Abstract
Background: Genetic variants in myocardial sodium and potassium channel genes are associated with prolonged QT interval and increased risk of sudden death. It is unclear whether these genetic variants remain relevant in subjects with underlying conditions such as diabetes that are associated with prolonged QT interval.
Methods: We tested single nucleotide polymorphisms (SNPs) in five candidate genes for association with QT interval in a family‐based study of subjects with type 2 diabetes mellitus (T2DM). Thirty‐six previously reported SNPs were genotyped in KCNQ1, HERG, SCN5A, KCNE1, and KCNE2 in 901 European Americans from 366 families. The heart rate‐corrected (QTc) durations were determined using the Marquette 12SL program. Associations between the QTc interval and the genotypes were evaluated using SOLAR adjusting for age, gender, T2DM status, and body mass index.
Results: Within KCNQ1 there was weak evidence for association between the minor allele of IVS12 +14T>C and increased QTc (P = 0.02). The minor allele of rs2236609 in KCNE1 trended toward significance with longer QTc (P = 0.06), while the minor allele of rs1805123 in HERG trended toward significance with shorter QTc (P = 0.07). However, no statistically significant associations were observed between the remaining SNPs and QTc variation.
Conclusions: We found weak evidence of association between three previously reported SNPs and QTc interval duration. While it appears as though genetic variants in previously identified candidate genes may be associated with QT duration in subjects with diabetes, the clinical implications of these associations in diabetic subjects at high risk for sudden death remain to be determined.
Keywords: QT interval, diabetes, association study, genetics, ion channels
Congenital long QT syndrome (LQTS) is caused by mutations leading to a gain or loss of expression of cardiac ion channel genes, which predispose to fatal arrhythmias. LQTS occasionally leads to sudden death in young, otherwise healthy individuals. While familial and sporadic mutations causing LQTS have been found in at least eight different genes, LQT1–8, acquired long QT syndrome (aLQTS) has generally been recognized in the presence of electrolyte disturbances or after an abnormal response to medications. 1 However, evidence suggests that single nucleotide polymorphisms (SNPs) in LQTS genes may also predispose to aLQTS. 2 , 3 Therefore, identification of these SNPs may provide additional insight in identifying those at increased risk for aLQTS and may subsequently lead to alternative approaches for prevention and treatment of this disorder.
Previous studies evaluating healthy populations have identified at least 151 SNPs in genes known for LQTS in an attempt to identify clinically important polymorphisms that may predispose to aLQTS (Cardiology Working Group on Arrhythmias database: http://www.fsm.it/cardmoc/). However, these studies are limited by small sample sizes and are generally composed of subjects who are not otherwise predisposed to fatal arrhythmia. 2 , 4 , 5 , 6 , 7 Hence, it is unclear whether genetic variants are relevant in subjects with (1) a predisposition to prolonged QT interval due to underlying conditions such as diabetes and (2) an already increased baseline risk of sudden cardiac death. Thus, the primary research objective of this study was to test 36 previously reported SNPs in five likely candidate genes (KCNQ1, HERG, SCN5A, KCNE1, and KCNE2) for association with QT interval in a family‐based study of subjects enriched for type 2 diabetes mellitus (T2DM). An analysis of SNPs in a large cohort of subjects with an already increased baseline risk for arrhythmic events could help substantiate the findings of previous studies and facilitate identification of common genetic variants associated with QT interval prolongation. The Diabetes Heart Study (DHS) is a large predominantly Caucasian cohort enriched for T2DM that offers a novel opportunity to confirm the importance of previously reported genetic polymorphisms in KCNQ1, HERG, SCN5A, KCNE1, and KCNE2 in aLQTS.
