Published online before print December 9, 2008, doi: 10.1161/CIRCGENETICS.108.792192
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Original Articles |
Common Genetic Variants in ANK2 Modulate QT Interval
Results From the KORA Study
From the Klinik und Poliklinik für Innere Medizin II, Universitätsklinikum Regensburg, Regensburg, Germany (Ka.S., Kl.S., S.W., I.B., C.H., A.J.); Clinic of Cardiology and Center for Cardiovascular Research, IKEM, Prague, Czech Republic (Ka.S.); Departments of Internal Medicine, Molecular Physiology, and Biophysics, University of Iowa Carver College of Medicine, Iowa City, Iowa (S.R.C., P.J.M.); Institut für Humangenetik (A.P.), HelmholtzZentrum München, Neuherberg, Germany; Institute for Biological and Medical Imaging (S.P.), HelmholtzZentrum München, Neuherberg, Germany; Medizinische Klinik und Poliklinik I (S.K.), Klinikum Großhadern, München, Germany; and Institut für Epidemiologie (H.E.W.), HelmholtzZentrum München, Neuherberg, Germany.
Correspondence to Christian Hengstenberg, MD, Clinic for Internal Medicine II–Cardiology, University Hospital of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Regensburg, Germany. E-mail christian.hengstenberg{at}klinik.uni-regensburg.de
Received May 14, 2008; accepted October 16, 2008.
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Abstract |
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Background— Spatial and timely variations in QT interval, even within its normal range, may underlie susceptibility to cardiac arrhythmias and sudden cardiac death. Given its important role in cardiac electrophysiology, we hypothesized that common genetic variation in ankyrin-B gene (ANK2) might modify QT interval length.
Methods and Results— The study population consisted of 1188 participants of the World Health Organizational Multinational Monitoring of Trends and Determinants in Cardiovascular Disease (WHO MONICA) general population survey Cooperative Health Research in the Region of Augsburg (KORA S3). Corrected QT interval was calculated using population specific linear regression formulas. A total of 22 single-nucleotide polymorphisms in the genomic region of ANK2 gene were genotyped using TaqMan technology. In a replication study, 6 single nucleotide polymorphisms were genotyped in 3890 individuals from a second population study (KORA S4). The rare variant of the single-nucleotide polymorphism rs6850768 (allele frequency, 0.28) significantly influenced duration of the QT interval, both in KORA S3 and KORA S4 populations. In homozygotes, the shortening of the QT interval was 3.79 ms (95% CI, 1.48 to 5.58; P=0.001 and P=0.0008 for log-additive and dominant model, respectively) in KORA S3 and 2.94 ms (95% CI, 1.11 to 4.77; P=0.001 and P=0.006 for log-additive and dominant genetic model, respectively) in KORA S4. A common 2-locus haplotype (rs11098171-rs6850768; population frequency, 28%) was associated with a QT interval difference of 2.85 ms (permutation; P=0.006) in KORA S3 and 1.23 ms (permutation; P=0.009) in KORA S4. Reverse transcription–polymerase chain reaction expression analysis of the human ANK2 5' genomic region in the human left ventricular tissue revealed 2 previously unidentified ANK2 5' exons in the proximity of the identified variants.
Conclusions— Common genetic variants juxtaposed with novel exons in the distant 5' genomic region of ANK2 influence the QT interval length in the general population. These findings support the role of ankyrin-B in normal cardiac electric activity.
Key Words: arrhythmia • genetics • long-QT syndrome • physiology • sudden cardiac death
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Introduction |
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The QT interval on 12-lead ECG reflects ion channel events of both myocardial depolarization and repolarization. The length of the QT interval is influenced by various determinants, such as heart rate, age, sex, electrolyte levels, and many medications. In the general population, the QT interval is normally distributed.1 Individuals with longer QTc display increased risk of malignant cardiac arrhythmias and sudden cardiac death, particularly in case of increased cardiovascular risk2 or manifest coronary artery disease.3
QT interval is a complex quantitative trait with a heritability of >30%.4 Genetic linkage of the QTc interval to LQT4 with its underlying gene ANK2 was demonstrated.5 Further studies identified single-nucleotide polymorphisms (SNPs) in multiple genomic regions that were associated with the QTc interval.1,6 ANK2 loss-of-function monogenic syndromes are responsible for a variety of cardiac phenotypes. However, currently no data support the role of common genetic variants in the ANK2 in normal cardiac repolarization.
