Skip main navigation

Rare Variation in Drug Metabolism and Long QT Genes and the Genetic Susceptibility to Acquired Long QT Syndrome

Originally publishedhttps://doi.org/10.1161/CIRCGEN.121.003391Circulation: Genomic and Precision Medicine. 2022;15

Abstract

Background:

Acquired long QT syndrome (aLQTS) is a serious unpredictable adverse drug reaction. Pharmacogenomic markers may predict risk.

Methods:

Among 153 aLQTS patients (mean age 58 years [range, 14–88], 98.7% White, 85.6% symptomatic), computational methods identified proteins interacting most significantly with 216 QT-prolonging drugs. All cases underwent sequencing of 31 candidate genes arising from this analysis or associating with congenital LQTS. Variants were filtered using a minor allele frequency <1% and classified for susceptibility for aLQTS. Gene-burden analyses were then performed comparing the primary cohort to control exomes (n=452) and an independent replication aLQTS exome sequencing cohort.

Results:

In 25.5% of cases, at least one rare variant was identified: 22.2% of cases carried a rare variant in a gene associated with congenital LQTS, and in 4% of cases that variant was known to be pathogenic or likely pathogenic for congenital LQTS; 7.8% cases carried a cytochrome-P450 (CYP) gene variant. Of 12 identified CYP variants, 11 (92%) were in an enzyme known to metabolize at least one culprit drug to which the subject had been exposed. Drug-drug interactions that affected culprit drug metabolism were found in 19% of cases. More than one congenital LQTS variant, CYP gene variant, or drug interaction was present in 7.8% of cases. Gene-burden analyses of the primary cohort compared to control exomes (n=452), and an independent replication aLQTS exome sequencing cohort (n=67) and drug-tolerant controls (n=148) demonstrated an increased burden of rare (minor allele frequency<0.01) variants in CYP genes but not LQTS genes.

Conclusions:

Rare susceptibility variants in CYP genes are emerging as potentially important pharmacogenomic risk markers for aLQTS and could form part of personalized medicine approaches in the future.

Acquired long QT syndrome (aLQTS) is associated with prolongation of the QT interval on the ECG and torsades de pointes (TdP) in the setting of triggering factors.1,2 It is rare, often unpredictable, and can be a serious adverse event and as such is a cause for relabeling and withdrawal of medications. It is more commonly seen during administration of antiarrhythmic drugs (eg, up to 3% of patients receiving dofetilide) but has a lower incidence in noncardiac drugs (1–10 per 100 000).3 It is an important issue for the pharmaceutical industry and public health and thorough QT studies have become a standard component of new drug evaluation.1,4

Several clinical factors have been identified that suggest an individual may be at increased risk for aLQTS including female gender; acute and chronic metabolic abnormalities, such as hypokalemia, renal, or liver disease; heart disease, including bradycardia and recent conversion from atrial fibrillation to normal rhythm; and drug-specific factors, such as dose, drug pharmacokinetics and pharmacodynamics, and route of administration.2,5 Inhibition of the cardiac repolarizing potassium current inward-rectifier potassium channel, and to a lesser extent IKs, is the most common mechanism across multiple drugs.1,6 In addition, inhibition of drug metabolism or elimination leading to high plasma concentrations has been implicated.7

The sporadic nature of aLQTS and the similarity with the congenital condition has, however, suggested that they may share some common genetic background.4,8 Indeed rare variants in genes associated with congenital LQTS (cLQTS) have been detected in other aLQTS cohorts, often with normal QT intervals after drug withdrawal. However, these studies were before the advent of large population databases and stringent variant calling with American College of Medical Genetics and Genomics criteria, and, therefore, may have over-called pathogenicity of some variants which have subsequently been identified to be more common than anticipated in the general population.9–12 A genome-wide association study did not identify any common single nucleotide polymorphisms with genome-wide significance for aLQTS suggesting that common variants do not contribute importantly to risk for TdP across multiple drugs.13 Other studies have shown that first-degree relatives of patients with TdP show more exaggerated QT interval responses to quinidine challenge than controls, suggesting that there is an important genetic influence.14 These observations support the concept of the repolarization reserve, the physiological capacity for cardiac repolarisation which is in part genetically predetermined, and upon which additional insults such as hypokalemia or a QT-prolonging drug may act and precipitate aLQTS.1,5

Human CYP450 (cytochrome P450) enzymes are responsible for drug metabolism, with induction or inhibition of these enzymes a key mechanism underpinning drug-drug interactions.15 Genetic changes in CYP genes may be responsible for individual variations in therapeutic efficacy and individual variations in disease susceptibility. This can lead to individuals with differing metabolizer status. However, the influence of genetic variation in CYP450 pathways in aLQTS has not been studied.

We hypothesized that there may be predisposition to aLQTS due to rare variation in genes associated with LQTS and CYP450 hepatic drug metabolism, and thus permit identification of personalized risk.

Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethical approval was gained from the London Multicentre Research Ethics Committee, reference number MREC/02/2/73 The full methods are available in the Supplemental Material.