METHODS
Study Subjects
The study sample consisted of 901 European American (EA) individuals (749 T2DM‐affected individuals, 152 unaffected individuals from 366 families) from the DHS. Ascertainment and recruitment have been described previously. 8 , 9 , 10 Briefly, siblings concordant for T2DM without evidence of advanced renal insufficiency were recruited. T2DM was defined clinically as diabetes developing after the age of 35 years treated with insulin and/or oral agents in the absence of historical evidence of ketoacidosis. Subjects with T2DM were receiving treatment at the time of recruitment. Subjects not of EA heritage were excluded due to their small number in the overall cohort. Individuals with serious health conditions, for example, renal insufficiency, were not eligible to participate; however, there were no inclusion/exclusion criteria based on a history of cardiovascular disease. The family structures of the subjects included in the analyses ranged from T2DM‐affected singletons to one family with ten individuals: 52 T2DM‐affected singletons, 185 families with two individuals, 78 families with three individuals, 29 families with four individuals, 11 families with five individuals, 7 families with six individuals, 2 families with seven individuals, 1 family with eight individuals, and 1 family with ten individuals. For the 185 families with two individuals, each sibling has T2DM. The larger families have combinations of T2DM‐affected and unaffected relatives, but each has at least one pair of T2DM‐affected siblings.
All protocols were approved by the Institutional Review Board of Wake Forest University School of Medicine, and all participants gave informed consent. Participant examinations were conducted in the General Clinical Research Center of the Wake Forest University Baptist Medical Center and included interviews for medical history and health behaviors, anthropometric measures, resting blood pressure, fasting total cholesterol, electrocardiography, and spot urine collection. All measurements were not available in all participants. Individuals identified by the Minnesota Code 11 as having had a myocardial infarction were excluded from these analyses.
QT Interval Measurement
A resting 12‐lead electrocardiogram (ECG) was obtained for each study participant using a Marquette MAC 5000 ECG instrument (Marquette Electronics, Milwaukee, WI, USA) with a standardized protocol and special attention to precise placement of electrodes. All ECGs were performed in the General Clinical Research Center at Wake Forest University School of Medicine and were electronically transmitted to a central reading center (EPICARE, Inc., Winston‐Salem, NC, USA). Each ECG was visually analyzed for recording errors and graded for quality. The ECGs were then analyzed using the Marquette 12‐SL Program, 2001 version (GE Healthcare, Waukesha, WI, USA). 12 QT interval duration was defined as the duration in milliseconds from the initial deflection of the QRS complex until the return of the T‐wave to the electrical baseline. The QT duration was measured in each of the 12 ECG leads and the longest QT duration was selected as the trait for analysis. The QT interval was corrected for heart rate according to Bazett's formula. 13
Genetic Analysis
Total genomic DNA was purified from whole blood samples obtained from subjects using the PUREGENE DNA isolation kit (Gentra, Inc., Minneapolis, MN, USA). DNA concentration was quantified using standardized fluorometric readings on a Hoefer DyNA Quant 200 fluorometer (Hoefer Pharmacia Biotech Inc., San Francisco, CA, USA). Each sample was diluted to a final concentration of 5 ng/μL.
Genotypes were determined using a MassARRAY SNP Genotyping System (Sequenom Inc., San Diego, CA, USA). 14 This genotyping system uses single‐base extension reactions to create allele‐specific products that are separated and scored in a matrix‐assisted laser desorption ionization/time of flight mass spectrometer. Primers for PCR amplification and extension reactions were designed using the MassARRAY Assay Design Software (Sequenom Inc., San Francisco, CA, USA).
Statistical Analysis
Maximum likelihood allele and genotype frequencies for each SNP were calculated from unrelated probands and were tested for departures from Hardy‐Weinberg equilibrium using both χ2 and exact tests. Estimates of linkage disequilibrium between SNPs were determined by calculating pairwise D′ and r2 statistics in unrelated individuals. As previously reported, the microsatellite markers from a 10 cM genome scan were used to examine and correct self‐reported familial relationships. 10 Association between each SNP and the phenotypes were tested using variance components methods implemented in SOLAR. 15
For each SNP, the two degree of freedom test of genotypic association with each phenotype was performed. 16 If there was evidence of a significant association (P < 0.05), three individual contrasts defined by the a priori genetic models (dominant, additive, and recessive) were computed. This is consistent with the Fisher's protected least significant difference (LSD) multiple comparison procedure, which controls for multiple comparisons within each SNP, adjusting for the overall two degrees of freedom comparison as well as the three a priori genetic models (dominant, additive, and recessive). Additional multiple comparison adjustments were not performed. Effects were estimated while adjusting for age, gender, diabetes affection status, and BMI. In post hoc analyses, we studied the effects of adjusting for heart rate in the QTc models and found no significant change in the results (data not shown). The QTc phenotypic data were normally distributed, and thus did not require any transformation.
