdds ratios are adjusted for age and sex; eTheP -values are corrected for genomic control factor. 70 Chapter 4

Supplementary Table 2 | Genotype distributions in the patient samples studied and in a sample of the general population Sample N Frequencies (95% CI) rs2824292 AA AG GG AGNES cases 513 0.22 (0.19-0.26) 0.48 (0.44-0.52) 0.30 (0.26-0.34) AGNES controls 457 0.37 (0.32-0.41) 0.48 (0.43-0.52) 0.15 (0.12-0.19) ARREST-MI (cases) 146 0.25 (0.18-0.32) 0.47 (0.39-0.55) 0.27 (0.20-0.35) GENDER-MI (controls) 391 0.35 (0.30-0.40) 0.48 (0.43-0.53) 0.17 (0.13-0.21) General Population 492 0.29 (0.25-0.33) 0.52 (0.48-0.57) 0.19 (0.16-0.23) rs1353342 GG GT TT AGNES cases 514 0.86 (0.83-0.89) 0.13 (0.10-0.16) 0.01 (0.00-0.01) AGNES controls 457 0.73 (0.69-0.77) 0.25 (0.21-0.29) 0.02 (0.01-0.03) ARREST-MI (cases) 146 0.82 (0.76-0.88) 0.17 (0.11-0.23) 0.01 (−0.01-0.02) GENDER-MI (controls) 391 0.79 (0.75-0.83) 0.2 (0.16-0.24) 0.01 (0.0-0.02) General Population 488 0.79 (0.75-0.83) 0.2 (0.16-0.23) 0.01 (0.0-0.02) rs12090554 GG AG AA AGNES cases 515 0.67 (0.63-0.71) 0.29 (0.25-0.33) 0.04 (0.02-0.06) AGNES controls 455 0.51 (0.46-0.56) 0.42 (0.38-0.47) 0.07 (0.04-0.09) ARREST-MI (cases) 143 0.66 (0.59-0.74) 0.31 (0.24-0.39) 0.02 (0.0-0.04) GENDER-MI (controls) 391 0.58 (0.53-0.62) 0.38 (0.34-0.43) 0.04 (0.02-0.06) General Population 477 0.57 (0.52-0.61) 0.39 (0.34-0.43) 0.05 (0.03-0.06)

Supplementary Table 3 | Genotype frequencies and odds ratio (95% CI) for rs2824292 Based on time-to-VF from Onset of Complaints Homozygous Heterozygous Homozygous Odds ratio P valuea P valueb for for (95% CI) major allele minor allele additive modela No VF 168(36.8%) 219(48.0%) 69(15.1%) 1.00 (ref) Early onset VFc 30(25.2%) 51(42.9%) 38(31.9%) 1.79(1.34-2.40) 7.4 × 10−5 0.76 Late onset VFd 51(25.0%) 92(45.1%) 61(29.9%) 1.71(1.35-2.17) 1.0 × 10−5 aCompares early onset or late onset VF against controls adjusted for age and sex; bCompares early onset vs. late onset VF; adjusted for age and sex; cVF occurring within 5 minutes of onset of chest pain; dVF occurring after more than 30 minutes of onset of chest pain. 4

Supplemental FigureGWASs identifi es a susceptibility locus at 21q21 for VF in MI 71

Supplemental Figures

Supplemental Figures Chapter 4

SupplementarySupplementary Figure 1 | Quantile-quantile Figure 1 | plotQuantile of test- qstatisticsuantile (pPlot-values) of t estfor thestatistics associations (P-values) of for the associations of single- single-nucleotidenucleotide polymorphisms polymorphisms with ventricular with v entricularfi brillation finibrillation the AGNES in Population.the AGNES Under Population. the Under the null hypothesis of null hypothesisno of association, no association, the the data data points would would be beexpected expected to fall toon the fall black on theline. blackP-values line. P-values corrected for possible Supplementarycorrected for Figurepopulation possible 1 | populationQuantile stratification-quantile stratifi p lotbycation ofmeans testby s meanstatisticsof the of genomic (theP-values) genomic control for control the a methodssociat methodions are are of presented spresent-ingle- (λ = 1.026). nucleotide polymorphisms with ventricular fibrillation in the AGNES Population. Under the null hypothesis of no association,ed (λ = 1.026). the data points would be expected to fall on the black line. P-values corrected for possible population stratification by means of the genomic control method are presented (λ = 1.026).

Supplementary Figure 2 | Haploview plot of linkage disequilibrium (D′) for all CEU HapMap SNPs in Supplementary Figure 2 | Haploview plot of linkage disequilibrium (D') for all CEU HapMap SNPs in a 500a 500 Kb Kb region region spanning spanningSupplementary rs2824292. rs2824292. Colour ColourFigure scheme: scheme: 2 | D′Haploview < D 1′

GWAS identifies a region at chr 21q21 as a susceptibility locus for VF in acute MI | 75

C hapter 5 Multiple genomic approaches for identifi cation of genetic modifi ers of ventricular fi brillation risk in the setting of acute myocardial infarction in the AGNES study

Manuscript in preparation for submission

Raha Pazoki, Lia Crotti, Reza Jabbari, Jonas S.S.G. de Jong, Nienke Bruinsma, Michiel E. Adriaens, Leander Beekman, Lukas R.C. Dekker, Anna M. Di Blasio, Jacob Tfelt-Hansen, Peter J. Schwartz, Gaetano M. De Ferrari, Arthur A.M. Wilde, Michael W.T. Tanck, Connie R. Bezzina 74 Chapter 5

Abstract

Background: Sudden cardiac death (SCD) from ventricular fibrillation (VF) during acute myocardial infarction (MI) is a leading cause of total and cardiovascular mortality. Genetic factors underlying risk for VF in the setting of acute MI remain largely unknown. We previously started exploring the role of common genetic variants in modulation of VF risk in acute MI by a genome-wide association study (GWAS) in the AGNES population, consisting of Dutch individuals with a first acute MI, where cases suffered VF and controls not. The aim of the current study was to carry out GWAS in an extended set of AGNES patients to identify additional susceptibility loci and to overlay this GWAS data with other genomic data in an effort to identify loci modulating risk of VF in this setting. Methods: In total, 1433 AGNES patients with a first acute MI (672 cases, 761 controls) were analyzed by GWAS. We sought to replicate SNPs displaying association P-values < 1 × 10−5 in two additional independent but similar case-control sets, namely PREDES- TINATION and GEVAMI, recruited in Italy and Denmark, respectively. We additionally reviewed literature to find SNPs previously implicated in SCD and investigated their association with VF in the AGNES study. In an effort to identify candidate causal genes at associating loci, the most strongly associating SNPs from the AGNES GWAS were searched in our human heart eQTL (expression quantitative trait locus) database and in publicly available heart and blood eQTL databases in order to investigate whether any of these SNPs could be related to the expression of genes located in cis. In a reverse approach, we selected previously-identified eQTLs from the human heart eQTL databases (representing putatively functional SNPs) and investigated their association with VF in the AGNES sample. We additionally performed pathway analysis on the SNPs identified in the AGNES GWAS in an effort to identify biological mechanisms that may be involved in the occurrence of VF in the setting of MI. Results: rs2824292 remained as most-strongly and only genome-wide significant SNP associated with VF in the AGNES study (OR = 0.65; 95% CI: 0.56, 0.76; P = 2.75 × 10−8). Out of the 10 top independent AGNES SNPs, only rs1750041 (OR = 1.64; 95% CI: 1.09, 2.46; P = 0.02) was nominally significant in the PREDESTINATION study. This SNP was however not associated with VF in the GEVAMI case-control set. Our candidate SNP analysis in AGNES uncovered a nominal association with VF for rs6795970 in the SCN10A gene (OR = 0.85; 95% C.I: 0.73, 0.99; P = 0.02). Among the 10 most-strongly associated SNPs in the AGNES GWAS, rs1750041 and rs10096381 displayed eQTL effects with genes in cis. in peripheral blood. In the reverse approach, we uncovered that rs6982308 (an eQTL SNP that controls the expression of XPR6 in human heart) increased the risk of VF by 1.58 fold (95% CI: 1.28, 1.94; P = 1.8 × 10−5). Pathway analysis showed that VF-associated genes from the current study are enriched in pathways mainly involved in signal transmission. Multiple genomic approaches to identify genetic susceptibility to VF in MI 75

Conclusion: Using several integrated approaches, we highlighted several SNPs (i.e. rs2824292, rs1750041, rs10096381, rs6982308, and rs6795970) that merit further analysis in future studies. We also highlighted several pathways that connected together to possibly form a larger relevant biological mechanisms for risk of VF in the setting of MI. Chapter 5 76 Chapter 5

Introduction

Sudden cardiac death (SCD) accounts for 20% of total mortality and up to 50% of cardiovascular mortality among adults in Western Societies 1,2. Ventricular fibrillation (VF) is the most common cardiac arrhythmia underlying SCD 3. SCD in the general population occurs in individuals with an average age of 65 years 4 who have complex disease stemming from multiple common acquired disorders, especially coronary artery disease (CAD) or associated conditions such as myocardial ischemia, infarction (MI), post-MI myocardial scarring, and ischemic cardiomyopathy 3. While multiple studies have provided evidence for a heritable component in the determination of SCD risk in these settings 5-8, the identification of genetic risk factors has only recently started to be explored and progress has been slow 9-11. Difficulties in gene identification are hindered by a number of factors, amongst which are the paucity of patients that are available for genetic studies and the likely complex underlying genetic architecture of SCD. While as for other complex phenotypes, the genetic architecture of SCD susceptibility is unknown, SCD in the individual patient is likely governed by the cumulative effect of a broad spectrum of inherited genetic variants that occur at different frequencies in the general population and that carry different effect sizes on risk for SCD 1. Molecular mechanism underlying SCD may differ between different cardiac pathologies and gene identification efforts are therefore likely to benefit from a deep characterization of the cardiac condition in which SCD occurs as this allows the identification of different disease sub-groups for genetic studies. In the last years, our group has been focused on the identification of genetic factors modulating risk of VF in the setting of a first acute MI 5,9,12. To this aim we established the Arrhythmia Genetics in the NEtherlandS (AGNES) study, consisting of patients with a first acute ST-segment elevation MI presenting with VF (ECG-documented; cases) and without VF (controls) 5. In this case-control set we established that family history of sudden death is a risk factor for VF 5, providing a strong rationale for the exploration of genetic factors modulating risk of VF. Subsequently, through a genome-wide association study (GWAS) for common genetic variants in this case-control set, we identified the first susceptibility locus at chromosome (chr.) 21q21 9. The aim of the current study was to identify additional VF susceptibility loci by (1) conducting GWAS in an extended set of AGNES, (2) testing the role of expression quantitative trait loci (eQTL), and (3) implementing a pathway-based analysis. Multiple genomic approaches to identify genetic susceptibility to VF in MI 77

Methods

Patient samples The AGNES sample: The AGNES case-control set has been described in detail previously 5,9. In brief, it consists of cases that survived to hospital admission and had ECG- registered VF that occurred within 24 hours after the onset of symptoms and before reperfusion therapy in the setting of an acute and first ST-segment elevation MI. The majority (89%) of the VF events occurred within the first 2 hours after the onset of the symptoms and 99% occurred in the first 12 hours. Controls were patients with a first acute ST-segment elevation MI without VF. Cases and controls (age > 18 and < 80 years 5) were recruited at seven heart centers in the Netherlands between 2001 and 2011. We excluded individuals with an actual non-ST-segment elevation MI, prior MI, congenital heart defects, known structural heart disease, severe co-morbidity, electrolyte disturbances, trauma at presentation, recent surgery, previous coronary artery bypass graft or use of class I and III antiarrhythmic drugs. Individuals who developed VF during or after percutaneous coronary intervention were not eligible. Furthermore, because early reperfusion limits the odds of developing VF, potential control subjects undergoing percutaneous coronary intervention within 2 hours after onset of myocardial ischemia symptoms were not included. This time interval was based on the observation that a majority (> 90%) of cases develop VF within 2 hours after the onset of symptoms. Chapter 5 The study protocol was approved by the Institutional Review Board of the Aca- demic Medical Centre, University of Amsterdam, and was conducted according to the principles of the Declaration of Helsinki. The medical ethics committees of the hospi- tals participating in the study approved the study protocols, and all participants gave written informed consent. AGNES cases and controls that were used in genetic studies were of self-declared Dutch European descent. Thus far, the AGNES study consists of two subsets; a first set (n = 972) collected up until 2008 and a second set (n = 485) collected from 2008 to 2011. The PREDESTINATION sample: The first replication sample for the current study was from the PREDESTINATION (PRimary vEntricular fibrillation and suDden dEath during a firST myocardial INfArcTION) study. Similar to the AGNES study conducted by us in the Netherlands, the PREDESTINATION study is a multicenter study performed prospectively at multiple coronary care units in Italy. The recruiting centers were part of the group ANMCO (Associazione Nazionale Medici Cardiologi Ospedalieri). Patients eligible for the study had at least one ECG-documented VF occurring within 24 hours from the onset of an acute MI. The controls had the same clinical features in the absence of VF. Patients older than 75 and patients with previous MI in medical history, pre-existing significant cardiac disease and/or ejection fraction ≤ 30%, and the 78 Chapter 5

presence of conditions favoring the risk of VT/VF independently from the ischemic episode (e.g. inherited cardiomyopathy or primary electrical disease) were excluded from the study. Patients in the PREDESTINATION study read and signed an informed consent form approved by the Ethics Committee of the University of Pavia. The GEVAMI sample: We used a second replication sample from a Danish sample called GEVAMI (the GEnetic causes to Ventricular Arrhythmia in patients during first ST-elevation Myocardial Infraction). Similar to the AGNES and PREDESTINATION studies, the GEVAMI sample consisted of ECG-documented VF cases that suffered from VF within 12 hours after a first ST-segment elevation MI. The controls had all the same clinical presentations except VF. Cases and controls were collected nationwide in Denmark. Patients older than 18 and younger than 80 were included in the GEVAMI study. Patients with prior MI, previous coronary artery disease, coronary artery bypass graft, percutaneous coronary intervention, and non-ST-segment-elevation MI were not included. Patients or their next-of-kin signed the informed consent. Procedures in the GEVAMI sample are in accordance with the ethical standards of the national ethics committee on human research and with the Helsinki Declaration of 1975, as revised in 1983. Permission from the Danish Data Protection Agency was also obtained before the study was initiated (Jr.nr. 2010-41-5688).

Genotyping Previously, genome-wide genotyping of single nucleotide polymorphisms (SNPs) was performed on 515 AGNES cases and 457 AGNES controls using the Human610- Quad SNP array 9. In the current study, an additional 172 AGNES cases and 313 AGNES controls were genotyped using Illumina HumanOmni2.5 arrays. The analyses were performed using the BeadStudio (Genotyping module version 3.2.33) and the Ge- nomeStudio V2011.1 software (Genotyping module version 1.9.0). For both arrays, the GenCall score cutoff was 0.15 as recommended by Illumina. The average sample call rate was greater than 99%. Genome-wide genotyping of SNPs was performed on 220 Predestination cases and 421 Predestination controls using the Illumina Human OmniExpress‑12 SNP array. The analysis was performed using the Genome Studio software as described above. The GenCall score cutoff was 0.15. The average sample call rate was 99.2%. A subset of SNPs was genotyped in 256 cases and 527 controls from the GEVAMI sample using KASP technology (LGC Genomics, Herts, UK) based on competitive allele-specific PCR.

Quality control and imputation Multiple quality control measures were implemented before imputation. The estimated sex for each individual as determined by genotyping was compared with the Multiple genomic approaches to identify genetic susceptibility to VF in MI 79 phenotypic sex. Exclusion criteria were deviation from Hardy-Weinberg equilibrium at P ≤ 10−4 (estimated in controls), sample call rate < 0.95, and SNP call rate < 0.98. To use the information of typed rare variants when imputing from the reference panels, we selected all typed SNPs without regard to minor allele frequency (MAF). Minimal MAF of the typed SNPs in the current study was 0.001. Genotypes for SNPs from GE- VAMI sample passed quality control thresholds applied in the GEVAMI study (call rate ≥92%, Hardy-Weinberg equilibrium P > 0.01 in control subjects). To correct for possible genetic heterogeneity within the study sample, we performed principal component analysis in the AGNES (n = 1457) and the PREDESTINATION (n = 641) case-control sets using a multi-dimensional scaling technique applied on the Identity-By-State (IBS) matrix implemented in the R package GenABEL 13. Pairwise IBS values were calculated in the AGNES and PREDESTINATION samples separately and Prim’s algorithm implemented in the R package nnclust 14 was used to identify clusters and outliers (threshold: 0.3). The outliers thus identified were excluded from the subsequent analyses. The first two principal components were included as covariates in subsequent analyses. In total, 1433 AGNES and 629 PREDESTINATION cases and controls successfully passed all the quality control steps. The resulting outliers were excluded from the analysis. Genotype imputation was done using the Markov-chain Monte Carlo method implemented in MACH1.0 15,16. Un-typed polymorphisms were imputed using data from HAPMAP separately into the two AGNES datasets and the PREDESTINATION Chapter 5 dataset. The two imputed data sets of the AGNES sample were quality checked for potential swaps in genotype coding and were then combined. After imputation, an r2 threshold of 0.5 was implemented to identify and discard low-quality imputed SNPs.

Genome-wide association analysis Differences in continuous phenotypic variables between cases and controls were tested using an independent t test where data were normally distributed or a Mann- Whitney U test otherwise. Here values are presented as mean ± s.d. or median and inter-quartile range, respectively. Differences in categorical variables were compared using a Fisher exact test and values are presented as number and percentages.

1. Discovery in the AGNES study Genome-wide association analysis was conducted in the combined AGNES case- control set using logistic regression method in ProbABEL 17 while assuming an additive genetic model and including the first two principal components, age and sex as covariates. The P-values were corrected for the genomic control factor. The P-value threshold for genome-wide statistical significance 18,19 was set at P ≤ 5 × 10−8. An arbitrary threshold of P ≤ 1 × 10−5 was also considered. SNPs that passed this arbitrary 80 Chapter 5

threshold and were not in LD with other SNPs were considered independent and were clustered into independent genomic loci. At each genomic locus passing the arbitrary threshold, the SNP with the lowest P-value was considered as the lead-SNP. To account for independent signals, we selected all the SNPs passing this arbitrary threshold at each genomic locus and performed a conditional analysis to account for independent signals within each locus. We did this by running a regression model including all the SNPs at each genomic locus. We also performed a conditional analysis on the lead- SNPs by running a multiple regression model including all lead-SNPs to check their statistical independence.

2. Replication in the PREDESTINATION and GEVAMI samples The lead-SNPs from the discovery stage were then carried forward for replication in PREDESTINATION. Next, we selected a subset of SNPs that displayed the strongest combined association with VF in the AGNES and PREDESTINATION studies and took them forward for replication and meta-analysis in the Danish GEVAMI study. Analyses for the AGNES and PREDESTINATION were performed in R 20. Data analysis for a subset of SNPs in GEVAMI was performed using the Stata software package V.12.0 (StataCorp, College Station, Texas, USA). Similar to the discovery stage, logistic regression was used to analyze the SNPs that were taken forward for replication. A P-value threshold corresponding to 0.05 with a Bonferroni correction for the number of SNPs tested in the replication phase was used. Meta-analysis of the replication SNPs was carried out in R package meta 21 using a random effect model. We also searched articles presenting association results in relevant case-control sets for our previously reported SNP (rs2824292) and meta-analyzed the results with the findings of the current study.

Literature-based candidate SNP analysis We reviewed publications indexed in the National Center for Biotechnology Informa- tion (NCBI) PubMed database to identify genetic variants associated with SCD. We focused on full text publications, written in English, and studies performed among human subjects of European ancestry, published from 1st January 1998 until 3rd De- cember 2013. We used a search query that included: “common missense variant”, “VF”, “single nucleotide polymorphism”, “genome-wide association”, “SNP”, “allele”, “sudden cardiac death”, “sudden death”, “arrhythmic cardiac death”, and “sudden cardiac arrest”. We excluded review articles and case reports. Publications reporting statistically significant findings were reviewed for selection of candidate SNPs 11,22-38. Out of 26 relevant SNPs, 24 were typed or imputed in the AGNES study. The remaining 2 SNPs (rs41312391, and rs16847549) were not available in the AGNES study. rs16847549 is monomorphic in the HapMap sample of European ancestry and rs41312391 is not a recognized HaPMaP SNP and has no available proxy and was therefore not imputed. Multiple genomic approaches to identify genetic susceptibility to VF in MI 81

The 24 typed or imputed SNPs were investigated for association with VF in the AGNES case-control set. Here, the association P-value threshold corresponded to a P-value of 0.05 with a Bonferroni correction for the 24 candidate SNPs in this analysis (P ≤ 0.002).

Overlap with expression quantitative trait locus data We used two different approaches to search for VF-associated SNPs that also display eQTL effects.

1. Forward approach (from VF-SNP to eQTL) We selected SNPs that were associated with VF at P ≤ 1 × 10−5 in the current AGNES GWAS and investigated whether these SNPs displayed effects on the mRNA transcript abundance of genes located within 1Mb region spanning the SNPs (cis-eQTL effects). To this end, we employed three different databases including (1) an in- house eQTL resource obtained in left ventricle from 129 donor human hearts that was generated in our laboratory in the setting of another study 39; (2) The human, left ventricle eQTL resource from the Genotype-Tissue Expression (GTEx) pilot project 40 obtained in 83 post- mortem human heart samples; and (3) the Genenetwork eQTL resource of peripheral blood samples 41. All 3 databases assumed an additive genetic model. The P-value threshold applied in this analysis corresponded to 0.05 with a Bonferroni correction for the total number of transcripts within 1 Mb spanning all the SNP tested. Chapter 5

(1.1) The human donor heart eQTL database: In brief, our in-house human heart genome-wide eQTL data was generated in 129 left ventricular samples that were collected from non-diseased human donor hearts. Genome-wide left ventricular transcript abundance and genotypic data were combined to identify SNPs associating with the abundance of specific transcripts. Each of the 18,402 transcripts and 897,683 SNP genotypes that passed pre-processing and stringent quality control were tested for eQTL effects, identifying 771 eQTLs, regulating 429 unique transcripts at genome- wide statistical significance.

(1.2) GTeX human heart, left ventricle eQTL database: The GTEx project 40 collected human heart tissue samples from post-mortem or organ transplant settings to identify the tissue specific expressions and their correlation with genotypes in human. At present, the GTEx project includes 83 human left ventricule samples.

(1.3) The Genenetwork peripheral blood eQTL database: Genenetwork 41 is a large eQTL database that collected eQTLs in 8086 whole blood and white blood cell samples. Various gene expression platforms were used to measure gene expression values in 82 Chapter 5

the Genenetwork eQTL database. The large sample size of the Genenetwork database provides higher power to detect eQTL effects which was an important reason for us to use this database in our search for eQTL effects of SNPs associating with VF, alongside the relatively small human heart eQTL databases that were selected because of tissue specificity.

2. Backward approach (from eQTL to VF-SNP) In a backward approach, we tested whether independent SNPs that display statistically significant eQTL effects in human heart also display association with VF in the AGNES study. For this we used the 2 human heart databases including (1) our in-house eQTL database and (2) the human heart, left ventricle tissue from the GTEx pilot project (for details see above). The genome-wide significant thresholds as applied in the respective studies were used in our selection of significant SNPs. The two databases together contained 911 independent SNPs (r2 ≤ 0.7) with a MAF > 15% displaying a significant eQTL effect in left ventricular tissue. TheP- value threshold for the association analysis with VF was thus set at P ≤ 5.5 × 10−5, corresponding to a P-value of 0.05 with a Bonferroni correction for 911 independent tests. We also set an arbitrary threshold of P ≤ 0.001.

Pathway analysis We used a gene set enrichment analysis (GSEA) approach to test whether specific biological pathways were enriched with the VF-associated genes from the current AGNES GWAS. We tested pathways annotated in the Ingenuity, BIOCARTA, REAC- TOME, Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG), and Protein Analysis THrough Evolutionary Relationships (PANTHER) databases. In total, 3216 gene sets were tested for enrichment of multiple modest associations. Pathway analyses were carried out using MAGENTA 42. In brief, all P-values of SNPs included in the AGNES GWAS were used as an input for MAGENTA. The analysis within MAGENTA consists of three steps: (1) Determine the smallest P-value at each gene. Each SNP was assigned to a gene using extended gene boundaries which correspond to 110kb upstream to the gene’s most extreme transcript start site and 40kb downstream to the gene’s transcript most extreme end site. These extended gene boundaries represent the 99th percentile of the distances of cis-eQTLs from their adjacent gene’s transcript start and end sites which is according to a comprehensive genome-wide analysis of putative functional regulatory elements (cis-eQTLs) using expression data from human lymphoblastoid cell lines 43. (2) Group the genes according to pathway information of each database, i.e. GO, PANTHER, Ingenuity, KEGG, REACTOME, and BIOCARTA. (3) Determine if a pathway is enriched for low-P-value genes by comparing the P-value distribution with the one under the null hypothesis Multiple genomic approaches to identify genetic susceptibility to VF in MI 83 while correcting for confounders, such as gene size, SNP density and LD-related properties using a multivariate linear regression model. In the current study, a GSEA P-value less than 2 × 10−3 (corresponding to false discovery rate (FDR) < 0.1) was considered as statistical significance threshold for pathway analysis.