Results

Clinical Characteristics

Baseline clinical characteristics for the cohort are shown in Table 1. A total of 153 unrelated aLQTS cases (94 females, 61.4%) were included, with a mean age at presentation of 58 years (range, 14–88). The vast majority of cases were White (148/150, 98.7%).

Table 1. Clinical Characteristics of All Cases and Variant Carriers Compared With Noncarriers

Overall (n=153)Carriers (n=39)Noncarriers (n=114)P value*
Age, y, mean±SD58±1757±1859±160.57
Median (range)58 (14–88)59 (14–84)63 (17–86)
White, n (%)148/150 (98.7)38 (97.4)110/111 (99.1)0.44
QTc on admission, ms (mean±SD, median [min–max])574.1±80.0592.1±100.5567.9±71.20.18
564 (460–743)
560 (475–827)
[n=78]
560 (460–827)
[n=27]
[n=105]
QTc after drug removal, ms [mean±SD, median (min–max)]452±42452.5±29.1451.9±46.60.95
442 (374–622)452 (410–520)437 (374–622)
[n=87][n=22][n=65]
Culprit drugs
 No. of culprit drugs per patient1.4±0.71.5±0.61.4±0.70.26
1 (1–4)1 (1–3)1 (1–4)
Intrinsic risk factors
 Female sex, n (%)94 (61.4)29 (74.3)65 (57.0)0.055
 Hypothyroidism26 (16.9)8 (20.5)18 (15.8)0.50
 Heart disease74 (48.3)20 (51.2)54 (47.4)0.67
 Liver dysfunction15 (9.8)4 (10.3)11 (9.6)0.91
 Renal dysfunction11 (7.2)3 (7.7)8 (7.0)0.89
Extrinsic risk factors
 Hypokalemia43 (28.1)15 (38.5)28 (24.6)0.08
 Hypomagnesemia16 (10.5)6 (15.4)10 (8.8)0.24
 Hypocalcemia20 (13.1)7 (17.9)13 (11.4)0.28
 Extreme bradycardia <40/min12 (7.8)1 (2.6)11 (9.6)0.16
Cardiac event
 TdP, VF, or cardiac arrest122 (79.7)29 (74.3)93 (73.6)0.33
 Syncope9 (5.9)4 (10.2)5 (4.3)0.18
 QTc prolongation alone22 (14.4)6 (15.4)16 (14.0)0.84

QTc indicates corrected QT; TdP, torsades de pointes; and VF, ventricular fibrillation.

* Comparison between carriers and noncarriers.

† In case of liver metabolism of the culprit drug.

‡ In case of renal elimination of the culprit drug.

Clinical Presentation

One hundred thirty-one cases (85.6%) presented with either TdP, ventricular fibrillation, or cardiac arrest (n=122, 79.7%) or with syncope (n=9, 5.9%). Twenty-two cases (14.4%) were asymptomatic. The corrected QT (QTc) interval, where available at presentation (n=105, 68.7%), was prolonged in all patients. The average QTc on admission was 574±80 ms which reduced to an average of 452±42 ms on removal of the culprit drug(s). Hypokalemia was documented in 28.1%.

Culprit Drugs

A total of 216 QT-prolonging drugs were observed: 1.4±0.7 drugs per patient (median, 1; range, 1–4). CredibleMeds categorized 161 drugs as known risk of torsades de pointes, 11 as possible risk of torsades de pointes, 42 as conditional risk of TdP, and 2 to be avoided by LQTS patients. Drug interactions that were likely to increase culprit drug bioavailability or pharmacodynamic synergy were found in 29 patients (19.0%), 18 (11.8%) of whom did not carry a genetic variant (Table 2 and Figure 1).

Table 2. Drug Interactions That Affect Culprit Drugs and Associated CYP450 and LQTS Variants in ARITMO Cohort