RESULTS
Demographic characteristics of the study population are listed in Table 1. The prevalence and severity of T2DM, hypertension, and hyperlipidemia illustrate the high cardiovascular risk shared in this cohort. The use of medications that mildly prolong the QT interval was moderately prevalent, with 27.6% of the sample taking a beta‐blocker and 18.5% taking a nondihydropyridine calcium channel blocker. However, the use of potent QT‐prolonging drugs, such as class III antidysrhythmics, was rare (n = 4).
Table 1.
Clinical Characteristics of European American Participants in the Diabetes Heart Study (n = 901)
| Mean ± SD or % (n) | Median (Range) | |
|---|---|---|
| Age (years) | 61.4 ± 9.5 | 61.4 (33.5–85.9) |
| Gender (% female) | 56.1 (505) | |
| BMI (kg/m2) | 31.8 ± 6.7 | 30.7 (16.6–58.0) |
| Diabetes diagnosis | 83.1 (749) | |
| Duration of diabetes (years) | 10.2 ± 7.0 | 8.0 (0–41.0) |
| Total cholesterol (mg/dL) | 189.0 ± 41.7 | 185.0 (74.0–350.0) |
| LDL cholesterol (mg/dL) | 105.9 ± 32.2 | 103.0 (14.0–230.0) |
| HDL cholesterol (mg/dL) | 43.7 ± 12.6 | 42.0 (18.0–90.0) |
| Triglycerides (mg/dL) | 207.3 ± 139.1 | 176.0 (30.0–1310) |
| Smoking | ||
| Current | 17.0 (152) | |
| Past | 41.4 (371) | |
| Never | 41.7 (374) | |
| Hypertension | 83.7 (754) | |
| Resting heart rate (beats/min) | 70.1 ± 12.3 | 69.0 (40.0–158.0) |
| QT interval duration (ms) | 394.0 ± 33.1 | 392.0 (270.0–564.0) |
| QTc interval duration (ms) | 421.9 ± 24.1 | 418.5 (332.6–536.1) |
| QT‐altering medications | ||
| Beta‐blockers | 27.6 (245) | |
| Calcium channel blockers | 18.5 (167) | |
| Class III antidysrhythmic drugs* | 0.44 (4) | |
*Class III antidysrhythmic drugs include dofetilide (n = 0), sotalol (n = 1), and amiodarone (n = 3).
DNA from the 901 EA participants was genotyped for 36 SNPs in 5 LQTS genes (SCN5A, HERG, KCNQ1, KCNE2, and KCNE1). Of these 36 SNPs, 6 were monomorphic in the DHS population, 3 failed to genotype using the Sequenom MassARRAY system, and 5 failed assay design, resulting in 22 SNPs that were analyzed in the study (Table 2). Three of the SNPs were nearly monomorphic in our study sample, including rs2236608 and rs1805128 (both in KCNE1), and IVS1 −16A>G (KCNE2). The genotype distributions of the latter two SNPs deviated from Hardy‐Weinberg proportions, likely due to the infrequency of the minor alleles of these SNPs. Rs1805123, located in the HERG gene, also deviated from Hardy‐Weinberg proportions.
Table 2.
