Vincent Probst
email: vincent.probst@chu-nantes.fr

Jean-Baptiste Gourraud

Julien Barc

Aurélie Thollet

Solena Le Scouarnec

Hervé Le Marec

Jean-Jacques Schott

Richard Redon

The Brugada Syndrome: A Rare Arrhythmia Disorder with Complex inheritance

Keywords: Brugada syndrome, genetics, sudden death, cardiac arrhythmias, SCN5A iNTRODUCTiON

The Brugada syndrome (BrS) is a rare inherited arrhythmia disease, first described in 1992, increasing the risk of ventricular fibrillation in apparently healthy young adults (1). It is suspected to be involved in 4-12% of cases of sudden cardiac death (SCD) in the general population and in at least 20% of SCD in patients with a structurally normal heart (1-3).

). The ECG pattern may be transient in affected

For the last 10 years, applying new sequencing technologies to thousands of whole exomes has revealed the high variability of the human genome. Extreme caution should thus be taken to avoid misinterpretation when associating rare genetic variants to disease susceptibility. The Brugada syndrome (BrS) is a rare inherited arrhythmia disease associated with high risk of sudden cardiac death in the young adult. Familial inheritance has long been described as Mendelian, with autosomal dominant mode of transmission and incomplete penetrance. However, all except 1 of the 23 genes previously associated with the disease have been identified through a candidate gene approach. To date, only rare coding variants in the SCN5A gene have been significantly associated with the syndrome. However, the genotype/phenotype studies conducted in families with SCN5A mutations illustrate the complex mode of inheritance of BrS. This genetic complexity has recently been confirmed by the identification of common polymorphic alleles strongly associated with disease risk. The implication of both rare and common variants in BrS susceptibility implies that one should first define a proper genetic model for BrS predisposition prior to applying molecular diagnosis. Although long remains the way to personalized medicine against BrS, the high phenotype variability encountered in familial forms of the disease may partly find an explanation into this specific genetic architecture. patients [START_REF] Miyazaki | Autonomic and antiarrhythmic drug modulation of ST segment elevation in patients with Brugada syndrome[END_REF]. To address this issue, unmasking drugs, such as ajmaline, flecainide, and procainamide, can be used to reveal this pattern [START_REF] Brugada | Sodium channel blockers identify risk for sudden death in patients with ST-segment elevation and right bundle branch block but structurally normal hearts[END_REF], ajmaline showing higher sensitivity than flecainide and procainamide [START_REF] Wilde | Proposed diagnostic criteria for the Brugada syndrome: consensus report[END_REF][START_REF] Wolpert | Intravenous drug challenge using flecainide and ajmaline in patients with Brugada syndrome[END_REF][START_REF] Priori | Natural history of Brugada syndrome: insights for risk stratification and management[END_REF]. The high variability of the ECG pattern impairs proper assessment of its prevalence in the general population. Epidemiological studies have produced heterogeneous results regarding BrS incidence across the World. While estimated at 5 for 10,000 in western Europe and the USA, the prevalence of BrS seems higher in Southeast Asia, reaching 20 for 10,000 [START_REF] Gallagher | Prevalence and significance of Brugada-type ECG in 12,012 apparently healthy European subjects[END_REF][START_REF] Miyasaka | Prevalence and mortality of the Brugada-type electrocardiogram in one city in Japan[END_REF][START_REF] Nademanee | Arrhythmogenic marker for the sudden unexplained death syndrome in Thai men[END_REF].

Aborted SCD is often the first symptom in BrS, with a mean age of 45 years at diagnosis and a four-time higher incidence in men than in women [START_REF] Probst | Longterm prognosis of patients diagnosed with Brugada syndrome: results from the FINGER Brugada Syndrome Registry[END_REF][START_REF] Benito | Gender differences in clinical manifestations of Brugada syndrome[END_REF]. A third of the affected patients are identified after syncope, frequently preceded by vagal symptoms [START_REF] Probst | Longterm prognosis of patients diagnosed with Brugada syndrome: results from the FINGER Brugada Syndrome Registry[END_REF]. The syncope could either be due to non-sustained VF or to a vaso-vagal episode without direct clinical relevance, rendering it hard for the practitioner to distinguish arrhythmic from non-arrhythmic etiology [START_REF] Sacher | Syncope in Brugada syndrome patients: prevalence, characteristics, and outcome[END_REF][START_REF] Take | Identification of high-risk syncope related to ventricular fibrillation in patients with Brugada syndrome[END_REF]. The majority of patients are asymptomatic at time of diagnosis. More than one-third of cases are identified during familial screening [START_REF] Probst | Longterm prognosis of patients diagnosed with Brugada syndrome: results from the FINGER Brugada Syndrome Registry[END_REF].

Implantation of a defibrillator is still the only efficient therapy in high-risk patients, with a 48% rate of appropriate device therapy at 10 years in patients with previous aborted sudden death. This rate falls to 12% among implanted asymptomatic patients, many affected patients remaining asymptomatic during all their life. Furthermore, device-related complications are frequent with a 30% risk at 10-year follow-up mainly due to lead dysfunction, inappropriate therapy, and infection [START_REF] Sacher | Outcome after implantation of a cardioverter-defibrillator in patients with Brugada syndrome: a multicenter study-part 2[END_REF][START_REF] Sarkozy | Long-term follow-up of primary prophylactic implantable cardioverter-defibrillator therapy in Brugada syndrome[END_REF]. These serious side effects in comparison to the very low arrhythmic risk for asymptomatic patients require accurate risk stratification and/ or efficient drug therapy.

Only few clinical parameters allow risk stratification in BrS. The effectiveness of ventricular stimulation is still a matter of debate, and symptoms and spontaneous ECG pattern are still the two major parameters enabling risk stratification for SCD (6, 14, 20-23).

There is still need for medical therapies that could reduce arrhythmia occurrence and prevent SCD. Because successful trials were reported in limited series of patients, quinidine has been expected to be "the drug" for BrS. However, several recent studies failed to demonstrate its beneficial effects (6, 24-27).

FiGURe 2 | The complex inheritance pattern of BrS. Modified from Ref. [START_REF] Probst | SCN5A mutations and the role of genetic background in the pathophysiology of Brugada syndrome[END_REF]. Incomplete penetrance of the SCN5A mutation is illustrated by the presence of unaffected carriers of the mutation (+). The patient highlighted by an ellipse presents with a BrS ECG aspect, despite the absence of the familial mutation. Affected family members carrying the SCN5A mutation present with progressive cardiac conduction disease (PCCD) (right half-filled symbol), BrS (left half-filled symbol), or both diseases (full-filled symbol). PCCD consists of right bundle branch block with PR interval lengthening and led to complete AVB in three patients, in whom a pacemaker (PM) was implanted. There is accumulating evidence that implantable defibrillator is an effective and accurate therapy for symptomatic patients [START_REF] Sacher | Outcome after implantation of a cardioverter-defibrillator in patients with Brugada syndrome: a multicenter study-part 2[END_REF]. Many clinical parameters have also been proposed for asymptomatic patients, but risk prediction in the latter group of patients remains particularly challenging because of the lack of reproducible and reliable data [START_REF] Adler | Risk stratification in Brugada syndrome: clinical characteristics, electrocardiographic parameters, and auxiliary testing[END_REF].

TwO PATHOPHYSiOLOGiCAL MODeLS FOR BrS

Those unresolved questions concerning diagnosis and risk stratification for arrhythmia and therapy underlie the need for a better understanding of pathophysiological mechanisms in BrS. Two main pathophysiological hypotheses have been proposed to explain the ECG pattern.

Soon after the description of BrS, the first pathophysiological model was proposed, based on the existence of a transmural voltage gradient due to a repolarization heterogeneity across the ventricular wall [START_REF] Yan | Cellular basis for the electrocardiographic J wave[END_REF][START_REF] Diego | I(to) and action potential notch are smaller in left vs. right canine ventricular epicardium[END_REF]. According to this hypothesis, ST segment elevation could be due to either a loss of function of the sodium channel NaV1.5 responsible for the depolarization phase (phase 0 of the AP) favoring the expression of repolarization heterogeneity, an aggravation of this heterogeneity by a gain of function in one of the cardiac potassium channels responsible of the repolarization phases (phases 1 and 3 of the AP), or a loss of function of the CaV1.2 calcium channel that participate to the phase 2 of the AP [START_REF] Yan | Cellular basis for the electrocardiographic J wave[END_REF].

This hypothesis has been matter to debate since the second hypothesis, based on a conduction delay in the right ventricular outflow tract, emerged from clinical observations [START_REF] Fujiki | ST segment elevation in the right precordial leads induced with Class IC antiarrhythmic drugs: insight into the mechanism of Brugada syndrome[END_REF][START_REF] Nagase | Epicardial electrogram of the right ventricular outflow tract in patients with the Brugada syndrome: using the epicardial lead[END_REF][START_REF] Tukkie | Delay in right ventricular activation contributes to Brugada syndrome[END_REF][START_REF] Wilde | The pathophysiological mechanism underlying Brugada syndrome: depolarization versus repolarization[END_REF][START_REF] Boukens | Reduced sodium channel function unmasks residual embryonic slow conduction in the adult right ventricular outflow tract[END_REF]. This conduction delay could be responsible for voltage gradients between RV and RVOT during and explain the BrS ECG pattern.

Twenty years of genetic research based on both technological and methodological progresses have started to depict the complexity of BrS pathophysiology [START_REF] Watanabe | Genetics of Brugada syndrome[END_REF][START_REF] Spoonamore | Genetic testing and genetic counseling in patients with sudden death risk due to heritable arrhythmias[END_REF]. This review aims to provide an integrated synopsis of those two decades of research and to suggest future directions for further genetic investigations against BrS.

FROM A FAMiLiAL DiSeASe TO THe iDeNTiFiCATiON OF RARe vARiANTS

With the initial report of two affected siblings, familial inheritance was suggested from the first description of the Brs in 1992 (1). Few years later, Kobayashi et al. described a two-generation family presenting with both SCD and persistent ST elevation in relatives [START_REF] Kobayashi | Familial occurrence of electrocardiographic abnormalities of the Brugada-type[END_REF], confirming the heritability of the disease. The genetic component of BrS was further demonstrated in several reports (39-41) (Figure 2). Today, familial history of SCD is Functionnal effect on current are described with arrow, except for HCN4 mutation for which it remain unclear (?).

