OUP user menu

The non-synonymous coding IKr-channel variant KCNH2-K897T is associated with atrial fibrillation: results from a systematic candidate gene-based analysis of KCNH2 (HERG)

Moritz F. Sinner, Arne Pfeufer, Mahmut Akyol, Britt-Maria Beckmann, Martin Hinterseer, Annette Wacker, Siegfried Perz, Wiebke Sauter, Thomas Illig, Michael Näbauer, Claus Schmitt, H.-Erich Wichmann, Albert Schömig, Gerhard Steinbeck, Thomas Meitinger, Stefan Kääb
DOI: http://dx.doi.org/10.1093/eurheartj/ehm619 907-914 First published online: 25 January 2008

Abstract

Aims Atrial fibrillation (AF) is the most frequent arrhythmia in humans. Rare familial forms exist. Recent evidence indicates a genetic susceptibility to common forms of AF. The α-subunit of the myocardial IKr-channel, encoded by the KCNH2 gene, is crucial to ventricular and atrial repolarization. Patients with mutations in KCNH2 present with higher incidence of AF. Common variants in KCNH2 have been shown to modify ventricular repolarization. We intended to investigate, whether such variants may also modulate atrial repolarization and predispose to AF.

Methods and results In a two-stage association study we analysed 1207 AF-cases and 2475 controls. In stage I 40 tagSNPs (single nucleotide polymorphisms) from the KCNH2 genomic region were genotyped in 671 AF-cases and 694 controls. Of five associated variants, the common K897-allele of the KCNH2-K897T variant was replicated in n = 536 independent AF cases and n = 1781 controls in stage II [overall odds ratio 1.25, 95% confidence interval 1.11–1.41, P = 0.00033]. This association remained significant after adjustment for gender and age.

Conclusion We report a genetic association finding including positive replication between the K897-allele and higher incidence of AF. This provides a molecular correlate for complex genetic predispositions to AF. The consequences of the K897T variant at the atrial level will require further functional investigations.

  • Atrial fibrillation
  • KCNH2
  • Gene
  • Single nucleotide polymorphism
  • Potassium channel
See page 843 for the editorial comment on this article (doi:10.1093/eurheartj/ehn066)

Introduction

Atrial fibrillation (AF) is the most common arrhythmia in humans. It has a prevalence of ∼1% in the general population which is increasing significantly with age.1

The etiology of AF is heterogeneous. Several predisposing conditions have been identified including hypertension, hyperthyroidism, coronary artery disease, congestive heart failure, mitral valve disease and both dilative and hypertrophic cardiomyopathy.2 Besides common risk factors, monogenic forms of the arrhythmia are known to be caused by rare mutations predominantly in genes encoding ion channels and transmitted as mendelian traits. Without providing a comprehensive overview, causal mutations have been identified in the cardiac potassium channel genes KCNQ1, KCNE2, and KCNA5.36 Furthermore unidentified genes map to genetic loci on chromosomes 5, 6, and 10, respectively.710 Recently somatic mutations have also been identified in the GJA5 gene in atrial tissue from a subset of patients with idiopathic AF.11 The gene encodes the gap junction protein connexin 40, which is involved in electrical conduction in the myocardium. Rare mutations in KCNH2 have been linked both to long- and short-QT-syndrome, the latter demonstrating a higher incidence of AF.12 Genetic as well as a multitude of experimental data highlight the role of altered atrial repolarization as a key substrate in AF initiation and maintenance.13

Recently evidence is increasing for familial clustering of AF indicating that genetic components are relevant also in common forms of AF. Results from the Framingham Heart Offspring Study14 and a large investigation on the Icelandic population15 indicate a relative risk of up to 3.2 for AF if one parent was affected before age 75 and up to 4.7 if one parent had onset of AF before age 60.

Until today only a small number of common variants have been associated with AF. Among these are single nucleotide polymorphisms (SNPs) in the KCNE1, KCNE5, and SCN5A genes as well as in those coding for Connexin 40, Angiotensinogen, Angiotensin Converting Enzyme and G-protein β3. However, none of these associations have been replicated reliably.1621 Recently, the first genome wide association study for AF identified three novel SNPs significantly associated with this arrhythmia. These SNPs are located outside commonly known genes, thus their pathophysiological background remains to be clarified.22

In several recent studies SNPs in KCNH2 and other genes have been associated with the QT interval, an ECG surrogate for cardiac repolarization.23,24 As altered atrial repolarization is a hallmark of AF we sought to determine whether common variants of this gene are as well a modifier of common AF.