SNPs Evaluated in the LQTS Genes
| Gene | SNP | Chromosomal Position (dbSNP Build 128) | Location in Gene | Protein Coding | Alleles (Major/Minor) | MAF | Genotype Counts | ||
|---|---|---|---|---|---|---|---|---|---|
| SCN5A | rs6599230 | chr3: 38649716 | Exon 2 | A29A | G/A | 0.176 | GG (n = 594) | GA (n = 257) | AA (n = 25) |
| IVS9 ‐3C > A (rs41312433) | chr3: 38622646 | Intron 9 | C/A | 0.205 | CC (n = 502) | CA (n = 285) | AA (n = 26) | ||
| rs7428779 | chr3: 38621427 | Intron 10 | G/A | 0.198 | GG (n = 526) | GA (n = 282) | AA (n = 28) | ||
| rs1805124 | chr3: 38620424 | Exon 12 | H558R | A/G | 0.223 | AA (n = 515) | AG (n = 305) | GG (n = 37) | |
| rs7430407 | chr3: 38597471 | Exon 17 | E1061E | G/A | 0.095 | GG (n = 698) | GA (n = 145) | AA (n = 3) | |
| IVS24 +53T>C (rs41312393) | chr3: 38573673 | Intron 24 | T/C | 0.045 | TT (n = 800) | TC (n = 72) | CC (n = 0) | ||
| IVS24 + 116G>A (rs41312391) | chr3: 38573610 | Intron 24 | G/A | 0.143 | GG (n = 626) | GA (n = 206) | AA (n = 22) | ||
| HERG | rs1805123 | chr7: 150276467 | Exon 7 | K897T | A/C | 0.236 | AA (n = 518) | AC (n = 305) | CC (n = 60) |
| KCNQ1 | rs760419 | chr11: 2639933 | Intron 11 | A/G | 0.497 | AA (n = 232) | AG (n = 439) | GG (n = 185) | |
| IVS12 +14T>C (rs11024034) | chr11: 2746739 | Intron 12 | T/C | 0.101 | TT (n = 697) | TC (n = 156) | CC (n = 11) | ||
| rs1057128 | chr11: 2753813 | Exon 13 | S546S | G/A | 0.221 | GG (n = 527) | GA (n = 313) | AA (n = 35) | |
| rs163150 | chr11: 2753896 | Intron 13 | G/A | 0.353 | GG (n = 358) | GA (n = 383) | AA (n = 112) | ||
| rs81204 | chr11: 2754881 | Intron 14 | T/C | 0.242 | TT (n = 507) | TC (n = 323) | CC (n = 45) | ||
| KCNE2 | rs9305548 | chr21: 34664592 | Intron 1 | C/T | 0.168 | CC (n = 610) | CT (n = 230) | TT (n = 23) | |
| IVS1 ‐16A > G (rs41314677) | chr21: 34664620 | Intron 1 | A/G | 0.007 | AA (n = 854) | AG (n = 8) | GG (n = 5) | ||
| KCNE1 | rs2236609 | chr21: 34743981 | Intron 3 | C/T | 0.385 | CC (n = 328) | CT (n = 400) | TT (n = 145) | |
| rs2236608 | chr21: 34743911 | Intron 3 | T/C | 0.012 | TT (n = 812) | TC (n = 7) | CC (n = 3) | ||
| rs1805127 | chr21: 34743691 | Exon 4 | G38S | G/A | 0.354 | GG (n = 361) | GA (n = 385) | AA (n = 124) | |
| rs1805128 | chr21: 34743550 | Exon 4 | D85N | G/A | 0.008 | GG (n = 871) | GA (n = 15) | AA (n = 0) | |
| C*132 A >G (rs41314071) | chr21: 34743281 | 3′ UTR | A/G | 0.044 | AA (n = 783) | AG (n = 66) | GG (n = 5) | ||
| rs2070357 | chr21: 34743289 | 3′ UTR | G/A | 0.47 | GG (n = 238) | GA (n = 429) | AA (n = 198) | ||
| rs2070356 | chr21: 34742957 | 3′ UTR | T/C | 0.478 | TT (n = 223) | TC (n = 406) | CC (n = 193) | ||
The minor allele frequency (MAF) and the number of observations of each genotype in unrelated European American probands are shown for each SNP. SNPs in bold indicate those that deviate from Hardy‐Weinberg proportions.
The associations between each SNP and the QTc interval duration are displayed in Table 3. The strongest evidence of association was with the intron 12 SNP (IVS12 +14T>C) in KCNQ1 (P = 0.02). The association with QTc duration is most consistent with either a dominant (P = 0.01) or an additive (P = 0.01) genetic model as demonstrated by a longer mean QTc interval as a function of the number of copies of the minor allele (Table 4). The minor allele of rs2236609 in the KCNE1 gene trended toward an overall association with QTc (P = 0.06). This association is most consistent with a recessive model of inheritance, as two copies of the minor allele result in an increased QTc interval (P = 0.02; Table 4). Within the HERG gene, the minor allele of rs1805123 trended toward association with QTc (P = 0.07). The association between this SNP and QT interval appears to be driven by a stepwise reduction in QT interval duration with increasing copies of the minor allele (dominant model P = 0.01, additive model P = 0.04; Table 4). However, these results are suspect given that this SNP deviates from Hardy‐Weinberg proportions in the study sample.