ANK2 encodes the adapter protein ankyrin-B, which is critical for targeting ion channels and transporters to membrane domains in ventricular cardiomyocytes. Specifically, ankyrin-B directly associates with and targets Na/K ATPase and Na/Ca exchanger to cardiomyocyte transverse-tubule membranes.7–9 Additionally, inositol triphosphate (IP3) receptors are targeted by ankyrin-B to the myocyte sarcoplasmic reticulum membrane.8,10 Cardiomyocytes with reduced ankyrin-B expression display abnormal ion channel and transporter targeting resulting in sarcoplasmic reticulum calcium overload and catecholamine-induced afterdepolarizations.8 Mice lacking ankyrin-B display similar whole animal arrhythmia phenotypes in response to catecholaminergic stimulation.8 Humans with ANK2 loss-of-function mutations display arrhythmia phenotypes. To date, 9 different ANK2 loss-of-function variants have been identified that give rise to a spectrum of clinical phenotypes including long QT syndrome, sinus node dysfunction, atrial fibrillation, ventricular arrhythmia, and sudden cardiac death.8,11,12 Although a small number of studies have analyzed key regions of the ANK2 gene in specific patient populations, a detailed analysis of the large ANK2 gene has not been performed in the general human population. In fact, little is known regarding the genomic organization of ANK2 or its resulting cardiac transcripts. We hypothesized that (1) ANK2 gene might play a pivotal role in coordinating the process of normal cardiac depolarization and repolarization and (2) common genetic variation in the ANK2 gene might modify the physiological variability of the QT interval in the general population.
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Methods |
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Study Populations
The initial cohort consisted of 1483 individuals from a general population who took part in a substudy of Cooperative Health Research in the Region of Augsburg (KORA S3) epidemiological survey conducted in Southern Germany around the city of Augsburg in 1994 to 1995 as part of the international World Health Organization Multinational Monitoring of Trends and Determinants in Cardiovascular Disease (WHO MONICA) project (Table 1). The replication sample, KORA S4, was an analogous consecutive population survey (n=4261) from the region of Augsburg in Southern Germany, performed in 1999 to 2001 (Table 1). Study procedures in both surveys were similar and have been described earlier.13,14 Informed consent to participate in the study was obtained from all individuals. Local institutional review committees approved the study protocols.
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ECG Phenotyping: Measurement of the QT Interval
Resting 12-lead ECGs were recorded in 1223 and 4115 individuals of KORA S3 and KORA S4 populations, respectively (Table 1). Digital ECG systems (Philips Hewlett-Packard; Bioset 9000, Hörmann Medizinelektronik; and Hannover ECG analysis software, version 3.22-12) were used to record and analyze ECGs in the KORA S3 and KORA S4 populations, respectively. QT intervals were determined by a computerized analysis of an average cycle. QT measurements over short- and long-term time intervals have been investigated and shown to be highly reproducible.15 Significant differences between QT intervals in KORA S3 and KORA S4 are attributable to different algorithms used. In contrast, relative differences between the subjects (the measurement relevant to QTL studies) have been shown to be well preserved across QT measurement platforms.16 Sex-specific linear regression formulas for both study populations, with covariates including heart rate (RR interval), age (A), and sex (S), were designed to correct the QT interval (QTc-RAS). More detailed information regarding QT interval correction formulas can be found in the online-only Data Supplement.
Individuals with significant QRS aberration (QRS 
120 ms) and pathological prolongation or shortening of the QTc-RAS value (defined as QTc-RAS 
320 ms or QTc-RAS 460 ms) were excluded from all analyses (Table 1).
Description of the ANK2 Genomic Region, SNP Selection, and Genotyping
A detailed annotation of the studied genomic region is displayed in Figures 1 and 2 of the Data Supplement. Genomic region of the canonical ANK2 gene is 334 kb. It contains 46 exons, and the length of the mRNA transcript is 14.2 kb (NM_001148). The initial set of markers was selected according to the following criteria: (1) minor allele frequency >0.1; (2) even distribution over the ANK2 gene region, including one distant 5' marker tagging a predicted alternative gene start; (3) preference given to assays in the proximity of coding regions and tagging linkage disequilibrium (LD) blocks of the gene; and (4) availability as validated commercial TaqMan SNP genotyping assays (Applied Biosystems) (Data Suppplement, Table 1 and Figure 1). Fourteen SNPs were located in introns; 1 was located in the promotor of the ANK2 gene, and 2 markers (rs1979086 and rs12508397) were situated upstream and downstream from the canonical gene. The SNP rs1979086 was selected because it was located near a predicted, but unconfirmed, alternative exon 1 of ANK2 (Data Supplement, Figures 1 and 2).