Methods

Study population

Our study population consisted of 1207 AF-cases and 2475 controls. Inclusion criteria for cases were signs and symptoms of AF confirmed by a cardiologist based on at least one 12-lead resting ECG upon hospital admission. Patients showing signs of moderate to severe congestive heart failure greater than NYHA II, any moderate to severe valve disease greater than grade II on a scale of I–IV or suffered from hyperthyroidism were considered to have a high risk of secondary AF and were excluded from the study. AF-cases were recruited from the Deutsches Herzzentrum München (AF-DHM, n = 671), Medizinische Klinik und Poliklinik I of the Ludwig-Maximilians University Hospital Grosshadern, Munich (AF-GRH, n = 192) and the nationwide German Competence Network for Atrial Fibrillation (AFNET, n = 344). This network’s main focus is on establishing a national registry on AF to perform both epidemiological and socio-economical as well as clinical studies and basic research. Blood samples were drawn after informed consent had been obtained. All studies involving humans were performed according to the declarations of Helsinki and Somerset West and were approved by the local medical ethics committees.

Control population

All control probands were from a population based epidemiological survey of persons living in or near the city of Augsburg, Southern Germany (KORA S4), conducted between 1999 and 2001. The survey population consisted of residents of German nationality born between July 1, 1925 and June 30, 1975 identified through the registration office. A sample of 6640 subjects was drawn with ten strata of equal size according to gender and age and 4261 individuals (66.8%) agreed to participate in the survey. Participants were of German descent with very few exceptions (>99.5%).25 During 2002 and 2003 we reinvestigated a subsurvey of 880 persons specifically for cardiovascular diseases (KORA-KMC).24 Exclusion criteria for control probands were reported history of AF, signs or symptoms of AF on physical examination or absence of sinus rhythm upon 12-lead resting ECG that all probands received. According to exclusion criteria, 694 individuals from this KMC subsurvey were used as controls in the screening phase and 1781 different individuals from the KORA S4 survey were used as controls in the confirmation phase. In the latter, AF was ruled out in the same way as in the controls of the screening phase. We selected a representative 50% sample of the entire KORA S4 survey, maintaining the age- and sex-distribution and excluding those individuals overlapping with the KMC subsurvey.

Association study design

We employed a two-stage association study design with a screening stage (stage I) and subsequent analysis of associated variants in an independent confirmation stage (stage II). In the screening stage 671 AF-DHM patients and 694 KMC control probands were used. In the confirmation stage 536 cases from AF-GRH / AFNET and 1781 independent controls from KORA S4 were analysed only for positive genetic associations from stage I. Detailed information on the population under investigation can be derived from Table 1.

View this table:
Table 1

Population characteristics

CasesCasesControlsControls
Sample nameAF-DHMAF-GRH / AFNETKMCKORA-S4
RoleScreeningConfirmationScreeningConfirmation
Sample size (n)6715366941781
Male (%)69.775.049.948.5
Age (years)69.1 ± 10.859.7 ± 11.157.7 ± 12.447.6 ± 13.4
Age-range (years)33–9518–9425–7425–74
  • In the case samples the older age-group and males were overrepresented according to the distribution of atrial fibrillation (AF) in the population. This age-distribution was imitated in the KMC sample. The KORA-S4 study was designed to have an equal distribution of sexes.

Single nucleotide polymorphism selection

A genomic region of 150 kb (chr 7: 150 030 000–150 180 000; hg17) was selected (for ref. please see http://www.ncbi.nlm.nih.gov/omim/ and26) which contains the entire KCNH2 gene coding for the pore-forming α-subunit of the myocardial IKr rapid delayed rectifier potassium channel. Haplotype tagging SNPs (htSNPs) were identified based on the data of HapMap phase II (release #20)27 using the software Tagger28 and the following tagging criteria: pairwise tagging of the HapMap CEU population, r2-cut-off: 0.8, minor allele frequency (MAF): 0.05. Since non-synonymous coding variants bear a higher a priori probability for positive genetic associations, all regional nsSNPs with a MAF ≥ 0.05 were selected irrespective of being htSNPs. In particular these were the variants rs1805123 (KCNH2 K897T), rs1799983 (NOS3 E298D) and rs17545756 (ABCB8 R304C), the latter two being also htSNPs. The genomic region and the pairwise linkage-disequilibrium (LD) pattern between the variants are shown in Figure 1.