Results

Genome-wide association analysis

1. Discovery in the AGNES study In total, 1433 AGNES patients with a first acute MI (672 patients with VF and 761 without VF) were included in the analysis. Baseline characteristics of the study populations are shown in Table 1A-C. AGNES cases had a lower prevalence of diabetes mellitus and hypercholesterolemia. The average body mass index (BMI) was lower in

Table 1A | Baseline characteristics of the AGNES case-control set Characteristic Na Total Cases Controls P-valueb (n = 1433) (n = 672) (n = 761) c Sex (male) 1433/672/761 1142(80) 537(80) 605 (79) 0.85 Chapter 5 Age at MI (years) d 1433/672/761 57.5±10.8 56.7±1 58.3±10.6 0.004 ST segment deviation (mm) e 491/251/240 15(16) 18(19) 14(12) < 0.001 MI location c 1366/619/747 0.001f Anterior MI 726(53) 359(58) 367(49) Inferior MI 640 (47) 260(42) 380(51) CK-MB (μg/L) e 1044/415/629 205(277) 226(356) 188(244) < 0.001 Family history of sudden 1416/657/759 400(28) 205(31) 195(26) 0.002 death c Beta blocker usage c 1384/632/752 139(10) 66(10) 73(10) 0.65 Cardiovascular risk factors Current Smoking c 1374/627/747 809(59) 396(63) 413(55) 0.003 Diabetes Mellitus c 1349/613/736 102(8) 29(5) 73(10) 0.0004 Hypertension c 1298/584/714 426(33) 185(32) 241(34) 0.43 Hypercholesterolemia c 1250/563/687 406(32) 164(29) 242(35) 0.02 BMI (kg/m2) d 1342/597/745 26.5±3.86 26.1±3.66 26.8±3.99 0.002 MI, myocardial infarction; CK-MB, creatine kinase-MB; aSample sizes of the total, case and control sets for whom information is available are given. bP-value for comparison of cases and controls for each item; cNumber (%); dmean ± s.d.; eMedian (interquartile range); fP-value for comparison of inferior and anterior MI between cases and controls. 84 Chapter 5

Table 1B | Baseline characteristics of the PREDESTINATION case-control set Characteristic Na Total Cases Controls P-valueb (n = 1433) (n = 672) (n = 761) Sex (male) c 754/316/438 642(85) 266(84) 376(86) < 0.001 Age at MI (years) d 750/316/434 58.3±10.1 58.5±10.3 58.1±10.0 0.9 ST segment deviation (mm) e - - - - MI location c 753/315/438 Anterior MI 374(50) 170(54) 204(47) 0.08 Inferior MI 339(45) 133(42) 206(47) < 0.001 CK-MB (μg/L) e 250/138/112 145.6(240) 183.2(282) 109.5(244) < 0.001 Family history of sudden death c 430/201/229 47(11) 27(13) 20(9) 0.3 Beta blocker usage c 379/134/245 101(25) 44(33) 57(23) 0.2 Cardiovascular risk factors Current Smoking c 716/299/417 387(54) 166(56) 221(53) 0.005 Diabetes Mellitus c 701/288/413 97(14) 35(12) 62(15) 0.006 Hypertension c 712/294/418 390(55) 172(59) 218(52) 0.02 Dyslipidemia c 701/288/413 296(42) 130(45) 166(40) 0.04 BMI (kg/m2) e 417/193/224 102(24) 45(23) 57(25) 0.2 MI, myocardial infarction; CK-MB, creatine kinase-MB; aSample sizes of the total, case and control sets for whom information is available are given. bP-value for comparison of cases and controls for each item; cNumber (%); dmean ± s.d.; eMedian (interquartile range); fP-value for comparison of inferior and anterior MI between cases and controls.

Table 1 C | Baseline characteristics of the GEVAMI case-control set Characteristic Na Total Cases Controls P-valueb (n = 1433) (n = 672) (n = 761) Sex (male) c 852/269/583 678(80) 231(86) 447(77) 0.002 Age at MI (years) d 852/269/583 60±10.4 59.0±9.7 60.5±10.7 0.04 ST segment deviation (mm) e 590/201/389 3(1-5) 4(2-6) 2(1-6) < 0.001 MI location c Anterior MI 396(47) 145 (55) 251(43) 0.002 Non-Anterior MI 453(53) 121(45) 332(57) CK-MB (μg/L) e 654/221/433 165(252) 232(318) 139(221) < 0.001 Family history of sudden death c 833/257/576 241(29) 100(39) 141(25) < 0.001 Beta blocker usage c 832/261/571 67(8) 21(8) 46(8) 1.00 Cardiovascular risk factors Current Smoking c 847/265/582 462(55) 147(55) 315(54) 0.08 Diabetes Mellitus c 852/269/583 81(10) 31(11) 50(9) 0.2 Hypertension c 852/269/583 316(37) 110(41) 206(35) 0.1 Hypercholesterolemia c 852/269/583 291(34) 106(39) 185(32) 0.03 BMI (kg/m2) d 849/269/580 27.2 ±4.3 31.7 ±71.7 27.2 ±4.3 0.4 MI, myocardial infarction; CK-MB, creatine kinase-MB; aSample sizes of the total, case and control sets for whom information is available are given. bP-value for comparison of cases and controls for each item; cNumber (%); dmean ± s.d.; eMedian (interquartile range); fP-value for comparison of inferior and anterior MI between cases and controls. Multiple genomic approaches to identify genetic susceptibility to VF in MI 85 the AGNES cases than in the controls. AGNES cases more often had a family history of sudden cardiac death than the controls. Additionally, AGNES cases were younger at the time of MI, smoked less frequently, and had greater creatine kinase myocardial isoenzyme (CK-MB) levels than AGNES controls (Table 1A). AGNES GWAS showed an excess of SNPs associated with VF compared to the number expected under the null hypothesis of no association (Figure 1). The genomic control factor was small (λ = 1.057), indicating that overall inflation of genome-wide statistical results due to possible residual population stratification was minimal. The distribution of P-values for the association of SNPs with VF is shown in Fig- ure 2. One locus (Table 2) exceeded the genome-wide significance threshold (P ≤ 5 × 10−8). The SNP displaying the most significant association at this locus was rs2824292 at chr. 21q21 (OR = 0.65; 95% CI: 0.56, 0.76; P = 2.75 × 10−8) that was also identified in the first AGNES GWAS 9. An additional 9 independent SNP clusters (Figures 3a & 3b) passed the arbitrary threshold of P ≤ 1 × 10−5 (Table 2). At each genomic locus, the SNP with the smallest P-value was considered as the lead-SNP. Lead-SNPs remained independently associated with VF in a multiple regression model containing all lead-SNPs. In multivariate models containing all SNPs with P ≤ 1 × 10−5 at each genomic locus, 2 SNPs i.e. rs4841508 (OR = 0.47; 95% CI: 0.27, 0.82; P = 0.007) and rs7812879 (OR = 0.72; 95% CI: 0.57, 0.90; P = 0.005) at locus 8p23 remained associated with VF independently. These 2 SNPs were independent of each Chapter 5 other (r2 = 0.05) and of the lead SNP rs10096381 (r2 = 0.4). We didn’t find such independent signals in other 9 genomic loci.

2. Replication in the PREDESTINATION and GEVAMI samples SNP rs2824292 (Figure 3b) and the lead-SNPs from the other 9 loci were then selected for the first replication attempt in the PREDESTINATION study Table( 1B, Table 2). None of the SNPs displayed an association in PREDESTINATION at the pre-set Bonferroni-corrected significance threshold for 10 independent tests (P ≤ 0.005). However, one SNP, rs1750041, showed a nominally significant association with VF in PREDESTINATION (OR = 1.64; 95% CI: 1.09, 2.46; P = 0.017; Table 2). The direction of effect was consistent across the AGNES and PREDESTINATION studies, with the C-allele increasing the risk for VF in both studies. Upon meta-analysis of the two sets, the association P-value for rs1750041 was improved (OR = 1.66; 95% CI: 1.36, 2.02; P = 5.22 × 10−7). After meta-analysis of the 10 SNPs, 2 SNPs (rs1750041 and rs2135787), which had the strongest combined association with VF, were taken forward to the second replication in the Danish GEVAMI study (Table 1C). Neither rs1750041 (OR = 1.00; 95% CI: 0.74 - 1.34; P = 0.99) nor rs2135787 (OR = 1.01; 95% CI: 0.81, 1.26; P = 0.99) 55

86 Chapter 5

FigureFigure 11 || QuantileQuantileFigure- 1Quantile | Quantile-quantile Plot of plotTest of Statisticstest statistics ((P P(P--values)-values)values) for forfor the thethe associations AssociationsAssociations of SNPs of withof SNPs SNPs VF with with VF VF in in Plotted on the x-axis are expected P-values under the null hypothesis thethe AGNESAGNES CaseCasein the-Control AGNES case-control Set. Plotted set. on the xx--axisaxis areare expectedexpected PP--valuesvalues under under the the null null hypothesis hypothesis and and and on the y-axis the observed P-values after genomic control (λ = 1.057) has been applied. onon thethe yy--axisaxis the observed P-values after genomic controlcontrol (λ(λ == 1.057)1.057) hashas beenbeen applied. applied.

Figure 2 | Overview of the genome-wide association scan in the AGNES case-control set. –log10 (P- Figure 2 | OverviewFigure 2 of | Overview the genome of the -genome-widewide association association scan scan in in the the AGNES AGNES case-control case-control set. −log set.10 –log10 (P- values)values) correctedcorrected(P- values)for age, corrected sex, population for age, sex, stratification population stratifi andand cation inflationinflation and infl factorfactor ation are are factor shown shown are shownfor for each foreach each single single nucleotide polymorphismsingle nucleotide (SNP) polymorphism tested (ordered (SNP) tested by physical (ordered by position). physical position). The horizontal Th e horizontal lines indicatelines the nucleotide polymorphism (SNP) tested (ordered by physical-8 position).−8 The horizontal lines indicate-5 the presetpreset thresholdthresholdindicate for genome the preset-wide threshold significance for genome-wide ((PP ≤≤ 55signifi ×× 1010 cance-8)) andand (P ≤ the5the × 10 arbitraryarbitrary) and the thresholdarbitrarythreshold threshold ( (PP ≤ ≤ 1 1 × × 10 10-).5). (P ≤ 1 × 10−5). -8 OneOne locuslocus (Table 2) exceeded the genome--widewide significancesignificance thresholdthreshold ( (PP ≤ ≤ 5 5 × × 10 10-8).). The The

SNPSNP displayingdisplaying the most significant association atat thisthis locuslocus waswas rs2824292rs2824292 at at chr chr. .21q21 21q21 -8 (OR = 0.65; 95% CI: 0.56-0.76; P = 2.75 × 10 -8) that was also identified in the first (OR = 0.65; 95% CI: 0.56-0.76; P = 2.75 × 10 ) that was also identified in the first 9 AGNES GWAS 9. An additional 9 independent SNP clusters (Figure 3a-c) passed the AGNES GWAS . An additional 9 independent SNP clusters (Figure 3a-c) passed the arbitrary threshold of P ≤ 1 × 10-5 (Table 2). arbitrary threshold of P ≤ 1 × 10-5 (Table 2). At each genomic locus, the SNP with the smallest P-value was considered as the lead- At each genomic locus, the SNP with the smallest P-value was considered as the lead- SNP. Lead-SNPs remained independently associated with VF in a multiple regression SNP. Lead-SNPs remained independently associated with VF in a multiple regression

94 | Chapter 5 94 | Chapter 5

Multiple genomic approaches to identify genetic susceptibility to VF in MI 87 a heterogenoty heterogenoty , 2 Genes I In JPH1 In In XKR6 In Closest In SYNJ2 In Near OR2B2 Near Near MAT2B Near Near KCND3 Near Near CXADRNear Near PCSK5-RFKNear Near SYN2-C3orf31 Near Near ANGPT4-RSPO4Near 7 c −7 0.36 0.38 0.17 0.23 0.21 0.66 0.03 0.11 value P- 5.22 × 10 × 5.22 9.26 × 10- × 9.26 b (n = 2062) = (n Combined OR(95%CI) 1.3(0.72,2.35) 0.81(0.52,1.27) 1.26(0.91,1.75) 0.57(0.22,1.44) 0.68(0.37,1.25) 0.72(0.63,0.82) 0.83(0.36,1.89) 1.68(1.04,2.73) 1.28(0.95,1.74) 1.66(1.36,2.02) Odds ratios and of 95% codedCI per Oddscopy allele adjusted ratios b c 0.8 0.7 0.7 0.7 0.9 0.3 0.6 0.07 0.18 0.02 value P- b (n = 629) = (n PREDESTINATION OR(95%CI) -value adjusted for genomic control. adjusted P -value c 1.03(0.81,1.30) 0.96(0.73,1.26) 1.05(0.83,1.34) 0.92(0.56,1.52) 0.97(0.56,1.69) 0.79(0.61,1.02) 1.28(0.89,1.82) 1.27(0.78,2.06) 1.08(0.82,1.41) 1.64(1.09,2.46) Chapter 5 c −8 −7 −6 −6 −6 −6 −6 −6 −6 −5 P -value 2.7 × 10 × 2.7 1.6 × 10 × 1.6 10 × 8.9 3.2 × 10 × 3.2 10 × 4.3 10 × 1.0 3.5 × 10 × 3.5 2.4 × 10 × 2.4 2.3 × 10 × 2.3 2.6 × 10 × 2.6 b AGNES (n = 1433) = AGNES (n OR(95%CI) 0.65(0.56,0.76) 1.48(1.26,1.73) 1.47(1.24,1.75) 0.70(0.60,0.81) 2.09(1.52,2.85) 1.66(1.33,2.09) 0.55(0.43,0.71) 0.35(0.23,0.55) 1.75(1.41,2.16) 0.52(0.39,0.68) Genes within an area of 1 Mb centered at the SNP are listed; the SNP are at Genes centered of 1 withinMb an area a 4/6 CAF 53/51 36/38 28/28 41/36 90/90 87/89 11/13 50/47 85/86 -value; P -value; C/T C/T C/T A/C A/C G/T G/T A/G A/G C/G C/N Chr: location 20:848416 8:75374629 3:11952551 9:78064589 6:27991180 8:10851314 21:17709047 1:112372262 5:163438447 6:158418507 SNP rs2824292 rs6988935 rs17739527 rs2135787 rs13173656 rs1750041 rs1353342 rs1497526 rs497602 rs10096381 Chr, chromosome; C/N, coded allele/ non coded allele; CAF, frequency of coded allele in the cases and controls for AGNES/PREDESTINATION; AGNES/PREDESTINATION; for controls and cases the in allele coded of frequency CAF, allele; coded non allele/ coded C/N, chromosome; Chr, Table 2 | Meta-analysis of Single Nucleotide Polymorphisms (SNPs) in the of Single PolymorphismsAGNES (SNPs) Nucleotide and Predestination 2 | Meta-analysis case-control set Table for age and sex, random eff ratios;ect model is used for the combined odds and sex,for age eff random I-squared; Het.P, heterogenoty heterogenoty Het.P, I-squared; 88 Chapter 5 5 were associated with VF in the GEVAMI sample. Meta-analysis of association sta- tisticsBonferroni from- correctedAGNES, PREDESTINATIONsignificance threshold and for GEVAMI10 independent resulted tests in ( Pa ≤combined 0.005). odds ratio of 1.39 (95% CI: 0.99, 1.96; P = 0.06) per copy of the C-allele for rs1750041, and However, one SNP, rs1750041, showed a nominally significant association with VF in a combined odds ratio of 0.82 (95%CI: 0.65, 1.02; P = 0.08) per copy of the A-allele for rs2135787.PREDESTINATION (OR = 1.64; 95% CI: 1.09, 2.46; P = 0.017; Table 2). The direction Additionally, we meta-analyzed our results for rs2824292 with previously pub- lishedof effect results was consistentfor this SNP across 9,44. We the included AGNES 4and case-control PREDESTINATION studies in thisstudies, meta-analysis: with the C- (1) results from the current AGNES GWAS; (2) results from replication sample of our allele increasing the risk for VF in both studies. Upon meta-analysis of the two sets, the previous GWAS 9; (3) results from the PREDESTINATION study; and (4) results from theassociation study by P -Bugertvalue for et rs1750041al. 44 After wasmeta-analysis improved (OR (Figure = 1.66; 4), 95% the CI:association 1.36, 2.02; was not statistically signifi cant (OR = 1.22; 95% CI: 0.93, 1.60; P = 0.14). P = 5.22 × 10-7).

Figure 3a | Locus-specific association map spanning around rs17739527 (top left), rs2135787 (top right), Figurers13173656 3a | Locus-specifi(bottom left), rs1497526 c association (bottom map right) spanning using thearound SNP annotationrs17739527 and (top proxy left), search rs2135787 (SNAP) (top 36. right),The plot rs13173656 is centered (bottom at the strongest left), rs1497526 SNP at each (bottom locus right)depicted using by redthe diamondSNP annotation at the highest and proxy point; searchthe (SNAP)color legend 36. Th ate theplot upper is centered right of at each the plotsstrongest corresponds SNP at toeach the locus color ofdepicted each SNP by redand diamondillustrates atthe the high- estimated LD (by means of r2) of that SNP with the strongest SNP; Superimposed on the plot are gene estlocations point; (green)the color and legend recombination at the upper rates right(blue). of Chromosome each plots corresponds positions are tobased the oncolor HapMap of each release SNP and22 2 illustratesbuild 36.2 .the estimated LD (by means of r ) of that SNP with the strongest SNP; Superimposed on the plot are gene locations (green) and recombination rates (blue). Chromosome positions are based on HapMap release 22 build 36.2.

96 | Chapter 5

Multiple genomic approaches to identify genetic susceptibility to VF in MI 89 5

5 5

Figure 3b | Locus-specific association map spanning around rs1750041(top left), Chapter 5 rs6988935 (top right), rs10096381(bottom left), rs1353342(bottom right) using the SNP annotation and proxy search (SNAP) 36. The plot is centered at the strongest SNP at each locus depicted by red diamond at the highest point; the color legend at the upper right of each plots corresponds to the color of each SNP and illustrates the estimated LD (by means of r2) of that SNP with the strongest SNP; Superimposed on the plot are gene locations (green) and recombination rates (blue). Chromosome positions are based on HapMap release 22 build 36.2.

FigureFigure 3c | Locus 3c | -Locusspecific-specific association association map spanning map spanning around around rs497602(left), rs497602(left), and rs2824292 and rs2824292 (right) (right)using using theFigure SNPthe annotation 3bSNP | Locus-specifiannotation and proxy and searchproxyc association search(SNAP) (SNAP) 36.map The spanning 36 plot. The is plotcentered around is centered at rs1750041(topthe strongestat the strongest SNP left), at SNP each rs6988935 at l ocuseach l (topocus depictedright),depicted byrs10096381(middle red bydiamond red diamond at the left), highestat the rs1353342(middle highest point; thepoint; color the legend colorright), legend at rs497602(bottom the upperat the rightupper of right left),each of plotsand each rs2824292corresponds plots corresponds (bot- 2 2 totom the color toright) the of color using each of SNPthe each SNP and SNP illustratesannotation and illustrates the and estimated the proxy estimated searchLD (by LD (SNAP)means (by meansof 36 .r Th) of ofe thatrplot) of SNPis thatcentered with SNP the with at strongest the the strongest strongest SNP;SNP SuperimposedSNP; at each Superimposed locus on depicted the onplot the by are plotred gen diamondaree locations gene locations at (green) the highest (green) and recombination point; and recombination the color rates legend (blue). rates at (blue). Chromosome the upper Chromosome right of positionseachpositions plots are basedcorresponds are onbased HapMap on to HapMap the release color release 22 of build each 22 36.2. buildSNP and36.2. illustrates the estimated LD (by means of r2) of that SNP with the strongest SNP; Superimposed on the plot are gene locations (green) and recombi- Afternation Aftermeta rates -metaanalysis (blue).-analysis Chromosome of the of 10 the SNPs, positions 10 SNPs, 2 SNPs are 2 based SNPs (rs1750041 on (rs1750041 HapMap and release rs2135787) and 22rs2135787) build, 36.2.which, which had the had the

strongeststrongest combined combined association association with VFwith, were VF, weretaken taken forwa forward to rdthe to second the second replication replication in in

the Danishthe Danish GEVAMI GEVAMI study study (Table (Table 1C). N1Ceither). Neither rs1750041 rs1750041 (OR =(OR 1.00; = 1.00;95% 95%CI: 0.74 CI: -0.74 -

1.34; 1.34;P = 0.99) P = 0.99)nor rs2135787 nor rs2135787 (OR =(OR 1.01; = 1.01;95% CI:95% 0.81 CI:- 1.26;0.81- 1.26;P = 0.99) P = 0.99)were wereassociated associated

with VFwith in VF the in GEVAMI the GEVAMI sample. sample. Meta -Manalysiseta-analysis of association of association statistics statistics from fromAGNES, AGNES, Multiple genomic approaches to identify genetic susceptibility to VF in MI | 97 PREDESPREDESTINATIONTINATION and GEVAMI and GEVAMI resulted resulted in a combinedin a combined odds oddsratio ratioof 1.39 of (95%1.39 (95% CI: CI:

0.99, 0.99,1.96; 1.96;P = 0.06) P = 0.06)per copy per copyof the of C -theallele C-allele for rs1750041 for rs1750041, and ,a andcombined a combined odds ratioodds ofratio of

0.82 (95%CI0.82 (95%CI: 0.65:- 1.0.0265;- 1.P 02= ;0. P08 =) 0.per08 copy) per copyof the of A the-allele A-allele for rs2135787 for rs2135787. .

Additionally,Additionally, we meta we - metaanalyzed-analyzed our results our results for rs2824292 for rs2824292 with with previously previously published published

resultsresults for this for SNP this 9,44 SNP. We 9,44 . includedWe included 4 case 4 -controlcase-control studies studies in this in meta this - metaanalysis:-analysis: (1) (1)

resultsresults from from the current the current AGNES AGNES GWAS GWAS; (2) ; results (2) results from from replication replication sample sample of our of our

previousprevi GWASous GWAS 9; (3) 9results; (3) results from fromthe PREDESTINATION the PREDESTINATION study; study; and (4) and results (4) results from from

the studythe study by Bugert by Bugert et al .et 44 alAfter. 44 After meta - metaanalysis-analysis (Figu (reFigu 4),re the 4) , associationthe association was not was not

statisticallystatistically significant significant (OR =(OR 1.22; = 1.22;95% CI:95% 0.93 CI:- 1.60;0.93- 1.60;P = 0.14) P = .0.14) .

98 | Chapter98 | Chapter 5 5

90 Chapter 5 5

FigureFigure 4 4 | | Forest plot plot for for meta-analysis meta-analysis of rs2824292. of rs2824292. SE, standardSE, standard Error; Error;OR, odds OR, Ratio; odds CI, Ratio; Confi CI,- Confidencedence Interval; Interval; W, W, weight. weight. Odds Odds ratios ratios are are given given per per copy copy of theof the G-allele. G-allele.

Literature-basedLiterature-based candidate candidate SNP SNP analysis analysis We selected 24 SNPs previously reported to be associated with SCD/VF (Table 3) to Wetest selected for association 24 SNPs withpreviously VF in thereported AGNES to becase-control associated set.with None SCD/VF of the (Table tested 3 )SNPs to test reached the Bonferroni-corrected P-value threshold (P ≤ 0.002). At a nominally forsignifi association cant level, with theVF inA-allele the AGNES at rs6795970 case-control was associated set. None with of thea decreased tested SNPs risk reached of VF (OR = 0.85; 95% C.I: 0.73, 0.99; P = 0.04). By testing this association in the PREDESTI- the Bonferroni-corrected P-value threshold (P ≤ 0.002). At a nominally significant level, NATION study, rs6795970 showed a trend for association with VF (OR = 0.80; 95% CI: the0.63, A-allele 1.01; P at = 0.06). rs6795970 After was meta-analysis associated of with the AGNES a decreased and the risk PREDESTINATION of VF (OR = 0.85; results for this SNP, each copy of the A-allele at rs6795970 decreased the risk of VF by 95%0.83 C.I, fold 0.73-0.99; (95% CI: 0.73, P = 0.95; 0.04). P = By 0.006). testing this association in the PREDESTINATION

study, rs6795970 showed a trend for association with VF (OR = 0.80; 95% CI, 0.63-1.01; Overlap with expression quantitative trait locus data P = 0.06). After meta-analysis of the AGNES and the PREDESTINATION results for 1. Forward approach (from VF-SNP to eQTL) thisWe SNP, assessed each thecopy 10 of independent the A-allele VF-associated at rs6795970 locidecreased from the the currentrisk of AGNESVF by 0.83 GWAS fold for cis-eQTL eff ects in three eQTL databases (see material and methods). A total of 235 (95% CI, 0.73-0.95, P = 0.006). genes were present in the 1 Mb regions spanning the 10 loci, resulting in a Bonferroni- corrected eQTL P-value threshold of 0.05/235 = 2 × 10−4). Overlap with expression quantitative trait locus data (1.1) Th e human donor heart eQTL database: In the human donor heart eQTL database 39, none of the 10 SNPs tested showed a Bonferroni-corrected signifi cant associa- 1. Forward approach (from VF-SNP to eQTL) tion; 3 SNPs however showed a nominally signifi cant association with the expression level of genes located in cis (Table 4 & Figure 5). rs2824292 decreased the expression Welevel assessed of the the BTG3 10 independent transcript ( PVF-associated = 0.02; Table loci 4). rs17739527from the current was nominallyAGNES GWAS associ- for ated with a decreased level of the ATP5F1 transcript (P = 0.001), and with increased cislevels-eQTL of effects three transcripts in three eQTL namely databases RHOC (see (P = 0.04), material CEPT1 and methods). (P = 0.047), A and total DDX20 of 235 genes were present in the 1 Mb regions spanning the 10 loci, resulting in a Bonferroni-

corrected eQTL P-value threshold of 0.05/235 = 2 × 10-4).