To further study a significant association found for rs1979086 in KORA S3 population (Data Supplement, Table 2), a fine-mapping panel of 5 additional SNPs was selected. Three of these additional markers (rs11098171, rs6850768, and rs10026837) were in the physical proximity to rs1979086 within the same large LD block (mean distance, 9.8 kb; mean D', 0.99; mean r2, 0.5) within highly evolutionary conserved sequences (Data Supplement, Figure 2). Two markers (rs4834308 and rs11098182) were selected to fill in the gap between the alternative exon and the originally annotated ANK2 gene. Genomic distance of rs4834308 and rs11098182 from rs1979086 was 70 and 150 kb, respectively. Subsequently, rs1979086 together with the 5 fine-mapping markers were used for replication in KORA S4 population.
Genotyping in the KORA S3 sample was performed using 5' exonuclease TaqMan technology with predesigned assays (Applied Biosystems). The replication panel (KORA S4 population) was genotyped using the primer extension MALDI-TOF MS platform (Autoflex HT, Sequenom) according to the manufacturer’s protocol. More detailed information regarding genotyping platforms can be found in the Data Supplement.
Human Tissue, RNA Isolation, Reverse Transcription, and Polymerase Chain Reaction Amplification of the Transcripts
RNA was isolated from left ventricular muscle tissue from healthy donor hearts that were not suitable for transplantation (eg, subclinical atherosclerosis, old age, no matching recipients) through the Iowa Donors Network and through the National Disease Research Interchange, Inc (Philadelphia, Pa). Age and sex were the only identifying information acquired from the tissue providers, and the University of Iowa Human Subjects Committee deemed that informed consent from each patient was not required. None of the patients died from cardiac-related causes. The protocol for RNA isolation, reverse transcription, and polymerase chain reaction (PCR) amplification of the ANK2 transcripts can be found in the Data Supplement.
Statistics
Discrete variables were compared by the 
2 test. Genotype distributions in the studied populations were compared with predicted distributions under HW equilibrium. LD was analyzed using D' and r2.17 Genotypic association tests including testing of QTc-RAS extremes in a case-control pattern assuming dominant, recessive, or log-additive genetic models were performed using ANOVA or logistic regression analysis.18 Arbitrarily, greater QTc-RAS values were considered a risk phenotype. Haplotypes were reconstructed using the EM algorithm,19 and haplotype association with the phenotype under study was tested using a regression-based method, called haplotype-trend regression.20 Analyses were performed for complete populations (KORA S3, KORA S4) and separately in male and female subgroups. Finally, combined analysis of KORA S3 and S4 samples was performed after standardization of the QTc-RAS raw data into z scores to account for differences between the KORA S3 and S4 populations in the absolute values of the QT interval that arose by using different ECG analyzing platforms. A sample-size weighted meta-analysis of the results in KORA S3 and KORA S4 populations was performed using the results of the standardized z score statistics.
Nominal probability values <0.001 were considered significant in the initial KORA S3 sample to account for the problem of multiple testing. Using a stringent Bonferroni correction for 17 nonindependent tests (17 SNPs), the nominal probability value would have been 0.003 (0.05/17). In the replication sample, nominal probability values of P<0.05 were considered significant. In haplotype analyses, global significance probability values were defined by 10 000 permutations for every combination of markers. Statistical software and genetics statistics tools used in this study are featured in the Data Supplement. The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agreed to the manuscript as written.
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Results |
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Descriptive Genetic Statistics
All 22 SNP markers were polymorphic and the mean minor (rare) allele frequency was 0.29 (range, 0.11 to 0.50). All studied markers were in Hardy-Weinberg equilibrium in both studied populations. An LD map constructed from all 22 markers revealed 3 LD blocks (Figure 1), which fit well with a detailed LD structure available from the HapMap project (Data Supplement, Figure 1). Specifically, a pair-wise LD between the fine-mapping markers rs11098171, rs6850768, rs1979086, and rs10026837 that lied in a larger LD block downstream from the canonical ANK2 ranged between 0.97 and 1.00 (D') and 0.11 and 0.98 (r2), respectively.
