Common Variants in Myocardial Ion Channel Genes Modify the QT Interval in the General Population
Results From the KORA Study
Altered myocardial repolarization is one of the important substrates of ventricular tachycardia and fibrillation. The influence of rare gene variants on repolarization is evident in familial long QT syndrome. To investigate the influence of common gene variants on the QT interval we performed a linkage disequilibrium based SNP association study of four candidate genes. Using a two-step design we analyzed 174 SNPs from the KCNQ1, KCNH2, KCNE1, and KCNE2 genes in 689 individuals from the population-based KORA study and 14 SNPs with results suggestive of association in a confirmatory sample of 3277 individuals from the same survey. We detected association to a gene variant in intron 1 of the KCNQ1 gene (rs757092, +1.7 ms/allele, P=0.0002) and observed weaker association to a variant upstream of the KCNE1 gene (rs727957, +1.2 ms/allele, P=0.0051). In addition we detected association to two SNPs in the KCNH2 gene, the previously described K897T variant (rs1805123, −1.9 ms/allele, P=0.0006) and a gene variant that tags a different haplotype in the same block (rs3815459, +1.7 ms/allele, P=0.0004). The analysis of additive effects by an allelic score explained a 10.5 ms difference in corrected QT interval length between extreme score groups and 0.95% of trait variance (P<0.00005). These results confirm previous heritability studies indicating that repolarization is a complex trait with a significant heritable component and demonstrate that high-resolution SNP-mapping in large population samples can detect and fine map quantitative trait loci even if locus specific heritabilities are small.
Pathological alteration of myocardial ventricular repolarization is a leading cause of ventricular tachycardia and fibrillation.1 It is also suspected to contribute to sudden cardiac death in the context of myocardial hypertrophy or heart failure as well as in drug-induced arrhythmias.2
The cardiac repolarization process is known to be strongly dependent on various parameters, among them heart rate,3 age,4 sex,5,6 plasma levels of electrolytes,7 and medications,8 as well as inherited and acquired pathological conditions.9 The QT interval measured in the surface ECG is the most accessible noninvasive marker of repolarization. After correction for heart rate, its strongest covariate, it is usually referred to as the corrected QT or QTc interval.
Apart from monogenic long QT syndrome (LQT), heritability studies have suggested that genetic factors are also involved in the control of cardiac repolarization at the population level. The heritability of the QTc interval has been estimated between 25% and 52% in three sibpair and in one family-based study.10–13 In a nonparametric linkage analysis, the authors of one of the above studies could demonstrate a significant linkage of the QTc interval to the KCNQ1 (LQT1) and the ANK2 (LQT4) gene loci.12
Several authors have investigated nonsynonymous SNPs in candidate genes for their effect on repolarization. The K897T variant in exon 11 of the KCNH2 gene encoding the α-subunit of the voltage-gated myocardial IKr channel (LQT2) was examined in a study of 226 males and 187 females of Finnish descent. Only in females the 897T-allele had a prolonging influence on the maximum QTc interval measured over all 12 leads, but not in lead V2.14 In 39 LQT patients with the KCNQ1-G589D mutation, the QT interval during exercise was prolonged in those with at least one KCNH2–897T-allele.15 The authors of this and another functional study16 noted that the IKr-897T channel exhibited a decreased current density.
In a study of 1316 Europeans, the 897T-allele shortened the QTc interval at rest in both males and females.17 The effect appeared to be recessive with a shortening of QTc by −10.0 ms in 897TT-homozygotes and was stronger in females than in males. The IKr-897T channel showed a decrease in steady state activation potential predicting a shortening of action potential duration due to an increase in IKr current.
Myocardial repolarization is a fine-tuned process dependent on the delicate coordination of low strength ionic currents at the end of the action potential.18 Gene variants conferring only subtle differences to gene regulation or function, such as intronic or promoter variants, may well influence the repolarization process similar to nonsynonymous variants. We tested the hypothesis that frequent gene variants in the long QT syndrome potassium channel genes KCNQ1, KCNH2, KCNE1, and KCNE2 cause phenotypic variation of myocardial repolarization in the general population and conducted a systematic and high-density linkage disequilibrium (LD)–based SNP association study with a resolution similar to the current HapMap effort19 in search of novel quantitative trait loci (QTL) of the QT interval.