Multiple genomic approaches to identify genetic susceptibility to VF in MI | 99 Multiple genomic approaches to identify genetic susceptibility to VF in MI 91

Table 3 | Association of candidate SCD SNPs with ventricular fibrillation in AGNES SNP Ref. Chr. Position C/N CAFa AGNES (n = 1433) Gene OR (95% CI) P-value rs6795970 24 3 38,741,679 A/G 39 0.85(0.73,0.99) 0.04 SCN10A rs2383207 34 9 22,105,959 A/G 49 0.86(0.74,1.01) 0.06 CDKN2B-AS1 rs10503929 29 8 32,733,525 C/T 18 0.86(0.71,1.04) 0.12 NRG1 rs7366407 38 1 116,219,426 A/T 27 0.89(0.75,1.05) 0.17 Near HNRNPA1P43 rs3864180 11 13 91,234,489 A/G 63 1.11(0.95,1.3) 0.19 GPC5 rs1042714 26,37 5 148,186,666 C/G 57 1.09(0.94,1.28) 0.26 ADRB2 (Gln27Glu) rs2283222 22 11 2,712,177 C/T 32 1.08(0.92,1.26) 0.34 KCNQ1 rs1805124 32 3 38,620,424 C/T 22 1.08(0.90,1.31) 0.40 SCN5A (Pl(A1/A2)) rs12084280 38 1 160,290,433 C/G 10 0.89(0.69,1.16) 0.40 Near NOS1AP rs17500488 38 1 116,013,286 C/T 08 1.09(0.84,1.43) 0.51 VANGL1 rs360717b 27 11 111,539,935 A/G 28 0.95(0.80,1.12) 0.53 IL‑18 rs9862154 38 3 32,096,706 C/G 81 0.94(0.78,1.14) 0.54 Near GPD1L rs10918859 38 1 160,435,892 A/G 18 1.06(0.87,1.30) 0.56 NOS1AP rs3010396 38 1 116,075,311 A/G 44 0.96(0.83,1.12) 0.62 CASQ2 rs2077316 28 10 63,895,454 A/C 90 0.92(0.64,1.32) 0.64 ZNF365

rs2200733 31 4 111,929,618 C/T 87 0.95(0.76,1.19) 0.67 Near MIR297 Chapter 5 rs6730157 28 2 135,623,558 A/G 67 1.03(0.88,1.2) 0.73 RAB3GAP1 rs12567209 25 1 160,303,103 A/G 08 0.95(0.72,1.26) 0.73 NOS1AP rs710448 36 3 187,935,579 A/G 61 0.98(0.84,1.14) 0.79 KNG1 rs1801253 45 10 115,795,046 C/G 73 1.02(0.86,1.21) 0.83 ADRB1 (Arg389Gly) rs6065 33 17 4,777,161 C/T 92 1.03(0.78,1.37) 0.84 GP1BA (HPA‑2 Met/ VNTR B) rs5918 32 17 42,715,729 C/T 15 1.01(0.82,1.25) 0.90 ITGB3 (Leu33Pro of GPIIIa) rs3766871 35 1 235,844,707 A/G 3 0.98(0.62,1.54) 0.91 RYR2 rs1492099 36 3 149,920,193 C/T 86 0.99(0.8,1.23) 0.94 AGTR1 Chr, chromosome; Ref, reference; C/N, coded allele/ non coded allele; CAF, coded allele frequency; OR, odds ratio per copy of coded allele. aCAF, frequency of coded allele in the cases and controls combined; bProxy for rs187238 (r2 = 1.0). 92 Chapter 5

(P = 0.037). rs1750041 was associated with a decreased level of the ARID1B transcript (Beta = −0.19; SE: 0.03; P = 0.03; Table 4 & Figure 5).

(1.2) GTeX human heart, left ventricle eQTL database: None of the 10 selected SNPs that were searched in the human heart left ventricle GTEx resource displayed eQTLs at the Bonferroni-corrected level of statistical significance, but seven out of ten SNPs showed associations with expression levels at the nominal significance level Table( 4). These were rs10096381 which was associated with FDFT1 (P = 0.03), rs6988935 associated with TMEM70 (P = 0.001), rs497602 with TRIB3 (P = 0.04), rs2135787 with TIMP4 (P = 0.03) and HRH1 (P = 0.01), rs17739527 with CEPT1 (P = 0.02) and ADORA3 (P = 0.004), and finally rs1750041 was associated with SERAC1 (P = 0.02).

(1.3) The Genenetwork peripheral blood eQTL database (n = 5311): Two SNPs (rs1750041 and rs10096381) out of 10 SNPs displayed eQTL effects in peripheral blood passing the Bonferroni-corrected statistical significance level. Each copy of the T-allele at rs1750041 was associated with a decreased level of transcript expression of GTF2H5 (P = 1.66 × 10−46), SERAC1 (P = 7.94 × 10−5) and SYNJ2 (P = 6.34 × 10−4). Each copy of the

Table 4 | The strongest AGNES SNP sought in the human eQTL databases SNP Chr:Positiona Allelesb MAF Gene Probea Human heart 39 Effect Size SE P-value rs2824292 21:18787176 G/A 0.48 BTG3 ILMN_1707339 −0.19 0.08 0.02 rs10096381 8:10813904 ** NA No eQTL NA NA NA rs6988935 8:75212074 C/A 0.37 TMEM70 ILMN_1739032 0.07 0.09 0.40 rs1497526 6:27883201 A/G 0.03 TRIM27 ILMN_1655482 −0.01 0.12 0.90 PGBD1 ILMN_1791949 0.08 0.12 0.51 rs497602 20: 900416 ** ** TRIB3 ** NA NA NA rs2135787 3: 11977551 A/C 0.36 TIMP4 ILMN_1663399 −0.20 0.16 0.22 HRH1 ILMN_1673953 −0.07 0.05 0.19 rs1353342 9: 78874769 A/C 0.08 NA No eQTL NA NA NA rs13173656 5: 163505869 T/C 0.11 NA No eQTL NA NA NA rs17739527 1: 112372262 C/T 0.25 ATP5F1 ILMN_1672191 −0.36 0.13 0.005 ATP5F1 ILMN_1721989 −0.2 0.06 0.001 RHOC ILMN_1673305 0.1 0.05 0.04 CEPT1 ILMN_1676588 0.09 0.05 0.05 DDX20 ILMN_1694983 0.07 0.03 0.04 rs1750041 6: 158498519 A/G 0.17 ARID1B ILMN_1660433 −0.07 0.03 0.03 SYNJ2 ILMN_1660433 0.06 0.08 0.4 Chr, Chromosome; MAF, Minor Allele frequency; SE, Standard Error; NA, not available; aPosition and Probe are based on Koopman et al. bAlleles refers to minor allele/major allele. ** SNP not available in Koopman et al. Multiple genomic approaches to identify genetic susceptibility to VF in MI 93

Table 4 | The strongest AGNES SNP sought in the human eQTL databases (continued) SNP Genes within 1 Mba rs2824292 NS: C21orf34, BTG3, C21orf91, PRSS7, C21orf37, NCRNA00157; BP: C21orf114, C21orf91-OT1, CXADR, U03241, CHODL, TBX1 rs10096381 NS: MSRA, T-SP1, GATA4, XKR6, SOX7, NEIL2, C8orf74; BP: MTMR9, C8orf49, AMAC1L2, FDFT1, BX105370, C8orf16, CTSB, PINX1, BG181200, FAM167A, TDH, BLK, C8orf13 rs6988935 NS: LY96, TMEM70, STAU2, GDAP1, STAU2, GDAP1, CRISPLD1, PI15, UBE2W, LOC100128126, JPH1, TCEB1; BP: AK125786, RDH10, TYW3, DB201663, PRKRIR, BM674958, DR033690 rs1497526 NS: GPX6, GUSBL1, NKAPL, PP14762, FKSG83, ZNF165, HIST1H2AI, HIST1H2BK, TRIM27, ZNF391, ZSCAN16, HIST1H4I, ZNF192, ZKSCAN4, RFP, ZKSCAN3, HIST1H4L, ZNF323, PGBD1, SCAND3, HIST1H1B, ZNF187, ZSCAN12L, HIST1H3J, GPX5, ZNF193; BP: AK311627, ZSCAN12, HIST1H2AL, OR2B2, HIST1H2AG, HIST1H2AM, HIST1H2BJ, HIST1H4J, PRSS16, HIST1H4K, BC043177, AX747641, HIST1H2BM, HIST1H2BN, HIST1H3I, RPSA, AK123552, HIST1H2BO, HIST1H2AH, BI915787, OR2B6, ZSCAN23, HIST1H2AJ, HIST1H3H, HIST1H2BL, POM121L2, ZNF204, ZNF184, HIST1H2AK rs497602 NS: DEFB128, SNPH, PSMF1, SDCBP2, SIRPA, NSFL1C, NRSN2, FKBP1A, C20orf55, DEFB127, TCF15, SRXN1, TBC1D20, ZCCHC3, SIRPD, C20orf54, C20orf46; BP: TRIB3, DEFB125, SIRPB1, SIRPG, AK000809, DEFB126, FAM110A, SCRT2, SOX12, C20orf96, RBCK1, AI948563, DEFB129, DEFB32, ANGPT4, CSNK2A1, DEFB132, RSPO4 rs2135787 NS: RPL32, TIMP4, SYN2, TSEN2, VGLL4, SLC6A11, C3orf31, SLC6A1, Chapter 5 LOC100129480, IQSEC1, HRH1, RAF1; BP: LOC100128644, BX111027, LOC100506990, MKRN2, ATG7, BG754745, CD240199, AI769555, GSTM1L, SNORA7A, TMEM40, PPARG, CAND2 rs1353342 NS: VPS13A, RFK, PRUNE2, PCSK5, LAMR1P15, KIAA0367; BP: BP432430, AF070595, FOXB2, LOC100130426, GCNT1, PCA3 rs13173656 NS: NUDCD2, HMMR, CCNG1, MAT2B; BP: AW836649 rs17739527 NS: WDR77, RAP1A, FAM19A3, CEPT1, LOC149620, DDX20, KCND3, RHOC, ADORA3, ST7L, MOV10, FAM212B, C1orf183, CHIA, WNT2B, C1orf88, SLC16A1, C1orf162, CTTNBP2NL, ATP5F1, LOC100129269; BP: AK123703, AX747733, PPM1J, CAPZA1, BC063548, OVGP1, TMEM77, DENND2D, FLJ36116, CR738423, CHI3L2, AFARP1 rs1750041 NS: SYNJ2, RSPH3, C6orf35, TAGAP, TULP4, TMEM181, ARID1B, ZDHHC14, SNX9, VIL2, DYNLT1 BP: BX117128, BX116720, BX119561, GTF2H5, SYTL3, EZR, AK125637, CU680699, SERAC1, OSTCP1 NS, not significant (i.e. probe passed quality control and background expression threshold, but no significant eQTL effect); BP, bad probe (i.e. probe did not pass quality control: bad probe design resulting in ambiguous mapping or potential spurious eQTL effects) or not detected (i.e. probe passed quality control, but is not expressed above background levels). a List of non-significant genes within 1Mb region spanning the SNP 94 Chapter 5 5

Figure 5 | Plot of mRNA intensities for strongest AGNES SNPs with eQTL eff ects in the human heart39 . Figure 5 | Plot of mRNA intensities for strongest AGNES SNPs with eQTL effects in the human heart 39.

(1.3) The Genenetwork peripheral blood eQTL database (n = 5311): Two SNPs

(rs1750041 and rs10096381) out of 10 SNPs displayed eQTL effects in peripheral blood passing the Bonferroni-corrected statistical significance level. Each copy of the T-allele at rs1750041 was associated with a decreased level of transcript expression of GTF2H5

Multiple genomic approaches to identify genetic susceptibility to VF in MI | 103 Multiple genomic approaches to identify genetic susceptibility to VF in MI 95 gene XKR6 Cognate 40 Ref. a 0.91 P -value OR PREDESTINATION (95% CI) 0.98(0.75,1.29) a −5 10 × AGNES P -value 1.80 Chapter 5 AGNES OR(95% CI) 1.58(1.28,1.94) 51 CAF AGNES C/G C/N AGNES 10,231,182 Position 8 Chr SNP rs6982308 value adjusted for age and sex for age study. in the AGNES adjusted value P- Table 5 | Association VF in a significant of MI the displaying eQTL effect SNPs (i.e. heart) with in human of eQTL SNPs setting Table a C/N, coded allele/ non coded allele; CAF, coded allele frequency; OR, odds ratio per copy of coded allele; Ref, reference. of coded allele; Ref, per copy coded allele frequency; OR, odds ratio coded allele/ non coded allele; CAF, C/N, 96 Chapter 5

G-allele at rs10096381 was associated with a decreased level of expression of XKR6 (P ≤ 9.12 × 10−5). No significant eQTLs were found for rs1353342 and rs13173656 in any of the databases we investigated.

2. Backward approach (from eQTL to VF-SNP) We selected 911 previously identified eQTLs in human heart 39,40 and tested their association with VF in the AGNES study (Table 5) using a Bonferroni-corrected significance threshold of P < 5.5 × 10−5 (0.05/911). In this analysis, one SNP, rs6982308 regulating XKR6 gene in the human heart left ventricle tissue of the GTEx database, displayed a Bonferroni-corrected significant effect on VF. At nominal statistical significant level 199 SNPs (45 independent SNPs at r2 < 0.7) were additionally associated with VF. Each copy of the C-allele at rs6982308 increased the risk of VF by 1.58 fold (95% CI: 1.28, 1.94; P = 1.8 × 10−5; Table 5). After meta-analysis of test statistics from the AGNES and the PREDESTINATION for these SNPs, each copy of the C-allele at rs6982308 increased the risk of VF by 1.31 fold (95% CI: 1.10, 1.55; P = 0.002).

Pathway analysis We used a gene set enrichment analysis (GSEA) approach to test whether previously identified biological pathways were enriched for VF-associated genetic loci in the current study (Table 6). A GSEA P-value of less than 2 × 10−3 corresponding to FDR < 0.1 was considered for the selection of significant pathways (see methods for detail). The associations in the current study were mainly enriched in pathways involved in (i) Pathways related to signal transmission to CREB phosphorylation (Figure 6) such as Neurotransmitter receptor binding and downstream transmission in the postsynaptic cell (P = 1.3 × 10−3), Trafficking of Amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA) receptors (P = 1.0 × 10−4), Post N-methyl-D-aspartate (NMDA) receptor activation events (P = 3.5 × 10−3), phosphorylation of cyclic AMP response element binding protein (CREB) through the activation of CAMKII (P = 8.0 × 10−4), CACAM pathway (P = 3.2 × 10−3); RAS activation upon Ca2+ influx through NMDA receptor (P = 9.0 × 10−4), CREB phosphorylation through the activation of RAS (P = 1.2 × 10−3), and PGC1A pathway (P = 3.0 × 10−4); (ii) Cell cycle pathways such as Packaging of telomere ends (P = 4.4 × 10−5), and Telomere maintenance (P = 3.0 × 10−4); and (iii) Other Pathways such as RNA polymerase I promoter opening (P = 1.0 × 10−4) and Glucuronidation (P = 7.4 × 10−3). Multiple genomic approaches to identify genetic susceptibility to VF in MI 97

Table 6 | Pathway analysis of SNPs tested in the AGNES case control set Database Pathway Genes Nominal GSEA P-value CREB phosphorylation RELATED pathways: REACTOME Trafficking of AKAP5, AP2A1, AP2A2, AP2B1, AP2M1, AP2S1, CACNG2, 1.0 × 10−4 AMPA receptors CACNG3, CACNG4, CACNG8, CAMK2A, CAMK2B, CAMK2D, DLG1, EPB41L1, GRIA1, GRIA2, GRIA3, GRIA4, GRIP2, LOC651907, MDM2, MYO6, NSF, PICK1, PRKCA, PRKCB, PRKCG BIOCARTA PGC1A pathway CALM1, CALM2, CALM3, CAMK1, CAMK1G, CAMK2A, 3.0 × 10−4 CAMK2B, CAMK2D, CAMK2G, CAMK4, CYCSP35, ESRRA, HDAC5, LOC124827, LOC147908, MEF2A, MEF2BNB-MEF2B, MEF2C, MEF2D, PPARA, PPARGC1A, PPP3CA, PPP3CB, PPP3CC, SLC2A4, YWHAH REACTOME CREB phosphory- AKAP9, CREB1, GRIN1, GRIN2A, GRIN2B, GRIN2C, GRIN2D, 8.0 × 10−4 lation through NEFL, CALM1, CALM2, CALM3, CAMK2A, CAMK2B, CAMK2D, ACTN2 the activation of CAMKII REACTOME RAS activation AKAP9, GRIN1, GRIN2A, GRIN2B, GRIN2C, GRIN2D, HRAS, 9.0 × 10−4 upon Ca2+ influx NEFL, RASGRF1, RASGRF2, CALM1, CALM2, CALM3, CAMK2A, CAMK2B, CAMK2D, ACTN2 through NMDA receptor REACTOME Post NMDA ACTN2, ADCY1, ADCY3, ADCY8, AKAP9, BRAF, CALM1, 3.5 × 10−3 receptor activation CALM2, CALM3, CAMK2A, CAMK2B, CAMK2D, CAMK4, CAMKK1, CREB1, GRIN1, GRIN2A, GRIN2B, GRIN2C,

events Chapter 5 GRIN2D, HRAS, MAPK1, NEFL, PDPK1, PRKACB, RAF1, RASGRF1, RASGRF2, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KA6, RRAS BIOCARTA CACAM pathway CALM1, CALM2, CALM3, CAMK1, CAMK1G, CAMK2A, 3.2 × 10−3 CAMK2B, CAMK2D, CAMK2G, CAMK4, CAMKK1, CAMKK2, CREB1, CYCSP35, LOC124827, LOC147908 REACTOME Neurotransmitter ACTN2, ADCY1, ADCY2, ADCY3, ADCY4, ADCY5, ADCY6, 1.3 × 10−3 receptor binding ADCY7, ADCY8, ADCY9, AKAP5, AKAP9, AP2A1, AP2A2, AP2B1, AP2M1, AP2S1, ARHGEF9, BRAF, CACNG2, CACNG3, and downstream CACNG4, CACNG8, CALM1, CALM2, CALM3, CAMK2A, transmission in the CAMK2B, CAMK2D, CAMK4, CAMKK1, CHRFAM7A, postsynaptic cell CHRNA1, CHRNA2, CHRNA3, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNA9, CHRNB2, CHRNB3, CHRNB4, CHRND, CHRNE, CHRNG, CREB1, DLG1, DLG3, EPB41L1, GABBR1, GABBR2, GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRG2, GABRG3, GABRR1, GABRR2, GNAI1, GNAI2, GNAI3, GNAL, GNB1, GNB2, GNB3, GNB4, GNB5, GNG10, GNG11, GNG12, GNG13, GNG2, GNG3, GNG4, GNG5, GNG7, GNG8, GNGT1, GNGT2, GRIA1, GRIA2, GRIA3, GRIA4, GRIK1, GRIK2, GRIK3, GRIK4, GRIK5, GRIN1, GRIN2A, GRIN2B, GRIN2C, GRIN2D, GRIP2, HRAS, KCNJ10, KCNJ12, KCNJ15, KCNJ16, KCNJ2, KCNJ3, KCNJ4, KCNJ5, KCNJ6, KCNJ9, LOC651907, LOC732445, MAPK1, MDM2, MYO6, NCALD, NEFL, NSF, PDPK1, PICK1, PLCB1, PLCB2, PLCB3, PRKACB, PRKCA, PRKCB, PRKCG, RAF1, RASGRF1, RASGRF2, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KA6, RRAS 98 Chapter 5

Table 6 | Pathway analysis of SNPs tested in the AGNES case control set (continued) Database Pathway Genes Nominal GSEA P-value REACTOME CREB phosphory- ACTN2, AKAP9, BRAF, CALM1, CALM2, CALM3, CAMK2A, 1.2 × 10−3 lation through the CAMK2B, CAMK2D, CREB1, GRIN1, GRIN2A, GRIN2B, GRIN2C, GRIN2D, HRAS, MAPK1, NEFL, PDPK1, RAF1, activation of RAS RASGRF1, RASGRF2, RPS6KA1, RPS6KA2, RPS6KA3, RPS6KA6, RRAS - Cell Cycle pathways: REACTOME Packaging of telo- HIST4H4, HIST3H2BB, HIST1H2BA, POT1, TINF2, 4.4 × 10−5 mere ends HIST1H2AE, HIST1H2AD, H2AFX, H2AFZ, HIST1H2BD, HIST1H2BB, TERF2IP, HIST2H4B, ACD, TERF1, TERF2, HIST2H2AA4, HIST3H3, HIST1H4I, HIST1H2AJ, HIST1H2AC, HIST1H2AB, HIST2H2AA3, HIST2H2AC, HIST1H2BG, HIST1H2BL, HIST1H2BN, HIST1H2BM, HIST1H2BF, HIST1H2BE, HIST1H2BH, HIST1H2BI, HIST1H2BC, HIST1H2BO, HIST2H2BE, HIST1H4A, HIST1H4D, HIST1H4F, HIST1H4K, HIST1H4J, HIST1H4C, HIST1H4H, HIST1H4B, HIST1H4E, HIST1H4L, HIST2H4A, HIST1H2BK, HIST1H2BJ REACTOME Telomere mainte- ACD, DKC1, DNA2, FEN1, H2AFX, H2AFZ, HIST1H2AB, 3.0 × 10−4 nance HIST1H2AC, HIST1H2AD, HIST1H2AE, HIST1H2AJ, HIST1H2BA, HIST1H2BB, HIST1H2BC, HIST1H2BD, HIST1H2BE, HIST1H2BF, HIST1H2BG, HIST1H2BH, HIST1H2BI, HIST1H2BJ, HIST1H2BK, HIST1H2BL, HIST1H2BM, HIST1H2BN, HIST1H2BO, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E, HIST1H4F, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L, HIST2H2AA3, HIST2H2AA4, HIST2H2AC, HIST2H2BE, HIST2H4A, HIST2H4B, HIST3H2BB, HIST3H3, HIST4H4, LIG1, NHP2, PCNA, POLA1, POLA2, POLD1, POLD2, POLD3, POLD4, POLE, POLE2, POT1, PRIM1, PRIM2, RFC2, RFC3, RFC4, RFC5, RPA1, RPA2, RPA3, RUVBL1, RUVBL2, TERF1, TERF2, TERF2IP, TERT, TINF2, WRAP53 - Other pathways: REACTOME RNA polymerase I H2AFX, H2AFZ, H3F3A, H3F3AP5, H3F3AP6, H3F3B, 1.0 × 10−4 promoter opening HIST1H2AB, HIST1H2AC, HIST1H2AD, HIST1H2AE, HIST1H2AJ, HIST1H2BA, HIST1H2BB, HIST1H2BC, HIST1H2BD, HIST1H2BE, HIST1H2BF, HIST1H2BG, HIST1H2BH, HIST1H2BI, HIST1H2BJ, HIST1H2BK, HIST1H2BL, HIST1H2BM, HIST1H2BN, HIST1H2BO, HIST1H3A, HIST1H3B, HIST1H3C, HIST1H3D, HIST1H3E, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I, HIST1H3J, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E, HIST1H4F, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L, HIST2H2AA3, HIST2H2AA4, HIST2H2AC, HIST2H2BE, HIST2H3A, HIST2H3C, HIST2H3D, HIST2H4A, HIST2H4B, HIST3H2BB, HIST4H4, LOC731820, MAPK3, MBD2, UBTF REACTOME Glucuronidation SLC35D1, UGDH, UGP2, UGT1A1, UGT1A3, UGT1A4, 7.4 × 10−3 (Metabolism) UGT1A5, UGT1A6, UGT1A7, UGT1A8, UGT1A9, UGT2A1, UGT2B10, UGT2B11, UGT2B17, UGT2B28, UGT2B4, UGT2B7 Multiple genomic approaches to identify genetic susceptibility to VF in MI 99

Discussion

Using a multi-tiered approach in three independent patient sets (AGNES, PREDESTI- NATION, GEVAMI) with a first acute MI and complimentary genetic approaches involving eQTL analysis, we identified 5 SNPs with varying evidence of association with VF that merit further investigation in future studies (rs2824292, rs1750041, rs10096381, rs6982308, and rs6795970). We also attempted to identify putatively causal candidate genes at these loci by overlaying our association data with eQTL data. Furthermore, by assessing SNPs associated with VF in AGNES we identified a number of enriched biological pathways that could be involved in predisposition to VF.

1. rs2824292 In our extended GWAS analysis in the AGNES population, the locus at chr. 21q21 (rs2824292), which we identified in the initial AGNES GWAS 9, remained associated with VF at a genome-wide statistical significance level. In the initial AGNES GWAS 9, the association of rs2824292 with VF was replicated in an independent Dutch case- control set (chapter 4 of this thesis) consisting of patients presenting with VF in the setting of MI (cases) drawn from the ARREST study 46 and MI-survivors (controls) drawn from the GENDER study 47. In spite of this initial replication, and the fact that the signal remained at genome-wide statistical significance in the extended GWAS presented herein, in the current study the effect of rs2824292 on VF was not replicated Chapter 5 in the PREDESTINATION case-control set which has inclusion criteria similar to AG- NES (Table 2). Similarly, another study 44 conducted in a small case-control set from Germany did not detect an association of this locus with VF. After meta-analysis of the test statistics for rs2824292 in these 4 case-control sets, the locus was not associated with VF. This meta-analysis also demonstrated a remarkable heterogeneity in the effect size and direction between the studies (Figure 4) which may be due to differences in phenotype definition and study design, statistical power or pure chance 10. The only genes within 1Mb of rs2824292 are CXADR (encoding the coxsackie and adenovirus receptor) and BTG3 (encoding B-cell translocation gene 3). The coxsackie and adenovirus receptor (CAR) is a transmembrane protein located at the intercalated disks of cardiomyocytes and has a role in conduction of the cardiac electrical impulse at the level of the atrio-ventricular node. We 48 investigated CXADR as a candidate gene for the effect on VF at this locus and demonstrated that decreased expression of CXADR slows conduction in the ventricles and modulates susceptibility to arrhythmia in the setting of myocardial ischemia. These observations make CXADR a likely causal gene for the effect on VF observed in AGNES at this locus. This is further supported by our observation 48 that the risk allele (G) at rs2824292 is associated with a lower expression of CXADR in human heart and has no effect on BTG3 transcript abundance. This 100 Chapter 5

data, generated by qRT-PCR, is at variance with the observation made by Koopmann et al. who using an array-based approach for determination of transcript abundance showed that rs2824292 was an eQTL for BTG3 albeit at a nominally-significant level. It is here imperative to realize that these two studies were conducted on the same human heart samples and therefore the differences in association observed across these studies is very likely due to the different approaches used in the measurement of mRNA abundance, with the qRT-PCR assessment of Marsman et al. being the most accurate. It is clear that the effects of rs2824292 on the expression ofCXADR and BTG3 require further assessment in additional heart samples which are unfortunately scarce. Thus far, in aggregate, data from functional and gene expression studies suggests that rs2824292 may increase the risk of VF by decreasing the level of expression of CXADR.