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reported for about 26% of affected patients. Additionally, 36% of affected patients are identified during familial screening after SCD or identification of BrS in the proband [START_REF] Probst | Longterm prognosis of patients diagnosed with Brugada syndrome: results from the FINGER Brugada Syndrome Registry[END_REF]. Brugada syndrome has been consistently reported as a monogenic disease with autosomal dominant mode of inheritance, caused by rare genetic variants with large effect size [START_REF] Brugada | Right bundle branch block, persistent ST segment elevation and sudden cardiac death: a distinct clinical and electrocardiographic syndrome: a multicenter report[END_REF][START_REF] Kobayashi | Familial occurrence of electrocardiographic abnormalities of the Brugada-type[END_REF]. Loss-of-function mutations in the SCN5A-encoded α-subunit of the cardiac sodium channel (Nav1.5) were first identified in 1998 [START_REF] Chen | Genetic basis and molecular mechanism for idiopathic ventricular fibrillation[END_REF]. Mutations in SCN5A are detected in 20-25% of cases, SCN5A appearing as the major susceptibility gene for BrS [START_REF] Crotti | Spectrum and prevalence of mutations involving BrS1-through BrS12susceptibility genes in a cohort of unrelated patients referred for Brugada syndrome genetic testing: implications for genetic testing[END_REF]. More than 300 rare variants in SCN5A have been reported, while the contribution of other genes remains extremely low [START_REF] Crotti | Spectrum and prevalence of mutations involving BrS1-through BrS12susceptibility genes in a cohort of unrelated patients referred for Brugada syndrome genetic testing: implications for genetic testing[END_REF][START_REF] Scouarnec | Testing the burden of rare variation in arrhythmia-susceptibility genes provides new insights into molecular diagnosis for Brugada syndrome[END_REF]. In a pediatric population affected by BrS, the prevalence of SCN5A mutations seems to be even higher [START_REF] Andorin | The impact of clinical and genetic findings on the management of young Brugada syndrome patients[END_REF].

In this context, genetics was initially expected to help the clinical management of patients with BrS. Although some SCN5A mutations -particularly those leading to premature truncation of Nav1.5 -have been reported as associated with higher arrhythmic risk, no such result has been further confirmed in randomized studies [START_REF] Probst | Longterm prognosis of patients diagnosed with Brugada syndrome: results from the FINGER Brugada Syndrome Registry[END_REF][START_REF] Sommariva | Genetics can contribute to the prognosis of Brugada syndrome: a pilot model for risk stratification[END_REF][START_REF] Meregalli | Type of SCN5A mutation determines clinical severity and degree of conduction slowing in loss-of-function sodium channelopathies[END_REF][START_REF] Nishii | SCN5A mutation is associated with early and frequent recurrence of ventricular fibrillation in patients with Brugada syndrome[END_REF].

Despite evidence for strong familial inheritance, familial linkage analyses on BrS have been largely unsuccessful. Only one gene, GPD1L, has been identified as a BrS-susceptibility gene using this approach [START_REF] London | Mutation in glycerol-3-phosphate dehydrogenase 1 like gene (GPD1-L) decreases cardiac Na+ current and causes inherited arrhythmias[END_REF]. The causing mutation in GPD1L has been shown to affect Na + channel trafficking to the plasma membrane, by modifying its oxydation state [START_REF] London | Mutation in glycerol-3-phosphate dehydrogenase 1 like gene (GPD1-L) decreases cardiac Na+ current and causes inherited arrhythmias[END_REF][START_REF] Fukuda | Oxidative mediated lipid peroxidation recapitulates proarrhythmic effects on cardiac sodium channels[END_REF]. Every other gene reported so far has been identified through a candidate approach based on direct sequencing of genes with a known (or suspected) role in cardiac electrical activity. So far, 23 genes have been related to BrS (Table 1). Based on pathophysiological hypotheses, those genes can be divided according to whether they affect the sodium current INa (SCN5A, SCN10A, GPD1L, SCN1B, SCN3B, RANGRF, SCN2B, PKP2, SLMAP, and FGF12), the potassium current IK (KCNJ8, KCNH2, KCNE3, KCND3, KCNE5, KCND2, SEMA3A, and ABCC9), or the calcium current ICa (CACNA1C, CACNB2B, and CACNA2D1).

LiMiTS iN iNTeRPReTiNG RARe vARiANTS CARRieD BY PATieNTS wiTH BrS

In the last decade, the emergence of massively parallel sequencing [or next-generation sequencing (NGS)] has considerably facilitated genetic screening and reduced its cost [START_REF] Brenner | Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays[END_REF][START_REF] Porreca | Multiplex amplification of large sets of human exons[END_REF][START_REF] Margulies | Genome sequencing in microfabricated high-density picolitre reactors[END_REF][START_REF] Tennessen | Evolution and functional impact of rare coding variation from deep sequencing of human exomes[END_REF][START_REF] Fu | Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants[END_REF]. Combined to the availability of the reference assembly of the human genome [START_REF] Venter | The sequence of the human genome[END_REF][START_REF] Lander | Initial sequencing and analysis of the human genome[END_REF], NGS-based approaches have revealed the high variability of the human genome, with at least 300-600 functional genetic variants detected in each exome (i.e., the whole coding portion of a single genome) (75) -and has retrospectively changed the interpretation of previous rare variants identified by candidate gene approach. The investigation of large number of exomes revealed the extraordinary prevalence of rare variants among each individual. As an illustration, the sequencing of 60,706 exomes identified about 7,500,000 variants from which 99% have a frequency of <1% (http://biorxiv.org/content/early/2015/10/30/030338).

Extreme caution should thus be taken when interpreting the rare genetic variants detected among patients with BrS, since the Furthermore, a recent study has illustrated the weakness of candidate approaches on small pedigrees, by highlighting the high frequency of some genetic variants previously associated with BrS among 6,500 individual exomes from the Exome Sequencing Project [START_REF] Risgaard | High prevalence of genetic variants previously associated with Brugada syndrome in new exome data[END_REF]. One variant in particular, which was related to BrS based on functional evidence, showed a minor allele frequency of 4.4% among the 6,500 individuals. This result was confirmed in a healthy Danish control population, suggesting that a proportion of the genetic variants reported as causing BrS are actually not pathogenic. Interestingly, 93% of the SCN5A variants reported as causing BrS are not present among the control population, thus reinforcing the pivotal role of this gene.

By testing the burden of rare coding variants in 45 arrhythmiasusceptibility genes among 167 BrS cases versus 167 control individuals, we have also recently demonstrated the limitation of previous candidate approaches [START_REF] Scouarnec | Testing the burden of rare variation in arrhythmia-susceptibility genes provides new insights into molecular diagnosis for Brugada syndrome[END_REF]. Indeed, for every tested gene except SCN5A, rare variants were found in the same proportion in cases than in controls. Figure 3 shows the distribution of rare variants among cases and controls for the protein products of four genes: SCN5A, SCN10A, CACNA1C, and PKP2. The distribution of rare variants across the functional domains of the CACNA1C product indicates that the C-terminal tail, which was previously considered as pathogenic in BrS, may in fact be highly polymorphic. On the opposite, most rare variants detected along the protein encoded by PKP2 among BrS patients reside in a small interval coding for four amino acids. The PKP2 gene has been previously associated with BrS by decreasing functional Na channel expression through modification of microtubule anchoring [START_REF] Cerrone | Missense mutations in plakophilin-2 cause sodium current deficit and associate with a Brugada syndrome phenotype[END_REF]. The small PKP2 interval emphasized in this study may be a preferential site of such interaction.

Rare genetic variants appear more evenly distributed across SCN10A and less predictive of any potential pathophysiological mechanism. In fact, the functional effects of these rare variants affecting SCN10A are largely debated. SCN10A gene, which encodes the sodium channel Nav1.8, was initially described in neurons physiology [START_REF] Akopian | A tetrodotoxin-resistant voltage-gated sodium channel expressed by sensory neurons[END_REF][START_REF] Renganathan | Contribution of Na(v)1.8 sodium channels to action potential electrogenesis in DRG neurons[END_REF]. Further investigations illustrated a potential role in cardiac electrophysiology, particulary as a modulator of cardiac conduction [START_REF] Pallante | Contactin-2 expression in the cardiac Purkinje fiber network[END_REF][START_REF] Yang | Blocking Scn10a channels in heart reduces late sodium current and is antiarrhythmic[END_REF]. Recently, Hu et al. described rare variants in the SCN10A gene, in 16.7% of 150 patients affected with BrS [START_REF] Hu | Mutations in SCN10A are responsible for a large fraction of cases of Brugada syndrome[END_REF]. Furthermore, they demonstrated that the SCN10A variants R1268Q and R14L reduced cardiac sodium currents [START_REF] Hu | Mutations in SCN10A are responsible for a large fraction of cases of Brugada syndrome[END_REF]. However, although relevant biological effects are reported for some variants, most variants are also reported in control populations. Behr et al. have recently underlined this issue [START_REF] Behr | Role of common and rare variants in SCN10A: results from the Brugada syndrome QRS locus gene discovery collaborative study[END_REF]. Using an extended control population, they decreased the yield of such variants from 16.7% in the Hu et al. 's study to 5.1% in a different set of BrS probands [START_REF] Hu | Mutations in SCN10A are responsible for a large fraction of cases of Brugada syndrome[END_REF]. Additionally, only two over seven familial pedigrees available with such variants demonstrated segregation with the BrS.

Coding genetic variants in candidate genes are usually classified as likely pathogenic if they are extremely rare or absent from control populations. However, private genetic variants are found in control populations, and many rare variants predicted as damaging are carried by apparently healthy individuals [START_REF] Scouarnec | Testing the burden of rare variation in arrhythmia-susceptibility genes provides new insights into molecular diagnosis for Brugada syndrome[END_REF][START_REF] Risgaard | High prevalence of genetic variants previously associated with Brugada syndrome in new exome data[END_REF]. As an example, in the SCN5A gene, rare functional variants can be found in about 2% of control patients and even in 5% in non-white population [START_REF] Kapa | Genetic testing for long-QT syndrome distinguishing pathogenic mutations from benign variants[END_REF]. Thus, considering SCN5A-mediated BrS account for about 20% of cases and that background noise of rare variant with minor allele frequency under 1/10,000 is approximately 2%, there is a 10/1 signal to noise ratio that means a 10% risk of false positive in possibly damaging rare SCN5A variants [START_REF] Ackerman | Genetic purgatory and the cardiac channelopathies: exposing the variants of uncertain/unknown significance issue[END_REF]. As prevalence of asymptomatic BrS in the general population is unknown, this percentage may be over estimated. However, as BrS is a rare disease, the proportion of false positive variants remains, in any case, too high to be confident with a direct translation of new rare variants in clinical practice. On the opposite, some rare variants detected among BrS patients are reported as benign by prediction algorithms though they modify the function of the protein. As an example, while one SCN3B variant has been associated with BrS and reported as impacting the sodium current density, it is considered as benign by prediction algorithms such as SIFT and PolyPhen-2 [START_REF] Hu | A mutation in the β3 subunit of the cardiac sodium channel associated with Brugada ECG phenotype[END_REF][START_REF] Kumar | Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm[END_REF][START_REF] Adzhubei | A method and server for predicting damaging missense mutations[END_REF]. This demonstrates the strong limitations of such prediction algorithms and the need for functional studies and/or segregation analyses to better assess the causality of rare variants.