Materials and Methods
Between 1999 and 2001, we conducted an epidemiological survey of the general population living in or near the city of Augsburg, Southern Germany (KORA S4). This was the fourth in a series of population-based surveys originating from our participation in the WHO MONICA project. The study 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 10 strata of equal size according to gender and age. After a pilot study of 100 individuals, 4261 individuals (66.8%) agreed to participate in the survey, which were ethnic Germans with very few exceptions (>99.5%). During 2002 and 2003, we reinvestigated a subsurvey of 880 persons specifically for cardiovascular diseases. From that subsurvey, 689 individuals were studied to screen for positive genetic associations (screening sample), whereas 3277 different individuals from the total survey were used to confirm positive findings (confirmation sample). A detailed description of samples and the list of exclusion criteria are given in Table 1. 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 committee.
In the initial survey, we recorded 12-lead resting electrocardiograms (ECGs) using a digital recording system (Bioset 9000, Hörmann Medizinelektronik). QT intervals were determined using the Hannover ECG analysis software (HES-Version 3.22-12) by computerized analysis of an averaged cycle computed from all cycles of the 10-second recording after exclusion of ectopic beats. The QT interval determined by this algorithm represents the earliest begin of depolarization until the latest deflection of repolarization between any two leads. In an international validation study, the HES software was among the best performing digital ECG systems.20 Reproducibility of HES QT-measurements over short- and long-term time intervals has been investigated.21
Covariate Analysis and Phenotype Correction
We adjusted QT for known covariates by a correction formula. Traditional formulas like Bazett’s3 correct only for heart rate in a nonlinear fashion. A linear correction formula for QT has been derived from Framingham Heart Study data.22 We based correction of QT on a multivariate linear regression model including covariates heart rate (RR interval), sex, and age. Correction factors were determined separately for each sex; the QT interval corrected for rate-, age-, and sex was called QTc_RAS. With the correction factors derived from the total sample of 3966 individuals, the formulas for QTc_RAS were determined for males:
where RR denotes RR interval in milliseconds.
Genotyping, Determination of Haplotype Blocks, and Haplotypes
We investigated genes encoding the α- and β-subunits of the myocardial delayed rectifier potassium channels IKs (IKs-α: KCNQ1; IKs-β: KCNE1) and IKr (IKr-α: KCNH2; IKr–β: KCNE2). A total of 270 SNPs distributed in and around these genes were chosen from the public dbSNP database, databases on monogenic long QT-syndrome genes,23–25 and diagnostic LQT-patient resequencing. SNPs in exons or intron/exon boundaries were chosen without exception (19 SNPs). Outside those regions SNPs were selected on the criterion of equidistant spacing of ≈1 SNP every 5 kb (251 SNPs). Information about local patterns of LD from HapMap or other sources was not available at the time of SNP selection.
DNA was extracted from EDTA anticoagulated blood using a salting out procedure.26 SNP genotypes were determined using PCR, primer extension, and MALDI-TOF mass spectrometry in a 384-well format (Sequenom). LD measures (D′, r2) and haplotypes were determined with Haploview software.27 Haplotype block boundaries were defined based on the confidence interval of the D′ measure as described in Gabriel et al.28 Haplotype-phenotype association analysis based on sliding window haplotypes was performed using the haplotype trend regression test as described in.29
Of the 270 SNP assays, 33 were not functional, with call rates below 0.8, and 36 were monomorphic. And 174 SNPs had call rates ≥0.8, minor allele frequencies ≥0.02, and Hardy-Weinberg-equilibrium (HWE) P values ≥0.01. The low cut-off value for HWE was accepted because of the relatively large number of SNPs genotyped in the project.
Genotype Phenotype Association Analysis
SNPs were tested for association by linear regression analysis using QTc_RAS as the dependent variable. Significance levels were determined for both the one-degree (1df) and the two-degree of freedom (2df) test. In the 1df test, the independent variable was derived by transforming SNP’s genotypes (AA, Aa, aa) to a relational scale by counting the number of minor alleles (0, 1, 2) assuming a strictly codominant model with identical trait increases between genotypes. This test has a relatively higher power to detect weak effects and was our primary test used during screening and confirmation. In the 2df test, a SNP was decomposed into two variables representing the two genotypic changes and both were included into a bivariate regression. This test accounts for dominance and recessivity by allowing the trait increase of each genotypic change to take an individual value. It was used to specifically quantify each genotype’s effect and significance level in the total sample. The average trait increase per allele was calculated as the mean of both genotypic changes weighted by the genotype frequencies and the variance attributable to a SNP was calculated as the adjusted r2 value from the bivariate regression analysis.