2. rs1750041 Besides rs2824292, 9 other independent loci with association below our arbitrary threshold were taken forward for replication in the PREDESTINATION and GEVAMI studies. Of these, one SNP (rs1750041 at chr. 6p25) was nominally associated with VF in PREDESTINATION. This SNP however did not display association with VF in GEVAMI. rs1750041 is located in an intron of the SYNJ2 gene encoding synaptojanin 2. This gene has not been previously implicated in arrhythmia susceptibility. SYNJ2 is widely expressed and is also expressed in human heart and cardiac myocytes and mostly localized in cytosol and plasma membrane. Synaptojanin 2 is a member of the inositol-polyphosphate 5-phosphatase family. This family is involved in synaptic transmitter release and interacts with ion channels 49 such as inward rectifier potassium (Kir) channels 50 and calcium channels 51,52. Phosphatidylinositol-4,5-bisphosphate (PIP2) malfunction has been implicated in arrhythmia 53 (reviewed in ref. 54), and in QT-interval prolongation in the Long-QT syndrome 55,56. A mutation in the Phospha- tidylinositol-4,5-bisphosphate binding site of the potassium channel encoded by KCNJ2 has been linked to a familial disorder featuring ventricular arrhythmias 57. In spite of these suggestive observations, we found no Bonferroni-corrected eQTL effect of rs1750041 on SYNJ2 expression in any of the eQTL databases that we investigated. However, we found that rs1750041 modulates the expression of GTF2H5 (in blood), and SERAC1 (in blood and left ventricle). The strongest eQTL effects were reported in blood. None of these have been previously implicated in arrhythmia susceptibility.

3. rs6795970 The current study builds further on our earlier analysis 12,24 that provided the first evidence that rs6795970 (a non-synonymous SNP in SCN10A) is associated with VF in AGNES. The statistical significance of this association improved in the larger Multiple genomic approaches to identify genetic susceptibility to VF in MI 101

AGNES case-control set studied here. Furthermore, this SNP also showed a trend for association in PREDESTINATION with a similar direction of effect and as expected, the association P-value was lower when the AGNES and PREDESTINATION sets were meta-analyzed. In recent years a large body of literature has accumulated on rs6795970 and the haplotype on which it occurs. This SNP was first linked to cardiac electrical function in GWAS on the electrocardiographic PR-interval 24,58,59 and subsequently QRS-duration 60. In all these studies, the A-allele of rs6795970 was associated with slower cardiac conduction (i.e. prolongation of the PR-interval and QRS duration). These same studies 58 and subsequent studies also linked this SNP to pacemaker implantation as a result of atrioventricular block 58, and atrial fibrillation (AF) 61-64. Surprisingly, the conduction-slowing A-allele is protective of VF in AGNES and PRE- DESTINATION and is also protective of AF in the respective studies 59,61,62. Several studies have attempted to identify the genetic and electrophysiological mechanism by which genetic variation in the haplotype tagged by rs6795970 modulates cardiac conduction. rs6795970 causes a Valine to Alanine substitution at residue 1073 in the Nav1.8 sodium channel encoded by SCN10A. Strong evidence exists linking another variant in this haplotype with the effects observed on conduction. Studies working on SCN10A identified a common variant (rs6801957) that resides in an enhancer element located in an intron of SCN10A. This variant is in high LD with rs6795970 (r2 = 0.93). rs6801957 contains a TBX3/TBX5 (T-box transcription factors) binding site and regulates the expression of the nearby SCN5A gene (which encodes Chapter 5 Nav1.5, the mediator of cardiomyocyte depolarization and a critical mediator of conduction in the heart). A series of studies have provided convincing evidence that the A‑allele at rs6801957 disrupts TBX3/TBX5 binding to the enhancer, thereby reducing the expression of SCN5A 65,66. In support of this, the A‑allele at SCN10A rs6801957 has been shown to be associated with reduced SCN5A mRNA in human heart 66.

4. rs6982308 and rs10096381 The second most significant SNP in the AGNES GWAS (rs10096381) is located in an intron of the XKR6 gene (encoding XK, Kell blood group complex subunit-related family, member 6). This SNP displayed an eQTL effect onXKR6 in peripheral blood 41. Of note, in our reverse approach (eQTL to VF-SNP) we uncovered that another SNP, rs6982308, which displays an eQTL effect onXKR6 in human heart 40, is also associated with VF in the AGNES study. rs6982308 and rs10096381 are distant SNPs separated by multiple genes and are not in LD with each other. In fact, SNP rs6982308 is located in the methionine sulfoxide reductase (MSRA) gene, which is located 467 kb far from XKR6 (Figure 3b). MSRA (the protein expressed by MSRA gene) is a regulator of oxi- dative stress and lifespan 67, protects cardiomyocytes against hypoxia/reoxygenation, stress and cell death 68 and protects the heart from ischemia-reperfusion injury 69. 102 Chapter 5

Currently, little is known about the XKR6 gene. Common genetic variants in XKR6 (e.g. rs7819412) have been previously linked to triglyceride levels 70 hypertri- glyceridemia 71 and systemic lupus erythematous (rs6985109, rs4240671, rs11783247, rs6984496) 72-75. The underlying pathology linking XKR6 to all these apparently different phenotypes is still unknown. rs10096381 that is identified in the current study is in high LD with rs6984496 and rs11783247 (r2 > 0.9; mentioned above) which have been previously identified for systemic lupus erythematous.

Pathway analysis In the current study, we demonstrated that SNPs associating with VF in the AGNES study were enriched in 12 biological pathways (Table 6). Few of these pathways such as cell cycle, RNA polymerase I promoter opening, PGC1a, and Glucuronidation could not be grouped into clear SCD-related molecular mechanisms. However, with extensive literature review we grouped the rest of the pathways (i.e. AMPA, NMDA, CaM, CACAM, CAMKII, Ras, CREB) to an important arrhythmia-related process that starts from signal transmission and eventually leads to CREB phosphorylation in a calcium-dependent manner (Figure 6). When signal arrives to the cell membrane of post synaptic cell, AMPA and NMDA receptors transmit the signal from the pre synaptic cell into the post-synaptic cell. These ligand-gated ion channels 76 regulate intracellular calcium 77. Chronic NMDA activation increases susceptibility to ventricular arrhythmias 78 whereas antagonists of NMDA or AMPA reduce calcium accumulation in cardiac mitochondria 79 and thus prevent ventricular arrhythmias 80,81. Calcium ions that are transferred inside the cell, bind to a calcium-regulating protein called

Figure 6 | Schematic figure of pathways starting from activation of AMPA and NMDA receptors to CREB phosphorylation (adapted from Figure 5 of ref. 92). Multiple genomic approaches to identify genetic susceptibility to VF in MI 103

Calmodulin (CaM) 82-84. CaM also regulates cardiac potassium 85 and sodium 86,87 channels. CaM decreases conduction in gap junction of ventricular myocytes at moderately elevated calcium level 88. Mutations in CaM gene cause various types of ventricular arrhythmias 89,90. Calcium and CaM form a calcium-calmodulin (CACAM) complex that activates enzymes called calcium-calmoduline kinases (CaMKs) 91 that mediate cardiac calcium current in ventricular myocytes and have the capacity to phosphorylate CREB in vitro and in vivo (reviewed in ref. 92 and 93). CAMKII is the most important member of CAMKs family in cardiomyocytes 94. CAMKII prevents CREB from binding to DNA and exerting its effects as a transcription factor 95. CAMKII modulates potassium current in cardiac sinoatrial (SA) node cells in an intracellular calcium dependent manner 96. Over-activation of CaMKII initiates ventricular arrhythmias 97 and inhibitors of CaMKII prevent arrhythmias 98 by reducing cardiac contractility 99. Another parallel pathway to CaMKII-mediated CREB phosphorylation is trough calcium-dependent activation of Ras (small G-protein that is involved in signal transduction) 100 which once activated can phosphorylate CREB. Ras signaling pathway is important in cardiac hypertrophy 101-103. Ras inhibitors improve cardiac performance, 104 reduce ischemia-reperfusion injury, 104-106 and decrease size of scar tissue 104.

Strengths and limitations

As mechanisms of VF likely differ across different cardiac pathologies, the fact that Chapter 5 this study was conducted exclusively in patients that presented with VF in the setting of a first MI, represents an important strength of the study. The presence of such a specific phenotype in the discovery and validation patient sets increases the likelihood of shared molecular mechanisms and consequently shared genetic risk factors among the cases; this strategy is thus expected to increase the statistical power for the discovery of genetic variants associated with VF. The replication sets used in this study (PREDESTINATION and GEVAMI) are highly suitable replication sets for associations uncovered in AGNES as their inclusion criteria are very similar to those of AGNES. However, although the AGNES set is large when one considers the challenges encountered in collecting clinical data and DNA from victims of VF/SCD, it is modest in comparison to those used in contemporary GWA studies on other phenotypes. The relatively small size of AGNES limits our ability to identify risk-SNPs with small effects. This underscores the necessity of continued recruitment of highly character- ized patient sets with VF to further our understanding of the underlying genetic risk factors. Due to ethical considerations, the inclusion of VF-cases in AGNES is limited to those who survive the event; individuals who do not survive at least until hospital admission are not included in the study. This implies that findings in AGNES might not be generalizable to non-survivors of VF. 104 Chapter 5

In an original approach in the current study we tested SNPs displaying eQTL effects in human heart tissue for a role in modulation of risk for VF. Such SNPs modulate the level of expression of genes in human heart and are thus highly relevant candidates for a cardiac phenotype like VF. This approach increases the power to identify biologically true associations. We acknowledge that the eQTL databases we used had a small sample size. The sample sizes for the most relevant tissues included 129 human heart samples in our earlier study 39 and 83 human heart left ventricle in the study by Lonsdale et al. 40. Hence, potential eQTLs with small effects may not be present in these eQTL databases. Furthermore, in the generation of the human heart eQTL database by Koopmann et al. not all transcripts were tested for eQTL effect due to sub-optimal probe design. Addi- tionally, quality control criteria such as MAF > 15% and optimal probe design limited the number of SNPs and transcripts included in generation of this eQTL database. Thus, SNPs with eQTL effects might have been missed here. In the study by Westra et al. 41 only eQTLs with FDR < 0.5 (corresponded to a P-value < 0.003) were accessible to us. Therefore, SNPs with an eQTLs effect below this threshold are also missed.

Conclusion We here integrated several approaches in an effort to identify genetic loci and molecular mechanisms modulating risk for VF in the setting of a first acute MI and possible causal genes therein. While the identification of robust and highly reproducible signals for this phenotype remains challenging, a number of SNPs from this study merit further investigation in future studies. These include rs2824292, which although remained highly-significantly associated with VF in an extended sample of AGNES, did not display association in case-control sets with a presumably similar phenotype. Another SNP that deserves investigation in the future is rs1750041 since it has been replicated in the PREDESTINATION study. rs6795970 stands out in our candidate SNP analysis. Although the direction of effect is unexpected (conduction slowing allele is protective for VF) this SNP is emerging as the most reproducible SNP thus far in a vari- ety of arrhythmias. Finally, rs6982308 stands out of our candidate eQTL analysis. This was also important since beside the association with VF, this SNP is an eQTL for XKR6 gene at which the second AGNES SNP rs10096381 occurs. All associations identified in the current study require further research in future studies.

Acknowledgement This study was supported by research grants from the Netherlands Heart Foundation (grants 2001D019, 2003T302 and 2007B202), the Leducq Foundation (grant 05-CVD) and the Interuniversity Cardiology Institute of the Netherlands. Multiple genomic approaches to identify genetic susceptibility to VF in MI 105

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C hapter 6 SNPs identifi ed as modulators of ECG traits in the general population do not markedly aff ect ECG traits during acute myocardial infarction nor ventricular fi brillation risk in this condition

PLoS One. 2013;8(2):e57216

Raha Pazoki*, Jonas S.S.G. de Jong*, Roos F. Marsman, Nienke Bruinsma, Lukas R.C. Dekker, Arthur A.M. Wilde, Connie R. Bezzina, Michael W.T. Tanck

* Th ese authors contributed equally to this paper 114 Chapter 6

Abstract

Background – Ventricular fibrillation (VF) in the setting of acute ST- segment elevation myocardial infarction (STEMI) is a leading cause of mortality. Although the risk of VF has a genetic component, the underlying genetic factors are largely unknown. Since heart rate and ECG intervals of conduction and repolarization during acute STEMI differ between patients who do and patients who do not develop VF, we investigated whether SNPs known to modulate these ECG indices in the general population also impact on the respective ECG indices during STEMI and on the risk of VF. Methods and Results – The study population consisted of participants of the Ar- rhythmia Genetics in the NEtherlandS (AGNES) study, which enrolls patients with a first STEMI who develop VF (cases) and patients who do not develop VF (controls). SNPs known to impact on RR interval, PR interval, QRS duration or QTc interval in the general population were tested for effects on the respective STEMI ECG indices (stage 1). Only those showing a (suggestive) significant association were tested for association with VF (stage 2). On average, VF cases had a shorter RR and a longer QTc interval compared to non-VF controls. Eight SNPs showed a trend for association with the respective STEMI ECG indices. Of these, three were also suggestively associated with VF. Conclusions – RR interval and ECG indices of conduction and repolarization during acute STEMI differ between patients who develop VF and patients who do not. Although the effects of the SNPs on ECG indices during an acute STEMI seem to be similar in magnitude and direction as those found in the general population, the effects, at least in isolation, are too small to explain the differences in ECGs between cases and controls and to determine risk of VF. ECG-modulating SNPs and risk of VF in MI 115

Introduction

Sudden cardiac death (SCD) is a leading cause of death in adults in the Western world 1. The overwhelming majority (~80%) of SCDs in adults are caused by the sequelae of coronary artery disease, namely myocardial ischemia or acute myocardial infarction (MI) 2. While the risk of VF is known to have a genetic component, the underlying genetic factors are yet largely unknown 3-5. Electrocardiographic indices of conduction and repolarization are considered important quantifiable intermediate phenotypes for arrhythmia risk 6. In support of this concept, ECG indices measured during the acute phase of ST elevation MI (STEMI) were associated with risk of ensuing VF 7. A number of studies have demonstrated that heart rate, PR interval, QRS duration and QTc interval are heritable traits 8-10 and over the last years, genome-wide association studies (GWAS) in large samples of the general population have uncovered several common single nucleotide polymorphisms (SNPs) affecting these ECG traits 11-21. The objective of this study was to assess whether SNPs, previously associated with heart rate and ECG indices of conduction and repolarization in GWAS in the general population, modulate ECG indices and risk of VF during acute STEMI. Specifically, we hypothesized that ECG-related SNPs modulate heart rate and ECG conduction and repolarization intervals in the setting of acute STEMI and thereby modulate (and predict) the risk of VF in this condition.

Methods Chapter 6 Ethics statement The study protocol was approved by the Institutional Review Board of the Academic Medical Center, University of Amsterdam, and was conducted according to the principles of the Declaration of Helsinki. All participants gave written informed consent.

Study samples The study population consisted of participants of the Arrhythmia Genetics in the NEtherlandS (AGNES) case-control study which has been described in detail previously 3,4. In brief, the AGNES study enrolls patients with a first acute STEMI. Cases are defined as patients who were successfully resuscitated after documented VF that occurred between the onset of symptoms and coronary intervention. Controls are defined as acute STEMI patients who did not develop VF. Patients with a previous MI or major co-morbidity were excluded. All individuals studied were of self-declared European ancestry. 116 Chapter 6

Measurement of ECG indices The first recorded ECG that was acquired during STEMI and before reperfusion treatment was used for analysis. For cases, both ECGs acquired pre-VF as well as post-VF were included. ECGs of insufficient quality (due toe.g. baseline drift or missing leads) and ECGs with rhythms other than sinus rhythm or atrial fibrillation (AF) were excluded. Patients with AF (n = 23) were excluded for the analyses of PR interval, but included for the other ECG indices. All ECGs were digitized at 400 dpi (giving a spatial resolution 0.064 mm or 1.6 ms / pixel on an ECG traced at 25 mm / sec). Calibrated measurements were performed on-screen after 4 times enlargement of the digitized ECGs in ImageJ (National Institutes of Health, Bethesda, Maryland, http://rsb.info. nih.gov/ij/). RR-interval (heart rate) was measured from the ECG. PR interval was measured from the onset of the P-wave to the onset of ventricular depolarization. QRS duration was measured from the onset of ventricular depolarization to the J point. QTc interval was measured using the tangent method 22. Lead II was used when the T wave was of sufficient amplitude to warrant QT measurement, otherwise leads V5 or V2 were used. QT was corrected for heart rate (QTc) using Fridericia’s formula (QTc = QT / (cube root (RR)). ST deviation was calculated as the sum of all ST deviation from baseline at 60ms after the J point in all 12 leads. ST deviation was not reported if individual leads were disconnected or in patients with left bundle branch block. The mean of three consecutive beats wherever possible was measured for all parameters and used in subsequent analyses. Patients with AV block, PR interval ≥200 ms or QRS duration ≥120 ms were excluded from the ECG analyses involving the continuous endpoints PR interval, QRS duration and QTc interval. For PR interval and QRS duration, additional dichotomous endpoints were assessed i.e. PR interval ≥200 ms and QRS duration ≥120ms. Patients with AV block were included in the dichotomized PR interval endpoint as PR > 200 ms.

Selection of SNPs and Genotyping We inspected the published GWAS concerning heart rate and ECG indices of conduction and repolarization and identified SNPs reported to be associated with these parameters at genome-wide significantP -values (P < 5 × 10−8) 23. In case of high linkage disequilibrium (r2 ≥ 0.75) between identified SNPs, only a single SNP, capturing the maximum amount of variation present in the correlated SNPs, was selected for our analysis. This was the case for SNPs at the SCN10A, SCN5A, KCNH2, KCNQ1, CNOT1, ARHGAP24, NOS1AP, CDKN1A, GJA1 and MYH6 loci. A total of 65 SNPs were identified in this way. Genotypes for these SNPs were either obtained by direct genotyping (Illumina610 Quad genotyping array) or were estimated by imputation using HapMap build 36 as the reference panel. Details on genotyping and imputation in the AGNES sample have been described previously 3. ECG-modulating SNPs and risk of VF in MI 117

Statistical Analysis We tested differences between cases and controls in continuous variables with an independent sample t-test, or the Mann-Whitney U test when the data was not normally distributed. We compared differences in the percentages of categorical variables with the Pearson χ2 test. We used linear regression modelling in association analyses of continuous endpoints and logistic regression modelling for association analyses of dichotomous endpoints (VF, PR ≥ 200 ms and QRS ≥ 120 ms). In all models, we assumed an additive genetic model and corrected for age, sex and the culprit artery (harbouring the occluding lesion) 7 as well as the possible interaction between culprit artery and the SNPs. The occurrence of the latter was first tested using a Wald test. Our analysis plan had a two-stage design. In the first stage, we tested for association of the selected SNPs with the corresponding ECG parameter during STEMI. In the second stage, we selected those SNPs with a (suggestive) significant effect on the ECG parameter and analyzed their effect on the occurrence of VF. The Bonferroni thresholds for statistical significance were P ≤ 0.0002 for the first stage (corrected for two tests per SNP and six outcomes: HR, QTc, PQ, PQ ≥ 200, QRS and QRS ≥ 120, resulting in a total of 210 tests) and P ≤ (0.05/number of SNPs carried over from stage 1) for the second stage. For both stages, P-values between the Bonfer- roni threshold and 0.05 were considered as a suggestive trend.

Power and detectable effects Given the observed standard deviations in our study population for heart rate and the ECG indices, the reported effects of the identified SNPs would result in effect sizes ≤ 0.2 SD. With the present range in sample sizes (417–515), the power to detect an effect size of 0.2 of a SNP with a minor allele frequency (MAF) ranging from 0.05 to Chapter 6 0.5 would range from 1 to 31% for a two-sided P-value of 0.0002 (Bonferroni threshold) and from 24–90% for a two-sided P-value of 0.05 (suggestive trend). The present study, therefore, lacks the power to significantly detect effect sizes as found in the general population (from the GWAS). However, given the fact that our heart rate and ECG indices were measured in patients experiencing a STEMI, we hypothesized that the SNP effects might be markedly increased in this sensitized population. Given the present range in sample sizes, the study had 80% power to detect effect sizes of 0.3 to 0.7 (± 5% explained variance) for the quantitative traits at MAFs ranging from 0.05 to 0.5 assuming an additive genetic model and a two-sided significance threshold of 0.0002 (Stage 1). For PR interval with an overall standard deviation of 20 ms, for example, this translates into detectable allele effects (beta) ranging from 14 to 6 ms. For the dichotomized endpoints, we were able to detect odds ratio’s ranging from 2.3 to 1.6 (Stage 1, α = 0.0002). 118 Chapter 6

Results

VF and ischemic ECG indices Baseline characteristics of the study population are shown in Table 1. As reported previously 3,4, AGNES cases had a lower prevalence of diabetes mellitus and hypercholesterolemia, and lower mean body mass index (BMI) than AGNES controls. AGNES cases more often had a family history of sudden cardiac death as compared to controls. STEMI ECGs of sufficient quality were available for 599 patients. Of these, 79 (13%) patients had QRS ≥ 120 ms, and 85 (14%) patients had a PR ≥ 200 ms or higher degree

Table 1 | Baseline characteristics of the AGNES case-control set Characteristic Na Total Cases Controls P-valueb (n = 969) (n = 516) (n = 453) Sex (male)c 783(80.6) 412(80.0) 371(81.2) 0.685 Mean age at MId 56.4±11 55.9±11.2 56.9±10.8 0.174 Family history of sudden death 909/459/450 291(32.0) 174(37.9) 117(26.0) < 0.0001 CVD risk factors Current smoking 914/473/441 565(61.8) 303(64.1) 262(59.4) 0.1531 Diabetes mellitus 893/462/431 62(6.9) 21(4.6) 41(9.5) 0.004 Hypertension 294(30.2) 149(28.9) 145(31.7) 0.3633 Hypercholesterolemia 297(30.6) 127(24.7) 170(37.2) < 0.0001 Body mass index (BMI, kg/m2) 879/439/440 26.5±3.9 26.1±3.8 26.9±4.0 0.0042 Peak CK-MBe 735/337/398 227(294) 241(354) 215(247) 0.006 Ischemic ECG RR interval (ms) 453/214/239 807±204 743±188 864±203 < 0.0001 ST segment deviation (mm)e 474/188/286 15(17) 20(20) 13(12) < 0.0001 PR interval (ms) 417/195/222 157±20 158±19 157±20 0.358 QRS duration (ms) 452/214/238 93±13 93±14 93±13 0.860 QTc interval (ms) 424/200/224 410±35 415±37 405±31 0.003 PR ≥ 200 ms 515/249/266 57(11.0) 32(12.7) 25(9.5) 0.153 QRS ≥ 120 ms 509/252/257 57(11.2) 38(15.1) 19(7.4) 0.007 Culprit artery 811/430/381 RCA 220(27.1) 109(25.3) 111(29.1) LAD 475(58.6) 260(60.5) 215(56.4) 0.202 LCX 116(14.3) 61(14.2) 55(14.4) MI, myocardial infarction; CK-MB, creatine kinase-MB; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery. aIn case of missing values, the sample sizes of the total, case and control sets (total, case, control) for which information was available are given. bP-value for comparison of cases and controls using independent t-test, Mann-Whitney test, or chi-square test where appropriate. cnumber (%); dmean ± SD; eMedian (interquartile range). ECG-modulating SNPs and risk of VF in MI 119

Table 2 | ECG characteristics of AGNES cases and controls according to the artery harbouring the stenotic lesion Culprit Cases Mean ± SD Controls Mean ± SD P-valuea Interaction artery (N) (N) P-value RR interval LAD 130 728±170 124 822±180 < 0.0001 LCX 25 751±185 35 866±213 0.047 0.974 RCA 43 791±230 70 944±214 0.001 PR interval LAD 117 158±19 112 159±18 0.88 LCX 24 150±17 33 151±19 0.65 0.423 RCA 39 163±20 67 157±22 0.17 QRS duration LAD 130 93±13 123 91±13 0.29 LCX 25 98±11 35 88±9 0.002 0.002 RCA 43 94±16 70 98±12 0.13 QTc interval LAD 120 422±37 113 413±28 0.04 LCX 24 415±28 33 391±30 0.02 0.026 RCA 40 399±38 68 399±35 0.94 LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery; aP-value of comparison between cases and controls using a logistic regression model adjusted for age and sex; all patients with AV block or PR ≥ 200 ms or QRS ≥ 120 ms & AF are excluded.

AV block; several patients had a combination of these ECG abnormalities. Because of these exclusions and missing values, the number of available observations varied between 417 and 515. VF cases showed, on average, a shorter RR interval, a longer QTc interval, a greater ST segment deviation and more often prolongation of the QRS interval (≥120 ms) as compared to controls (Table 1). Location of the culprit coronary lesion modified the effect of QRS duration and Chapter 6 QTc interval on risk of VF (Table 2). For instance, QRS duration was more prolonged in cases compared to controls only among patients with a left circumflex artery (LCX) occlusion (Pinteraction = 0.002). QTc-interval tended to be more prolonged among patients with left anterior descending artery (LAD) or LCX occlusion (Pinteraction = 0.026).