From that perspective, mutations in L-type calcium channels (CACNA1C, CACNB2B, and CACNA2D1) that were considered as associated with about 4% of BrS cases are of particular interest [START_REF] Crotti | Spectrum and prevalence of mutations involving BrS1-through BrS12susceptibility genes in a cohort of unrelated patients referred for Brugada syndrome genetic testing: implications for genetic testing[END_REF]. The L-type calcium current ICa-L is a perfect candidate to explain BrS physiopathology, due to its central role in action potential dome (phases 2 and 3) and in the "depolarization" hypothesis [START_REF] Antzelevitch | J wave syndromes: molecular and cellular mechanisms[END_REF]. However, functional studies on mutations in L-type calcium channels are scarce in the literature. Moreover, mutations in CACNA1C among BrS cases and controls are mostly located within the C-terminal tail of Cav1.2, thus suggesting a high genetic variability of the domain (Figure 3). Although CACNA1C mutations seem to play lesser role than previously reported, this particular gene remains involved in a small subset of BrS cases, in particularly those with combined phenotypes of BrS and short QT syndrome [START_REF] Watanabe | Role of mutations in L-type calcium channel genes in Brugada syndrome, early repolarization syndrome, and idiopathic ventricular fibrillation associated with right bundle branch block[END_REF].

These accumulated data demonstrate that in order to avoid misinterpretation of genetic variants: (1) functional prediction algorithms should be used cautiously and (2) ancestry-matched control populations should be systematically considered. Furthermore, familial segregation analysis and/or extended functional testing are mandatory before associating rare coding variants to disease susceptibility.

Following these guidelines, no previously reported susceptibility gene except SCN5A seems to contribute significantly to BrS pathophysiology. Although SCN5A remains the major gene involved in BrS with about 20% of carriers among probands [START_REF] Crotti | Spectrum and prevalence of mutations involving BrS1-through BrS12susceptibility genes in a cohort of unrelated patients referred for Brugada syndrome genetic testing: implications for genetic testing[END_REF][START_REF] Scouarnec | Testing the burden of rare variation in arrhythmia-susceptibility genes provides new insights into molecular diagnosis for Brugada syndrome[END_REF], a proportion of rare variants residing in this gene -particularly among those of uncertain functional effect -could play no role in relation with the disease [START_REF] Ackerman | Genetic purgatory and the cardiac channelopathies: exposing the variants of uncertain/unknown significance issue[END_REF].

THe COMPLeX iNHeRiTANCe OF BrS: TOwARD A New GeNeTiC MODeL

Since the discovery of SCN5A as the first susceptibility gene for BrS, this disorder has been consistently reported as a monogenic disease with autosomal dominant mode of inheritance, caused by rare genetic variants with large effect size [START_REF] Brugada | Right bundle branch block, persistent ST segment elevation and sudden cardiac death: a distinct clinical and electrocardiographic syndrome: a multicenter report[END_REF][START_REF] Kobayashi | Familial occurrence of electrocardiographic abnormalities of the Brugada-type[END_REF][START_REF] Chen | Genetic basis and molecular mechanism for idiopathic ventricular fibrillation[END_REF]. SCN5A remains the only major susceptibility gene for BrS, with more than 300 coding variants described among more than 75% of the genetically diagnosed patients [START_REF] Crotti | Spectrum and prevalence of mutations involving BrS1-through BrS12susceptibility genes in a cohort of unrelated patients referred for Brugada syndrome genetic testing: implications for genetic testing[END_REF][START_REF] Kapplinger | An international compendium of mutations in the SCN5A-encoded cardiac sodium channel in patients referred for Brugada syndrome genetic testing[END_REF]. However, hardly any of the large family pedigrees with BrS provides evidence for Mendelian inheritance. Most familial forms indicate a genetic model with incomplete penetrance and remain genetically undiagnosed.

We have investigated the cosegregation of SCN5A mutations with BrS among large genotyped families [START_REF] Probst | SCN5A mutations and the role of genetic background in the pathophysiology of Brugada syndrome[END_REF]. SCN5A mutations exhibit low penetrance (61% after drug testing) in families, leading to poor genotype/phenotype correlations. More surprisingly, among five pedigrees, we could identify eight affected members who did not carry the familial SCN5A mutation (Figure 2). This lack of genotype/phenotype correlation is further emphasized in other families with variable cardiac phenotypes associated with a same SCN5A mutation. Indeed, although a Na current decrease could lead to cardiac conduction or sinus node dysfunction, the description of relatives sharing the same SCN5A mutation but presenting with either BrS or a progressive cardiac conduction disease question about the relevance of a monogenic model [START_REF] Schott | Cardiac conduction defects associate with mutations in SCN5A[END_REF][START_REF] Kyndt | Novel SCN5A mutation leading either to isolated cardiac conduction defect or Brugada syndrome in a large French family[END_REF]. A similar issue involving SCN5A mutation has been described with BrS and long QT syndrome [START_REF] Bezzina | A single Na+ channel mutation causing both long-QT and Brugada syndromes[END_REF].

These observations have led us to seek for genetic factors modulating the risk of Brugada ECG phenotype. To explore the potential role of common genetic variants in susceptibility to Brs, we have recently coordinated an international genomewide association study (GWAS) on BrS. By comparing allele frequencies of common haplotypes genome wide among 312 index cases versus 1,115 control individuals, we identified three loci associated with susceptibility to BrS (Figure 4A). The three hits were then replicated on independent case-control sets from Europe and Japan. We found that their cumulative effect on disease susceptibility was unexpectedly large, with an estimated odds ratio of 21.5 in the presence of more than four risk alleles versus less than two (Figure 4B). This study demonstrates that an aggregation of genetic polymorphisms can strongly influence the susceptibility to BrS and confirms that the mode of inheritance for this arrhythmia disorder is far more complex than previously described.

Two association signals reside at the SCN5A-SCN10A locus. Both common risk alleles have previously been associated with cardiac conduction traits in the general population [START_REF] Pfeufer | Genome-wide association study of PR interval[END_REF]. This finding demonstrates that genetic polymorphisms modulating cardiac conduction can also influence susceptibility to cardiac arrhythmia. One haplotype is located inside the SCN10A gene, of which involvement in the pathophysiology of BrS is still matter to debate. van den Boogaard et al. provided evidence that the SCN10A haplotype contain was an enhancer region for both SCN10A and SCN5A genes [START_REF] Van Den Boogaard | Genetic variation in T-box binding element functionally affects SCN5A/SCN10A enhancer[END_REF]. They further demonstrated that a common variant (rs6801957) of this locus, associated with cardiac conduction trait and in high linkage disequilibrium with rs10428132, alters a transcription factor binding site for TBX3/TBX5 and reduces the SCN5A expression [START_REF] Van Den Boogaard | A common genetic variant within SCN10A modulates cardiac SCN5A expression[END_REF]. This may explain the high phenotype variability observed in BrS patients even within a same family.

The third association signal resides near the Hey2 gene, which encodes a basic helix-loop-helix transcriptional repressor expressed in the cardiovascular system. The implication of this gene in susceptibility to BrS was previously unknown (100). Interestingly, Hey2 presents a gradient of expression across the ventricular wall in mirror image with SCN5A expression suggesting a possible (indirect) regulation mechanism. Despite no ECG changes, Hey2 heterozygous knockout mice (Hey2 +/-) present interesting findings for BrS pathophysiology. Conduction velocity seems specifically increase in the right outflow tract in which cellular action potential present both increase in AP upstroke velocity and repolarization (101). These data uncovered the role of Hey2 in the cardiac electrical function and more specifically in the pathogenesis of BrS. Among its role on BrS phenotype, common variant in this gene could also presented with a protective role from ventricular fibrillation in BrS patients by regulating the repolarization current (102).

CONCLUSiON

Almost two decades ago, the first description of a mutation in SCN5A gene has paved the way of genetics in BrS. As BrS was initially described as a Mendelian disease with low penetrance, many studies have been performed to track genetic variants in families affected by this syndrome. However, in most cases, studies were unable to show positive linkage. In a very large majority of cases, putative causing genes were identified through a "candidate gene approach" based on pathophysiological hypotheses. In these a priori approaches, the results were "validated" by the rarity of the genetic variants identified, while aberrant linkage results were "explained" by non-penetrance or phenocopies.

In the recent years, NGS technologies have dramatically expanded our capacity to sequence genomes. It has also revealed the high variability of the human genome, underlying the extreme caution that should be taken to avoid misinterpretation of the potential association of rare variants with BrS. Thus, recent burden tests have questioned the implication of several genes previously identified as there distribution was similar in the normal population and affected patients. For now, only rare variants in SCN5A gene seem to be significantly associated with the syndrome. However, genotype/phenotype studies among BrS families with SCN5A mutation carriers have highlighted a complex mode of inheritance for this syndrome. In line with these reports, a GWAS has recently identified three common risk haplotypes for the Brugada ECG pattern.

It is now established that the molecular mechanisms leading to BrS involve both rare and common genetic variants, underlying the need for better understanding the genetic architecture of BrS prior to applying genetics as a diagnostic tool. For the next future, one of the challenges that could contribute to a more efficient strategy for BrS would be to decipher the role of the combination of variants both for diagnosis and prognosis. Another source of progress regarding risk stratification among BrS patients could go through the identification of specific ECG indices associated with higher risk of (fatal) arrhythmia. Genetic variants at the SCN5A, HEY2, and SCN10A loci have been associated with arrhythmia occurrence in independent studies [START_REF] Meregalli | Type of SCN5A mutation determines clinical severity and degree of conduction slowing in loss-of-function sodium channelopathies[END_REF]102,103). Integrating such effects toward establishing a global genetic model for BrS is the next step before including genetic testing into the clinical management of BrS.