To determine the independence of effects, we performed multivariate linear regression analysis, incorporating the genotypic changes of several SNPs into one model. To determine combined effects, we counted the number of significant genotypic changes in each person to give a QT-prolongation score and performed ANOVA analysis using the score as the independent variable. To investigate if associated SNP-markers had also been identified in a categorical trait analysis, we analyzed groups of individuals with extreme QTc_RAS values in both and individual sexes in a case control–like design using the Cochran-Armitage test for trend.
Association Study Design and Adjustment for Multiple Testing
We designed a two-step association procedure using a small screening and a larger confirmation sample in an attempt to minimize the false-positive error rate. We genotyped the screening sample for all designed SNP assays. Without adjusting for multiple testing, we genotyped all SNPs significantly associated with QTc_RAS (P<0.05 in the 1df test) and nonredundant to each other (pairwise r2<0.6) in the confirmation sample. To adjust for multiple testing in this step, we calculated an adjusted table-wide significance level using 1000 rounds of permutation. As the question if adjustment for multiple testing is necessary in two-step designs is not resolved, we used both the unadjusted and the adjusted significance levels in the discussion of confirmation results. In haplotype blocks of confirmed SNPs, we investigated additional nonredundant markers even if in the screening sample they had not been significantly associated with QTc_RAS.
To determine gender-specific differences of SNP-phenotype associations, we performed sex-specific regression analysis in the total sample. Sample sizes of males (n=1959) and females (n=2007) were similar and therefore comparable for effect strength. To investigate if SNPs with confirmed association to QTc_RAS had also been identified by a categorical trait analysis, we analyzed groups of individuals with extreme QTc_RAS values in both and individual sexes against each other using the Cochran-Armitage test for trend.
Association Analysis of Individual SNPs
In the total sample of 3966 individuals, QTc_RAS corrected to a 60-year-old male with a heart rate of 60 bpm had a mean value of 417.6 ms and a SD of ±17.2 ms.
Of the 174 successfully genotyped SNPs, the average call rate was 0.953 and the average minor allele frequencies were 0.258 (mean) and 0.251 (median). Haplotype blocks are shown in Figure 1 and described in Table 2. In the screening sample, 34 of these SNPs showed association to QTc_RAS in the 1df-test, 18 of these being also significant in the 2df test. We genotyped 13 nonredundant SNPs in the confirmation sample (Table 3a; supplemental Table I, available online at http://circres.ahajournals.org) plus one additional SNP that tags another frequent haplotype in the block of an associated marker. Association was confirmed for four SNPs if the unadjusted significance level of 0.05 was used and for three SNPs if the adjusted table-wide significance level of 0.0041 was applied.
We detected a previously undescribed QTL in intron 1 of the KCNQ1 gene. Although the gene shows remarkably little LD, intron 1 contains a large haplotype block of ≈50-kb size and high LD (both D′ ≥0.94 and r2≥0.79 for 6 of 7 markers) (Figure 1). It contains two major haplotypes with frequencies of 0.570 and 0.379 that can be tagged by rs757092. This SNP showed association in both subsamples (Table 3a), the rare G-allele being associated to a QTc_RAS prolongation of +1.7 ms in heterozygotes and +3.3 ms in homozygotes (Table 3b; 0.38% of variance; P=0.0002).
In the KCNH2 gene, we confirmed the previously published effect of SNP KCNH2-K897T (rs1805123) on the QT interval. The rare 897T-allele was associated with a shortening of QTc_RAS of −1.9 ms in heterozygotes and −3.5 ms in homozygotes (0.36% of variance; P=0.0006). The effect was stronger in females. The K897T variant resides on a large haplotype block extending over 60 kb from exon 3 to 30 kb 3′ of the gene (KCNH2-block 2 in Figure 1), in which four haplotypes with allele frequencies above 0.05 exist (Table 4) among which KCNH2-K897T tags haplotype h2 (Hf=0.205). Typing the confirmation sample with a SNP tagging haplotype h3 (Hf=0.195) revealed a second effect. The rare A-allele of SNP rs3815459 was associated with a prolongation of QTc_RAS of +1.5 ms in heterozygotes and +4.5 ms in homozygotes (0.35% of variance; P=0.0004).
In the KCNE1 gene region, SNP rs727957 showed a positive association to QTc_RAS in both the screening (P=0.0081) and the confirmation sample (P=0.0498), but did not exceed the adjusted significance level. In the total sample, the rare T-allele of the marker was associated with a prolongation of QTc_RAS of +1.0 ms in heterozygotes and +4.5 ms in homozygotes (0.23% of variance P=0.0051).