ECG candidate SNPs and STEMI ECG indices (Stage 1) None of the 65 SNPs tested displayed an association with the corresponding ECG trait that passed the Bonferroni-corrected significance threshold of P ≤ 0.0002, nor showed an interaction with culprit artery. Eight SNPs displayed a trend (0.0002 < P < 0.05) in association with STEMI ECG indices (Table 3). Regarding the continuous ECG outcomes, SNPs rs223116 and rs281868 showed a trend for association with RR interval (P = 0.004 and 0.014, respectively) and SNP rs6795970 showed a trend for association with PR interval (P = 0.004). SNPs rs1886512 and rs883079 showed a trend for association with QRS duration (P = 0.010 and 0.007, respectively). SNPs rs17779747 and rs8049607 120 Chapter 6

Table 3 | Association analysis of SNPs with ECG indices of conduction and repolarization during myocardial ischemia SNP End C/N GWASa MA(F) Beta (SE)b P-value P.intc Gene point Effect RR SNPs rs11154022 RR A/G Inc A (0.33) 6.5 (14.9) 0.66 0.98 8 kb from GJA1 rs12666989 RR C/G Dec C (0.20) −10.7 (17.6) 0.54 0.17 UFSP1 rs12731740 RR T/C Inc T (0.12) 10.3 (22.3) 0.65 0.28 CD3-MIR29C- IR29B2 rs17287293 RR G/A Inc G (0.14) 22.6 (19.7) 0.27 0.13 SOX5-BCAT1 rs174547 RR C/T Dec C (0.31) −11.7 (15.0) 0.44 0.34 FADS1 rs223116 RR A/G Dec A (0.24) −45.6 (15.9) 0.004 0.46 THTPA-NGDN- FHX2-MYH7 rs2745967 RR G/A Inc G (0.38) −1.2 (14.1) 0.93 0.17 CD34-PLXNA2 rs281868 RR G/A Dec A (0.49) −33.8 (13.8) 0.01 0.06 SLC35F1 rs314370 RR C/T Dec C (0.20) −5.5 (17.4) 0.75 0.2 SLC12A9 rs452036 RR A/G Dec A (0.39) −10.4 (14.5) 0.47 0.82 MYH6 rs885389 RR A/G Dec A (0.37) −17.2 (14.7) 0.24 0.57 GPR133 rs9398652 RR A/C Dec A (0.12) −9.2 (21.9) 0.67 0.29 GJA1-HSF2 PR SNPs rs11047543 PR A/G Dec A (0.14) −1.97 (1.94) 0.31 0.7 SOX5 rs11708996 PR C/G Inc C (0.16) −0.64 (2.11) 0.76 0.72 SCN5A rs11897119 PR C/T Inc C (0.38) 0.41 (1.42) 0.77 0.41 MEIS1 rs1896312 PR C/T Inc C (0.29) 0.37 (1.49) 0.81 0.4 TBX5-TBX3 rs251253 PR C/T Dec C (0.41) −1.59 (1.37) 0.25 0.74 NKX2-5 rs3807989 PR A/G Inc A (0.42) 0.17 (1.42) 0.90 0.17 CAV1-CAV2 rs3825214 PR G/A Inc G (0.20) −0.16 (1.74) 0.92 0.77 TBX5 rs4944092 PR G/A Dec G (0.33) −0.74 (1.48) 0.62 0.04 WNT11 rs6795970 PR A/G Inc A (0.39) 4.06 (1.42) 0.004 0.03 SCN10A rs7660702 PR T/C Inc C (0.30) −0.04 (1.42) 0.98 0.46 ARHGAP24 QRS SNPs rs10850409 QRS A/G Dec A (0.25) −0.39 (0.98) 0.7 0.38 TBX3 rs10865879 QRS T/C Inc C (0.25) −0.45 (1.07) 0.67 0.79 SCN5A/EXOG rs11153730 QRS C/T Inc T (0.49) −0.72 (0.87) 0.41 0.38 C6orf204-SLC35F1- PLN-BRD7P3 rs11708996 QRS C/G Inc C (0.16) 0.94 (1.37) 0.49 0.23 SCN5A C/N, Coded/Non Coded Allele; MA(F), Minor Allele (Frequency); SE, Standard Error; P.int, P-value for Interaction; Inc, Increasing effect; Dec, Decreasing effect; aDirection of effect estimate per copy coded allele, data from previous GWA studies; bEffect estimate is given per copy of the coded allele adjusted for age, sex and culprit artery (all patients with AV block or PR ≥ 200 ms or QRS ≥ 120 ms are excluded); cP-values for interaction between SNPs and culprit artery on risk of VF. ECG-modulating SNPs and risk of VF in MI 121

Table 3 | Association analysis of SNPs with ECG indices of conduction and repolarization during myocardial ischemia (continued) SNP End C/N GWASa MA(F) Beta (SE)b P-value P.intc Gene point Effect rs11710077 QRS T/A Dec T (0.18) 0.59 (1.12) 0.6 0.52 SCN5A rs11848785 QRS G/A Dec G (0.27) −0.34 (1.05) 0.74 0.76 SIPA1L1 rs13165478 QRS A/G Dec A (0.37) −1.45 (0.91) 0.11 0.56 HAND1-SAP30L rs1321311 QRS A/C Inc A (0.24) −0.43 (1.08) 0.69 0.86 CDKN1A rs1362212 QRS A/G Inc A (0.16) 0.65 (1.29) 0.62 0.83 TBX20 rs17020136 QRS C/T Inc C (0.21) 0.24 (1.08) 0.83 0.25 HEATR5B-STRN rs1733724 QRS A/G Inc A (0.27) −0.51 (1.03) 0.62 0.27 DKK1 rs17391905 QRS G/T Dec G (0.02) −2.68 (3.13) 0.39 0.98 C1orf185-RNF11- CDKN2C-FAF1 rs17608766 QRS C/T Inc C (0.13) 1.31 (1.31) 0.32 0.45 GOSR2 rs1886512 QRS A/T Dec A (0.37) −2.39 (0.92) 0.01 0.88 KLF12 rs2051211 QRS G/A Dec G (0.26) −0.35 (1.03) 0.73 0.27 EXOG rs2242285 QRS A/G Inc A (0.43) −0.24 (0.87) 0.78 0.89 LRIG1-SLC25A26 rs4074536 QRS C/T Dec C (0.32) 1.19 (0.99) 0.23 0.75 CASQ2 rs4687718 QRS A/G Dec A (0.12) 1.70 (1.34) 0.21 0.49 TKT-PRKCD- CACNA1D rs6795970 QRS A/G Inc A (0.39) 0.52 (0.91) 0.57 0.33 SCN10A rs7342028 QRS T/G Inc T (0.25) 1.89 (1.03) 0.07 0.05 VTI1A rs7562790 QRS G/T Inc G (0.43) −0.22 (0.91) 0.81 0.53 CRIM1 rs7784776 QRS G/A Inc G (0.41) −0.82 (0.93) 0.38 0.26 IGFBP3 rs883079 QRS C/T Inc C (0.26) 2.77 (1.02) 0.007 0.15 TBX5 rs9436640 QRS G/T Dec G (0.48) −0.68 (0.89) 0.44 0.28 NFIA rs9851724 QRS C/T Dec C (0.35) −1.25 (0.92) 0.18 0.53 SCN10A Chapter 6 rs991014 QRS T/C Inc T (0.43) −0.40 (0.91) 0.66 0.43 SETBP1 rs9912468 QRS G/C Inc G (0.40) 0.28 (0.96) 0.77 0.51 PRKCA QTc SNPs rs10919071 QTc A/G Inc G (0.11) −0.01 (2.09) 0.997 0.77 ATP1B1 rs11970286 QTc T/C Inc T (0.47) 0.13 (2.31) 0.954 0.99 PLN rs12029454 QTc A/G Inc A (0.14) 4.74 (3.20) 0.139 0.44 NOS1AP rs12053903 QTc C/T Dec C (0.33) −4.13 (2.55) 0.106 0.91 SCN5A rs12143842 QTc T/C Inc T (0.24) 3.47 (2.69) 0.197 0.87 NOS1AP rs12210810 QTc C/G Dec C (0.04) 1.34 (5.33) 0.801 0.59 PLN C/N, Coded/Non Coded Allele; MA(F), Minor Allele (Frequency); SE, Standard Error; P.int, P-value for Interaction; Inc, Increasing effect; Dec, Decreasing effect; aDirection of effect estimate per copy coded allele, data from previous GWA studies; bEffect estimate is given per copy of the coded allele adjusted for age, sex and culprit artery (all patients with AV block or PR ≥ 200 ms or QRS ≥ 120 ms are excluded); cP-values for interaction between SNPs and culprit artery on risk of VF. 122 Chapter 6

showed a trend for association with QTc interval (P = 0.004 and 0.036, respectively). Regarding the dichotomized ECG endpoints, the C-allele of SNP rs11708996 showed a trend for association with PR ≥ 200ms with an odds ratio of 2.39 (95% CI: 1.33, 4.30; P = 0.004). None of the QRS SNPs showed any significant association with QRS ≥ 120 ms. rs8049607 showed a trend for association with QTc interval (P = 0.004 and 0.036, respectively). Regarding the dichotomized ECG endpoints, the C-allele of SNP rs11708996 showed a trend for association with PR ≥ 200ms with an odds ratio of 2.39 (95% CI: 1.33, 4.30; P = 0.004). None of the QRS SNPs showed any significant association with QRS ≥ 120 ms.

STEMI ECG SNPs and risk of VF (Stage 2) We next tested the eight SNPs from Stage 1 showing a trend for association with the ECG parameters for association with VF. Of these eight, none passed the Bonferroni- corrected significance threshold (P ≤ 0.0063) for association with risk of VF during STEMI. However, two SNPs, namely rs6795970 and rs17779747, were nominally associated with VF (P = 0.009 and 0.026, respectively) independent of the culprit artery. Another SNP, rs223116 displayed a nominal association with VF in patients with an occlusion in the LCX (P = 0.039) or RCA (P = 0.037) only (Table 4). ECG-modulating SNPs and risk of VF in MI 123

Table 4 | Association analysis of SNPs with VF in AGNES cases versus AGNES controls SNP GWAS C/N GWASa MA(F) OR(95% CI)b P-value P.intc Gene End point Effect rs223116 RR A/G Dec A(0.24) THTPA-NGDN- LAD 0.86(0.64,1.17) 0.34 0.016 ZFHX-MYH7 LCX 2.08(1.04,4.17) 0.04 RCA 1.61(1.03,2.51) 0.04 rs281868 RR G/A Dec A(0.49) 0.99(0.81,1.21) 0.93 0.24 SLC35F1 rs6795970 PR A/G Inc A(0.39) 0.77(0.63,0.94) 0.01 0.90 SCN10A rs11708996 PR ≥ 200ms C/G Inc C(0.16) 1.24(0.93,1.65) 0.14 0.23 SCN5A rs1886512 QRS A/T Dec A(0.37) 0.98(0.80,1.20) 0.82 0.55 KLF12 rs883079 QRS C/T Inc C(0.26) 0.90(0.72,1.13) 0.36 0.82 TBX5 rs17779747 QTc T/G Dec T(0.34) 1.27(1.03,1.57) 0.03 0.92 KCNJ2 rs8049607 QTc T/C Inc C(0.49) 1.05(0.87,1.29) 0.60 0.94 LITAF C/N: Coded/Non Coded Allele; P.int, P-value Interaction; OR, Odds ratio; MA(F):, Minor Allele (Fre- quency); Inc, Increasing effect; Dec, Decreasing effect; aDirection of effect estimate per copy coded allele, data from previous GWA studies; bEffect estimate is given per copy of the coded allele adjusted for age, sex and culprit artery cP-values for interaction between SNPs and culprit artery on risk of VF

Discussion

In the current study, we confirm and extend previous observations that heart rate and ECG indices of conduction and repolarization differ between acute STEMI patients who develop VF and acute STEMI patients who do not develop VF. Although our analysis only detected nominal association with the STEMI ECG traits for only a minority of SNPs, the effect size and direction for the association of these SNPs was comparable Chapter 6 to that found for the corresponding trait in the general population. Furthermore, of the eight SNPs displaying such association, three also displayed a trend for association with VF.

Ischemic ECG indices and VF Ischemic ECGs of patients with VF (cases) showed, on average, shorter RR intervals, longer QTc intervals and more ST segment deviation than patients without VF (controls). When the culprit artery harbouring the occluding lesion was taken into account, cases with an LCX occlusion had a longer QRS duration. This finding is similar to our previous findings in a smaller sample of STEMI patients with and without VF 7. However, the observation in this previous study of longer QRS interval in cases with an RCA occlusion was not observed in the present study. With respect to QTc interval, cases with either an LAD or LCX occlusion had a longer QTc-interval compared to controls. This extends our earlier findings, where similar (though non-significant) -dif 124 Chapter 6

ferences in QTc interval were found for the same culprit arteries. The observed longer QRS duration and QTc interval in cases with VF reflect cardiac conduction delay and prolonged repolarization time, respectively, consistent with a more pro-arrhythmic substrate in cases 24.

ECG candidate SNPs, STEMI ECG indices and VF Recent GWAS have identified multiple common genetic variants affecting heart rate, conduction and repolarization in individuals from the general population 11-21. How- ever, whether these variants also influence ECG indices in the setting of STEMI has not yet been investigated. In our study, none of the association P-values exceeded our stringent pre-set threshold for statistical significance. Eight SNPs displayed a suggestive association to the corresponding STEMI ECG parameter, and of note all eight SNPs displayed an effect that was similar in direction and magnitude to that observed in the general population 11-21. However our findings also demonstrate that SNPs which are known to exert only small effects on ECG parameters in the general population, do not have a markedly increased effect on ECG indices in the setting of acute STEMI. None of the eight SNPs that displayed nominal association with STEMI ECG parameters passed our stage 2 Bonferroni threshold (P < 0.0063) for the association with VF. However, as previously reported 12, the A-allele of rs6795970 in the SCN10A gene appears to be associated with a decreased risk of VF in the AGNES population (P = 0.009). The rs6795970 SNP has consistently been shown to impact on PR interval in different studies carried out in the general population 12,15,20, with the A-allele being the PR-prolonging allele. We now show that this SNP may also be associated with a longer PR interval during acute STEMI. In addition to PR interval, rs6795970 has been found associated with QRS in GWA studies, underscoring the shared molecular underpinning of atrio-ventricular and ventricular conduction. In spite of this, the effect of rs6795970 on QRS duration in our study was small and non-significant. SNP rs6795970 is in high LD (r2 = 0.93) with rs6801957 with both SNPs being localized within the SCN10A gene located immediately downstream of SCN5A on chromosome 3. SNP rs6801957 is located in a T-box transcription factor binding motif within an enhancer element and the PR- and QRS-prolonging minor allele of this SNP was recently shown to affect TBX3/TBX5 binding and T-box factor response of the enhancer 25. These recent findings suggest that rs6801957 is the causal SNP and that this SNP may modulate SCN5A and / or SCN10A expression levels in human heart and thereby impact on conduction and arrhythmia risk. The common genetic variants identified to date as modulators of ECG indices in the general population all carry very small effect sizes and even when considered in aggregate, explain only a very small percentage of the variation in these indices. For instance, in a meta-analysis for identification of QTc-associating SNPs by Pfeufer and ECG-modulating SNPs and risk of VF in MI 125 co-workers 19, SNPs at 10 different loci in aggregate explained only around 3% of the variance in this trait. The effects of these common genetic variants on the STEMI ECG are small and insufficient to explain the differences found in STEMI ECGs of cases and controls. This underscores the need for further studies aimed at uncovering additional genetic variants, such as rare variants associated with larger effects, which would lead to a more-complete representation of the allelic architecture of these differences in STEMI ECGs.

Strengths and Limitations In this study, we had the unique opportunity to study the effect of genetic variants, known to impact on ECG indices in the general population, on VF in a well-defined case-control set of patients with a first acute STEMI. Our study is rather unique in that the availability of STEMI ECGs and DNA from patients in whom STEMI is complicated by VF is very scarce due to the high mortality in these patients. On the other hand, as a consequence, our sample size is limited, which can result in insufficient statistical power. Furthermore, the STEMI ECGs used in this study were retrieved retrospectively and, therefore, the time between the onset of complaints and acquisition of the STEMI ECG varied among the study participants. Also since the ECGs were retrieved retrospectively, some were of insufficient quality for analysis, limiting the size of the sample available for analysis. The first recorded ECG that was acquired during STEMI and before reperfusion treatment was used for this analysis. Due to the nature of this complex and specific phenotype under investigation a resting ECG without STEMI prior to the index event is missing for most of the patients and the effects of SNPs on baseline ECGs from these patients could, therefore, not be tested. Our analysis did not account for effects which may arise from differences in infarct size, intake of amio- Chapter 6 darone or cardioversion. However, with respect to infarct size, additional adjustment for peak CKMB as a marker of infarct size, resulted in similar effect estimates but with larger confidence intervals, mainly related to the reduced sample size as peak CKMB was not available in all patients (Supplementary Table 1).

Conclusion In conclusion, ECG indices of conduction and repolarization differ between STEMI patients with VF and STEMI patients who do not develop VF. Although the effects of some SNPs on ECG parameters during an acute STEMI were similar in magnitude and direction as those found in the general population, the effects were too small to explain the differences in conduction and repolarization indices and to exert any marked impact on risk of VF. Nevertheless, rs6795970, located within the SCN10A gene, associated with longer PR interval in the general population and with longer PR interval and risk of VF during STEMI in our study, merits investigation in future larger studies. 126 Chapter 6

References

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18. Nolte, I. M. et al. Common Genetic Variation Near the Phospholamban Gene Is Associated with Cardiac Repolarisation: Meta-Analysis of Three Genome-Wide Association Studies. Plos One 4, doi:10.1371/journal.pone.0006138 (2009). 19. Pfeufer, A. et al. Common variants at ten loci modulate the QT interval duration in the QTSCD Study. Nature Genetics 41, 407-414, doi:10.1038/ng.362 (2009). 20. Pfeufer, A. et al. Genome-wide association study of PR interval. Nature Genetics 42, 153- U189, doi:10.1038/ng.517 (2010). 21. Sotoodehnia, N. et al. Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction. Nature Genetics 42, 1068-U1062, doi:10.1038/ng.716 (2010). 22. Postema, P. G., De Jong, J. S. S. G., Van der Bit, I. A. C. & Wide, A. A. M. Accurate electrocardiographic assessment of the QT interval: Teach the tangent. Heart Rhythm 5, 1015-1018, doi:10.1016/j.hrthm.2008.03.037 (2008). 23. Pe’er, I., Yelensk, R., Altshuler, D. & Daly, M. J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genetic Epidemiology 32, 381-385, doi:10.1002/gepi.20303 (2008). 24. Zareba, W., Moss, A. J. & le Cessie, S. Dispersion of ventricular repolarization and arrhythmic cardiac death in coronary artery disease. The American journal of cardiology 74, 550-553 (1994). 25. van den Boogaard, M. et al. Genetic variation in T-box binding element functionally affects SCN5A/SCN10A enhancer. Journal of Clinical Investigation 122, 2519-2530, doi:10.1172/ jci62613 (2012). Chapter 6 128 Chapter 6

Supplementary Table 1 | Association analysis of SNPs with VF in AGNES cases versus AGNES controls with additional correction for peak CKMB levels SNP GWAS C/N GWASa MA(F) OR(95% CI)b P-value P.intc Gene End point Effect rs223116 RR A/G Dec A (0.24) 1.04 (0.79,1.36) 0.78 0.29 THTPA- NGDN- ZFHX2 rs281868 RR G/A Dec A (0.49) 0.95 (0.76,1.19) 0.65 0.2 SLC35F1 rs6795970 PR A/G Inc A (0.39) 0.81 (0.64,1.02) 0.08 0.7 SCN10A rs11708996 PR ≥ 200 ms C/G Inc C (0.16) 1.26 (0.91,1.77) 0.17 0.12 SCN5A rs1886512 QRS A/T Dec A (0.37) 0.93 (0.74,1.18) 0.55 0.15 KLF12 rs883079 QRS C/T Inc C (0.26) 1.02 (0.79,1.31) 0.90 0.84 TBX5 rs17779747 QTc T/G Dec T (0.34) 1.15 (0.90,1.47) 0.26 0.64 KCNJ2 rs8049607 QTc T/C Inc C (0.49) 0.97 (0.77,1.22) 0.78 0.94 LITAF C/N: Coded/Non Coded Allele; P.int, P-value Interaction; OR, Odds ratio; MA(F), Minor Allele (Fre- quency); Inc, Increasing effect; Dec, Decreasing effect; aDirection of effect estimate per copy coded allele, data from previous GWA studies; bEffect estimate is given per copy of the coded allele adjusted for age, sex, culprit artery and CKMB; cP-values for interaction between SNPs and culprit artery on risk of VF

C hapter 7 SNPs modulating infl ammatory biomarkers as intermediate phenotype of susceptibility to ventricular fi brillation in the setting of myocardial infarction

Manuscript in preparation for submission

Raha Pazoki, Jonas S.S.G. de Jong, Nienke Bruinsma, Lukas R.C. Dekker, Arthur A.M. Wilde, Connie R. Bezzina, Michael W Tanck 132 Chapter 7

Abstract

Background: Sudden cardiac death from ventricular fibrillation (VF) during acute myocardial infarction (MI) is a leading cause of total and cardiovascular mortality. Genetic factors underlying risk for VF in the setting of acute MI remain largely unknown. Epidemiological and experimental data suggest that inflammation may be involved in susceptibility to VF. In this study we investigated the role of single nucleotide polymorphisms (SNPs) modulating inflammatory biomarkers on risk of VF in the setting of MI. Methods: The study was performed in the context of the Arrhythmia Genetics in the NEtherlands Study (AGNES), an ongoing case-control study aimed at identification of genetic risk factors of VF. In total, 1433 AGNES patients with a first acute MI (672 of which had VF, ‘cases’, and 761 of which had no VF, ‘controls’) were analysed. We selected independent SNPs previously linked to inflammatory biomarkers by genome- wide association studies (GWAS) in the general population. We then tested these SNPs for modulatory effects on VF risk. We included SNPs associated with C-reactive Protein (CRP), interleukin 1, interleukin 6, interleukin 18, erythrocyte sedimentation rate (ESR), and monocyte chemotactic protein 1 (MCP‑1). Results: Two CRP-modulating SNPs (rs6901250 and rs4420638) were associated with VF at nominally significant P-value threshold (P < 0.05). Each additional A-allele at rs6901250 increased the risk of VF by 1.2 (95% CI: 1.03, 1.4; P = 0.02) while each additional A-allele at rs4420638 increased the risk of VF by 1.2 (95% CI: 1.01, 1.5; P = 0.05). Considering the 18 CRP SNPs in aggregate in the form of a weighted genetic risk score (wGRS), we demonstrated an association between the wGRS and VF. VF risk increased with 1.58 (95% CI: 1.01, 2.49; P = 0.04) per point wGRS increase. Conclusions: Although this data awaits further verification, these initial findings may suggest that pro-inflammatory SNPs may play a role in arrhythmia susceptibility. Pro-inflammatory SNPs and risk of VF in MI 133

Introduction

Sudden Cardiac Death (SCD) is a major cause of cardiovascular mortality and is commonly caused by ventricular fibrillation (VF) in the setting of sequela of coronary artery disease in adults 1,2. While a genetic component in susceptibility to VF in this setting is recognized 3-6 the underlying genetic risk factors and molecular mechanisms involved in SCD are yet largely unknown. Our current work focuses on the identification of genetic risk factors of VF in the setting of a first ST-segment elevation myocardial infarction (MI) in the Arrhythmia Genetics in the NEtherlandS (AGNES) study 3. To complement our previous agnostic approach entailing genome-wide association analysis (GWAS) for genetic risk variant discovery in AGNES 7, we here undertook a candidate approach testing the role of genetic variants previously associated with inflammation, a likely intermediate phenotype for VF. The underlying principal is that genetic variants modulating an intermediate phenotype for VF are likely to modulate VF susceptibility as well 8. Inflammatory response increases dramatically during MI 9,10, and several lines of evidence support a role of inflammation in susceptibility to VF. A series of studies among patients with MI demonstrate an increased pro-inflammatory status among VF patients compared to those without VF 11-13. In a prospective study conducted in men from the Physician’s Health Study, a high baseline level of the inflammatory biomarker C-reactive protein (CRP) was significantly associated with SCD 14. Similar findings were reported for interleukin‑6 (IL6) among participants from the Cardiovascular Health Study 15. In patients treated with ICD after MI, an increased serum CRP was associated with a higher incidence of ventricular tachycardia 16. Furthermore, a common genetic variant in the promoter region of interleukin 18 gene (IL‑18) was found to be associated with SCD 17,18. Studies conducted in vitro have shown that the pro-inflammatory cytokine interleukin‑1β (IL‑1β) produced by myofibroblasts downregulates the gap junctional protein Cx43, suggesting an effect of IL‑1β on cardiac electrical function 19. A recent study conducted in mice provided evidence that myocardial inflammation contrib- Chapter 7 utes to adverse electrophysiological remodeling (prolonged action potential duration and slowed conduction) and increased post-MI arrhythmia risk 20. Collectively the above studies support the general hypothesis that inflammatory biomarkers can be regarded as intermediate phenotypes for SCD and genetic variation influencing these inflammatory markers, are likely candidates for susceptibility to SCD. GWAS have in recent years identified single nucleotide polymorphisms (SNPs) modulating inflammatory biomarkers in the general population 21-25. In the current study, we hypothesized that SNPs modulating inflammatory biomarkers from these GWASes may impact on VF in the setting of a first ST-segment elevation MI. The aim of this study was to investigate this hypothesis in the AGNES population. 134 Chapter 7

Methods

Study sample The AGNES sample: Study individuals consisted of patients enrolled in the AGNES case-control study 3,7. The AGNES study is an ongoing study consisting of patients with a first acute ST-segment elevation MI. Patients who developed (ECG-documented) VF before percutaneous coronary intervention (i.e. reperfusion) were defined as AGNES cases. Controls were defined as acute ST- segment elevation MI patients who did not develop VF in the course of their MI. All individuals included were Dutch and of self-declared European descent and were recruited at seven heart centers in the Netherlands from 2001–2011. Patients with a previous MI or major co-morbidities were excluded. Details on the AGNES study sampling have been described earlier 3. The study protocol was approved by the Institutional Review Board of the Aca- demic Medical Center, University of Amsterdam, and was conducted according to the principles of the Declaration of Helsinki. The medical ethics committees of the hospi- tals participating in the study approved the study protocols, and all participants gave written informed consent.

Selection of inflammatory loci We reviewed publications indexed in the National Center for Biotechnology Informa- tion (NCBI) PubMed database to identify genetic variants associated with inflammatory biomarkers at genome-wide statistical significance. We focused on full text publications, written in English, and studies performed among human subjects of European ancestry, published until 5th October 2014. We used search queries that included: “single nucleotide polymorphism”, “genome-wide association”, “SNP”, “allele”, “C-reactive Protein”, “CRP”, “interleukin”, and “inflammation”. We excluded review articles and case reports. We additionally inspected the Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies/) to check for any additional SNPs modulating serum inflammatory biomarkers in populations of European ancestry and at genome-wide statistical significance. Independent SNPs were chosen for genotyping based on pairwise linkage disequilibrium (LD; r2 ≤ 0.7). We used LD information from 1000 Genomes pilot1 and HapMap projects compiled on the SNP Annotation and Proxy Search (SNAP) web page 26.