Besides the direct benefit of this research on the BrS for itself, it appears increasingly that this primary electrical disorder affecting the young adult (with no identifiable structural abnormalities and presenting limited exposure to environment side effect) may represent a relevant model for the identification of markers and mechanism implied into broader common cardiac arrhythmias.

Retrospectively, SCN10A common variant identified in the BrS GWAS study have been also associated with the risk of VF in the context of myocardial infarction and with the pacemaker implantation rate (103, 104). Additionally, a protective role against developing AF has been suggested for both common variants previously identified as risk alleles for BrS at the SCN10A-SCN5A locus. This reinforces the interest of rare diseases to help identifying the pathophysiological bases of common pathologies. As they constitute homogenous groups of patients, rare arrhythmia disorders can provide new molecular insights that may be relevant to the broader health issue of SCD (105).

FiGURe 1 |

 1 FiGURe 1 | Ajmaline testing reveals the Brugada eCG pattern. ECG pattern is recorded at 1 mm/10 mV and 25 mm/s. Baseline ECG without aspect of BrS (left side). Type 1 Brugada pattern on the right precordial leads at the end of the test (right side).

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  Gourraud et al. Genetics of Brugada Syndrome Frontiers in Cardiovascular Medicine | www.frontiersin.org April 2016 | Volume 3 | Article 9

FiGURe 3 |

 3 FiGURe 3 | The distribution of rare coding variants detected across four selected arrhythmia-susceptibility genes among 167 BrS cases and 167 healthy individuals. Modified from Ref. (44). SCN5A (A), SCN10A (B), CACNA1C (C), and PKP2 (D) are the four genes exhibiting the largest numbers of rare coding variants among BrS cases. Rare coding variants (minor allele frequency <0.1%) are represented in red (cases) and blue (controls). Green variants are detected in both cases and controls.

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  in Cardiovascular Medicine | www.frontiersin.org April 2016 | Volume 3 | Article 9

FiGURe 4 |

 4 FiGURe 4 | A genome-wide association study on BrS. Modified from Ref. (101). (A) Manhattan plot revealing signal associations between two SNP (SCN10A and HEY2) and BrS. Statistical significance is represented with a red line (P = 5 × 10 -8 ). A third haplotype of SCN5A reached genome-wide significance after replication analysis. (B) The cumulative effect of the six common risk alleles on susceptibility to BrS. Odds Ratio (OR) is plotted on the vertical axis and the cumulative number of alleles (from 0 to 6) in horizontal axis.

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TABLe 1 |

 1 The 23 reported susceptibility genes for BrS.

	OMiM	Gene	Protein	Prevalence in	Functional effect	Reference
	ranking			BrS cases	of the mutation
	BrS1	SCN5A	α subunit of the Nav1.5 sodium channel	20-25%	 INa	(42)
	BrS2	GPD1L	Glycerol-3-phosphate dehydrogenase 1-like	Rare	 INa	(49)
	BrS3	CACNA1C	α subunit α1C of the Cav1.2 calcium channel	1-2%	 ICa-L	(51)
	BrS4	CACNB2b	β subunit Cavβ2b of calcium channel	1-2%	 ICa-L	(51)
	BrS5	SCN1b	β subunit Navβ1 of sodium channel	Rare	INa	(52)
	BrS6	KCNE3	β subunit MiRP2 of potassium channel	Rare	 Ito	(53)
	BrS7	SCN3b	β subunit Navβ3 of sodium channel	Rare	 INa	(54)
	BrS8	HCN4	Hyperpolarization-activated cyclic nucleotide-gated channel 4	Rare	?	(55)
	BrS9	KCND3	α subunit of the KV4.3 potassium channel	Rare	 Ito	(56)
	BrS10	KCNJ8	α subunit of the KIR6.1 potassium channel	Rare	 IKATP	(57)
	BrS11	CACNA2D1 δ subunit Cavα2δ1 of calcium channel	Rare	 ICa-L	(58)
	BrS12	KCNE5	β subunit of potassium channel	Rare	 Ito	(59)
	BrS13	RANGRF	RAN guanine nucleotide release factor	Rare	 INa	(60)
	BrS14	KCND2	α subunit of the KV4.2 potassium channel	Rare	 Ito	(61)
	BrS15	TRPM4	Calcium-activated non-selective ion channel	Rare	?	(62)
	BrS16	SCN2B	β subunit Navβ2 of sodium channel	Rare	 INa	(63)
	BrS17	PKP2	Plakophilin 2	Rare	 INa	(64)
	BrS18	ABCC9	ATP-sensitive potassium channels	Rare	 IKATP	(65)
	BrS19	SLMAP	Sarcolemma-associated protein	Rare	 INa	(66)
	BrS20	KCNH2	α subunit of the HERG potassium channel	Rare	 IKr	(67)
	BrS21	SCN10A	α subunit of the Nav1.8 sodium channel	<5%	 INa	(68, 69)
	BrS22	FGF12	Fibroblast growth factor 12	Rare	 INa	(70)
	BrS23	SEMA3A	Semaphorin family protein	Rare	 Ito	(71)

Frontiers in Cardiovascular Medicine | www.frontiersin.org April 2016 | Volume 3 | Article 9

AUTHOR CONTRiBUTiONS

All authors authored sections of the manuscript, contributed to the figure design, and approved the final version.

Conflict of Interest Statement:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PhD Iris C R M Kolder

PhD # Michael W T Tanck

MD, PhD # , Pieter G Postema

PhD Julien Barc

MD, MPH Moritz F Sinner

MD Sven Zumhagen

PhD Anja Husemann

PhD Birgit Stallmeyer

PhD Tamara T Koopmann

PhD Nynke Hofman

MD, MSc Arne Pfeufer

PhD Peter Lichtner

MD Thomas Meitinger

MD Britt M Beckmann

MD Robert J Myerburg

MD Nanette H Bishopric

MD Dan M Roden

MD Stefan Kääb

PhD Arthur A M Wilde

PhD Jean-Jacques Schott

PhD # Eric Schulze-Bahr

MD # Connie R Bezzina
email: c.r.bezzina@amc.uva.nl

Analysis for Genetic Modifiers of Disease Severity in Patients With Long-QT Syndrome Type 2

Keywords: arrhythmias, cardiac, ion channels, long-QT syndrome

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The congenital long-QT syndrome (LQTS) is a heritable disorder associated with corrected QT (QTc)-interval prolongation on the ECG and an increased risk of sudden cardiac death from torsade de pointes polymorphic ventricular tachycardia. Mutations in multiple genes, primarily encoding ion channel subunits have been identified in patients with the disorder. In ≈75% of cases, the disease is caused by the inheritance of a mutation in either KCNQ1 (LQT1), KCNH2 (LQT2), or SCN5A (LQT3). [START_REF] Schwartz | Impact of genetics on the clinical management of channelopathies[END_REF] Despite previous achievements in gene discovery, important issues in the clinical management of patients with LQTS remain. As for most Mendelian disorders, patient management is complicated by the variability in disease severity among mutation carriers. [START_REF] Priori | Low penetrance in the long-QT syndrome: clinical impact[END_REF] Variability is observed both in the extent of the QTc-interval prolongation as well as in the occurrence of arrhythmic events. Although some mutation carriers display a severely prolonged QTc-interval, the QTc-interval of others may be within the normal range. Similarly, not all patients suffer arrhythmic events. Established modulators of disease severity include sex, age, heart rate, intake of QTc-prolonging drugs, and affected gene and mutation location. [START_REF] Priori | Risk stratification in the long-QT syndrome[END_REF][START_REF] Schwartz | Neural control of heart rate is an arrhythmia risk modifier in long QT syndrome[END_REF][START_REF] Makita | Drug-induced long-QT syndrome associated with a subclinical SCN5A mutation[END_REF][START_REF] Shimizu | Genotypephenotype aspects of type 2 long QT syndrome[END_REF] Furthermore, in ≈10% of cases, clinical disease severity can be explained by compound heterozygosity. [START_REF] Westenskow | Compound mutations: a common cause of severe long-QT syndrome[END_REF] However, although additional genetic factors are also expected to play a role, these are largely unexplored. [START_REF] Crotti | NOS1AP is a genetic modifier of the long-QT syndrome[END_REF][START_REF] Tomás | Polymorphisms in the NOS1AP gene modulate QT interval duration and risk of arrhythmias in the long QT syndrome[END_REF][START_REF] Duchatelet | Identification of a KCNQ1 polymorphism acting as a protective modifier against arrhythmic risk in long-QT syndrome[END_REF] We here investigated the role of common genetic variants (minor allele frequency >10%) in the form of single-nucleotide polymorphisms (SNPs) in patients with LQT2. In the first approach, we conducted a comprehensive analysis of haplotype-tagging SNPs in 18 candidate genes. In a second approach we investigated the effect of SNPs that have been associated with the QTc-interval through genome-wide association studies (GWAS) conducted in the general population over the past years. [START_REF] Newton-Cheh | Common variants at ten loci influence QT interval duration in the QTGEN Study[END_REF][START_REF] Pfeufer | Common variants at ten loci modulate the QT interval duration in the QTSCD Study[END_REF][START_REF] Marroni | A genome-wide association scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project[END_REF][START_REF] Chambers | Genetic variation in SCN10A influences cardiac conduction[END_REF][START_REF] Arking | A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization[END_REF]

Methods

LQT2 Patients

The study population consisted of 639 individuals from 254 families of European descent, all harboring a mutation in KCNH2. Patients carrying >1 mutation in KCNH2 or carrying a second mutation in another LQTS gene were excluded. These subjects were drawn from the LQTS registries of 4 European clinical centers: Amsterdam (The Netherlands), Münster (Germany), Munich (Germany), and Nantes (France). The Medical Ethical Committee at each center approved the study. All subjects or their guardians provided informed consent for genetic and clinical studies. Analyses were conducted in a set of 353 patients (Set 1), a non-overlapping set of 286 patients (Set 2), and in Set 1 and Set 2 combined. Patient Set 1 and Set 2 were drawn a few years apart of each other from the LQTS registries of the same 4 European academic centers. Routine clinical and ECG parameters were acquired at the time of patient enrollment in each of the registries (see QTc-interval measurement in the Data Supplement). A first cardiac event was defined as a first unexplained syncope, a first documented ventricular tachycardia, or a first aborted cardiac arrest. The observation period for cardiac events started at birth and lasted either to the initiation of antiadrenergic therapy (β-blockers) or the date of the last medical visit (without antiadrenergic therapy).