Among the 174 investigated common SNPs were two further nonsynonymous gene variants, KCNH2-R1047L [Af(min)=0.024], for which functional data indicate no allele differences,16,17 and KCNE1-S38G [Af(min)=0.355], neither of which were associated to QTc_RAS. To clarify the importance of rare nonsynonymous SNPs, we additionally genotyped KCNE2-T8A [Af(min)=0.0073], which had previously been described associated to drug-induced long QT syndrome30 and KCNQ1-G643S [Af(min)=0.00072], for which only one heterozygote was observed. Also, these showed no significant effect (supplemental Table III).
Combined and Categorical Association Analysis
The comparison of multivariate linear regression analysis of both QTc_RAS and QT including covariates demonstrated highly similar significance levels from both methods and independence of SNPs’ effects enabling combined association analysis (Table 5; supplemental Table II). Analysis of the intragenic KCNH2 variants K897T and rs3815459 against all other haplotypes of that block revealed an increase of QTc_RAS mean values among the six genotype groups from 415.0 to 421.1 ms (0.52% of variance; P=0.0002; Table 6a; Figure 2b). The combined intergenic analysis of the KCNH2-K897T and the KCNQ1-rs757092 variants showed an increase of QTc_RAS mean values among the nine genotype groups from 412.9 to 421.2 ms (0.74% of variance; P<0.00005; Table 6b; Figure 2c).
Five of six genotypic changes of the three confirmed SNPs KCNQ1-rs757092, KCNH2-rs1805123, and rs3815459 were independently significant (P<0.05; Table 5). Individuals harboring the maximum possible number of five QT-prolonging alleles had on average a 10.5 ms longer QTc_RAS than individuals that had no QT-prolonging allele (0.95% of variance; P<0.00005) (Table 6c). When the genotypic change KCNE1-rs727957(aa) was included, a +14.3 ms increase was observed (1.13% of variance; P<0.00005)
Categorical analysis of individuals with extreme QTc_RAS values for all SNPs genotyped in the total sample detected significant effects in 2 of the 4 associated SNPs in 200 individuals from the extremes and in 3 of the 4 associated SNPs in 600 individuals from the extremes (supplemental Table IV). After adjustment for multiple testing, categorical analysis results were only significant for KCNQ1-rs707592 in the analysis of 600 individuals.
Covariate Correction of QT
The linear correction factors for heart rate we determined were well in agreement with published ones.22 The comparison between a formula correction and a multivariate linear regression model of QT for detecting SNP association (Table 5; supplemental Table II) supports the view that none of the two methods is superior.
Effects of Individual SNPs
The KCNQ1 gene locus had previously been shown to influence the QT interval in a quantitative trait linkage study.12 We could map this QTL to a 50-kb haplotype block in intron 1 in which only two major haplotypes existed. Of all identified effects, this was the most significant and most robust against testing in both genders. The 50-kb block does not contain any known or predicted exonic or regulatory sequences. Its high LD precludes further fine mapping in our population. The causal variant and its functional nature thus remain elusive at this point. Several association studies have demonstrated the nonsynonymous KCNH2-K897T variant to be significantly associated with repolarization, but results were conflicting. Our data show that in Caucasians of both sexes, the 897T-allele (block 2, h2) shortens the QT interval. The conflicting results may indicate that in other ethnic groups, the LD-relationship of the K897T variant may vary or that the smaller previous association studies were affected by increased type 1 error rates. We have identified another QT-modifying haplotype (h3) in the same block. In Caucasians, the presence of a common nonsynonymous SNP on h3 is unlikely, given the large number of individuals others and we have sequenced to detect mutations in the KCNH2 gene. An effect of the two synonymous SNPs I489I and F513F might be causal as for both amino acids less common codons are present on h3 [I489I: ATC (0.48) > ATT (0.35); F513F: TTC (0.55) >TTT (0.45)].31 In humans, codon usage has been shown to correlate with expression breadth, which covaries with expression levels32 but convincing evidence for codon usage effects in humans and other higher organisms has not been demonstrated.
SNP KCNE1-rs727957 did not fulfill all our significance criteria but showed evidence for association in the combined analysis (P=0.0051). It is located in the 5′ region 50 kb upstream, its haplotype block ending 20 kb upstream of the KCNE1 gene. We argue that an independent replication of this SNP’s effect should be conducted before it is considered a significant QTL.