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Genetic Association Analyses
Study Samples Characteristics
A detailed demographic overview of both studied populations is presented in Table 1. In the KORA S3 sample (n=1483), ECG data were available in 1223 individuals. After exclusion of study participants with QRS complex width
120 ms and QTc-RAS >460 ms or <320 ms, 1188 individuals remained as the primary study population. In the KORA S4 sample (n=4261), ECG recordings were available in 4115 individuals. After exclusion of study participants with QRS complex width 120 ms and QTc-RAS longer than 460 ms, 3890 individuals were left as the validation (replication) study population. In both populations, men and women were similarly represented. Significant differences in ECG parameters in the KORA S3 and S4 populations were attributable to different ECG platforms used.
Association Analysis in the Primary Population (KORA S3)
In the primary sample (KORA S3), one marker (rs1979086) in the distant 5' upstream genomic region containing an ANK2 alternative exon 1 prediction displayed a significant association with the QTc-RAS (Data Supplement, Table 2). This genomic region was of potential biological interest as it contained a predicted alternative exon 1 of ANK2 and some highly evolutionary conserved sequences suggesting so far unknown coding or regulatory elements. Therefore, additional 5 SNPs (rs11098171, rs6850768, rs10026837, rs4834308, and rs11098182) were used for fine mapping of this region (Data Supplement, Figure 2). The marker rs6850768 displayed the most significant association and remained the principal finding of the study throughout all following analyses (P=0.001 and P=0.0008 for the log-additive and dominant genetic model, respectively). Genetic effects of fine-mapping SNPs in the ANK2 gene 5' genomic region in the primary population (KORA S3) are shown in Table 2. The minor allele of the rs6850768 was associated with a 3.47-ms shortening in QTc-RAS in heterozygotes and a 3.79-ms shortening of the QTc-RAS in rare homozygotes. The initial associated marker in this region, rs1979086, was linked with a mean prolongation of the QTc-RAS by 4.00 ms in rare homozygotes (P=0.005 for log-additive model).
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To analyze the robustness of this finding, the study population was divided into QTc-RAS quartiles. Individuals from the quartile with the shortest QTc-RAS were arbitrarily used as control subjects and were compared with individuals from the quartile with the longest QTc-RAS, arbitrarily defined as cases. The most significant marker was rs6850768 (OR, 0.74; 95% CI, 0.58 to 0.96; P=0.02).
Haplotype analyses in KORA S3 paralleled the findings in single SNPs. Relevant 2 locus haplotypes are reviewed in Table 3. Haplotypes created from markers rs11098171–rs6850768 and rs6850768–rs1979086 were similar in distribution (prevalent haplotypes with a frequency of 0.49 and 0.28, respectively) and genetic effect, bringing about a 2.85-ms shortening for both alternative prevalent haplotypes (Table 3). Three and 4 locus haplotypes revealed a similar picture but did not provide any additional information (data not shown).
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Validation of Association Findings in a Second Population (KORA S4)
In the validation sample (KORA S4), rs1979086 and all 5 fine-mapping markers from KORA S3 were genotyped (rs11098171, rs6850768, rs10026837, rs4834308, and rs11098182). Genetic effects of the 4 most relevant replication markers are displayed in Table 2. The most significant marker from the initial KORA S3 population (rs6850768) remained highly significantly associated with the QTc-RAS also in the KORA S4 population. Minor allele in rs6850768 was associated with a 1.08-ms shortening in QTc-RAS in heterozygotes and a 2.94-ms shortening of the QTc-RAS in rare homozygotes (P=0.001 and P=0.006 for the log-additive and dominant test, respectively).
Haplotype analysis in the KORA S4 population revealed a similar pattern when compared with findings in KORA S3 (Table 3). Specifically, a common alternative 2-locus haplotype (population frequency 0.29) composed from the markers rs11098171 and rs6850768 was significantly associated with QTc-RAS shortening of 1.23 ms (global permutation; P=0.009).