Selection of SNPs and Genotyping

Candidate Gene SNPs-Eighteen candidate genes (listed in Table I in the Data Supplement) were selected based on their involvement in cardiac arrhythmia syndromes or their role as functionally important subunits of these genes. Because at the time of assay design, the NOS1AP locus was already associated with the QTc-interval in GWAS, this gene was also included in the candidate gene study. SNPs for genotyping were selected from all HapMap SNPs available for the CEU (Northern Europeans from Utah) population within the genes and the 50-kb flanking regions. Tag-SNPs were selected using Tagger [START_REF] De Bakker | Efficiency and power in genetic association studies[END_REF] with the following criteria: pairwise only tagging with r 2 ≥0.8 and a minor allele frequency ≥10%. A total of 1424 SNPs were derived in this way for genotype analysis using an Illumina GoldenGate custom assay (Data Supplement).

The systematic analysis of haplotype-tagging SNPs in the 18 candidate genes was conducted in LQT2 patient Set 1 (n=353). SNPs found to be significantly associated with the QTcinterval in this analysis were subsequently investigated in LQT2 patient Set 2 (n=286).

SNPs From QTc-Interval GWAS-We also investigated SNPs previously associated with the QTc-interval in GWAS conducted in the general population. Twenty-two independent SNPs were identified from the literature. [START_REF] Newton-Cheh | Common variants at ten loci influence QT interval duration in the QTGEN Study[END_REF][START_REF] Pfeufer | Common variants at ten loci modulate the QT interval duration in the QTSCD Study[END_REF][START_REF] Marroni | A genome-wide association scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project[END_REF][START_REF] Chambers | Genetic variation in SCN10A influences cardiac conduction[END_REF][START_REF] Arking | A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization[END_REF] SNPs were pruned based on their extent of linkage disequilibrium (LD, R 2 <0.5). SNPs thus selected were genotyped in patient Sets 1 and 2 combined (n=639; Set 1+Set 2) using iPLEX Gold chemistry (Data Supplement).

Calculation of the Genotype Risk Scores-The genotypes from the 22 SNPs from GWAS studies were used to calculate an unweighted multilocus genetic risk score (GRS 22 ). In addition, a GRS based on the 6 NOS1AP SNPs was also generated (GRS NOS1AP ). The directionality of the effect of each SNP was based on the original publication. [START_REF] Newton-Cheh | Common variants at ten loci influence QT interval duration in the QTGEN Study[END_REF][START_REF] Pfeufer | Common variants at ten loci modulate the QT interval duration in the QTSCD Study[END_REF][START_REF] Arking | A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization[END_REF][START_REF] Marroni | A genome-wide association scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project[END_REF][START_REF] Chambers | Genetic variation in SCN10A influences cardiac conduction[END_REF] For each QT-shortening allele, 1 point was subtracted from the score, whereas 1 point was added for each QT-prolonging allele. A negative GRS indicates an excess of QT-shortening alleles, whereas a positive GRS indicates an excess of QT-prolonging alleles.

Statistical Analyses-QTc-interval data were normally distributed (Shapiro-Wilk statistic, W>0.90) and are reported as mean±SD.

Effects of KCNH2 mutation type and location, effects of covariates, and effects of SNPs and the GRS on the QTc-interval were estimated using the linear mixed effect model function (lmekin). The model fit of the GRS NOS1AP was compared with the GRS 22 using Akaike's information criterion (AIC). The effect of SNPs and the GRS on the secondary end point age at first cardiac event was estimated using the Cox proportional hazards function (coxme). Both Imekin and coxme are functions from the coxme package in R [START_REF] Therneau | Coxme: mixed effects Cox models. R package version 2[END_REF] and are correlated random effect models. The models allow for a per-patient random effect that are correlated based on a matrix containing the kinship coefficients for each pair of individuals. This way, dependency between some of the study subjects because of familial relatedness is taken into account. For each SNP-phenotype relationship, an additive genetic model was assumed.

The effects of the SNPs and the GRS on QTc-interval were adjusted for center, sex, age at ECG, proband status, β-blocker use at the time of the ECG, and mutation type and location, whereas the effects of the SNPs on age at first cardiac event were adjusted for center, sex, and mutation type and location only. With respect to mutation type and location, mutations were classified into 5 different classes: (1) nonsense, frameshift (small indels or splice-site mutations), large deletions and insertions, independent of location; (2) missense, N terminal;

(3) missense, transmembrane S1 to S4; (4) missense, transmembrane S5-loop-S6; and (5) missense, C terminal. The classes were treated in the models as an unordered factor. The annotation of mutation location was based on the Uniprot database (http://www.uniprot.org/ uniprot/Q12809).

For the 22 SNPs from QTc-interval GWAS, we also compared genotype counts between the probands from Set 1 and Set 2 combined (n=278), with those of 498 general population controls drawn from the Genome of the Netherlands (GoNL) project. [START_REF] Boomsma | The genome of the Netherlands: design, and project goals[END_REF] For 20 of the 22 GWAS SNPs investigated, genotypes were available in all 498 GoNL controls. For the remaining 2 SNPs, genotype information was available in 497 (rs4725982) and 472 (rs2074238) individuals, respectively. Genotype counts were compared using logistic regression assuming an additive genetic model (no covariates were included because of lack of access to individual participant data for GoNL).

The SNAP tool (http://www.broadinstitute.org/mpg/snap/) was used to assess LD between SNPs using the CEU reference population. The significance thresholds applied and a statement on statistical power can be found in the Data Supplement.

Results

Study Populations

The characteristics of the patients with LQT2 studied are presented in Table 1. Patient Set 1 was comparable with patient Set 2 and only differed in the occurrence of cardiac events (P=0.039). Considering Sets 1 and 2 together, QTc-intervals differed significantly between probands (479±50 ms) and relatives (460±40 ms, P=8×10 -7 ). Males and females had similar QTc-intervals (462±44 ms, males; 468±43 ms, females; P=0.06). β-Blocker use at the time of the ECG did not affect the QTc-interval (464±43 in nonusers; 469±49 in users; P=0.32). The relatively low β-blocker use at the time of ECG (≈16%) most likely reflects the fact that the ECGs used in this study were acquired at enrollment.

Effects of KCNH2 Mutation Type and Location

Because the type and location of the KCNH2 mutation may affect the extent of QTc-interval prolongation, [START_REF] Shimizu | Genotypephenotype aspects of type 2 long QT syndrome[END_REF] we evaluated such effects in the patients in this study. Considering Sets 1 and 2 combined, a total of 197 different KCNH2 mutations were present among the 639 patients who originated from 254 different families. The number of patients per family ranged from 1 to 20. We grouped the nonsense, frameshift (small indels or splice site mutations) and large duplications/deletions as 1 category because these mutation types are all expected to have a drastic effect on the protein structure and likely lead to haploinsufficiency. We detected no difference in extent of QTc-interval prolongation when this category of mutations was compared with missense mutations (P=0.13). We then classified the missense mutations according to the channel subdomain in which they occurred (locations of missense mutations in the channel are represented in Figure I in the Data Supplement); we found that carriers of a missense mutation in the transmembrane nonpore region (S1-S4) had on average a longer QTc-interval compared with individuals carrying a missense mutation in any of the other 3 locations, that is, transmembrane pore region (S5-pore-S6), N terminus, or C terminus (P=2.0×10 -4 ; Table 2). When 25 patients with S1 to S4 region missense mutations were excluded (8 different mutations), patients with a missense mutation in the pore region (transmembrane S5-loop-S6) displayed a longer QTc-interval compared with patients with a nonpore missense mutation (N or C terminal; P=0.046). Missense mutations were primarily located at the N and C termini and the S5-pore-S6 region (Figure I in the Data Supplement).

Individual SNP Effects

Candidate Gene Study-After quality control, a total of 1201 SNPs across the 18 candidate genes were left for analysis for effects on the QTc-interval as a quantitative variable in the 353 LQT2 patients of Set 1 (all association results are listed in the Data Set file/Table II in the Data Supplement). Three SNPs passed the preset Bonferroni-corrected P value threshold for association of P<4.2×10 -5 (0.05/1201); these included a SNP at the NOS1AP locus (rs16847548), a SNP at KCNH2 (rs956642), and a SNP at CASQ2 (rs1935778; Table 3). The minor allele at both the NOS1AP and CASQ2 loci was associated with a longer QTc-interval, whereas that at KCNH2 was associated with a shorter QTcinterval. The absolute effect sizes per minor allele were >12 ms in all 3 cases. The 3 SNPs that were significantly associated with the QTc-interval in Set 1 were subsequently tested in Set 2 (Table 3). In Set 2, only the NOS1AP SNP (rs16847548) displayed a significant association at the Bonferroni-corrected P value threshold of P<0.016 (0.05/3). The direction of the effect was consistent with that found in Set 1, with the minor allele being associated with a longer QTc-interval. The other 2 SNPs (rs1935778, rs956642) showed a nonsignificant effect on QTc-interval in Set 2 (effect <0.5 ms). Combining the results improved the accuracy for all estimates, but the effects of rs1935778 and rs956642 were reduced with 40% to 50% (Table 3).

SNPs From QTc-Interval GWAS-Analysis of SNP effects on the QTc-interval analyzed as a quantitative variable. Twenty-two SNPs previously found to associate with the QTc-interval in the general population were analyzed for modulatory effects on the QTcinterval as a quantitative variable in Sets 1 and 2 combined (Table 4). Three SNPs (rs10494366, rs12143842, and rs2880058), all from the NOS1AP locus were found to associate with the QTc-interval at the preset Bonferroni-corrected P value threshold of 2.27×10 -3 (0.05/22). In all cases, the minor allele was associated with a longer QTc-interval and the effect size per minor allele was >12 ms. Of note, rs16847548 which was found to associate with the QTc-interval in the candidate gene study (Table 3) is in LD with rs12143842 (R 2 =0.88). Four SNPs displayed nominal statistical significance; these were rs12029454 and rs16857031 at NOS1AP, rs2074238 at KCNQ1, and rs17779747 at KCNJ2.