Sliding window haplotype analysis did not reveal additional associations or improve significance levels (data not shown). For the effect in KCNQ1-intron 1 this result is intuitive, as most of the associated haplotype block’s diversity can be captured by typing only a single SNP. Using the information from the International HapMap Project, currently providing data at an average coverage of 1 SNP per 3.8 kb,19 will aid the capturing of relevant haplotype diversity in future studies.
Combined Effects at Several SNP Loci
We have observed two kinds of combined QTL effects on the QT interval. SNPs in high LD tagging different haplotypes in one block had opposite additive effects on QTc_RAS as seen in the KCNH2 gene. Similarly, SNPs in complete linkage equilibrium also exerted additive effects as seen between the KCNQ1 and KCNH2 genes. The fact that these three gene variants, although together only explaining 0.95% of trait variance, were associated to a monotonous rise in average QTc_RAS of up to 10.5 ms, supports their concerted mode of action irrespective whether they are in LD or not.
Previous publications of the KCNH2-K897T variant’s association to the QT interval had noted its more pronounced effect in females. We have confirmed and extended this finding, as especially marker KCNE1-rs727957 and to a lesser extent also KCNQ1-rs757092 and KCNH2-rs3815459 showed gender-dependent association. This underscores the importance of considering gender as a potent confounder variable when designing complex trait genotype-phenotype association studies.
Categorical Analysis of the QT Interval
A categorical analysis in the confirmation step using only 200 individuals with extreme QTc_RAS values would only have confirmed KCNQ1-rs757092 (P=0.002, OR=1.92) and KCNH2-K897T (P=0.004, OR=0.44) (supplemental Table IV). Notably, these effects were the most significant ones from the quantitative association analysis. Using a larger sample (n=600) also KCNH2-rs3815459 (P=0.008, OR=1.47) would have been confirmed. Categorical confirmation analysis thus can be considered in similar projects if a focus on the strongest effects at significantly reduced cost is desirable.
Implications for Future Investigations
Although the overall heritability of the QT interval is high, all gene variants identified in this study are only minor quantitative trait loci each explaining less than 1% of trait variance. This finding is in common with the view that important physiological mechanisms are unlikely to tolerate large genetic variance at a single locus. The authors of an early heritability study on electrocardiographic traits already noted that these reflected critical biologic functions, which evolved to an evolutionary optimum and the attainment of this optimum would necessarily tend to eliminate interindividual differences.33
We show that in a large sample of thoroughly phenotyped individuals even minor QTLs can be detected. The population-representative recruiting of individuals from one geographic area with limited recent immigration was helpful to this aim, as complex population genealogies can confound association signals. In two of the known monogenic long QT disease genes KCNQ1 and KCNH2, the common disease or in this case common phenotypes/common variants hypothesis holds true. The confirmation of this hypothesis for cardiac rhythm phenotypes appears a prerequisite to investigate whether common gene variants also influence cardiac patients’ predisposition toward arrhythmias.
Common intronic gene variants may influence repolarization to a similar extent as common nonsynonymous exonic variants. Future fine mapping studies of complex and quantitative trait loci should avoid to focus on exonic effects, but apply SNP coverage based on LD.
Besides studies of monogenic arrhythmogenic diseases and functional studies of recombinant cardiac ion channels, the genome-wide investigation of heritable surface ECG signatures may provide a valuable third route toward the identification of novel genes involved in cardiac electrophysiology that up to now went undetected by other methods.
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) and the Bioinformatics for the Functional Analysis of Mammalian Genomes program (BFAM) by grants to Stefan Kääb (01GS0109) and to Thomas Meitinger (01GR0103). The KORA platform is funded by the BMBF and by the State of Bavaria. The KORA group (Cooperative Research in the Region of Augsburg) consists of H.E. Wichmann (speaker), H. Löwel, C. Meisinger, T. Illig, R. Holle, J. John, and their coworkers who are responsible for the design and conduct of the KORA studies. We acknowledge the information on SNPs from diagnostic gene resequencing provided by Hanns Georg Klein (IMGM Martinsried).
This manuscript was sent to Harry A. Fozzard, Consulting Editor, for review by expert referees, editorial decision, and final disposition.
This publication contains part of the doctoral thesis of S.J., M.A., and A.S.-W.
Original received November 9, 2004; revision received February 16, 2005; accepted February 21, 2005.
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