Figure 1

The top line displays the genomic 150 kb region under investigation on chromosome 7. The gene KCNH2 and its exons and introns are shown below. The little red triangles below the ruler indicate all single nucleotide polymorphisms (SNPs) genotyped in the region within the scope of the HapMap project. The 40 SNPs genotyped in our study are written vertically with the thin lines indicating the map location within the genome. The large triangular structure in the figure’s main body represents the linkage disequilibrium (LD) between the variants measured as D-prime based on our genotyping results. The SNP rs1805123 is highlighted for better orientation.

Deoxyribonucleic acid extraction and genotyping

Deoxyribonucleic acid (DNA) was extracted from ethylene diamine tetraacetic acid (EDTA) anticoagulated blood using either a commercially available kit (Qiagen, Hilden, Germany) or a salting out procedure.29 SNPs for both stages were genotyped using polymerase chain reaction (PCR), iPlex single base primer extension and MALDI-TOF mass spectrometry in a 384-well-format (Sequenom, San Diego, CA, USA) as described elsewhere.24 Sequences of PCR- and extension-primers can be found in the online Supplementary material, Table S1. LD measures were determined with the Haploview software.30 Haplotype boundaries were defined as previously described.31

Statistical analysis and power calculation

Deviation of SNP genotypes from the Hardy–Weinberg-equilibrium was calculated according to Ref.32 SNPs were tested for genotype-phenotype-associations using the STATA SE 8.0 statistical package. We calculated the associations of SNP-genotypes to AF under the log-additive model of inheritance. Without correction for multiple testing after stage I, we genotyped all SNPs associated with AF in this stage (P < 0.05) in the confirmation stage II. Replicated associations were considered truly associated with the phenotype. To obtain the most reliable estimation, the effect size of confirmed associations was calculated in the entire study population combining samples from stages I and II. In each stage and the overall analysis we additionally calculated a logistic regression model to adjust for the covariates sex and age.

Pre-analysis power calculations based on the CaTS power calculator33 applying a log-additive model of inheritance using 671 cases and 694 controls of stage I (49.2% cases) and a MAF of 0.05 revealed a statistical power of 78% to detect a variant conferring an odds ratio (OR) as low as 1.25. The lowest MAF of the variants analysed in stage II was 0.235. Our power to redetect a variant conferring this OR in the confirmation analysis was 93%.

The population attributable risk of associated variants was calculated as the attributable risk per allele and the allele frequency in the general population.

Results

In stage I all 40 selected SNPs were successfully genotyped with an average call rate of 98.6% and an average MAF of 26.2%. All SNP-genotypes of the control population were in Hardy–Weinberg-equilibrium. Five SNPs showed an association with AF below the stage I cutoff level of P = 0.05 (Table 2). Only rs1805123 (K897T) remained positively associated to AF in stage II (P = 0.0164) (Table 2). Effect size estimation in the combined set determined an OR of 1.25 (95% CI 1.11–1.41, P = 0.00033), indicating that the common K897-allele predisposes to AF while the rare T897-allele is protective (Table 3). This SNP is located in the coding sequence of exon 11 and exchanges the nucleic acid A with C at position 2690 of the cDNA (c.2690 A>C) and the amino acid lysine with threonine at position 897 of the protein (p.K897T), respectively.

View this table:
Table 2

Genotype-phenotype association: screening and confirmation results

SNPPositionAlleleLocalizationScreening stage IConfirmation stage IICombined
rs1805123150,083,182K/TKCNH2-K897TP = 0.0279, OR 1.23 (1.02–1.48)P = 0.0164, OR 1.23 (1.04–1.45)P = 0.00033, OR 1.25 (1.11–1.41)
rs4725984150,106,162C/TKCNH2-IntronP = 0.0409, OR 1.18 (1.01–1.38)P = 0.8213, OR 0.98 (0.86–1.13)
rs2373962150,118,625C/GNOS3-PromotorP = 0.0467, OR 0.85 (0.73–1.00)P = 0.7153, OR 1.03 (0.89–1.18)
rs6951150150,119,562C/TNOS3-PromotorP = 0.0424, OR 0.85 (0.72–0.99)P = 0.6001, OR 1.04 (0.90–1.19)
rs1799983150,133,759E/DNOS3-E289DP = 0.0224, OR 1.21 (1.03–1.43)P = 0.6601, OR 1.03 (0.90–1.19)
  • All associations calculated assuming a log-additive genetic model. P-values ≤ 0.05 were considered significant. Genomic position as referred to HapMap release 20. Alleles are nucleic bases or allelic amino acid where applicable. Genomic localization as indicated by the single nucleotide polymorphism (SNP) per database. Odds ratios (ORs) are provided including 95% confidence interval.