Table 3.
Genotypic Association of SNPs in LQTS Genes for European Americans in the Diabetes Heart Study
| Gene | SNP | P‐value |
|---|---|---|
| SCN5A | rs6599230 | 0.91 |
| IVS9 ‐3C > A (rs41312433) | 0.38 | |
| rs7428779 | 0.34 | |
| rs1805124 | 0.14 | |
| rs7430407 | 0.13 | |
| IVS24 + 53T>C (rs41312393) | 0.78 | |
| IVS24 + 116G>A (rs41312391) | 0.85 | |
| HERG | rs1805123 | 0.07 |
| KCNQ1 | rs760419 | 0.65 |
| IVS12 +14T>C (rs11024034) | 0.02 | |
| rs1057128 | 0.69 | |
| rs163150 | 0.69 | |
| rs81204 | 0.41 | |
| KCNE2 | rs9305548 | 0.90 |
| IVS1 ‐16A > G (rs41314677) | 0.55 | |
| KCNE1 | rs2236609 | 0.06 |
| rs2236608 | 0.79 | |
| rs1805127 | 0.13 | |
| rs1805128 | 0.35 | |
| C*132 A > G (rs41314071) | 0.63 | |
| rs2070357 | 0.40 | |
| s2070356 | 0.13 |
SNPs in bold indicate those that deviate from Hardy‐Weinberg proportions. P‐values in bold indicate significant association at the P ≤ 0.05 level. P‐values are adjusted for age, gender, diabetes affection status, BMI.
Table 4.
Untransformed Mean Trait Values by Genotype and Model‐Specific P‐values for Associated SNPs
| Gene | SNP | Genotypic Means | P‐values* | |||||
|---|---|---|---|---|---|---|---|---|
| Overall | Additive | Dominant | Recessive | |||||
| KCNQ1 | IVS12 +14T>C (rs11024034) | TT (n = 697) | TC (n = 156) | CC (n = 11) | 0.02 | 0.01 | 0.01 | 0.43 |
| 421.37 ± 23.98 | 426.63 ± 24.03 | 424.13 ± 33.55 | ||||||
| KCNE1 | rs2236609 | CC (n = 328) | CT (n = 400) | TT (n = 145) | 0.06 | 0.15 | 0.76 | 0.02 |
| 421.88 ± 24.18 | 420.79 ± 22.77 | 424.91 ± 27.60 | ||||||
| HERG | rs1805123 | AA (n = 518) | AC (n = 305) | CC (n = 60) | 0.07 | 0.12 | 0.04 | 0.81 |
| 423.55 ± 23.14 | 419.38 ± 25.41 | 422.39 ± 25.18 | ||||||
P‐values ≤ 0.05 are shown in bold. P‐values trending toward association (0.05 < P < 0.10) are shown in italics. SNPs in bold indicate those that deviate from Hardy‐Weinberg proportions.
*P‐values are adjusted for age, gender, diabetes affection status, BMI.
DISCUSSION
We evaluated a previously reported panel of common SNPs in sodium and potassium channel genes in a high‐risk sample of predominantly diabetic participants in the DHS. We found weak evidence for associations between these SNPs and the duration of the QT interval, consisting of three borderline‐significant associations that have been previously reported. However, the number of significant associations was few and the magnitude of the observed genetic effects was small. Therefore, these results provide additional support to the strong pretest rationale for sodium and potassium channel genes as likely contributors to the genetic variance in QT interval duration. However, inconsistencies in the observed associations with previously reported SNPs between this study and prior studies warrant a broader reassessment of the precise location of the informative regions of these genes using haplotype‐tagging SNPs or other approaches.