Genotyping, quality control and imputation AGNES samples (n = 1457) were genotyped using either the Illumina Human610- Quad (2001–2008) or the Illumina HumanOmni2.5 (2009–2011) arrays. Genotypes were called using the GenomeStudio V2011.1 software (Genotyping module version Pro-inflammatory SNPs and risk of VF in MI 135

1.9.0) and the GenCall score cutoff was 0.15 as recommended by Illumina. The average sample call rate was larger than 99%. Multiple quality control measures were implemented before imputation. The estimated sex for each individual as determined by genotyping was compared with the phenotypic sex. Exclusion criteria were deviation from Hardy-Weinberg equilibrium at P ≤ 10−4 (estimated in controls), sample call rate < 0.95 and SNP call rate < 0.98. The minimal observed minor allele frequency (MAF) was 0.001 in the present sample and no selection based on MAF was performed. To correct for possible genetic heterogeneity within the AGNES study sample, we performed principal component analysis using a multi-dimensional scaling technique applied on the Identity-By-State (IBS) matrix (R package GenABEL 27). Prim’s algorithm implemented in R package nnclust 28 was used to identify clusters and outliers (threshold: 0.3). The outliers thus identified were excluded from the subsequent analyses. In total, 1433 AGNES cases and controls successfully passed all the quality control steps. The resulting outliers were excluded from the analysis. Imputation of non-genotyped SNPs was done using the Markov-chain Monte Carlo method implemented in MACH1.0 29,30 using the HAPMAP reference panel. After imputation, an r2 threshold of 0.3 was implemented to identify and discard low- quality imputed SNPs.

Statistical Analysis Differences in continuous phenotypic variables between cases and controls were tested using an independent t test where data was normally distributed or a Mann- Whitney U test otherwise. Values are presented as means ± SD or median and interquartile range, respectively. Differences in categorical variables were compared using a Fisher exact test, and values are presented as number and percentages. Logistic regression models assuming an additive genetic model of inheritance were used to test the association between pro-inflammatory SNPs and VF. We tested the additive assumption using the le Cessie - van Houwelingen - Copas - Hosmer un- weighted sum of squares test for global goodness of fit using rms package in R 31. The Chapter 7 analyses were adjusted for age, sex, and the first 2 principal components. In addition to main effects, possible interactions between a SNP and sex and/or age were also investigated. Since at the CRP locus a total of 18 SNPs were investigated, we additionally tested the in-aggregate effect of these 18 SNPs on VF susceptibility by means of a weighted genetic risk score (wGRS) where the CRP increasing alleles were treated as coded allele. To calculate wGRS, the allele counts per SNP were multiplied by the size of the CRP increasing effect and were subsequently summed up. The weights used were based on the effect estimates per SNP from the original GWAS in the general popula- 136 Chapter 7

tion. Nagelkerke’s R square 32 was used as the estimate of percentage of variance in the risk of VF explained by individual SNPs or the SNPs in aggregate (combined in wGRS). A P-value of 0.05 with a Bonferroni correction for the number of independent SNPs was used as the threshold for statistical significance in the analysis involving individual SNP effects. AP -value of 0.05 was considered as the threshold for statistical significance in the genetic risk score analysis and as a nominal significance threshold for individual SNP analysis. All analyses were performed in R.

Results

Patients In total, 1433 AGNES patients with a first acute MI (672 patients with VF, ‘cases’, and 761 without, ‘controls’) were included in the statistical analysis. Baseline characteristics of the study population are shown in Table 1. AGNES cases had a lower prevalence of diabetes mellitus and hypercholesterolemia, a lower average body mass index (BMI),

Table 1 | Baseline characteristics of the AGNES case-control set Characteristic Na Total Cases Controls P-valueb (n = 1433) (n = 672) (n = 761) Sex (male) c 1433/672/761 1142 (80) 537 (80) 605 (79) 0.847 Age at MI (years) d 1433/672/761 57.5 ± 10.85 56.7 ± 11.03 58.3 ± 10.63 0.004 ST segment deviation (mm) e 491/251/240 15(16) 18(19) 14(12) < 0.001 MI location c 1366/619/747 0.001f Anterior MI 726(53) 359(58) 367(49) Inferior MI 640(47) 260(42) 380(51) CK-MB (μg/L) e 1044/415/629 205(277) 226(356) 188(244) < 0.001 Family history of sudden 1416/657/759 400(28) 205(31) 195(26) 0.002 death c Beta blocker usage c 1384/632/752 139(10) 66(10) 73(10) 0.65 Cardiovascular risk factors Current Smoking c 1374/627/747 809(59) 396(63) 413(55) 0.003 Diabetes Mellitus c 1349/613/736 102(8) 29(5) 73(10) 0.0004 Hypertension c 1298/584/714 426(33) 185(32) 241(34) 0.428 Hypercholesterolemia c 1250/563/687 406(32) 164(29) 242(35) 0.022 BMI (kg/m2) d 1342/597/745 26.5±3.86 26.1±3.66 26.8±3.99 0.002 MI, myocardial infarction; CK-MB, creatine kinase-MB; aSample sizes of the total, case and control sets for whom information is available are given. bP-value for comparison of cases and controls for each item; cNumber (%); dmean ± s.d.; eMedian (interquartile range); fP-value for comparison of inferior and anterior MI between cases and controls. Pro-inflammatory SNPs and risk of VF in MI 137 and less frequency of smoking. AGNES cases more often had a family history of sudden cardiac death than the controls, were on average younger at the time of MI, and had higher creatine kinase-MB (CK-MB) levels than AGNES controls (Table 1).

Inflammatory SNPs and VF By reviewing published GWAS, we selected 28 independent SNPs that modulate inflammatory biomarkers. These SNPs included 18 SNPs modulating serum CRP, 2 SNPs modulating serum erythrocyte sedimentation rate (ESR), 2 SNPs modulating serum monocyte chemotactic protein 1 (MCP‑1), 4 SNPs modulating IL18, 1 SNP modulating serum interleukin 1 receptor antagonist (IL1ra), and 1 SNP modulating serum IL6. The selected SNPs were independent (r2 ≤ 0.7) and were identified in the general population to be associated with inflammatory biomarkers in individuals of European descent at genome-wide significance level (see Table 2 where the coded allele is the biomarker increasing allele). In the model corrected for age, sex and principal components, none of the 28 SNPs displayed an association with VF passing the Bonferroni-corrected significance threshold (P ≤ 0.002; 0.05/ 28). Also, none of the SNPs showed interaction with age or sex at the Bonferroni-corrected threshold. Two CRP-modulating SNPs (rs6901250 and rs4420638) were associated with VF at nominally-significant P-value threshold (P < 0.05). Each additional A allele at rs6901250 increased the risk of VF by 1.2 (95% CI: 1.03, 1.4; P = 0.02) while each additional A allele at rs4420638 increased the risk of VF by 1.2 (95% CI: 1.01, 1.5; P = 0.05) (Table 2). In a multivariable model restricted to the 18 CRP SNPs, rs6901250 and rs4420638 remained independently associated with VF (P = 0.02 for both). This model explained 2% of the variability in susceptibility to VF. Inclusion of the remaining 10 pro-inflammatory SNPs increased the explained variability to 3%. Addition of age, sex, and principal components increased the explained variance in susceptibility to VF to 6.6%.

Genetic risk score analysis on CRP SNPs AGNES cases and controls carried at least 15 and at most 32 CRP-increasing alleles. Chapter 7 For the wGRS (range 1.2 to 2.6; Figure 1) the risk of VF increased by 1.58 fold (95% CI: 1.01, 2.49; P = 0.04) per point wGRS increase, and the wGRS explained 0.3% of the VF risk. Frequency of VF increased with increasing wGRS quartile and was 34.9% in the lowest quartile and 44.2% in the highest quartile. 138 Chapter 7

Table 2 | Association of inflammatory biomarker SNPs with VF in the AGNES sample SNP CA(F)a Ref. GWAsb Chr Trait Total AGNES P-value P-int Gene Effect (N = 1433) age/sex OR (95% CI)c rs10745954 A(47) 21 0.039 12 CRP 0.94(0.81,1.1) 0.42 0.54/0.98 Near ASCL1 rs1183910d G(70) 21,22 0.149 12 CRP 0.97(0.83,1.14) 0.72 0.30/0.21 TCF1 rs12037222 A(26) 21 0.045 1 CRP 1.06(0.89,1.27) 0.47 0.69/0.37 Near PABPC4 rs12239046 C(61) 21 0.047 1 CRP 1(0.85,1.17) 0.99 0.65/0.79 NLRP3 rs1260326 T(37) 21 0.072 2 CRP 1.06(0.91,1.24) 0.44 0.75/0.43 GCKR rs13233571 C(87) 21 0.054 7 CRP 0.96(0.77,1.19) 0.74 0.22/0.03 BCL7B rs1800961 C(97) 21 0.088 20 CRP 1.2(0.76,1.88) 0.44 0.35/0.29 HNF4A rs2794520 C(68) 21 0.160 1 CRP 1.13(0.96,1.32) 0.15 0.78/0.04 Near CRP rs2847281 A(59) 21 0.031 18 CRP 0.94(0.81,1.1) 0.40 0.40/0.08 PTPN2 rs340029 T(61) 21 0.032 15 CRP 1.11(0.94,1.29) 0.19 0.74/0.59 RORA rs3845624 C(40) 21 0.10 1 CRP 0.98(0.84,1.15) 0.82 0.11/0.31 DARC rs4129267 C(60) 21 0.079 1 CRP 0.95(0.81,1.11) 0.51 0.36/0.75 IL6R rs4420065 C(60) 21 0.090 1 CRP 1.06(0.91,1.24) 0.46 0.95/0.11 Near LEPR rs4420638d A(82) 21,22 0.236 19 CRP 1.23(1.01,1.5) 0.05 0.68/0.85 Near APOC1 rs4705952 G(20) 21 0.042 5 CRP 0.92(0.77,1.1) 0.37 0.26/0.31 Near IRF1 rs6734238 G(40) 21 0.050 2 CRP 1.02(0.87,1.19) 0.82 0.12/0.28 Near IL1F10 rs6901250 A(31) 21 0.035 6 CRP 1.21(1.03,1.41) 0.02 0.91/0.11 GPRC6A rs9987289 G(92) 21 0.069 8 CRP 1.12(0.83,1.5) 0.44 0.37/0.68 Near PPP1R3B rs12034598 G(18) 25 0.14 1 ESR 1.07(0.88,1.3) 0.48 0.37/0.39 CR1 rs12075 A(55) 25 0.33 1 MCP‑1 0.96(0.82,1.12) 0.57 0.99/0.46 DARC rs1834481 G(26) 23 0.09 11 Il18 1.03(0.86,1.23) 0.76 0.08/0.90 Il18 rs2115763 T(31) 23 0.09 11 Il18 0.93(0.8,1.09) 0.42 0.75/0.08 Il18 rs2250417 T(45) 24 0.1 11 IL18 1.01(0.86,1.18) 0.92 0.59/0.08 BCO2, IL18, TEX12 rs3026968 T(21) 25 0.22 1 MCP‑1 0.9(0.76,1.08) 0.27 0.04/0.84 CADM3 rs4910742 G(5) 25 0.22 11 ESR 1.34(0.94,1.9) 0.12 0.29/0.76 HBB rs643434 A(37) 25 0.22 9 IL‑6 0.98(0.84,1.15) 0.83 0.11/0.70 ABO rs6743376 A(63) 24 0.13 2 IL‑1ra 1.13(0.96,1.32) 0.11 0.35/0.12 IL1F10 rs7577696 G(41) 24 0.08 2 IL18 1(0.85,1.17) 0.95 0.14/0.32 SRD5A2, DPY30, SPAST, SLC30A6, NLRC4 CA(F), coded allele (frequency); Ref. reference; chr, chromosome; CRP, c-reactive protein; OR, odds Ratio; P-int, P-value for interaction; aAllele refers to the allele that is associated with increased level of the corresponding inflammatory trait; bBeta coefficient represents 1-unit change in the natural log- transformed CRP (mg/L) per copy increment in the coded allele; cEffect estimate is given per copy of the CRP-increasing allele adjusted for age, and sex and PCs; dSNPs found in more than one study but could not be meta-analyzed due to incomparable unit differences. Coefficients from the larger sample size were then considered. 7

P = 0.02) while each additional A allele at rs4420638 increased the risk of VF by 1.2

(95% CI: 1.01, 1.5; P = 0.05) (Table 2).

In a multivariable model restricted to the 18 CRP SNPs, rs6901250 and rs4420638 remained independently associated with VF (P = 0.02 for both). This model explained

2% of the variability in susceptibility to VF. Inclusion of the remaining 10 pro- inflammatory SNPs increased the explained variability to 3%. Addition of age, sex, and principal components increased the explained variance in susceptibility to VF to 6.6%.

Genetic risk score analysis on CRP SNPs

AGNES cases and controls carried at least 15 and at most 32 CRP-increasing alleles

(Figure 1). For the wGRS (range 1.2 to 2.6) the risk of VF increased by 1.58 fold

(95% CI : 1.01, 2.49; P = 0.04) per point wGRS increase, and the wGRS explained 0.3% of the VF risk. Frequency of VF increased with increasing wGRS quartile and was 34.9% in the lowest quartile and 44.2% in the highest quartile.Pro-infl ammatory SNPs and risk of VF in MI 139

Figure 1 | CRP-weighted genetic risk score and percentage of VF in the AGNES case-control set. Figure 1 | CRP-weighted genetic risk score and percentage of VF in the AGNES case-control set. Error bars represent 95% confi dence interval for percentage of VF cases in each score category pro- Error bars represent 95% confidence interval for percentage of VF cases in each score category proportional portionalto the total to number the total of AGNESnumber casesof AGNES and controls cases andin that controls category. in that category.

Discussion Pro-inflammatory SNPs and risk of VF in MI |155

In the current study, we selected SNPs previously demonstrated modulating infl ammatory biomarkers and subsequently tested their eff ect on susceptibility to VF in the setting of a fi rst acute ST-elevation MI in the AGNES case-control set. We demonstrated that 2 SNPs, respectively at chromosome 6q22 (rs6901250) and chromosome 19q13 (rs4420638), displayed a suggestive association with the risk of VF. Considering CRP SNPs in aggregate in the form of a weighted genetic risk score (wGRS), we demonstrated an association between the wGRS and VF. Although this data awaits further verifi cation, these initial fi ndings may suggest that pro-infl ammatory SNPs might play a role in arrhythmia susceptibility. rs6901250 Chapter 7 Th e data presented here may suggest that the CRP-increasing allele at rs6901250 may be associated with increased risk of VF in the setting of a fi rst acute MI. Th e exact gene mediating the eff ect of rs6901250 on serum CRP is not known. rs6901250 is located in the GPRC6A gene that encodes the G protein-coupled receptor, family C, group 6, member A. GPRC6A which is expressed in multiple organs including heart, is a transmembrane calcium-sensitive receptor which is involved in numerous pathways including calcium signaling 33. It is known to be triggered by increased extracellular calcium as occurs for example during necrosis, to activate assembly of infl ammatory cells 34. 140 Chapter 7

rs4420638 The A-allele of rs4420638 which increases serum CRP in the general population was suggestively associated with VF in the current study. rs4420638, which is located immediately downstream of the apolipoprotein C-I (APOC1) gene 35 has been linked to multiple phenotypes in GWAS including Lipoprotein-associated phospholipase A2 (Lp-PLA2) mass 36, coronary artery disease 37, and treatment response to lipid lower- ing function of statin and fenofibrates 38-40. As for rs6901250, the biological mechanism linking rs4420638 to variation in serum CRP are still unexplored. However, APOC1 is known to have immunosuppressive properties and to decrease secretion of pro inflammatory cytokines from human macrophages 41. Furthermore APOC1 forms part of the statin pathway, which besides the major role in lipid metabolism has also been implicated in immunomodulation 41. Since rs4420638 is associated with multiple phenotypes in the general population, the effect of this SNP on CRP or VF may be possibly mediated through multiple factors and mechanisms.

Genetic risk score Next to single SNP effects, we also constructed a weighted genetic risk score (wGRS) that considered the SNPs from the CRP loci in aggregate. This uncovered an association of the CRP wGRS with VF. This is the first time that SNPs impacting on phenotypes considered intermediate phenotypes for VF, were associated with VF when considered in aggregate. A similar approach has been employed by our group 8 (chapter 5 in this thesis) and others 42,43 for SNPs related to ECG parameters. We previously tested the collective effect of ECG-modulating SNPs on risk of VF in the AGNES case-control set and did not demonstrate any effect for such score 8. Noseworthy et al. investigated the effect of SNPs modulating the QTc-interval on risk of SCD and did not demonstrate a linear relation between genetic risk score of QTc-interval SNPs on SCD risk 43,44.

Role of inflammation in VF susceptibility The CRP genetic risk score in the current study explained a marginal part of the variance in risk of VF (0.3%). This is not surprising given the fact that the CRP-modulating SNPs explain only 5% of the variability in CRP in the general population 21 which is rather small in relation to the 35-40% heritability estimated for CRP 45,46. Furthermore, VF in the setting of a first acute MI is most likely governed by numerous pathways of which inflammation may be one. Mechanistic studies aimed at understanding the processes by which inflammation may affect cardiac electrophysiology and arrhythmia susceptibility are largely lacking. One recent study conducted in mice that investigated the role of post-MI inflammation on cardiac electrophysiology and arrhythmogenesis demonstrated prolonged repolarization, slowed conduction and exacerbation of arrhythmia as a consequence Pro-inflammatory SNPs and risk of VF in MI 141 of inflammation. While this study supports a role for modulation of cardiac electrophysiology by inflammation, it looked at changes in hearts on the fifth date post-MI, which differs markedly from the timeline of arrhythmia in AGNES (where arrhythmias arise within 2 hours of commencement of complaints in almost 90% of patients). Furthermore, one should distinguish between the “pre-conditioning” effects of long- standing inflammation (existing prior to the MI, e.g. systemic) on cardiac electrical properties as opposed to the acute effects of inflammation on cardiac electrophysiology during the MI.

Strengths and Limitations Considering the difficulty in ascertaining patients presenting with VF, the sample size of our study is rather large. Furthermore, we here study patients presenting with VF in the setting of a specific cardiac pathology entailing a first MI. This limits the variability in molecular mechanisms underlying VF which should enhance genetic variant discovery. However, we have here tested common genetic variants that have been associated with inflammatory markers in very large sets of the general population and which have been found to carry (very) small effect sizes (as expected of common variants). Thus although our current findings may be considered as encouraging evidence for an effect of pro-inflammatory SNPs (at least when considered in aggregate) in modulation of VF risk in acute MI, the current findings call for validation in independent patient sets and investigation in larger studies. Our hypothesis that inflammation is involved in VF risk is entirely based on published epidemiological and experimental studies. We have as yet not studied the role of inflammation in predisposition to VF in the AGNES study where CRP measurements were available but only in a non-random subset of the total set. Further whenever CRP levels were determined, these measurements were done post MI and at various time- points after MI. For these reasons, we refrained from assessing the relation between serum CRP and VF risk in the AGNES study. This will require a dedicated study. In our GWA studies on the AGNES sample (chapters 4 and 5 of this thesis), we identified a VF susceptibility locus at chromosome 21. Interestingly this locus harbors Chapter 7 the Coxsackie and Adenovirus Receptor (CAR) which besides playing a role in cardiac conduction 47 has a long-recognized role as viral receptor in the pathogenesis of viral myocarditis 48. Of note, active coxsackie B virus infection has been reported at a high frequency in a group of individuals with MI who died suddenly 49. This may suggest a role for the genetic locus in mediating variability in susceptibility to myocarditis and ensuing inflammation that may in turn be pro-arrhythmic. While this remains purely speculative, these observations together with previously published observations emerged from epidemiological 14,15,50 and experimental studies 20 collectively argue for further investigation of the role of inflammation in risk of VF/SCD. 142 Chapter 7

Conclusion In the current study, two CRP SNPs located in the GPRC6A and APOC1 gene were nominally associated with VF. Additionally, a genetic risk score constructed of CRP- increasing alleles appeared to increase the risk of VF in the AGNES study. The genetic underpinnings of inflammation system may play a role in arrhythmia susceptibility during MI and our findings here merit further validation and investigation in animal, clinical or population-based studies.

References

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C hapter 8 Summary and conclusions

Summary and conclusions 151

Sudden cardiac death (SCD) is a catastrophic cardiovascular event most commonly occurring as a consequence of ventricular fibrillation (VF) in adults suffering from acute myocardial infarction (MI) or its late consequences. Several studies have demonstrated the role of family history of SCD in the determination of risk of SCD and VF 1-4; however, the genetic underpinnings of SCD and VF remain largely unknown. In this thesis, we investigated genetic determinants of VF using multiple genomic approaches including genome-wide association studies (GWAS), candidate gene approaches, pathway analysis as well as integration with expression quantitative loci (eQTL) data. These approaches were undertaken in the Arrhythmia Genetics in the NEtherlands (AGNES) study. In this final chapter, we summarize the main findings of this thesis and discuss future perspectives.

Main Findings

In the first two chapters of the thesis (Chapters 2 and 3) 5,6, we provide an overview of SCD and the current knowledge on clinical and genetic risk factors of VF/SCD. In Chapters 4-7, we provide original information about the genetic architecture of SCD in the context of the AGNES study 7,8. GWAS analyses are presented in Chapters 4 and 5. In Chapter 5, next to GWAS, we additionally present results from the complementary eQTL investigations, pathway analysis and data from a candidate gene approach assessing single nucleotide polymorphism (SNPs) previously implicated in published studies. Chapters 6 and 7 describe results from two further candidate gene approaches, including the assessment of the effects of genetic variants in aggregate by means of genetic risk score analysis, focusing on genes involved in biological pathways that may constitute intermediate phenotypes of VF.

1. Genetic predisposition to SCD: Overview In Chapters 2 and 3, we reviewed existing literature that investigated genetic risk factors of SCD including biological mechanisms of SCD during MI, and recent knowledge about genetic predisposition to SCD. We concluded that while significant progress has been made concerning gene discovery for inherited cardiac disorders associated with SCD in the young (e.g. in the primary electrical disorders and the cardiomyopathies), progress in dissecting the genetic underpinnings of SCD risk in complex cardiac pa- Chapter 8 thologies has been slow. Disorders associated with SCD in the young usually display monogenic / Mendelian inheritance, which favors gene discovery. In contrast, the genetic architecture of SCD occurring in the setting of complex cardiac pathologies such as MI is complex and multiple genetic factors are expected to conspire to determine risk. These genetic factors may occur at different frequencies in the general popula- 152 Chapter 8

tion and may be frequent or rare. They are also expected to carry different effect sizes on risk of VF. Such complex genetic architecture hinders gene discovery. Moreover, genetic studies on VF in the setting of these complex cardiac pathologies are hindered by difficulties that are inherent to the ascertainment of patients experiencing VF/SCD.

2. GWAS approach for identification of genetic loci predisposing to VF in MI Chapters 4 and 5 present the results of two GWAS we conducted in the AGNES study. The initial GWAS Chapter( 4) included 515 cases and 457 controls, whereas the second (extended) GWAS (Chapter 5) included 672 cases and 761 controls. The GWAS presented in Chapter 4 was the first GWAS investigating the genetic architecture of VF during MI. We identified a locus on chr.21q21 (rs2824292) at which the G-allele increases the risk of VF. This effect was replicated in an independent Dutch case- control sample (Chapter 4), while it did not replicate in small case-controls sets from Italy and Germany (Chapter 5). The closest genes to this locus are the CXADR gene encoding the Coxsackie and Adenovirus Receptor and the BTG3 gene encoding B- cell translocation gene 3. The protein encoded by CXADR was previously implicated in virus uptake and in cardiac conduction. Our group showed that the VF risk allele at rs2824292 is associated with a decreased CXADR mRNA abundance in human heart 9. We also demonstrated that mice haplo-insufficient for CXADR have slowed conduction in the ventricles and greater susceptibility to arrhythmia in the setting of myocardial ischemia 9. This data supports a biologically plausible role of CXADR in predisposition to VF in the setting of a first acute MI, in spite of the fact that the locus is not uniformly associating across studies. In our view, this warrants further investigation in additional cohorts and an effort to determine the factors underlying the observed heterogeneity across studies. The locus at rs2824292 remained associated with VF during MI in the second GWAS in the extended set of AGNES (Chapter 5). The effect of this locus and nine other single nucleotide polymorphisms (SNPs) identified in this second GWAS were tested for replication in the PREDESTINATION study, an independent case-control sample of patients with a first MI from Italy (n = 641) with inclusion criteria similar to AGNES. One locus (rs1750041) was replicated with a nominally significant P-value. After meta-analysis, two of the SNPs displaying the strongest association with VF were taken forward for replication and meta-analysis in an additional similar case-control study from Denmark (GEVAMI, n = 783). A review of online eQTL databases (Chapter 5) showed that rs1750041 was associated with the mRNA abundance of multiple genes (including the SYNJ2 gene in which it occurs) in human heart tissue and whole blood. Our multiple independent genomic approaches in Chapter 5 converged at another promising gene: XKR6 (encoding XK, Kell blood group complex subunit-related family, member 6). SNP rs10096381 located in XKR6 showed the second strongest Summary and conclusions 153 association with VF in GWAS after the SNP at chr.21q21 and it was associated with increased levels of XKR6 mRNA. In addition, a SNP (rs6982308) located in the methionine sulfoxide reductase (MSRA) gene (identified through its eQTL effect on XKR6) was also associated with VF (chapter 5). In spite of non-replication of these SNPs in the PREDESTINATION case-control set, this convergence on XKR6 is potentially interesting and deserves further investigation.