View this table:
Table 3

Combined genotype distribution

GenotypeCasesControls
K/K747 (65.0%)1414 (58.9%)
K/T352 (30.6%) 842 (35.1%)
T/T 50 (4.4%) 143 (6.0%)
Sum11492399
Allele frequenciesK: 80.3%, T: 19.7%K: 76.5%, T: 23.5%
Effect sizeOdds ratioP-value
Log-additive1.25 (1.11–1.41)0.00033
  • Genotype-distribution of the atrial fibrillation (AF)-associated variant KCNH2-K897T for the combined study population. Numbers represent successfully genotyped samples, relative distribution in brackets. Effect size of the association calculated for the log-additive genetic model. Odds ratio provided including 95% confidence interval.

After multivariable adjustment for covariates gender and age both at stages I and II as well in the combined analysis the K897T-genotype association remained significant as an independent factor (stage I: OR = 1.41 (1.14–1.75), P = 0.0015; stage II: OR = 1.22 (1.01–1.47), P = 0.0454; combined: OR = 1.30 (1.13–1.50), P = 0.0002) (Table 4).

View this table:
Table 4

Multivariable logistic regression model adjusting for gender and age

CovariateRisk factorOR (95% CI)P-value
CombinedKCNH2-K897TK897–allele1.30 (1.13–1.50)0.0002
Gendermale2.70 (2.27–3.21)<0.0001
AgePer year of age1.09 (1.08–1.09)<0.0001
  • Atrial fibrillation (AF) remains an independent factor after adjustment for gender and age in a multivariable logistic regression model. Odds ratios (OR) including 95% confidence intervals (CIs) are given per K897-allele, per year of age increase, and for male gender.

The population attributable risk conferred by the K897 allele (allele frequency = 0.765, risk increase = 0.25 per allele) was estimated to be about 19%.

Discussion

Association studies are known to be affected by a high rate of false positive errors (type 1), especially when the number of samples is small.34 Studying more than 1200 AF-cases we could show an association between the common non-synonymous coding variant K897T of the KCNH2-gene and AF. Recently three SNPs associated with AF were identified and confirmed by a genome wide association study.22 To our knowledge this is the first report of a genetic variant predisposing to AF that was identified by a candidate-gene based approach based on pathophysiological considerations that was significant in a large two-stage association study.

Retrospectively, including all successfully genotyped 1149 cases and 2399 controls, our investigation was powered with 99% to detect the presented association underlying the log-additive model of inheritance (Table 3).

As the AF-cases have been recruited across Germany, especially in the case of the AFNET samples, population stratification may lead to an inflated or false positive association result.35,36 Population stratification has been investigated across Germany according to the Genomic controls method.37 The upper limit of the 95% confidence interval (CI) of the lambda correction factor comparing two German samples was estimated to be λ = 1.372.38 No lambda values exceeding λ ≥ 1.8 have been observed in this study. Given these data, our association finding is still highly significant even taking the population structure across Germany into account. Finally it is worth noting that the major part of cases and all of the controls were recruited in closely neighbouring cities of Munich and Augsburg and thus from a very narrow geographic region.

The atrial excitation depends on precisely balanced ion currents. Imbalances in this system can lead to arrhythmias. Under physiologic conditions the sinus node functions as the pacemaker, however, in principle, cardiac contractions can be triggered by all cardiac myocytes. This is prevented by the refraction period which can be influenced by the velocity of repolarization, mainly by potassium currents. Shortened refraction periods, e.g. by increased potassium currents, could thus increase the susceptibility for arrhythmias.39

The functional role of the common K897 allele and its effect on ventricular repolarization are still under investigation. This allele has been repeatedly associated with delayed repolarization at the ventricular level.23,24 At the atrial level delayed repolarization and prolonged atrial action potentials at first sight may protect against reentry mechanisms known to perpetuate AF. On the other hand, an increased dispersion of atrial repolarization may predispose to AF and prolongation of the atrial action potential has been shown to promote atrial tachyarrhythmias in both experimental40,41 and clinical42 studies. Recently it was postulated that the loss of function mutation E375X of the KCNA5 gene, encoding the ultrarapid delayed rectifier potassium channel, leads to prolonged atrial action potential duration and early afterdepolarizations and can thus cause AF yet a cause and effect relation has not been demonstrated.5 Similar results were found for a 33 base-pair coding deletion in the same gene.43 In another study the rarer G-allele of the KCNE1 SNP S38G was significantly associated with AF.18 Functional analyses of this variant’s minor allele revealed a reduced current density of the IKs-channel, again with prolonged atrial repolarization as a potential pathomechanism of AF in these patients.44