Case-Control Analysis-The 22 SNPs from QTc-interval GWAS were also investigated for association with LQTS status using a case-control design using independent probands from Sets 1 and 2 combined as cases and general population individuals from the GoNL project as controls (Table 5). [START_REF] Boomsma | The genome of the Netherlands: design, and project goals[END_REF] In this analysis, 2 SNPs were significantly (P<2.27×10 -3 ) associated with LQTS status and displayed the expected directionality of effect, that is, the allele associated with a longer QTc-interval in the general population was the risk allele. The SNPs were located at NOS1AP (rs12029454) and KCNQ1 (rs12576239), respectively. Another 7 SNPs displayed a nominal association.

Individual SNP Effects on Cardiac Events-SNPs that displayed a significant or nominal association in any of the above analyses were LD-pruned (R 2 <0.5) and assessed for association with cardiac events in Sets 1 and 2 combined (n=639). Because it has been previously suggested that SNP effects on risk of cardiac events might be more pronounced in patients with QTc<500 ms, [START_REF] Tomás | Polymorphisms in the NOS1AP gene modulate QT interval duration and risk of arrhythmias in the long QT syndrome[END_REF] we also tested for association with cardiac events in this subgroup alone. Of the 12 SNPs tested (Table 6), none were associated with cardiac events after correction for multiple testing (0.05/12; P<4.2×10 -3 ). However, 3 SNPs, 2 at NOS1AP and 1 at KCNE1, were nominally associated with cardiac events. In all 3 cases, the allele associated with a longer QTc-interval increased risk. The results differed somewhat when LQT2 patients with QTc-interval <500 ms were analyzed separately, with the effect of the KCNE1 SNP no longer remaining (nominally) significant (Table 6). Reanalysis of the 3 SNPs by adding QTc-interval as an additional covariate in the model resulted in lower and nonsignificant relative risks for all 3 (rs10494366, odds ratio, 1.15; 95% confidence interval [CI], 0.92-1.44; rs12029454; odds ratio, 1.27; 95% CI, 1.00-1.62; and rs1805128, odds ratio, 1.26; 95% CI, 0.95-1.67).

Genetic Risk Score-We finally tested the effect of the 22 SNPs from GWAS, and a subset of 6 NOS1AP SNPs thereof, in aggregate by first generating 2 multilocus GRSs (GRS 22 and GRS NOS1AP ) per individual and then testing these GRS for association with QTc-interval and occurrence of cardiac events. This analysis was conducted in patients from Sets 1 and 2 combined. The GRS 22 , that varied from -8 to 14 with a mean (±SD) of 3.0±3.8, was strongly associated with the QTc-interval with an increase of 2.3 (SE, 0.50) ms per point increase in GRS 22 (P=4.3×10 -6 ; Figure 1A). There was a linear increase in QTcinterval with increasing GRS 22 ; patients with GRS 22 in the second, third, or fourth quartile had mean QTc-intervals that were 7 (SE, 5), 13 (SE, 6), and 19 (SE, 5) ms, respectively, longer than individuals in the lowest GRS 22 quartile. When the 6 NOS1AP SNPs were not included in the GRS calculation, the correlation between the GRS and the QTc-interval was no longer significant (P=0.15). The GRS NOS1AP , consisting of the 6 NOS1AP SNPs only, showed a similar/better fit than the GRS 22 (AIC GRS22 , 5199.2; AIC GRSNOS1AP , 5194.9). The GRS NOS1AP , varying from 0 to 11, was strongly associated with the QTc-interval with an increase of 3.5 (SE, 0.69) ms per point increase in GRS NOS1AP (P=4.2×10 -7 ; Figure 1B). Patients with GRS NOS1AP in the second, third, or fourth quartile had mean QTc-intervals that were 14 (SE, 5), 15 (SE, 5), and 23 (SE, 5) ms, respectively, longer than individuals in the lowest GRS NOS1AP quartile.

No associations were found between GRS 22 or GRS NOS1AP quartiles and the occurrence of a cardiac event, neither in the entire LQT2 patient sample (GRS22, P=0.192; GRS NOS1AP , P=0.119; Figure 1C and1D; Figure IIA and IIB in the Data Supplement) nor in the subset of patients with a QTc-interval <500 ms (data not shown). The results did not differ when only patients with documented VT or aborted cardiac arrest/VF were considered (data not shown). Although risk of a cardiac event did not increase linearly between quartiles, inspection of the data in Figure 1C and 1D and Figure II in the Data Supplement suggested that individuals in the quartile with the lowest GRS (Q1) might be protected as opposed to individuals in any of the other 3 quartiles (Q2-Q4). A statistical comparison of the cumulative event-free survival in these 2 groups, that is, Q1 versus Q2-Q4 uncovered a protective effect for patients in Q1 (GRS 22 : RR, 0.67; 95% CI, 0.46-0.98; P=0.041; GRSNOS1AP: RR, 0.69; 95% CI, 0.48-0.98; P=0.039; Figure III in the Data Supplement).

The QTc-interval was a strong predictor of cardiac events in patients with a QTc-interval in the highest quartile with a RR of 2.11 (95% CI, 1.35-3.30) when compared with patients in the lowest QTc-interval quartile (P=7.9×10 -7 ; Figure IV in the Data Supplement).

Discussion

Considerable interest exists in the identification of genetic factors that modulate disease severity in the LQTS as the identification of such factors is expected to contribute to the refinement of risk stratification in the individual patient. However, studies aimed at the identification of these genetic factors are scarce. [START_REF] Crotti | NOS1AP is a genetic modifier of the long-QT syndrome[END_REF][START_REF] Tomás | Polymorphisms in the NOS1AP gene modulate QT interval duration and risk of arrhythmias in the long QT syndrome[END_REF][START_REF] Duchatelet | Identification of a KCNQ1 polymorphism acting as a protective modifier against arrhythmic risk in long-QT syndrome[END_REF] In this study, we undertook 2 approaches to identify common genetic variants that modulate the QTc-interval and the occurrence of cardiac events in a large set of patients with LQT2. In 1 approach, we conducted an exploratory analysis of SNPs tagging common haplotypes within and around 18 candidate genes. In a second approach, we investigated the role of 22 independent SNPs from 14 chromosomal loci that were previously identified as modulators of the QTc-interval in GWAS studies conducted in the general population. Our analysis confirms and extends on previous observations that common genetic variants at the NOS1AP locus modulate disease severity in the LQTS. We identified multiple SNPs at this locus displaying markedly large effects on the QTc-interval among LQT2 patients and enrichment of the QTc-prolonging allele in LQT2 patients versus general population controls. In addition, 2 NOS1AP SNPs also appeared to affect the risk of cardiac events. Similar effects on the QTc-interval and risk of cardiac events were observed when the NOS1AP SNPs were considered in aggregate as a GRS. Our data also implicate for the first time common genetic variation at KCNQ1 as a risk factor for LQTS.

NOS1AP

GWAS conducted in the general population have consistently shown that SNPs at the NOS1AP locus exert the strongest influence of any of the common genetic variation known to influence the QTc-interval. [START_REF] Newton-Cheh | Common variants at ten loci influence QT interval duration in the QTGEN Study[END_REF][START_REF] Pfeufer | Common variants at ten loci modulate the QT interval duration in the QTSCD Study[END_REF][START_REF] Arking | A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization[END_REF][START_REF] Marroni | A genome-wide association scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project[END_REF] In this study, SNPs at NOS1AP have similarly emerged as the strongest modifiers of the QTc-interval and possible modifiers of cardiac events among LQT2 patients, both when considered as single variants, as well as when considered cumulatively as a GRS. Of the 6 independent signals (R 2 <0.4) that we tested at this locus, 3 (rs10494366, rs12143842, and rs2880058) displayed highly significant associations with the QTc-interval; 1 of these (rs10494366) also displayed a suggestive association with the occurrence of cardiac events. Besides these, rs12029454 was significantly enriched in LQT2 probands versus controls and displayed a suggestive association with both the QTc-interval and cardiac events.

Three studies have previously investigated the role of NOS1AP SNPs as modulators of disease severity in LQTS. One study investigated NOS1AP SNPs in 135 carriers of the founder mutation KCNQ1-A341V and identified rs4657139 (in high LD with our rs2880058) and rs16847548 (in high LD with our rs12143842) as modifiers of the QTcinterval and risk of cardiac events. [START_REF] Crotti | NOS1AP is a genetic modifier of the long-QT syndrome[END_REF] A second study analyzed NOS1AP SNPs in 901 patients with LQTS of different genetic subtypes (primarily LQT1-3). [START_REF] Tomás | Polymorphisms in the NOS1AP gene modulate QT interval duration and risk of arrhythmias in the long QT syndrome[END_REF] This study also identified rs4657139 and rs16847548 as modifiers of the QTc-interval and detected effects on cardiac events for rs4657139 and rs10494366. A third study tested NOS1AP SNPs in 112 phenotypically discordant (1 clinically affected and 1 not) patient duos carrying the same mutation in either KCNQ1 or KCNH2 and identified a suggestive association between rs12029454 and the QTc-interval. [START_REF] Duchatelet | Identification of a KCNQ1 polymorphism acting as a protective modifier against arrhythmic risk in long-QT syndrome[END_REF] Keeping in mind that these 3 studies and ours are for several reasons not directly comparable (eg, different sizes of the patient study sample which affects the statistical power, different study design, patients studied harbor mutation in different LQTS gene, and the fact that not all studies investigated every independent signal linked thus far to the QTc-interval in the general population), in aggregate their findings allow us to start drawing some conclusions about the role of NOS1AP SNPs in modulation of disease severity in the LQTS. It is obviously clear that common genetic variation at this locus also modulates the QTc-interval in patients with LQTS, with some individual SNPs (such as rs12143842 and rs2880058) now displaying highly convincing associations with the QTc-interval in the majority of the studies. Another observation emerging from these studies is that the effect of NOS1AP SNPs on the QTc-interval is larger in patients with LQTS when compared with that observed in the general population in previous GWAS. In our analysis, for example, each T-allele at rs12143842 increased the QTc-interval by an average of 13.2 ms, whereas its effect in a large sample of the general population was of 3.15 ms. [START_REF] Newton-Cheh | Common variants at ten loci influence QT interval duration in the QTGEN Study[END_REF] We also detected similarly large effects for rs10494366 and rs2880058 (see Table 4). Effect sizes of 7 and 8 ms were observed for rs4657139 and rs16847548, respectively, in the study of Tomás et al. [START_REF] Tomás | Polymorphisms in the NOS1AP gene modulate QT interval duration and risk of arrhythmias in the long QT syndrome[END_REF] The larger effect sizes among patients with LQTS are likely because of the sensitized genetic background of these patients: they are all carriers of a rare genetic variant with a putatively large deleterious effect on repolarization reserve which in turn may make the repolarization process more permissive to the effect of common genetic variation. This observation brings forward the possibility that further genetic studies in patients with LQTS may uncover QTcmodulating genetic variants that would otherwise remain unidentified in GWAS conducted in the general population because of the small effect size in the latter. However, although strong associations have been laid in patients with LQTS between NOS1AP SNPs and the QTc-interval, this cannot be said of the effect of the same SNPs on the risk of cardiac events. In our study, although 2 of 6 NOS1AP SNPs we tested were nominally associated with risk of cardiac events, none, even those displaying robust effects on the QTc-interval, displayed association P values for cardiac events that exceeded the Bonferroni-corrected threshold for multiple testing. Notwithstanding, considering the fact that QTc-modulating NOS1AP SNPs have already been implicated in modulation of risk for cardiac events in 2 studies, [START_REF] Crotti | NOS1AP is a genetic modifier of the long-QT syndrome[END_REF][START_REF] Tomás | Polymorphisms in the NOS1AP gene modulate QT interval duration and risk of arrhythmias in the long QT syndrome[END_REF] one could argue that the Bonferroni correction we applied is too harsh. Of the 2 SNPs that showed a nominal association with cardiac events in our study, rs10494366 was previously associated with risk of cardiac events by Tomás et al. [START_REF] Tomás | Polymorphisms in the NOS1AP gene modulate QT interval duration and risk of arrhythmias in the long QT syndrome[END_REF] NOS1AP encodes a nitric oxide synthase adapter protein. Functional studies have suggested that it regulates action potential duration of cardiomyocytes via calcium and potassium currents. [START_REF] Chang | CAPON modulates cardiac repolarization via neuronal nitric oxide synthase signaling in the heart[END_REF] The NOS1AP SNPs that affect the QTc-interval are located in the noncoding regions of the gene, and if their effect on the QTc-interval indeed occurs through NOS1AP, it is then likely that this occurs through modulation of the level of NOS1AP transcript abundance and consequently protein levels.