3. Candidate gene approach to VF in MI Previously, Lemmert et al. demonstrated that PR interval and QRS duration are associated with risk of VF during MI 10. In Chapter 6 of this thesis we investigated AGNES patients for such correlations between ECG parameters during STEMI and occurrence of VF, uncovering shorter mean RR interval and longer mean QTc-interval among patients who developed VF compared to patients who did not develop VF 8. In this chapter we also investigated whether 65 candidate SNPs previously associated with heart rate and/or ECG indices of conduction and repolarization in GWAS in the general population also play a role in variation of these traits during MI. We identified eight SNPs displaying a nominal association with either heart rate, PR interval, QRS duration or QTc interval during MI. We subsequently investigated these eight SNPs for association with VF during MI and demonstrated that three (located in SCN10A, KCNQ1 and near MYH7) displayed a nominal association with VF. The effect of the MYH7 (Myosin heavy chain 7, cardiac muscle, beta) locus was modified by the culprit artery showing an association only in patients with either an occlusion in the left circumflex or right coronary artery (i.e. non-anterior wall MI). The SNP rs6795970 in the SCN10A gene, which encodes a sodium channel isoform primarily expressed in neurons, turned out to be especially interesting. This SNP was associated with PR interval and with VF in the initial AGNES set (Chapter 6), as well as with VF in the extended AGNES set (Chap- ter 5), and with VF after meta-analysis of AGNES with the Italian cases and controls from the PREDESTINATION study (Chapter 5). Quite intriguingly, the PR-prolonging allele of rs6795970 is protective against VF. Similarly, the PR-prolonging allele of this SNP has been demonstrated to be protective against atrial fibrillation 11,12. Thus, in spite of the fact that the protective effect of the PR-prolonging allele appears counter- intuitive, consistent observations appear to be emerging relating to the modulatory effects of this SNP on cardiac arrhythmia susceptibility. SCN10A is located near the

SCN5A gene, which encodes the major sodium channel isoform in heart and underlies Chapter 8 depolarization in cardiomyocytes. Experimental studies have shown that a common variant in SCN10A, which is in high LD with rs6795970, regulates expression of the SCN5A gene in heart through disruption of a T-box (TBX5/TBX3) binding element 13-15. Another candidate SNP approach that we undertook involved pro-inflammatory SNPs. Inflammatory response increases dramatically during MI and several lines of 154 Chapter 8

evidence support a role of inflammation in susceptibility to VF Chapter( 7). Therefore, in chapter 7, we selected SNPs from previous GWAS of inflammatory-biomarkers and tested their individual and aggregate effects on VF through construction of a genetic risk score (GRS). We showed that rs6901250 located in GPRC6A (G protein-coupled receptor, family C, group 6, member A), and rs4420638 located in the vicinity of the apolipoprotein C-I (APOC1) gene were associated with VF. While the GRS was associated with VF at the nominal statistical significance level, it only explained a marginal part of the variation in VF risk. This data nevertheless, points to a possible role of genetic variation in the inflammation system on predisposition to VF.

Future Perspective

Using various approaches, this thesis uncovered a number of common genetic variants that may be involved in risk of VF during MI. While this data is interesting and has already instigated functional studies aimed at understanding molecular pathways involved in the predisposition to VF 9, these findings await further validation in additional patients. Furthermore, the number of variants identified and the variance in VF susceptibility that is explained by them is very limited, precluding their imminent clinical utility in VF prediction. The identification of additional common genetic variants will require larger patient sets, justifying ongoing efforts to enlarge the AGNES patient set and similar sets. The AGNES study was designed to specifically include patients presenting with VF in the setting of a first ST-segment elevation MI. The rationale behind this study design was that including one specific (homogeneous) phenotype would increase statistical power favoring gene discovery. However, one should realize that sub-groups can still be distinguished within AGNES. For instance analysis could be conducted in sub-groups defined by culprit artery or time-to-VF, as one could hypothesize those VF mechanisms may differ among these subgroups. This again argues in favor of expanding the patient sets for such studies. Efforts should, therefore, be made to increase the sample size through world-wide collaborations between studies to achieve sufficiently large and homogeneous patient (sub) groups 16. Variants identified could, subsequently, be tested in populations with SCD occurring in the setting of other cardiac pathologies to investigate the generalizability of the effects outside the specific cardiac pathology of first MI. Larger patient sets will additionally provide in the future the opportunity to start investigating the role of rare genetic variants that presumably carry a larger effect on risk. By virtue of their rarity the investigation of these variants will also necessitate large sets of uniformly phenotyped patients. Summary and conclusions 155

In conclusion, this thesis has set the first steps towards the dissection of the genetic underpinnings of VF risk in the context of MI, providing the first insights and high- lighting the challenges involved. Chapter 8 156 Chapter 8

References

1. Dekker, L. R. et al. Familial sudden death is an important risk factor for primary ventricular fibrillation: a case-control study in acute myocardial infarction patients. Circulation 114, 1140-1145, doi:10.1161/circulationaha.105.606145 (2006). 2. Friedlander, Y. et al. Sudden death and myocardial infarction in first degree relatives as predictors of primary cardiac arrest. Atherosclerosis 162, 211-216 (2002). 3. Jouven, X., Desnos, M., Guerot, C. & Ducimetiere, P. Predicting sudden death in the population: the Paris Prospective Study I. Circulation 99, 1978-1983 (1999). 4. Kaikkonen, K. S., Kortelainen, M. L., Linna, E. & Huikuri, H. V. Family history and the risk of sudden cardiac death as a manifestation of an acute coronary event. Circulation 114, 1462-1467, doi:10.1161/circulationaha.106.624593 (2006). 5. Pazoki, R., Tanck, M. W., Wilde, A. A. & Bezzina, C. R. Complex inheritance for susceptibility to sudden cardiac death. Current pharmaceutical design 19, 6864-6872 (2013). 6. Pazoki, R., Wilde, A. A. & Bezzina, C. R. Genetic Basis of Ventricular Arrhythmias. Current cardiovascular risk reports 4, 454-460, doi:10.1007/s12170-010-0128-2 (2010). 7. Bezzina, C. R. et al. Genome-wide association study identifies a susceptibility locus at 21q21 for ventricular fibrillation in acute myocardial infarction. Nature genetics 42, 688-691, doi: 10.1038/ng.623 (2010). 8. Pazoki, R. et al. SNPs identified as modulators of ECG traits in the general population do not markedly affect ECG traits during acute myocardial infarction nor ventricular fibrillation risk in this condition. PloS one 8, e57216, doi:10.1371/journal.pone.0057216 (2013). 9. Marsman, R. F. et al. Coxsackie and adenovirus receptor is a modifier of cardiac conduction and arrhythmia vulnerability in the setting of myocardial ischemia. J Am Coll Cardiol 63, 549-559, doi:10.1016/j.jacc.2013.10.062 (2014). 10. Lemmert, M. E. et al. Electrocardiographic factors playing a role in ischemic ventricular fibrillation in ST elevation myocardial infarction are related to the culprit artery. Heart rhythm: the official journal of the Heart Rhythm Society 5, 71-78, doi:10.1016/j.hrthm.2007.09.011 (2008). 11. Jabbari, J. et al. Common and Rare Variants in SCN10A Modulate the Risk of Atrial Fibrilla- tion. Circulation. Cardiovascular genetics, doi:10.1161/circgenetics.113.000442 (2014). 12. Ritchie, M. D. et al. Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk. Circulation 127, 1377-1385, doi:10.1161/circulationaha.112.000604 (2013). 13. van den Boogaard, M. et al. A common genetic variant within SCN10A modulates cardiac SCN5A expression. The Journal of clinical investigation 124, 1844-1852, doi:10.1172/jci73140 (2014). 14. van den Boogaard, M. et al. Genetic variation in T-box binding element functionally affects SCN5A/SCN10A enhancer. The Journal of clinical investigation 122, 2519-2530, doi:10.1172/ jci62613 (2012). 15. Arnolds, D. E. et al. TBX5 drives Scn5a expression to regulate cardiac conduction system function. The Journal of clinical investigation 122, 2509-2518, doi:10.1172/jci62617 (2012). 16. Wellens, H. J. et al. Risk stratification for sudden cardiac death: current status and challenges for the future. European heart journal 35, 1642-1651, doi:10.1093/eurheartj/ehu176 (2014). Nederlandse samenvatting 157

Nederlandse samenvatting

Plotse hartdood (sudden cardiac death) is een ernstige cardiovasculaire gebeurtenis die meestal optreedt als gevolg van een hartritmestoornis en dan vooral ventrikelfibril- leren (VF). Bij volwassenen treedt VF vaak op tijdens een acuut hartinfarct of de latere gevolgen daarvan. Verscheidene studies hebben aangetoond dat het risico van plotse hartdood en VF verhoogd is bij mensen waarvan (directe) familieleden plotseling zijn overleden. Dit wijst erop dat het risico van VF (deels) erfelijk bepaald is. Er is tot nu toe echter maar weinig vooruitgang geboekt in het vinden van de betrokken genen. In dit proefschrift hebben we naar genen gezocht die het VF risico beïnvloeden. Hierbij hebben we gebruik gemaakt van verschillende benaderingen. We hebben gekeken of het risico van VF gerelateerd was aan (1) genetische varianten, die over het gehele genoom verspreid voorkomen (genome-wide association studies, GWAs), (2) plaatsen in het genoom, waarvan bekend is dat ze de genexpressie reguleren (expression quan- titaive loci, eQTL), (3) bekende reactiewegen en (4) varianten in kandidaatgenen, d.w.z. genen die op basis van hun functie een hogere kans hebben een relatie met risico van VF te hebben. Bij al deze onderzoeken is gebruik gemaakt van de AGNES studie (Arrhythmia Genetics in the Netherlands). De AGNES studie is een patiënt- controle onderzoek, waarin personen met VF tijdens een acuut hartinfarct (patiënten) vergeleken worden met personen zonder VF tijdens een acuut hartinfarct (controles). In dit hoofdstuk vatten we de belangrijkste bevindingen van het proefschrift samen en geven enkele aanbevelingen voor vervolgonderzoek. Deze Nederlandse samenvatting is bedoeld voor niet-ingewijden. Voor een meer gedetailleerde samenvatting verwij- zen we u naar de Engelse samenvatting (Hoofdstuk 8).

Belangrijkste resultaten

In de eerste twee hoofdstukken van dit proefschrift (Hoofdstuk 2 en 3) beschrijven we de huidige kennis op het gebied van genetische risicofactoren voor VF en plotse dood. In Hoofdstukken 4–7 presenteren we de resultaten van de verschillende onderzoeken in de AGNES studie. De genoombrede associatie analyses (GWAs) worden in Hoofd- stuk 4 en 5 besproken. Daarnaast keken we in Hoofdstuk 5 ook naar verbanden tussen het risico of VF en regio’s waarvan bekend is dat ze genexpressie beïnvloeden (eQTL), genen in bekende reactiewegen en varianten in kandidaatgenen. Deze eQTL, genen in reactiewegen en kandidaatgenen werden op basis van eerder gepubliceerd werk geselecteerd. Hoofdstukken 6 en 7 beschrijven resultaten van twee andere studies met kandidaatgenen. Hierbij hebben we o.a. het gezamelijke effect van verschillende 158 Nederlandse samenvatting

varianten in genen, die betrokken zijn bij dezelfde biologische reactieweg, onderzocht door het gebruik van een genetische risicoscore.

Genetische aanleg voor plotse hartdood: Overzicht

In Hoofdstukken 2 en 3 geven we op basis van bestaande publicaties een overzicht van de huidige kennis wat betreft de genetische risicofactoren voor plotse hartdood. Hierbij hebben we gekeken naar biologische mechanismen die het optreden van plotse hartdood tijdens een hartinfarct en de genetische risicofactoren voor plotse hartdood kunnen verklaren. We kwamen tot de conclusie, dat terwijl er een aanmer- kelijke vooruitgang is geboekt m.b.t. het vinden van genen welke betrokken zijn bij erfelijke aandoeningen die kunnen leiden tot plotse hartdood in jongeren (bijv. de primaire aritmiesyndromen), er maar weinig voortuitgang is in het onderzoek naar genetische risicofactoren van plotse hartdood in het kader van complexe cardiale pathologieën. De primaire aritmiesyndromen worden vaak veroorzaakt door een variant in een enkel gen wat opsporing vergemakkelijkt. Dit in tegenstelling tot plotse hartdood tijdens complexe cardiale pathologieën, bijv. een hartinfarct, waar varianten in meerdere genen samen het risico bepalen dat een persoon loopt. Deze varianten komen met verschillende frequenties voor in de algemene populatie en het kunnen zowel veelvoorkomend als zeldzaam zijn. De genetische risicofactoren kunnen dus van persoon tot persoon verschillen wat opsporing bemoeilijkt. Daarnaast worden studies belemmerd door factoren die onlosmakelijk met plotse hartdood verbonden zijn, zoals bijv. problemen met het verkrijgen van toestemming.

Genoombrede associatie-analyses (GWAs): genen vinden voor het risico van VF

In Hoofdstukken 4 en 5 beschrijven we de resultaten van twee genoombrede associatie studies (GWAs), die in de AGNES studie gedaan zijn. Voor de eerste GWAs (Hoofdstuk 4) beschikten we over gegevens van 515 patiënten en 457 controles. Voor de tweede GWAs (Hoofdstuk 5) was dit uitgebreid tot 672 patiënten en 761 controles. De GWAs in Hoofdstuk 4 was de eerste GWAs naar de genetische aanleg voor VF tijdens een hartinfarct. We vonden een genetische variant op chromosoom 21 die het risico van VF tijdens een hartinfarct verhoogde. Dit effect werd ook gevonden in een tweede, onafhankelijke, Nederlandse patiënt-controle studie (Hoofdstuk 4), maar niet in twee kleinere patiënt–controle studies uit Italië en Duitsland (Hoofdstuk 5). De genen die het dichts bij de gevonden variatie op chromosoom 21 lagen, waren het Nederlandse samenvatting 159

CXADR gen (coderend voor de Coxsackie en adenovirus receptor) en het BTG3 gen (coderend voor het B-cel translocatie gen 3). Van het CXADR eiwit was al bekend dat het naast virusopname ook cardiale geleiding beïnvloedt. Vervolgonderzoek binnen onze groep liet zien dat de variant die het VF risico verhoogde ook geassocieerd was met een verminderde CXADR expressie in het menselijke hart. Tevens toonden we ook aan dat in muizen met maar één i.p.v. de normale twee kopieën van dit CXADR gen de geleiding in de ventrikels vertraagd was, wat leidde tot een grotere gevoeligheid voor ritmestoornissen tijdens een hartinfarct. Deze gegevens ondersteunen de hypothese dat het CXADR gen een rol speelt in de genetische aanleg voor VF tijdens een acuut hartinfarct. Dit ondanks de gevonden verschillen tussen de Nederlandse studies enerzijds en de Duitse en Italiaanse studies anderzijds. De redenen voor deze verschillen moeten nader onderzocht worden. De genetische variant op chromosoom 21 bleef ook tijdens de uitgebreidere tweede GWAs een relatie houden met de kans op VF tijdens een hartinfarct (Hoofdstuk 5). De effecten van deze en negen andere varianten op het risico van VF werden vervolgens getest in de PREDESTINATION studie, een vergelijkbare Italiaanse patiënt - controle studie van 641 personen met een acuut hartinfarct. Eén variant op chromosoom 6 had ook in dit cohort een significant effect. De resultaten van de twee patiënt - controle studies werden samengevoegd en de twee varianten met het sterkste effect werden vervolgens getest in een derde vergelijkbare patiënt–controle studie uit Denemarken (GEVAMI). Uit onderzoek in online genexpressie (eQTL) databases bleek tevens dat deze variant geassocieerd was met de expressie van verscheidene genen in menselijk hartweefsel en bloed, waaronder het SYNJ2 gen, waarin deze variant ligt. Combinatie van onze verschillende onderzoeksbenaderingen leidden ook naar een derde veelbelovend gen: XKR6 (coderend voor XK, Kell bloedgroep complexe- subunit gerelateerde familie, lid 6). Een variant in dit gen vertoonde een sterke relatie met het risico van VF en was ook geassocieerd met verhoogde expressieniveaus van XKR6. Tevens bleek een variant in het methionine sulfoxide reductase (MRSA), die we vonden door zijn relatie met de expressie van XKR6, ook een relatie te hebben met het risico van VF (hoofdstuk 5). Ondanks het feit, dat deze effecten niet in de Italiaanse PREDESTINATION studie gevonden werden, is dit samenkomen van de verschillende effecten interessant en verder onderzoek is nodig.

De kandidaatgen associatie-analyses

Eerder onderzoek vond dat PR interval en QRS duur een relatie hadden met het risico van VF tijdens een hartinfarct. In Hoofdstuk 6 van dit proefschrift onderzochten we of dergelijke correlaties tussen ECG-parameters tijdens een acuut hartinfarct en het 160 Nederlandse samenvatting

optreden van VF ook in de AGNES studie aanwezig waren. We vonden dat AGNES patiënten gemiddeld een korter RR en een langer QTc interval hadden dan de AGNES controles. Tevens onderzochten we in Hoofdstuk 6 of 65 genetische varianten in kandidaatgenen, welke eerder in GWAs in de algemene bevolking met hartslag en/of ECG-indices van de geleiding en repolarisatie geassocieerd waren, ook een rol spelen in de variatie van deze eigenschappen gemeten tijdens een acuut hartinfarct. Acht van de 65 geteste varianten lieten een (nominaal significante) relatie zien met ofwel de hartslag, QRS duur, PR of QT interval tijdens het infarct. Vervolgens onderzochten we de relatie van deze acht varianten met het risico van VF. Drie varianten, respectievelijk gelegen in (de buurt van) het SCN10A, KCNQ1 of het MYH7 gen, lieten een (nominaal significante) associatie zien met het risico van VF. De grootte van het effect van de MYH7 (myosine zware keten 7, hartspier, beta) variant hing af van in welke kransslagader de occlusie optrad. De variant in het SCN10A gen, dat codeert voor een natrium kanaal wat voornamelijk in neuronen tot expressie komt, bleek vooral interessant te zijn. Deze variant was geassocieerd met de lengte van het PR interval en met het risico van VF in de initiële (Hoofdstuk 6) en de uitgebreidere AGNES studie populatie (Hoofdstuk 5). Deze associatie werd sterker toen de resultaten van de AGNES studie samengevoegd werden met die van de Italiaanse PREDESTINATION studie. Een op- vallende, en gevoelsmatig verkeerde, bevinding was dat de variant die het PR interval verlengde, ook leidde tot een lager risico van VF. Dit schijnbaar omgekeerde effect werd eerder echter ook al waargenomen bij patiënten met boezemfibrilleren. Het SCN10A gen ligt dicht bij het SCN5A gen, dat codeert voor één van de belangrijkste natriumkanalen in het hart dat zorgt voor de depolarisatie in hartspiercellen. Experi- menten hebben aangetoond dat een variant in SCN10A, die vaak samen overerft met de door ons gevonden variant, de binding van een T-box (TBX5/TBX3) verstoort en zo de expressie van het SCN5A gen verandert. Een hartinfarct gaat gepaard met een sterk verhoogde ontstekingsreactie, welke op zijn beurt de gevoeligheid voor VF beïnvloedt (Hoofdstuk 7). Om dit verder te onderzoeken hebben we genetische varianten op basis van hun, in andere GWAs gevonden, relatie met ontstekings-biomarkers geselecteerd en vervolgens de individuele en gezamenlijke effecten van deze varianten op het risico van VF bepaald in de AGNES studie. We vonden een relatie tussen de aanwezigheid van een variant in GPRC6A (G eiwit-gekoppelde receptor, familie C, groep 6, lid A) en een variant in de buurt van het apolipoproteine CI (APOC1) gen en het risico van VF. Daarnaast was het aantal CRP verhogende varianten dat een persoon droeg ook geassocieerd met het risico van VF. Ondanks het feit dat deze genetische risico score maar een zeer klein gedeelte van het risico van VF kon verklaren, wijzen deze resultaten op een mogelijke rol van genen betrokken bij de ontstekingsreactie in de genetische aanleg voor VF. Nederlandse samenvatting 161

Aanbevelingen voor vervolgonderzoek

Met behulp van de verschillende benaderingen beschreven in dit proefschrift heeft ons onderzoek een aantal veelvoorkomende genetische varianten kunnen identifice- ren die betrokken zijn bij het risico van VF tijdens een acuut hartinfarct. Hoewel deze resultaten al tot vervolgstudies geleid hebben, welke gericht zijn op het ontrafelen van de reactiewegen op moleculair niveau, wachten deze bevindingen op verdere validatie in extra patiëntstudies. Bovendien is het aantal geïdentificeerde varianten en de verklaarde variatie in het risico van VF zeer beperkt, waardoor toepassing in de kliniek als voorspellers van VF vooralsnog uitgesloten is. Voor het vinden van verdere veelvoorkomende varianten, die het risico van VF beïnvloeden, zijn meer patiënten en controles nodig, wat verdere inclusie van patiënten en controles in de AGNES studie en soortgelijke studies rechtvaardigt. De AGNES studie werd ontworpen om specifiek patiënten met VF in de setting van een eerste hartinfarct met ST-segment verhoging te includeren. De grondgedachte achter dit studie ontwerp was dat het opnemen van personen met een zo’n specifiek (homogeen) kenmerk het statistisch onderscheidend vermogen zou verhogen en zo de ontdekking van genen makkelijker zou maken. Men moet echter beseffen dat ook in deze homogene AGNES patiëntengroep nog subgroepen onderscheiden kunnen worden. De patiënten verschillen bijv. in de kransslagader waar de occlusie plaats vond en in de tijd tussen het begin van de klachten en het optreden van het VF. Het is dus denkbaar dat de VF mechanismen verschillen tussen deze subgroepen. Dit pleit weer voor uitbreiding van dergelijke studies. Tevens moet door wereldwijde samenwerking tussen studies gestreefd worden naar homogene patiënt (sub)groepen van voldoende grootte. De effecten van de gevonden varianten kunnen vervolgens getest worden in studies naar SCD t.g.v. andere cardiale pathologieën om de generaliseerbaarheid van de effecten buiten de setting van VF tijdens een hartinfarct te onderzoeken. Dergelijke grotere patiëntpopulaties bieden bovendien de mogelijkheid om in de toekomst te beginnen met het onderzoeken van de rol van zeldzame genetische varianten. Deze zeldzame varianten hebben mogelijk een groter effect op het risico van VF, maar door hun zeldzaamheid vereist dit soort onderzoek grote aantallen patiënten met hetzelfde ziektebeeld. Tot slot heeft dit proefschrift de eerste stappen gezet naar de ontrafeling van de genetische aanleg voor VF tijdens een hartinfarct, de eerste inzichten hierin gegeven en een beeld geschetst van de uitdagingen horende bij dit type onderzoek.

PhD Portfolio 163

PhD Portfolio

Name PhD student: Raha Pazoki PhD period: Sep 2008- Feb 2013 Name PhD supervisor: Prof. dr. C.R. Bezzina, Prof. dr. A.A.M. Wilde Name PhD Co- supervisor: Dr. ir. M.W.T. Tanck

1. PhD training YEAR ECTS Courses • Project Management, Academic Medical Center, Amsterdam, the 2012 0.5 Netherlands • Statistical Computing in R, Academic Medical Center, Amsterdam, the 2011 0.5 Netherlands • Infectious Diseases, Academic Medical Center, Amsterdam, the Netherlands 2011 1.3 • Evidence-Based Searching, Academic Medical Center, Amsterdam, the 2011 0.1 Netherlands • Web of Science, Academic Medical Center, Amsterdam, the Netherlands 2011 0.1 • PubMed Biomedical Sciences, Academic Medical Center, Amsterdam, the 2011 0.1 Netherlands • Reference Manager, Academic Medical Center, Amsterdam, the Netherlands 2011 0.1 • Conceptual Foundation of Epidemiologic Study Design, Erasmus MC, the 2010 0.7 Netherlands • Clinical Decision Analysis, Erasmus MC, the Netherlands 2010 0.7 • Causal Inference, Erasmus MC, the Netherlands 2010 0.7 • Markers and Prognostic Research, Erasmus MC, the Netherlands 2010 0.7 • Family-based Genetic Analysis, Erasmus MC, the Netherlands 2010 1.4 • Crash Course: Basic Biochemistry, and Physics 2010 0.4 • Introduction to Bioinformatics, Academic Medical Center, Amsterdam, the 2009 1.1 Netherlands • Advances in Genome Wide Association Studies, Erasmus MC, the 2009 1.4 Netherlands • Advanced Topic in Clinical Epidemiology, Academic Medical Center, 2009 1.4 Amsterdam, the Netherlands • AMC World of Science, Academic Medical Center, Amsterdam, the 2009 0.7 Netherlands Seminars, workshops and master classes • The Workshop Writing Successful Grant Proposals VII, Erasmus MC, the 2011 1.4 Netherlands • Cardiology master class, Prof. Günter Breithatdt, The Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, the Netherlands 2011 0.2 (oral presentation) 164 PhD Portfolio

YEAR ECTS • Workshop on Basic Analysis of Gene Expression Arrays, Erasmus MC, the 2010 1.4 Netherlands • Foundation LeDucq "Alliance against SCD" network meeting, Munich, 2009 1 Germany • Weekly department seminars (Dept. of Epidemiology, Biostatistics, and Bio- 2008- 6 informatics) 2013 (Inter)national conferences and presentations • American Society of Human Genetics, Montreal, Canada (poster 2011 0.5 presentation) • Rembrandt Symposium, Leiden, the Netherlands (poster presentation) 2011 1 • Denis Escande Symposium, Nantes, France (poster presentation) 2011 1.25 • European Society of Human Genetics, Amsterdam, the Netherlands (poster 2011 2.1 presentation) • The International Conference: “From DNA to Phenotype”, Erasmus MC, the 2011 1.4 Netherlands • American Society of Human Genetics, Washington, USA (oral presentation) 2010 2.1 • Genetica Retraite, Kerkrade, the Nehterlands (oral presentation) 2010 1.25 • PhD training course Cardiac Function and Adaptation, Dutch Heart 2010 1.8 Foundation (poster presentation) • Weekly seminar at the Dept. of Epidemiology, Biostatistics, and Bio- informatics (oral presentation) 2009 0.2

2. Teaching • Teaching assistant, Practical Biostatistics, Academic Medical Center, 2008- 1.4 Amsterdam, the Netherlands 2010 • Lecturer, Public Health in Low and Middle Income Countries (LMICs): General Principles, Erasmus University, Rotterdam 2012 0.1

3. Parameters of Esteem Awards and Prizes • Nominee for Poster Award, European Society of Human Genetics, the 2011 Netherlands • Semifinalist Award, American Society of Human Genetics, USA 2010 Grants • Travel Grant, Netherlands Heart Foundation, the Netherlands 2011 Publications 165

Publications

Muka, T., Imo, D., Jaspers, L., Colpani, V., Chaker, L., van der Lee, S. J., Mendis, S., Chowdhury, R., Bramer, W. M., Falla, A., Pazoki, R. & Franco, O. H. The global impact of non-communicable diseases on healthcare spending and national income: a systematic review. European journal of epidemiology, doi:10.1007/s10654-014-9984-2 (2015).