Analysis in Combined KORA S3 and KORA S4 Population
A joint analysis of study populations (KORA S3 and KORA S4) was performed to assess whether merging both populations would amplify or abolish genetic effects seen in separate analyses. Interestingly, of all 6 replication markers, only the marker rs6850768 increased in significance in this analysis, suggesting a similar nature and magnitude of the genetic effect in both studied populations (z score difference, –0.10; 95% CI, –0.14 to –0.05; P=0.00001 for the log-additive genetic model; Figure 2). The most significant 2-locus haplotype was composed of the markers rs6850768 and rs1979086 (alternative prevalent haplotype, population frequency 0.22, global permutation; P<0.0001). A sample-size weighted meta-analysis of the standardized data (z scores) from the KORA S3 and KORA S4 samples confirmed the synergistic result in the separate populations (meta-analysis; P=0.00001). In multivariate analysis, no statistically significant contribution of sex and age was found when the QTc-RAS correction formula was used (data not shown). Confirmatory analyses using the conventional QTc correction formula revealed similar results when compared with the QTc-RAS correction formula (data not shown).
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Human QTc SNP Markers are Associated With Novel ANK2 Exons
Analysis of the genomic region upstream of ANK2 demonstrated that markers rs6850768, rs11098171, rs1979086, and rs10026837 were not localized in the coding region of any identified gene and were therefore likely associated with the ANK2 locus. In fact, in silico analysis of human expressed sequence tags as well as computational searches for unidentified intron/exon borders identified 2 putative ANK2 exons located approximately 230 and 145 kb from previously identified ANK2 exon 1 (Figure 3) and near markers rs11098171, rs6850768, rs1979086, and rs10026837.
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Putative novel ANK2 exons were confirmed by reverse transcription (RT)-PCR experiments from human left ventricular tissue (Figure 4). Novel exons (now termed ANK2 exon 0 and ANK2 exon 1') were identified in 4 human heart samples. Novel ANK2 exon 1' encodes an alternative start sequence (sequence Met-Thr-Thr-Met-Leu-Gln-Lys) for cardiac ankyrin-B. In addition, RT-PCR experiments supported by expressed sequence tags demonstrate that exon 0 is associated with exon 1', but not ANK2 exon 1. Markers rs6850768 and rs11098171 were localized within
29 kb of ANK2 exon 0. Both markers rs1979086 and rs10026837 were localized within 2 kb (1.7 and 0.3 kb, respectively) of novel ANK2 exon 0. Together, common markers that significantly influence the QT interval duration in this study are associated with novel exons of ANK2.
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Discussion |
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In this report, we describe identification of common genetic variants in the distant 5' genomic region of the ANK2 that significantly contribute to QT-interval variation. The effect size is in the range of milliseconds. The marker rs6850768 explains only 0.83%, 0.29%, and 0.4% of the overall QTc-RAS variance in the KORA S3, KORA S4 and combined KORA S3 and KORA S4 populations, respectively. This is negligible from an individual point of view but nonetheless statistically robust and comparable to effect sizes detected in other studies.1 Previous studies, including genome-wide association study by Arking et al,21 failed to identify association of this genomic region with the normal QT-interval variation. Multiple factors can explain this discrepancy. First, in the previous versions of the high-scale genotyping platforms, the distant 5' ANK2 genomic region reported here was not covered. Second, the moderate genetic effect size as in this study is likely to be missed in a genome-wide association due to a severe multiple testing problem and consequently very low cutoff values for the statistical significance measures. Finally, although regions of ANK2 have been extensively screened for human variants, this specific 5' ANK2 genomic region has never been tested before. In fact, before this study, this distal genomic sequence was not even considered to exist within the ANK2 gene.
In the planning phase of this study, public genome annotations suggested an alternative ANK2 gene prediction based on in silico analysis of human expressed sequence tags as well as computational search for unidentified intron/exon borders. In addition, a high degree of interspecies DNA sequence conservation made this region particularly interesting, because it was likely to be associated with evolutionary important sequences. Genetic variants in such sequences may exert particularly strong phenotypic effects.
Our new findings revealed 2 previously unidentified ANK2 exons. Exons 0 and 1' represent previously undefined alternative translational start sites for ANK2. Although quantitative RT-PCR experiments demonstrate that this alternative transcript is present in human heart, the role of this alternative transcript is currently unknown. Future experiments will be important for defining the role of the novel ANK2 transcript.