The SNP panel used in the present report was derived from a panel developed by Aydin et al. 4 In this previous report, genotypic data were reported on 35 SNPs in SCN5A, HERG, KCNQ1, KCNE2, and KCNE1, 10 of which were novel. Among these 35 SNPs, there were four significant associations with the duration of the QT interval, two in SCN5A, and one each in KCNE1 and KCNE2. In our attempt to replicate these findings in our sample, we found that the KCNE1 and KCNE2 SNPs were monomorphic. This is not surprising, given the low minor allele frequencies for these SNPs (KCNE1 MAF = 0.01, KCNE2 MAF = 0.02) reported by Aydin et al. In addition, Aydin et al. reported an increase in QT duration with the minor allele of the intronic SCN5A SNP, IVS24 + 116G>A. We observed no statistical differences in QTc duration with this SNP at the α= 0.05 level. The only significant association from Aydin et al. that was similar in our analysis was an increase in raw QT interval duration with the minor allele of the nonsynonymous SCN5A SNP, rs1805124 (called C1673 A > G in the Aydin manuscript; data not shown). However, in our study, there was no significant association observed between this SNP and QTc. Furthermore, differences in the analytic approach used by Aydin et al. make comparison with our results problematic.
The voltage‐gated sodium channel, SCN5A, is well known for its contributions to the genetic variance in the QT duration as well as for its pathogenic role in the Brugada syndrome. We observed no significant associations between SNPs within the SCN5A gene and QTc variation. Our observations reflect the incomplete understanding of the true relationship of SNPs within this gene and variation in QT interval duration. For example, within the D.E.S.I.R. cohort, the minor allele of rs1805124 was more prevalent among the subjects with a QT duration above the 95th percentile, compared to those with the QT duration less than the 5th percentile. 17 In contrast, the minor allele of rs1805124 was not associated with QT duration in 2042 population‐based subjects from Olmsted County, Minnesota. 18 In addition, Yang et al. found no difference in the distribution of the SCN5A rs1805124 SNP, comparing patients who suffered torsades des pointes with controls. 2 Of related interest, the rs1805124 minor allele has also been reported as a predisposing factor to lone atrial fibrillation. 19 For all of these reasons, an expanded evaluation of both common and rare variants of SCN5A, particularly the rs1805124 SNP and surrounding regions, may provide additional insight regarding genetic variation in QT duration and resulting cardiovascular disease.
The rapidly activating Ikr potassium current is made of up four alpha subunits, each with six transmembrane segments and an auxiliary beta subunit. Each of the alpha subunits is encoded by HERG. With Iks, Ikr is responsible for the termination of the plateau phase of cardiac repolarization and is highly susceptible to binding by QT‐prolonging medications.
We observed that the minor allele of the HERG rs1805123 SNP (also known as KCNH2 K897T) trended toward a significant association with shorter QTc duration (P = 0.08). Conflicting findings about the association between rs1805123 and the QT duration exist in the current literature. Pietila et al. 20 reported a shorter QT duration among carriers of the TT genotype compared with the TG and GG genotypes in 187 Finnish females. However, studies with a greater number of participants favor a shorter QT interval with GG as compared with the TT and TG genotypes. For example, Pfueufer et al. 21 reported in 689 participants that each G allele was associated with a 1.6 ms decrease in the QT interval. The shorter QT interval duration with increasing copies of the HERG rs1805123 G allele has been replicated in many other reports as well. 17 , 22 , 23 , 24 This association between GG and shorter QT duration appears to be especially prominent among females. 21 , 22 Therefore, our finding of a decrease in the QTc duration with the rs1805123 minor (G) allele is consistent with the bulk of the prior literature. However, in our sample the distribution of the HERG rs1805123 SNP did not conform to Hardy‐Weinberg proportions, and therefore we must treat these results with some caution.
Mutations in the KCNQ1 and KCNE1 genes are associated with LQTS types 1 and 5, respectively. The rapid (Ikr) and slow (Iks) components of the delayed‐rectifier K current (Ik) are elicited by the co‐expression of the α and beta ion channel subunits encoded by the KCNQ1 and KCNE1 genes, respectively. 25 It is thought that mutations in the alpha subunit of the voltage‐gated potassium channel encoded by KCNQ1 lead to prolonged QTc due to near‐complete loss of Iks current. A twin study using microsatellite markers by Busjahn et al. provided the first evidence that common variants in the KCNQ1 gene contribute to variation in the QTc. 26 We observed a modest increase in the QTc with the minor allele of one SNP in the KCNQ1 gene (IVS12 +14T>C, also known as rs11024034). The association with QTc duration was consistent with either a dominant or an additive inheritance model. However, no other SNPs in KCNQ1 showed any association with QTc duration. While our results support the conclusions of Busjahn et al., more recent findings, including those of Gouas et al., 17 and Aydin et al., 4 failed to show associations between any KCNQ1 SNPs and QT interval duration, including IVS12 +14T>C. We believe that the discrepancy in findings between reports suggests that the IVS12 +14T>C SNP is likely not a causative SNP related to the QTc, but is instead a SNP that may be correlated with the true causative SNP in this important gene.