Sedaghat, S., Pazoki, R., Uitterlinden, A. G., Hofman, A., Stricker, B. H., Ikram, M. A., Franco, O. H. & Dehghan, A. Association of uric acid genetic risk score with blood pressure: the Rotterdam study. Hypertension 64, 1061-1066, doi:10.1161/hypertensio- naha.114.03757 (2014).

Jaspers, L., Colpani, V., Chaker, L., van der Lee, S. J., Muka, T., Imo, D., Mendis, S., Chowdhury, R., Bramer, W. M., Falla, A., Pazoki, R. & Franco, O. H. The global impact of non-communicable diseases on households and impoverishment: a systematic review. European journal of epidemiology, doi:10.1007/s10654-014-9983-3 (2014).

Pazoki, R., Tanck, M. W., Wilde, A. A. & Bezzina, C. R. Complex inheritance for susceptibility to sudden cardiac death. Current pharmaceutical design 19, 6864-6872 (2013).

Pazoki, R.*, de Jong, J. S. *, Marsman, R. F., Bruinsma, N., Dekker, L. R., Wilde, A. A., Bezzina, C. R. & Tanck, M. W. SNPs identified as modulators of ECG traits in the general population do not markedly affect ECG traits during acute myocardial infarction nor ventricular fibrillation risk in this condition. PLoS One 8, e57216, doi:10.1371/ journal.pone.0057216 (2013).

Eghbali, S. S., Amirinejad, R., Obeidi, N., Mosadeghzadeh, S., Vahdat, K., Azizi, F., Pazoki, R., Sanjdideh, Z., Amiri, Z., Nabipour, I. & Zandi, K. Oncogenic human papillomavirus genital infection in southern Iranian women: population-based study versus clinic-based data. Virology journal 9, 194, doi:10.1186/1743-422x-9-194 (2012).

Arking, D. E., Junttila, M. J., Goyette, P., Huertas-Vazquez, A., Eijgelsheim, M., Blom, M. T., Newton-Cheh, C., Reinier, K., Teodorescu, C., Uy-Evanado, A., Carter-Monroe, N., Kaikkonen, K. S., Kortelainen, M. L., Boucher, G., Lagace, C., Moes, A., Zhao, X., Kolodgie, F., Rivadeneira, F., Hofman, A., Witteman, J. C., Uitterlinden, A. G., Mars- man, R. F., Pazoki, R., Bardai, A., Koster, R. W., Dehghan, A., Hwang, S. J., Bhatnagar, P., Post, W., Hilton, G., Prineas, R. J., Li, M., Kottgen, A., Ehret, G., Boerwinkle, E., Coresh, J., Kao, W. H., Psaty, B. M., Tomaselli, G. F., Sotoodehnia, N., Siscovick, D. S., 166 Publications

Burke, G. L., Marban, E., Spooner, P. M., Cupples, L. A., Jui, J., Gunson, K., Kesaniemi, Y. A., Wilde, A. A., Tardif, J. C., O’Donnell, C. J., Bezzina, C. R., Virmani, R., Stricker, B. H., Tan, H. L., Albert, C. M., Chakravarti, A., Rioux, J. D., Huikuri, H. V. & Chugh, S. S. Identification of a sudden cardiac death susceptibility locus at 2q24.2 through genome-wide association in European ancestry individuals. PLoS Genet 7, e1002158, doi:10.1371/journal.pgen.1002158 (2011).

Pazoki, R., Wilde, A. A. & Bezzina, C. R. Genetic Basis of Ventricular Arrhythmias. Current cardiovascular risk reports 4, 454-460, doi:10.1007/s12170-010-0128-2 (2010).

Bezzina, C. R. *, Pazoki, R. *, Bardai, A. *, Marsman, R. F. *, de Jong, J. S. *, Blom, M. T., Scicluna, B. P., Jukema, J. W., Bindraban, N. R., Lichtner, P., Pfeufer, A., Bishopric, N. H., Roden, D. M., Meitinger, T., Chugh, S. S., Myerburg, R. J., Jouven, X., Kaab, S., Dekker, L. R., Tan, H. L., Tanck, M. W. & Wilde, A. A. Genome-wide association study identifies a susceptibility locus at 21q21 for ventricular fibrillation in acute myocardial infarction. Nat Genet 42, 688-691, doi:10.1038/ng.623 (2010).

Nabipour, I., Larijani, B., Beigi, S., Jafari, S. M., Amiri, M., Assadi, M., Pazoki, R., Amiri, Z. & Sanjdideh, Z. Relationship among insulinlike growth factor I concentrations, bone mineral density, and biochemical markers of bone turnover in postmeno- pausal women: a population-based study. Menopause (New York, N.Y.) 15, 934-939, doi:10.1097/gme.0b013e31816665a7 (2008).

Vahdat, K., Jafari, S. M., Pazoki, R. & Nabipour, I. Concurrent increased high sensitivity C-reactive protein and chronic infections are associated with coronary artery disease: a population-based study. Indian journal of medical sciences 61, 135-143 (2007).

Pazoki, R., Nabipour, I., Seyednezami, N. & Imami, S. R. Effects of a community-based healthy heart program on increasing healthy women’s physical activity: a randomized controlled trial guided by Community-based Participatory Research (CBPR). BMC public health 7, 216, doi:10.1186/1471-2458-7-216 (2007).

Nabipour, I., Vahdat, K., Jafari, S. M., Amiri, M., Shafeiae, E., Riazi, A., Amini, A. L., Pazoki, R. & Sanjdideh, Z. Correlation of hyperhomocysteinaemia and Chlamydia pneumoniae IgG seropositivity with coronary artery disease in a general population. Heart, lung & circulation 16, 416-422, doi:10.1016/j.hlc.2007.02.100 (2007). Publications 167

Nabipour, I., Vahdat, K., Jafari, S. M., Pazoki, R. & Sanjdideh, Z. The association of metabolic syndrome and Chlamydia pneumoniae, Helicobacter pylori, cytomegalo- virus, and herpes simplex virus type 1: the Persian Gulf Healthy Heart Study. Cardio- vascular diabetology 5, 25, doi:10.1186/1475-2840-5-25 (2006).

Nabipour, I., Imami, S. R., Mohammadi, M. M., Heidari, G., Bahramian, F., Azizi, F., Khosravizadegan, Z., Pazoki, R., Soltanian, A. R., Ramazanzadeh, M., Emadi, A., Arab, J. & Larijani, B. A school-based intervention to teach 3-4 grades children about healthy heart; the Persian Gulf healthy heart project. Indian journal of medical sciences 58, 289-296 (2004).

* These authors contributed equally 168 Contributing authors

Contributing authors

Lia Crotti Department of Molecular Medicine, University of Pavia, Italy. IRCCS Istituto Auxologico Italiano, Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular Genetics, Milan, Italy. Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany.

Michiel E. Adriaens AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Abdennasser Bardai AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Leander Beekman AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Connie R. Bezzina AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Navin R. Bindraban Department of Cardiology, Academic Medical Center, University of Amsterdam, Am- sterdam, the Netherlands. Department of Social Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.

Nanette Bishopric Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA.

Marieke T. Blom AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands. Contributing authors 169

Nienke Bruinsma AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Sumeet S. Chugh The Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Gaetano M. De Ferrari Departments of Cardiology and Cardiovascular Clinical Research Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.

Jonas S.S.G. de Jong AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Lukas R.C. Dekker Department of Cardiology, Catharina Hospital, Eindhoven, The Netherlands.

Anna M. Di Blasio Laboratory of Molecular Biology, Istituto Auxologico Italiano, Milan, Italy.

Reza Jabbari The Danish National Research Foundation Centre for Cardiac Arrhythmia (DARC) & Laboratory for Molecular Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.

Xavier Jouven Université Paris Descartes, AP-HP, Hôpital Européen Georges Pompidou, Paris. France.

J. Wouter Jukema Department of Cardiology, Leiden University Medical Center, Leiden, the Nether- lands. Durrer Center for Cardiogenetic Research, Amsterdam, the Netherlands.

Stefan Kääb Department of Medicine I, Klinikum Grosshadern, Munich, Germany. 170 Contributing authors

Peter Lichtner Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany.

Roos F. Marsman AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Thomas Meitinger Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany. Helmholtz Zentrum München, Neuherberg; Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany.

Robert J. Myerburg Division of Cardiology, University of Miami Miller School of Medicine, Miami, FL, USA.

Arne Pfeufer Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany. Helmholtz Zentrum München, Neuherberg; Institute of Human Genetics, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany.

Dan M. Roden Departments of Medicine and Pharmacology, Vanderbilt University School of Medi- cine, Nashville, TN, USA.

Peter J. Schwartz Department of Molecular Medicine, University of Pavia, Italy. IRCCS Istituto Auxologico Italiano, Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular Genetics, Milan, Italy. Laboratory of Molecular Biology, Istituto Auxologico Italiano, Milan, Italy. Department of Medicine, University of Stellenbosch, South Africa. Chair of Sudden Death, Department of Family and Community Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia. Departments of Cardiology and Cardiovascular Clinical Research Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy. Contributing authors 171

Brendon P. Scicluna Center for Experimental Molecular Medicine and Center for Infection and Immunity, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Hanno L. Tan AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands.

Michael W.T. Tanck Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medi- cal Center, Amsterdam, The Netherlands.

Jacob Tfelt-Hansen The Danish National Research Foundation Centre for Cardiac Arrhythmia (DARC), Copenhagen, Denmark.

Arthur A.M. Wilde AMC Heart Centre, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands. Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disor- ders, Jeddah, Kingdom of Saudi Arabia.

Curriculum Vitae 173

Curriculum Vitae

Raha Pazoki Academic Medical Center, Amsterdam, the Netherlands

Raha Pazoki is born in Bijar, Iran. Daughter of Rahmatollah Pazoki and Shamsalhajie Ardalani; and sister of Toumaj and Soha Pazoki. She started primary school in Tehran, and due to her outstanding progress, she was able to graduate two years early from primary school. In 1991, together with her family, she travelled to Bushehr (a city in the southern coast of Iran). At age 16, she successfully passed the national university entrance exam of Iran scoring above the 99th percentile of scores of all competing university applicants and thus received a 7-year medical training schol- arship from the ministry of health to study medicine at her home town at Bushehr University of Medical Sciences. Later during her medical education, she decided to dedicate herself to cardiovascular research. Thus for her medical thesis, she designed a healthy heart program to test effectiveness of a short-term cardiovascular health education program in primary schools.1 In 2002, she received her Medical Doctor degree while achieving 84% of the maximum attainable grade point average. She consequently started working as cardiovascular health education expert under management of Dr. Gholamreza Heidari in the provincial public health center at Bushehr University of Medical Sciences producing cardiovascular public health strategic plan- ning and conducting public health research. In 2004, under management of Prof. Iraj Nabipour at the Research department at Bushehr University of Medical Sciences, she supervised volunteers among medical and bio-medical students to perform clinical and bio-medical research where she also conducted cardiovascular health education trials among women as well as research on cervical cancer prevalence and its pathologic subtypes. In 2007, she obtained the Young Investigator Award from Bushehr University of Medical Sciences. In the same year she was awarded by the Netherlands Fellowship Program to start a master program at the Netherlands Institute for Health Sciences where she conducted research at the laboratory of Prof. Jacqueline Wit- teman on the effect of genetic variants within the EIG121 gene on HDL cholesterol and myocardial infarction. In 2008, she received her Master of Science degree and later started a Ph.D. program at the laboratory of Prof. Arthur Wilde and Prof. Connie Bezzina which resulted in this thesis on genetic modulators of ventricular fibrillation in the setting of myocardial infarction. During this Ph.D. program, she obtained the semifinalist award from the American Society of Human Genetics for her genome- wide association analysis on risk of ventricular fibrillation 2 and was nominated to obtain poster award from the European Society of Human Genetics for her study on the role of genetic modulators of ECG parameters on risk of ventricular fibrillation3 . 174 Curriculum Vitae

In summer 2013, she conducted research at the laboratory of Dr. Nicole Soranzo at Sanger Institute (Hinxton, U.K.) on rare and common genetic modifiers of inflammation and renal biomarkers. In 2014, she conducted research at the laboratory of Prof. Oscar Franco at Erasmus MC (Rotterdam, the Netherlands) on the global economic impact of non-communicable diseases (funded by the world health organization) and on exome chip and exome sequencing data analysis of hematologic traits. In summer 2014, with the help of a travel fund from the European (FP7) project COMBI-BIO (http://www.combi-bio.eu/) she conducted research at the laboratory of Prof. Paul Elliot at Imperial College (London, U.K.) in collaboration with the laboratory of Prof. Oscar Franco (Rotterdam, the Netherlands) on the identification of metabolites impacting on obesity. She wishes to start and establish collaborations with researchers in her country of origin in order to help increase quantity and quality of research in the field she has expertise in. She is married to Abbas Dehghan and is mother of Nick Dehghan.

References

1. Amiri, M. & Pazoki, R. Effects of a short-term school-based healthy heart program: An interventional study, 2001-2002. American Journal of Epidemiology 161, S27 (2005). 2. Bezzina, C. R. et al. Genome-wide association study identifies a susceptibility locus at 21q21 for ventricular fibrillation in acute myocardial infarction.Nature Genetics 42, 688-U664, doi: 10.1038/ng.623 (2010). 3. Pazoki, R. et al. SNPs Identified as Modulators of ECG Traits in the General Population Do Not Markedly Affect ECG Traits during Acute Myocardial Infarction nor Ventricular Fibril- lation Risk in This Condition. Plos One 8, doi:10.1371/journal.pone.0057216 (2013). Acknowledgements 175

Acknowledgements

With no doubt, completion of this thesis was not possible without the impact of many individuals including the patients, colleagues, co-authors, or experts (e.g. program and software developers) who contributed in various stages of this project. I wish to express my sincerest appreciation to all whose contribution made creation of this thesis possible. My promotores Prof. Connie Bezzina and Prof. Arthur Wilde Connie, when I started working in your group, I was a young doctor coming from a totally different culture. During the last couple of years, I evolved to a different person and you contributed to that major step in my life. You demonstrated to me that it is possible to be a woman and a mother and still a strong successful scientist, full of energy. Your talent in scientific writing and your ability to create beautiful master- ful pieces of work has always been amazing to me. I learned from you to be precise, double check my work and aim for high. It’s all because of you that once again I can feel the nice taste of success and achievement. Thank you very much. Arthur, I am grateful to you for providing perceptive insight and vision during this Ph.D. project. I always admired your great personality, your seniority, reputation and network. Thank you very much for giving me the opportunity to be proudly among your alumni. My co-promotor Dr. Michael Tanck Michael, There is a piece of newspaper on the board at your office with “Geen Dag Zonder Michael” written on in. Indeed you were my daily mentor during this PhD project. I learned a lot from you. You were always there to help me find a solution for challenging problems. Every time, I walked into your office, you had a piece of information to add to my knowledge. Thanks a lot for the insight and education and of course the coffee times. Members of the doctorate committee Dr. L. Crotti, Prof. dr. O.H. Franco, Dr. J.R. de Groot, Prof. dr. J.W. Jukema, Prof. dr. M.M.A.M. Mannens , Prof. dr. A.H. Zwinderman, I am very grateful for your expertise in assessment of my thesis. Dear Lia, it was a great pleasure for me to work with you and the PREDESTINATION study. Oscar, working with you has been a good produc- tive experience for me. I am very thankful for giving me the opportunity to contribute to various research activities in your group. Koos, thanks for facilitating this PhD project by amongst others providing infrastructure and computing facility. Without that, it would definitely be very difficult to fulfil this project. Also thank you for all those memorable department outdoor and indoor activities. My Paranymphs Erik van Iperen and Wouter Ouwerkerk 176 Acknowledgements

Thank you for accepting to stand next to me at the time of my defense and helping me further with preparation for the ceremony. Erik, the two of us were working on GWAs and therefore we learned a lot from each other. Thank you for suggestions, helps and brainstorming. Wouter, the coffee lover of 207, thanks for computer and software advices and koffietijds of 207. Members of J1B-207 Iris, Erik, Michel, Wouter, Willem, and Sandra, with you guys the 207 was the best room in the KEBB. I had a lot of fun with you at the time I was working on my PhD project. The most exciting life changes happened to me while I was working in 207 and at all these steps I received useful, in-time insight and advices from all of you. My whole experience in 207 was fantastic. Thank you all for being part of this journey. Michel, the R guru of 207, it was wonderful and inspiring to have a young talented PhD student like you in our room. Iris, you were the private financial advisor of 207 and always had something exciting to let others know about tax returns; what a hot topic! Thanks for the advices and chats.Sandra , you were the calm and nice lady of 207 who makes one of the most beautiful cake decorations I have ever seen. Willem, you were my first R helper and the person who encouraged me to start working with R. Thanks for the in-time tips, for the chats and for being friendly. My (old) colleagues at the KEBB, Umesh, and Jammbe, it was nice that you were participating in 207 chats every on and off. Umesh thanks for your technical and expert advices with Linux. Shayan, Parvin, and Mehdi, you guys made me feel at home. Thanks for all those delightful Farsi chats. Mariska, I appreciate you for constantly being nice and for your kind advice and help. Annet, Petra and Gre, I am grateful for helping me work in a nice, comfortable environment. Ronald, thanks for statistical advices, tips and tricks and for your kindness in having a chat about various topics every on and off.Saphire , you are a source of happiness and energy and always made everyone cheerful every time you entered 207. I wish you good luck with your new career. Nazanin, I am happy that KEBB was the reason for us to know each other and start a friendship. You are a very nice person and it’s always pleasant to have a chat with you about anything. I wish you the best my friend. Hans wouters, Annet van Abeelen, Aldo, Silvia, Antoine, Miranda, Teodora, Elea- nor, Barbara, I wish you all the best with your careers. Colleagues from the experimental cardiology department Leander, Svitlana, Carol Ann, Elisabeth, Stephania, Julien, Maaike, Margriet, Mathilde, Elizabeth, Annalisa, Najib, it was a pleasure to work with you in the same team. Acknowledgements 177

Michiel, for part of my thesis, we collaborated with each other directly which I enjoyed very much. Working with you was effortless. Not only because we share the same skills but also because of your cooperative, friendly and trustworthy personality. Yuka, you have a pleasant personality and it’s always amusing to have a chat with you. Thanks for your hospitality and the most delicious sushi’s ever. Tamara, thanks for supporting me in the American Society of Human Genetics. Your tips were pretty useful and guided me to have a lovely, stress free talk. I enjoyed our chats during breakfasts and the group dinner in Washington and also in the center of Nantes at the Denis Escande symposium. Roos, I am truly grateful for your contribution in reading ECG data to be used in chapter 6 and genotyping of the strongest SNPs in the AGNES and in the replication sets to be used in chapter 4 of this thesis. I wish you all the best with your future career as an internist. AGNES team Dr. Lukas Dekker, Dr. Jonas de Jong and Nienke Bruisma Thank you very much for your contribution in collection and management of AGNES data. Nienke, I am grateful for creating, maintaining, and providing the AGNES data in the most convenient way. Jonas, I am pleased that you were happy about the ECG paper. My colleagues and friends in AMC Brendon, Abdenaser, Amin, Soha, and Irina, I wish you good luck with your research projects or clinical works. Abdenaser, it was a pity that the NOS1AP project didn’t end up with the results we expected but I am sure there is always room for team-working on other ideas. My colleagues at ErasmusMC Jacqueline, you were my first supervisor in the Netherlands. Because of you I got the chance to work on this Ph.D. project which I was enthusiastic about. Thanks a lot for your support. Janine, to me you represent a smart successful lady who is always ready to help others with almost any question. Thanks for insight and information. Ayse, Paula, Adel , Klodian, Ana, Anna, Kozeta, Jelena, Olta, Jana, Maarten, Jessica, Paul, Symen, Paula, Audri, Mirte, Anna, Trudy, Mariana, Ingrid, carolina, Inge, I wish you the best of luck with your careers and Ph.D. projects. The SOAP team,Layal , Taulant, Abby, David, Loes, Veronica, Sven, you are all young talented researchers who amazingly worked with each other to fulfill the WHO project. I am proud that I worked as part of such team. Dear Toos, I appreciate you for being nice to me and my family and for your spectacu- lar foot shoots and accepting to beautifully record my defense ceremony. My colleagues at Sanger Institute and Imperial College 178 Acknowledgements

Nicole, you provided me with the opportunity to visit your group and provided a warm welcome and support during my stay in the Sanger Institute. I am sincerely grateful. Yasin, klaudia, max, Jie, valentina, Ele, Kalia, Lucy, Ester, Ioanna, Luc, thanks for the fantastic work environment, volleyball games, and group dinners. Ioanna, thanks for your direct supervision on my work during my stay at the Imperial College London and for useful tips. Raphaelle, Ibrahim, Diana, Claire, Manuja, and Andrea, it was a pleasure for me to work with you and learn from you. Thanks for your helps and advices and I wish you all the best with the rest of the COMBI-BIO project and your careers. My former mentor and manager, Prof. Iraj Nabipour, all the joy, energy, success and achievement that I feel today is in the first place because of your helps, trust, and support. I would like to express my sincere gratitude for the positive changes you caused in my career and I wish to be able to help and contribute back as much as possible. My friends and family Jolanda, you are one of my special Dutch friends. I still remember our first chat at the class of biostatistics when you invited me for a tour in Numansdorp and a dinner at your place. Coming from Iranian culture, it was quite usual to be invited by your classmate. Since that time, you have always been there as one of my best friends providing nice company and hospitality. I am very thankful to have a good friend like you. Eric, Falco, and Nathan, I am very pleased that Jolanda let us meet her family. Just like Jolanda you are amazing friends. Thanks for being nice and friendly.Eric , the rope hand crafts you make are amazingly beautiful. Martha, you were one of my first friends after I entered the Netherlands starting my master project. Although our original countries were thousands miles distant from each other, something that I believe has a root in cultural similarities connected us tightly with each other. I admire your honesty, kindness, company and trust in me. Now, you are the busy mother of three kids and there is an ocean between where each of us live but I hope to see you soon and I wish you good luck my friend with your new Brazilian life. Azadeh, starting a friendship is about finding something in common with the other person. For us it happened in the first instance. The funny initial bind between us was the close meaning of our names and realizing that we had one day age difference (luckily, I was the older one!). But I am sure these are not the secret of our long-time friendship. You are always there for your friends, no matter where you live and how busy you are. That is a beautiful asset in you. You were the one I could always count on when I needed support of a friend. Mr and Mrs Amiri, Arman and Armin, Sanaz and Behnam, Maryam and Payam, Azadeh and Ramin, thanks for longtime friendship, hospitality, outdoor activities and nice company. Sahar Esteghamat, Leila Karimi, Fatemeh Saberi, Mohsen and Acknowledgements 179

Sara, Mehrnaz and Saeed, Azadeh and Farhad, Sima and Farzin, thanks for group activities, birthdays, chats, advices, and hospitality. Alireza and Sheida, meeting you was a great pleasure for us at the time we were at Imperial College in London. You didn’t let us feel alone. Outdoor summer activities together with you and your circle of friends is one of my most wonderful memories from London. Thanks for all the kindness and friendship that you offered. Maman Mahin, my dear mother-in-law, thanks for being so compassion and raising the nicest guy in the world to be my husband. I am proud and lucky to have you as my mother-in-law . Shirin, Shiva, Shahrzad, Kourosh, and Marzieh, you are the best in laws, I could ever have. Thanks for the lovely online chats and the nice environment you create every time we come for a visit. Taraneh and Arad, sweeties, just like uncle Abbas and Nick, I love you so much. Taraneh, thanks for your adorable gifts and paintings. Shiva, my dear sister-in law, you are a mirror of me. I see my strengths and weaknesses in you. During the times you were in the Netherlands, I never felt alone. Thanks for your honesty and trust and for being there. I wish you all the best in following your dreams. Toumaj and Soha, my darling brother and sister, life has made the three of us fall apart but our hearts are still together. I am sure you are proud of me today for what I have achieved. I want you to know that I love you so much and in every minute of my life, I am looking forward to seeing days that we can live close to each other again to be playful and cheerful just like the olden days. Maziar, my dear brother-in law, I am pleased that my sister lives a happy and successful life together with you. I wish you all the best and a happy life for many years to come. Maman & Baba, you are the reason for who I am and what I have. Thanks for the unlimited unique love and kindness. Baba, I love you so much. I miss you a lot especially when I need someone like you to spoil me as a little princess and make sure that the life is going smooth, calm and stress free for me. You have always believed that I deserve the bests and nothing less. Since the time I left our country to study abroad, you were the one who believed I can handle it successfully. Thanks for your unconditioned love, support, kindness and trust. Maman, when it comes to you I am truly speechless. There is a lot I’d like to say but I can’t choose where to start. You are the best. I miss you so much and I love you. You were the one who believed in my abili- ties and wisdom since I was a little girl. You were the one who have always been there for me to support me and make sure that I have access to the most desired education, work and life environment. You were the one who directed me into the correct way of success and when I finally reached the stage in science that I was able to study abroad, you accepted it bravely with a lot of pain in your heart since you never wanted to fall apart from any of your children. I know that I can’t be thankful enough for what you 180 Acknowledgements

have done and you are doing for me. Thanks for everything and kisses from the bottom of my heart! Nick, my dearest son, at some point during completion of this thesis you were playing hard in my tummy and some other times you were playing happily on my shoulders! Sweetie, you are the most important person for me in the whole world. Since the time you were born, my life has been changed dramatically in a positive way. I have been evolving since then to a more mature and complete person. I love you mamy’s little smart prince. Abbas, my best friend and my sweet heart, you came into my love life during the years I was busy with this thesis and you were there for me as the most trusted person when I needed someone to share my feelings and my work with. Thanks for believing in me, for advices, for listening and the beautiful fact that you were always there to ensure my wishes come true. I Love you so much.

Raha Pazoki, Capelle aan den IJssel, 2015