Our data underscore both strengths and weaknesses of genetic LD mapping. Using a classical candidate gene approach, we identified small genotype-dependent differences in the normal cardiac repolarization. These findings illustrate the power of genetic association mapping for the identification of novel physiological pathways and potential pathophysiological targets. On the other hand, primary genetic mapping itself can rarely decipher underlying molecular mechanisms responsible for the finding. Additional genetic mapping in the ANK2 5' genomic region might add some information, but high degree of LD in this region would probably render most of genotyping redundant. Although none of the associated SNPs lies in the coding region of the ANK2 gene, all are juxtaposed with an alternative first ANK2 exon (named here exon 0). Therefore, their presence may affect promoter sequence and hence transcriptional regulation, or they might point to yet unidentified markers within exon 0.
In 2003, Mohler et al8 identified ANK2 as the gene responsible for LQT4 syndrome. Subsequently, multiple mutations in the C-terminus of the gene were identified that were associated with a variety of clinical arrhythmic phenotypes. Even preceding the identification of the underlying LQT4 gene, Busjahn et al5 suggested that the LQT4 genomic locus was linked with normal variation of the QT interval. To our knowledge, we present the first genetic association data that may explain this previous linkage finding. On the other hand, only a joint analysis of the previous linkage and our association data will show whether the linkage signal reported by Busjahn et al was related to the genetic variation in the genomic locus identified in this study.
A quantitative trait locus for the resting heart rate on chromosome 4q was demonstrated in man and in homologues rat chromosomes.22–24 Among other candidate genes, the 1-logarithm (base 10) of odds (LOD) support interval includes ANK2. Because heart rate is an important confounder of the QT-interval duration, it was essential for us to identify any confounding effect on the QT interval related to genetic variation of heart rate. Apart from using population specific QT correction formulas accounting for heart rate, sex, and age (QTc-RAS), direct association analysis in our data sets demonstrated no influence of the genotyped SNPs on the resting heart rate. Consequently, the effect on the QT interval identified in this study is independent of any potential genetic variation influencing the heart rate.
Our findings have some limitations. We report results of an LD mapping study together with expression data on alternative ANK2 initial exons. Further work is necessary to define the functional role of common genetic variants in this genomic region. Moreover, additional large populations need to be analyzed to define the role of the common variants described here in populations other than that of European descent. Last but not the least, the difference between the mean of the QT interval between the KORA S3 and KORA S4 populations is relatively large. This difference arose by the use of different computerized algorithms in each population survey. However, it is not the mean QT interval value, but its variance that is the subject of study in genetics of quantitative traits. As interindividual reproducibility (relative differences in the QT interval between subjects) of the measurement platforms has been validated, the absolute difference between the QT interval means does not reduce validity of our analyses.
In conclusion, we identified common genetic variants in the 5' genomic region of the ANK2 that significantly influence the QT interval duration. In addition, expression analysis revealed 2 unidentified ANK2 5' exons tagged by these genetic variants. These findings confirm the importance of ankyrin-B in cardiac electrophysiology and demonstrate the role of common genetic variation in the normal cardiac repolarization.
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Acknowledgments |
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We appreciate an invaluable contribution of participants of the MONICA and KORA studies. We gratefully acknowledge the excellent technical assistance of Martina Köhler, Josef Simon, and Michaela Vöstner.
Sources of Funding
This study was supported by the Deutsche Forschungsgemeinschaft (He1921/9-1 to C.H.), the German National Genome Network (01GS0418 to C.H. and 01GS0838 to S.K.), the Ernst- and Berta-Grimmke-Stiftung (C.H.), the Wilhelm-Vaillant-Stiftung (C.H.), the Deutsche Stiftung für Herzforschung (C.H.), the National Institutes of Health (HL084583 and HL083422 to P.J.M.), the Pew Scholars Trust (P.J.M.), the Czech Ministry of Education, Youth and Sports (1M0510 to Ka.S.), the Grant Agency of Charles University (63808 258081 to Ka.S.), and the Leducq Foundation (S.K.).
The KORA group 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. The MONICA Augsburg Study was initiated and conducted by Ulrich Keil and coworkers. The KORA research platform and the MONICA Augsburg studies were initiated and financed by the GSF National Research Centre for Environment and Health, which is funded by the German Federal Ministry of Education, Science, Research and Technology and by the State of Bavaria.
Disclosures
None.
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Footnotes |
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Kamil Sedlacek, Klaus Stark, Christian Hengstenberg and Andreas Jaron contributed equally to this work.
The online-only Data Supplement is available at http://circgenetics.ahajournals.org/cgi/content/full/CIRCGENETICS.108.792192/DC1.
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