SNPs at Other Loci

In our analysis, of the 22 SNPs from GWAS for quantitative effects on the QTc-interval, besides the SNPs at NOS1AP discussed above, no additional SNPs passed the Bonferronicorrected significance threshold. Two SNPs (rs2074238 at KCNQ1 and rs17779747 at KCNJ2), however, displayed a nominal association with a direction of effect consistent with that found previously in the general population. [START_REF] Newton-Cheh | Common variants at ten loci influence QT interval duration in the QTGEN Study[END_REF][START_REF] Pfeufer | Common variants at ten loci modulate the QT interval duration in the QTSCD Study[END_REF] The T-allele at rs2074238 is associated with a shorter QTc-interval. Of note, this SNP was recently reported to be associated with a shorter QTc-interval and decreased risk of symptoms in the study of Duchatelet et al. [START_REF] Duchatelet | Identification of a KCNQ1 polymorphism acting as a protective modifier against arrhythmic risk in long-QT syndrome[END_REF] We detected no effect of this SNP on cardiac events in the patients with LQT2 studied here. The study by Duchatelet et al, however, detected larger effects for this SNP, both on the QTcinterval and cardiac events, when compared with our study, and although our study was sufficiently powered to detect those effects, it was underpowered to uncover an association with the small effects we detected.

Besides our quantitative trait analysis of the 22 SNPs from GWAS with the QTc-interval, we additionally investigated their association with LQTS status in a case-control association analysis of the LQT2 probands versus individuals from the general population (Table 5). This additional analysis uncovered 2 significant associations with the expected direction of effect (ie, the QTc-interval prolonging allele being enriched among the cases versus the controls) highlighting the potential use of this approach as recently also demonstrated by us for the Brugada syndrome. [START_REF] Bezzina | Common variants at SCN5A-SCN10A and HEY2 are associated with Brugada syndrome, a rare disease with high risk of sudden cardiac death[END_REF] Our current analysis, for the first time, linked rs12576239 at KCNQ1 with susceptibility to the LQTS.

Genetic Risk Scores

We considered for the first time the combined effect of all 22 SNPs linked to the QTcinterval by constructing a GRS for each individual (GRS 22 ) and relating it to the QTcinterval and occur-rence of cardiac events. We demonstrated a significant positive linear relationship between GRS 22 and the QTc-interval. The correlation between GRS 22 and the QTc-interval, however, seems to be largely driven by the effect of the 6 NOS1AP SNPs as the association between the GRS and the QTc-interval did not remain significant when these SNPs were removed from the GRS calculation. A GRS based on the 6 NOS1AP SNPs only (GRSNOS1AP) showed a similar predictive value for QTc to that of the GRS 22 . No significant (linear) relation was found between either GRS 22 or GRSNOS1AP and the risk of cardiac events, but patients with scores in the first quartile had significantly less events than the patients in the other 3 quartiles combined. The latter observation will require further investigation in additional patients.

Effect on Cardiac Events

Our single SNP analysis did not uncover significant associations between any of the investigated SNPs and the occurrence of cardiac events. Furthermore, our GRS analyses did not reveal a linear relationship between the GRS and risk of cardiac events. However, when one considers the fact that the SNPs tested are candidates with a strong a priori probability of being involved, one could argue that in the single SNP analysis, our correction for multiple testing might be too conservative. However, one can posit that while the QTcinterval is governed by an appreciable genetic component, the precipitation of arrhythmias in the LQTS may be heavily influenced by other factors, such as environmental triggers, that vary largely across patients. In any case, the low relative risk associated with these variants currently precludes their immediate clinical use for arrhythmia risk stratification.

Many SNPs previously shown to affect the QTc-interval in the general population were silent with respect to their effect on the QTc-interval in our analysis. Some investigators have argued that the effect of SNPs in patients with LQTS is dwarfed by the large effect of the primary mutation (so-called ceiling effect). [START_REF] Crotti | NOS1AP is a genetic modifier of the long-QT syndrome[END_REF] Although this seems a plausible explanation, it is unclear why NOS1AP SNPs are not affected by this phenomenon. One possibility could be the larger effect size among patients with LQTS of NOS1AP SNPs when compared with the others, which would argue for investigation of the nonassociating SNPs in larger patient sets.

Candidate Gene Study

Besides SNPs from QTc-interval GWAS, we also systematically investigated the effect of haplotype-tagging SNPs in 18 candidate genes in LQT2 patient Set 1 (Table 3). Besides rs16847548 in NOS1AP, this analysis uncovered 2 associations, at CASQ2 (rs1935778) and KCNH2 (rs956642), respectively. Neither of the latter 2 SNPs were, however, validated in patient Set 2. Although these 2 SNPs may merit further investigation in additional samples, these signals may represent a false-positive association. One could argue that our correction for multiple testing in Set 1 may be too stringent and that true associations may exist above the Bonferroni-corrected P value threshold we used as the 18 genes were selected based on their high a priori probability for modulating the QTc-interval. Nevertheless, we preferred to apply stringent criteria for the most reliable findings with the current data.

Limitations

In this study, we limited genetic heterogeneity by considering only LQTS patients with a KCNH2 genetic defect. Nevertheless, although we accounted for this in the statistical analysis, some confounding may remain as a consequence of the variability in the severity of the haploinsufficient defect and the biophysical defect associated with the different KCNH2 mutations among the patients. Considering the fact that the LQTS is a rare disorder, we have here studied a substantial number of patients. However, the patient set may yet be considered modest for the study of common genetic variants with small effects. The effect of SNPs that we describe here may be different in the setting of other LQTS genetic subtypes. Furthermore, SNP effects may be allele-dependent, as we previously demonstrated for SNPs in the 3′ untranslated region of the KCNQ1 gene. [START_REF] Amin | Variants in the 3' untranslated region of the KCNQ1-encoded Kv7.1 potassium channel modify disease severity in patients with type 1 long QT syndrome in an allele-specific manner[END_REF] The design of this study precludes the analysis for such effects.

Conclusions

Our comprehensive analysis demonstrates that among SNPs previously linked to the QTcinterval in the general population, NOS1AP SNPs are the strongest modulators of the QTcinterval in patients with LQT2. The effect of these SNPs in patients with LQT2 is markedly larger than that observed in the general population. Our study also uncovered common genetic variation at KCNQ1 as a risk factor for LQTS.

CLINICAL PERSPECTIVE

The congenital long-QT syndrome is a heritable disorder associated with QTc-interval prolongation on the ECG and an increased risk of sudden cardiac death. Although gene discovery in the disorder has had a considerable impact, variability in disease severity among mutation carriers still hinders patient care. Although the inheritance of additional genetic factors is expected to contribute, the identity of these remains largely unknown. The identification of such genetic modifiers of disease severity may contribute to refinement of risk stratification in these patients. We here investigated the role of common genetic variants in the form of single-nucleotide polymorphisms (SNPs) in a large set of patients with long-QT syndrome caused by mutations in the KCNH2 gene. In the first approach, we conducted a comprehensive analysis of SNPs in 18 candidate genes. In a second approach, we investigated the effect of SNPs that have been previously associated with the QTc-interval in genome-wide association studies conducted in the general population. Our analysis identified SNPs at the NOS1AP gene locus that, in isolation or in aggregate, displayed relatively large effects on the QTcinterval. NOS1AP SNPs also displayed a trend for an association with cardiac events. Our observations thus confirm and extend on previous findings that common genetic variants at the NOS1AP locus modulate disease severity in the long-QT syndrome. As knowledge about the identity of modulatory genetic factors such as those identified here continues to increase, it is hoped that these may be implemented clinically for improved risk stratification in long-QT syndrome. A and B, Association between the genetic risk scores, GRS 22 and GRS NOS1AP , and the QTcinterval in LQT2 patient Set 1 and Set 2 combined (n=639; GRS 22 : P=4.3×10 -6 ; GRS NOS1AP : P=4.2×10 -7 ). C and D, Analysis of the relation between the genetic risk scores, GRS 22 and GRS NOS1AP , and event-free survival in LQT2 patient Set 1 and Set 2 combined (n=639; GRS 22 : P=0.192, GRS NOS1AP : P=0.119). Q1 is the quartile with the lowest genetic risk score.