While major genetic disruption of KCNE1 is known to cause LQTS type 5 as well as the Jervell and Lange‐Nielsen syndrome, the search for minor variants in KCNE1 is immature at present. We observed a borderline association between the T (minor) allele of the KCNE1 rs2236609 SNP with a longer QTc. No significant associations with the QTc were observed in the two previous reports about this SNP. 4 , 17 In one of these studies, 17 the rs2236609 SNP was found to participate in a haplotype (with KCNE1 rs1805127) that was associated with a longer QTc. However, in this prior study, the A allele was associated with a longer QTc, not the T allele as we observed.
There are several limitations to the current study. The study sample evaluated was highly prevalent in diabetes, hypertension, and cardiovascular disease—disorders that are known to have separate influences on the QT interval duration. Statistical methods to adjust for the effects of these conditions—all of which produce a wide continuum of diseases—can leave residual confounding. In addition, given the specific characteristics of our sample, these results may not be generalizable to all populations. In addition, while we attempted to limit the likelihood of population stratification by restricting our analysis to EAs, the possibility of such confounding remains. Finally, the genotype distributions of three SNPs evaluated did not conform to expected Hardy‐Weinberg proportions, decreasing the confidence in the significant results observed with the HERG rs1805123 SNP.
In conclusion, we have found weak evidence for association between three SNPs and variation in QT interval duration in subjects who already have a predisposition to prolonged QT interval due to other underlying conditions such as diabetes. While it appears as though genetic variants in previously identified candidate genes may be associated with QT interval duration in subjects with diabetes, the clinical implications of these associations in diabetic subjects at high risk for sudden death remain to be determined. However, given the few and weak associations observed with these selected SNPs and QTc, a more exhaustive search for the true causative SNPs within these genes is warranted.
Acknowledgments
Acknowledgments: This study was supported in part by the General Clinical Research Center of the Wake Forest University School of Medicine grant M01 RR07122 and by the National Heart, Lung, and Blood Institute R01 HL67348 to D.W.B.
REFERENCES
- 1. Modell SM, Lehmann MH. The long QT syndrome family of cardiac ion channelopathies: A HuGE review. Genet Med 2006;8:143–155. [DOI] [PubMed] [Google Scholar]
- 2. Yang P, Kanki H, Drolet B, et al Allelic variants in long‐QT disease genes in patients with drug‐associated torsades de pointes. Circulation 2002;105:1943–1948. [DOI] [PubMed] [Google Scholar]
- 3. Anson BD, Ackerman MJ, Tester DJ, et al Molecular and functional characterization of common polymorphisms in HERG (KCNH2) potassium channels. Am J Physiol Heart Circ Physiol 2004;286:H2434–2441. [DOI] [PubMed] [Google Scholar]
- 4. Aydin A, Bahring S, Dahm S, et al Single nucleotide polymorphism map of five long‐QT genes. J Mol Med 2005;83:159–165. [DOI] [PubMed] [Google Scholar]
- 5. Ackerman MJ, Tester DJ, Jones GS, et al Ethnic differences in cardiac potassium channel variants: Implications for genetic susceptibility to sudden cardiac death and genetic testing for congenital long QT syndrome. Mayo Clin Proc 2003;78:1479–1487. [DOI] [PubMed] [Google Scholar]
- 6. Iwasa H, Itoh T, Nagai R, et al Twenty single nucleotide polymorphisms (SNPs) and their allelic frequencies in four genes that are responsible for familial long QT syndrome in the Japanese population. J Hum Genet 2000;45:182–183. [DOI] [PubMed] [Google Scholar]