The pathophysiological consequences of the K897 and the T897 alleles have been well described previously by different authors, with conflicting results. A study by Scherer et al.45 did not detect any functional differences between recombinant channels expressing either a lysine or a threonine at position 897. In contrast, a number of other studies demonstrated a variety of electrophysiological differences between channels encoding a lysine or a threonine allele. In particular, Anson et al.46 concluded that a reduced current density due to the T allele could slightly lengthen the action potential duration. Paavonen et al.47 found altered properties in channel inactivation and smaller current density due to decreased expression of the 897T channels. This again led to longer QTc intervals associated with the T allele in their study population.47 In concordance with these findings, Crotti et al.48 detected reduced current densities when expressing channels with the T allele. By contrast, Bezzina et al.23 described altered channel properties of the 897T variant for the voltage-dependence of steady-state activation resulting in augmented net outward potassium currents and consequently a hastened repolarization reflected by a shorter QTc interval on the surface ECG. Accordingly, an independent SNP association study demonstrated a positive association of the T allele with a shorter QT interval in the general population.24 In all studies the detected effects ascribed to the K897T polymorphism were small. In addition there are potential differences in the effects of the two alleles on an atrial and ventricular myocyte level that remain to be identified. For example, the rare gain-of-function mutation S140G of the KCNQ1 gene has been identified as a causal mutation for the development of AF in a large Chinese family. Although the accelerated repolarization leads to shorter cardiac action potentials, prolonged QTc intervals were measured in the surface ECG.3 Thus our understanding of the relationship between atrial and ventricular repolarization even within a given channel remains limited.

Limitations

Although we provided convincing data that the K897-allele is associated with AF, this study still has some limitations that should be considered in interpreting the results and should be addressed in further investigations. One prominent question arises as to correction for multiple testing. We are well aware that our results from stage I would not have withstood a Bonferroni correction for 40 tested SNPs and thus a threshold of P-values of P = 0.05/40 = 0.00125. Neither would have the stand-alone results of stage II. Uncorrected however, we showed positive results for the same SNP in two different populations with comparable effect sizes and effects in the same direction. Since the strength of an association is highly dependent on the number of underlying observations, we subsequently combined cases and controls from stages I and II for a combined analysis. As expected, here the described association was much stronger and would have withstood correction also for more than 40 tests.

We were further limited by our ability to adjust for differences in covariates between the cases and controls. We were able to adjust for the prominent differences in age and gender between cases and controls and were able to show that the associated K897-allele remained an independent factor after such correction. However, some controls were considerably younger than the cases and could thus develop AF later in their lives. This could weaken the reported association at a later point of time.

Due to limited data on the patients’ phenotype we were not able to adjust for a number of risk factors predisposing to AF. Since most of the ECGs in the AF cohorts were recorded during AF and not in digitized form, we were not able to systematically study the effects of K897T alleles on the QT intervals in this study.24

Our results were not biased by population stratification as demonstrated above. Yet the selection of htSNPs was based on the HapMap CEU population, which is why transferability to other races and ethnicities has to be performed cautiously.

For the present study, besides further replications of the genotype-phenotype-association in independent populations, functional analyses in an atrial model will be needed to elucidate the pathophysiology behind the described effect.

Clinical perspective

This is the first study demonstrating a contribution of a common genetic variant to AF-risk in a candidate gene based, LD based SNP association study. While hypothesis-free genome wide association studies are under way, this study demonstrates a proof of concept and supports the common disease – common variant hypothesis.

Supplementary material

Supplementary material is available at European Heart Journal online.

Conflict of interest: none declared.

Funding

This work was funded by the German Federal Ministry of Education and Research (BMBF) in the context of the German National Genome Research Network (NGFN), the German National Competence network on atrial fibrillation (AFNet) and the Bioinformatics for the Functional Analysis of Mammalian Genomes programme (BFAM) by grants to Stefan Kääb (01GS0499, 01 GI 0204 and 01 GI 0204/N), H. -Erich Wichmann and Arne Pfeufer (01GI0204) and to Thomas Meitinger (01GR0103). The KORA platform is funded by the BMBF and by the State of Bavaria.

Footnotes

  • These authors contributed equally to the work.

References

View Abstract