Figure.

  Figure.

Table 1

 1 Characteristics of the Patients With LQT2 Studied

LQT2 Patient Set 1, n=353 LQT2 Patient Set 2, n=286 LQT2 Patient Set 1+Set 2, n=639

	Female	208 (59%)	157 (55%)	365 (57%)
	Proband	86 (24%)	88 (31%)	174 (27%)
	Median (IQR) age at ECG, y	30 (28)	27 (31)	29 (30)
	β-Blocker use at time of ECG	60 (17%)	44 (15%)	104 (16%)
	Mean (±SD) QTc-interval, ms	467±43	463±44	465±44
	Cardiac event	126 (36%)	76 (27%)	202 (32%)
	Median (IQR) follow-up, y

Table 2

 2 Effect of KCNH2 Mutation Type and Location on the QTc-Interval

	Mutation Type and Location	Patient Set 1,	QTc-Interval (ms)	Patient Set 1+Set	QTc-Interval (ms)
		n=353		2, n=639
	Nonsense, frameshift, large deletions and insertions,	150 (42%)	466±40	277 (43%)	463±40
	all locations
	Missense, N terminus	77 (22%)	460±49	150 (23%)	458±47
	Missense, transmembrane S1-S4	11 (3%)	522±48	25 (4%)	496±55
	Missense, transmembrane S5-loop-S6	86 (24%)	474±40	132 (20%)	474±40
	Missense, C terminus	29 (8%)	455±32	55 (9%)	462±46
	QTc indicates corrected QT.

Table 3

 3 SNPs From the Candidate Gene Study That Were Associated With the QTc-Interval

Effect on QTc-Interval in Set 1 * (n=353) Effect on QTc-Interval in Set 2 * (n=286) Effect on QTc-Interval in Set 1+Set 2 * (n=639) SNP Chr Candidate Gene Major Allele Minor Allele MAF

	P Value	4.8×10 -7	0.001	0.003
	β±SE (ms)	13.2±2.6	7.6±2.4	-7.1±2.4
	P Value ‡	0.007	0.894	0.942
	β±SE (ms)	10.1±3.8	0.5±3.6	0.3±3.7
	β±SE (ms) P Value †	rs16847548 1 N0S1AP A G 0.255 16.9±3.5 1.0×10 -6	rs1935778 1 CASQ2 A G 0.419 12.4±2.9 2.1×10 -5	rs956642 7 KCNH2 A G 0.405 -14.8±3.1 1.3×10 -6	Chr indicates chromosome; MAF, minor allele frequency; QTc, corrected QT; and SNPs, single-nucleotide polymorphisms.	* The coded allele is the minor allele in all cases.	† SNPs passing the discovery-phase Bonferroni-corrected P-value threshold (P<4.2×10 -5

) are listed. ‡

The P value for the SNP passing the replication-phase Bonferroni-corrected P value threshold (P<0.016) is depicted in bold.

Table 4

 4 Effects of SNPs Previously Associated With the QTc-lnterval in the General Population, in LQT2 Sets 1 and 2 Combined † P values for SNPs passing the Bonferroni-corrected P-value threshold (P<2.3×10 -3 ) are depicted in bold. ‡ In LD with rs16847548 from the candidate gene study (R 2 =0.88); see Table3.

	Effect on QTc-interval * (n=639)

* The coded allele is the minor allele in all cases. The direction of effect found in GWAS conducted in the general population is denoted in parenthesis: ↑, increase in QTc-interval; and ↓, decrease in QTc-interval.

Circ Cardiovasc Genet. Author manuscript; available in PMC 2016 February 29.

Table 5

 5 Case-Control Analysis of SNPs Previously Associated With the QTc-lnterval in the General Population, in Probands From LQT2 Sets 1 and 2 Combined

	SNP From GWAS Closest Gene Coded Allele	Frequency Coded Allele (Cases/Controls)	OR (95% CI)	P Value	Concordance With SNP Effect on QTc-Interval
	rs10494366	N0S1AP	G	0.43/0.34	1.35 (1.03-1.76)	0.028	Yes
	rs12029454	N0S1AP	A	0.21/0.13	1.85 (1.32-2.59) 0.0003 *	Yes
	rs12143842	N0S1AP	T	0.31/0.23	1.49 (1.13-1.96)	0.005	Yes
	rs16857031	N0S1AP	G	0.17/0.14	1.25 (0.89-1.76)	0.20
	rs2880058	N0S1AP	G	0.40/0.32	1.39 (1.08-1.80)	0.014	Yes
	rs4657178	N0S1AP	T	0.31/0.23	1.45 (1.10-1.93)	0.009	Yes
	rs10919071	ATP1B1	G	0.12/0.11	1.20 (0.82-1.77)	0.35
	rs37062	CN0T1	G	0.25/0.24	1.04 (0.79-1.37)	0.77
	rs1805128	KCNE1	A	0.04/0.02	1.95 (1.06-3.57)	0.03	Yes
	rs2968863	KCNH2	A	0.22/0.24	0.89 (0.65-1.21)	0.45
	rs4725982	KCNH2	T	0.20/0.20	1.00 (0.74-1.34)	0.98
	rs12576239	KCNQ1	T	0.20/0.12	1.84 (1.31-2.60) 0.0005 *	Yes
	rs2074238	KCNQ1	T	0.06/0.08	0.77 (0.49-1.21)	0.26
	rs2074518	LIG3	A	0.46/0.46	0.97 (0.75-1.25)	0.82
	rs8049607	LITAF	T	0.46/0.53	0.77 (0.60-0.98)	0.03	Yes
	rs846111	RNF207	G	0.28/0.31	0.86 (0.65-1.13)	0.27
	rs12053903	SCN5A	C	0.32/0.35	0.88 (0.68-1.14)	0.31
	rs3825214	TBX5	G	0.22/0.21	1.09 (0.81-1.47)	0.58
	rs17779747	KCNJ2	T	0.36/0.33	1.14 (0.87-1.50)	0.34
	rs2478333	Intergenic	A	0.41/0.33	1.43 (1.11-1.86)	0.007	Yes
	rs11970286	PLN	T	0.46/0.45	1.08 (0.83-1.40)	0.56
	rs12210810	PLN	C	0.03/0.06	0.56 (0.29-1.10)	0.09

CI indicates confidence interval; GWAS, genome-wide association study; LQT2, long-QT 2; OR, odds ratio; QTc, corrected QT; and SNPs, singlenucleotide polymorphisms.

Table 6

 6 Effect of SNPs on Event-Free Survival in Sets 1 and 2 Combined

Effect on Event-Free Survival * Effect on Event-Free Survival in Patients With QTc<500 ms *

	SNP	Closest Gene Major Allele Minor Allele RR (95% CI)	P Value †	RR (95% CI)	P Value †
	rs10494366	N0S1AP	T	G	1.30 (1.04-1.61)	0.020	1.35 (1.04-1.77)	0.027
	rs12029454	N0S1AP	G	A	1.37 (1.08-1.74)	0.011	1.45 (1.07-1.95)	0.015
	rs12143842	N0S1AP	C	T	1.14 (0.91-1.42)	0.246	1.13 (0.86-1.48)	0.373
	rs16857031	N0S1AP	C	G	1.03 (0.78-1.35)	0.855	0.93 (0.66-1.32)	0.694
	rs4657178	N0S1AP	C	T	1.22 (0.97-1.53)	0.08	1.32 (1.00-1.74)	0.06
	rs2880058	N0S1AP	A	G	1.16 (0.94-1.44)	0.167	1.22 (0.95-1.57)	0.118
	rs1805128	KCNE1	G	A	1.33 (1.01-1.76)	0.044	1.29 ((0.89-1.85)	0.174
	rs2074238	KCNQ1	C	T	0.83 (0.53-1.31)	0.422	0.78 (0.46-1.32)	0.356
	rs12576239	KCNQ1	C	T	1.13 (0.86-1.48)	0.38	1.02 (0.74-1.42)	0.89
	rs17779747	KCNJ2	G	T	1.14 (0.92-1.42)	0.221	1.21 (0.94-1.55)	0.137
	rs8049607	LITAF	C	T	0.91 (0.74-1.14)	0.42	0.93 (0.73-1.20)	0.59
	rs2478333	Intergenic	C	A	1.04 (0.83-1.31)	0.73	1.05 (0.82-1.34)	0.70

CI indicates confidence interval; GWAS, genome-wide association study; LQT2, long-QT 2; QTc, corrected QT; RR, relative risk; and SNPs, single-nucleotide polymorphisms.

* The coded allele is the minor allele. † P values for nominally associating SNPs are displayed in italics.

Circ Cardiovasc Genet. Author manuscript; available in PMC 2016 February 29.

Circ Cardiovasc Genet. Author manuscript; available in PMC 2016 February 29.

(30)

(33) 26 (32) IQR indicates interquartile range; LQT2, long-QT 2; and QTc, corrected QT. Circ Cardiovasc Genet. Author manuscript; available in PMC 2016 February 29.

Acknowledgments

Sources of Funding This study was supported by a grant from Fondation Leducq (Alliance Against Sudden Cardiac Death, 05 CVD 01). We acknowledge the support from the Netherlands CardioVascular Research Initiative (CVON-PREDICT project): the Dutch Heart Foundation, Dutch Federation of University Medical Centres, the Netherlands Organisation for Health Research and Development, and the Royal Netherlands Academy of Sciences. Dr Schulze-Bahr is supported by an IZKF (Interdisciplinary Centre for Clinical Research) grant. Dr Barc was supported by the Netherlands Heart Institute (ICIN). This study makes use of data generated by the GoNL Funding for GoNL was provided by the Netherlands Organization for Scientific Research under award number 184021007, dated July 9, 2009 and made available as a Rainbow Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL). Samples where contributed by LifeLines (http://lifelines.nl/lifelinesresearch/general), The Leiden Longevity Study (http://www.healthy-ageing.nl; http://www.langleven.net), The Netherlands Twin Registry (NTR: http://www.tweelingenregister.org), The Rotterdam studies, (http:// www.erasmus-epidemiology.nl/rotterdamstudy), and the Genetic Research in Isolated Populations program (http:// www.epib.nl/research/geneticepi/research.html#gip). The sequencing was performed in collaboration with the Beijing Institute for Genomics.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Author Manuscript

Kolder et al.