- 7. Laitinen P, Fodstad H, Piippo K, et al Survey of the coding region of the HERG gene in long QT syndrome reveals six novel mutations and an amino acid polymorphism with possible phenotypic effects. Hum Mutat 2000;15:580–581. [DOI] [PubMed] [Google Scholar]
- 8. Wagenknecht LE, Bowden DW, Carr JJ, et al Familial aggregation of coronary artery calcium in families with type 2 diabetes. Diabetes 2001;50:861–866. [DOI] [PubMed] [Google Scholar]
- 9. Lange LA, Lange EM, Bielak LF, et al Autosomal genome‐wide scan for coronary artery calcification loci in sibships at high risk for hypertension. Arterioscler Thromb Vasc Biol 2002;22:418–423. [DOI] [PubMed] [Google Scholar]
- 10. Bowden DW, Rudock M, Ziegler J, et al Coincident linkage of type 2 diabetes, metabolic syndrome, and measures of cardiovascular disease in a genome scan of the diabetes heart study. Diabetes 2006;55:1985–1994. [DOI] [PubMed] [Google Scholar]
- 11. Prineas RJ, Crow RS, Blackburn H. The Minnesota Code: Manual of Electrocardiographic Findings. Boston, John Wright , 1982. [Google Scholar]
- 12. Marquette R. 12SL ECG Analysis Program: Physician's Guide, Revision B. Waukesha , Wisconsin , GE Healthcare, 2006. [Google Scholar]
- 13. Bazett HC. An analysis of time relations of the electrocardiogram. Heart 1920;7:353–370. [Google Scholar]
- 14. Buetow KH, Edmonson M, MacDonald R, et al High‐throughput development and characterization of a genomewide collection of gene‐based single nucleotide polymorphism markers by chip‐based matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry. Proc Natl Acad Sci U S A 2001;98:581–584. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Almasy L, Blangero J. Multipoint quantitative‐trait linkage analysis in general pedigrees. Am J Hum Genet 1998;62:1198–1211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Searle SR. Linear Models. New York , John Wylie & Sons, Inc., 1971. [Google Scholar]
- 17. Gouas L, Nicaud V, Berthet M, et al Association of KCNQ1, KCNE1, KCNH2 and SCN5A polymorphisms with QTc interval length in a healthy population. Eur J Hum Genet 2005;13:1213–1222. [DOI] [PubMed] [Google Scholar]
- 18. Hobday PM, Mahoney DW, Urban L, et al Influence of the common H558R‐SCN5A sodium channel polymorphism on the electrocardiographic phenotype in a population‐based study. Heart Rhythm 2006;3:S279–S280. [Google Scholar]
- 19. Chen LY, Ballew JD, Herron KJ, et al A common polymorphism in SCN5A is associated with lone atrial fibrillation. Clin Pharmacol Ther 2007;81:35–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Pietila E, Fodstad H, Niskasaari E, et al Association between HERG K897T polymorphism and QT interval in middle‐aged Finnish women. J Am Coll Cardiol 2002;40:511–514. [DOI] [PubMed] [Google Scholar]
- 21. Pfeufer A, Jalilzadeh S, Perz S, et al Common variants in myocardial ion channel genes modify the QT interval in the general population: Results from the KORA study. Circ Res 2005;96:693–701. [DOI] [PubMed] [Google Scholar]
- 22. Bezzina CR, Verkerk AO, Busjahn A, et al A common polymorphism in KCNH2 (HERG) hastens cardiac repolarization. Cardiovasc Res 2003;59:27–36. [DOI] [PubMed] [Google Scholar]
- 23. Paavonen KJ, Chapman H, Laitinen PJ, et al Functional characterization of the common amino acid 897 polymorphism of the cardiac potassium channel KCNH2 (HERG). Cardiovasc Res 2003;59:603–611. [DOI] [PubMed] [Google Scholar]
- 24. Newton‐Cheh C, Guo CY, Larson MG, et al Common genetic variation in KCNH2 is associated with QT interval duration: The Framingham Heart Study. Circulation 2007;116:1128–1136. [DOI] [PubMed] [Google Scholar]
- 25. Watanabe E, Yasui K, Kamiya K, et al Upregulation of KCNE1 induces QT interval prolongation in patients with chronic heart failure. Circ J 2007;71:471–478. [DOI] [PubMed] [Google Scholar]
- 26. Busjahn A, Knoblauch H, Faulhaber HD, et al QT interval is linked to 2 long‐QT syndrome loci in normal subjects. Circulation 1999;99:3161–3164. [DOI] [PubMed] [Google Scholar]