PatientVariant (s)ClassMAF (gnomAD)MAF (European non-Finnish)Culprit drug-1RiskCYP450 metabolismCYP-drug matchCulprit drug-2 (and 3)RiskCYP metabolismCYP-drug matchDrug-drug interaction (type)
ARITMO-6KCNH2-c.3163C>T-p.R1055WPRA00FlecainideKRn/an/aDisopyramideKRn/an/aDisopyramide and flecainide both increase QTc interval. Pharmacodynamic synergy
ARITMO-9No variantn/an/an/aFluconazoleKRn/an/aClarithromycinKRn/an/a1. Fluconazole will increase the level or effect of clarithromycin by affecting hepatic/intestinal enzyme CYP3A4 metabolism.
2. Clarithromycin and fluconazole both increase QTc interval. Pharmacodynamic synergy
ARITMO-13No variantn/an/an/aMethadoneKRn/an/aVenlafaxineKRn/an/aMethadone and venlafaxine both increase QTc interval. Pharmacodynamic synergy
ARITMO-15CYP2B6-c.1172T>A-p. I391NPRA0.0040.007ErythromycinKR1A2, 2B6, 2J2, 3A4, 3A7yesn/an/an/an/an/a
ARITMO-161. CAV3-c.233C>T-p. T78MVUS0.00310.004AmiodaroneKRn/an/aSotalolKRn/an/aAmiodarone and sotalol both increase QTc interval. Pharmacodynamic synergy
2. SCN5A-c.569G>A-p. R190QPRA0.000030.00003
ARITMO-23No variantn/an/an/aSotalolKRn/an/aFluoxetineCRn/an/aFluoxetine and sotalol both increase QTc interval.
Pharmacodynamic synergy
ARITMO-26CYP3A4-c.1000G>T-p. E333*PRA00AmiodaroneKR1A1, 1A2, 2C8, 2C9, 2C19, 2D6, 3A4, 3A5, 3A7yesn/an/an/an/an/a
ARITMO-28KCNQ1c.727C>T-p. R243CLP00.0000009AmiodaroneKRn/an/aAmitriptylineCRn/an/a1. Amiodarone will increase the level or effect of amitriptyline by P-glycoprotein (MDR1) efflux transporter.
2. Amitriptyline and amiodarone both increase QTc interval. Pharmacodynamic synergy
ARITMO-29CYP2B6-c.1172T>A-p. I391NPRA0.0040.007ErythromycinKR1A2, 2B6, 2J2, 3A4, 3A7yesn/an/an/an/an/a
ARITMO-30No variantn/an/an/aCitalopramKRn/an/aIndapamideCRn/an/aindapamide and citalopram both increase QTc interval. Pharmacodynamic synergy
ARITMO-31CYP2B6-c.1172T>A-p. I139NPRA0.0040.007AmitriptylineCR1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4yesn/an/an/an/an/a
ARITMO-34CYP2B6-c.415A>G-p. K139EPRA0.00180.004FluoxetineCR1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 2J2, 3A4yesAmiodaroneKR1A1, 1A2, 2C8, 2C9, 2C19, 2D6, 3A4, 3A5, 3A7no1. Amiodarone will increase the level or effect of fluoxetine by affecting hepatic enzyme CYP2D6 metabolism.
2. Amiodarone and fluoxetine both increase QTc interval. Pharmacodynamic synergy
ARITMO-36CYP2B6-c.1172T>A-p. I391NPRA0.0040.007DomperidoneKR1A2, 2B6, 2C8, 2D6, 3A4, 3A5, 3A7yesn/an/an/an/an/a
ARITMO-38No variantn/an/an/aProcainamideKRn/aAmitriptyline and quinineCR and CRn/an/an/a1. Procainamide will increase the level or effect of quinine by basic (cationic) drug competition for renal tubular clearance.
2. Procainamide and quinine both increase QTc interval
3. Amitriptyline and procainamide both increase QTc interval. Pharmacodynamic synergy
4. Procainamide and citalopram both increase QTc interval. Pharmacodynamic synergy
5. Quinine and citalopram both increase QTc interval. Pharmacodynamic synergy
6. Amitriptyline and quinine both increase QTc interval.
ARITMO-50No variantn/an/an/aAmiodaroneKRn/an/aErythromycinKRn/an/a1. Erythromycin base will increase the level or effect of amiodarone by affecting hepatic/intestinal enzyme CYP3A4 metabolism.
2. Amiodarone and erythromycin base both increase QTc interval. Pharmacodynamic synergy.
ARITMO-61No variantn/an/an/aThioridazineKRn/an/aFluoxetineCRn/an/a1. Thioridazine will increase the level or effect of fluoxetine by affecting hepatic enzyme CYP2D6 metabolism.
2. Fluoxetine increases levels of thioridazine by decreasing metabolism.
3. Fluoxetine will increase the level or effect of thioridazine by affecting hepatic enzyme CYP2D6 metabolism.
4. Thioridazine and fluoxetine both increase QTc interval. Pharmacodynamic synergy
ARITMO-73No variantn/an/an/aAmiodaroneKRn/an/aTrimethoprimTAn/an/a1. Amiodarone will increase the level or effect of trimethoprim by basic (cationic) drug competition for renal tubular clearance.
2. Amiodarone and trimethoprim both increase QTc interval. Pharmacodynamic synergy
ARITMO-84No variantn/an/an/aAmiodaroneKRn/an/aFlecainideKRn/an/a1. Amiodarone will increase the level or effect of flecainide by affecting hepatic enzyme CYP2D6 metabolism.
2. Amiodarone and flecainide both increase QTc interval. Pharmacodynamic synergy
ARITMO-86CYP2B6-c.499C>G-p.P167APRA0.00020.000007Pimozide overdoseKR1A2, 2C8, 2C9, 2C19, 2D6, 2E1, 2J2, 3A4, 3A5, 3A7non/an/an/an/an/a
ARITMO-88CYP2D6-c.404C>T-p. S135FPRA0.000030.00007AmiodaroneKR1A1, 1A2, 2C8, 2C9, 2C19, 2D6, 3A4, 3A5, 3A7yesDomperidone
+fluconazole
KR1A2, 2B6, 2C8, 2D6, 3A4, 3A5, 3A7yes1. Fluconazole will increase the level or effect of amiodarone by affecting hepatic/intestinal enzyme CYP3A4 metabolism.
KRno2. Amiodarone: CYP2D6 inhibitor
3. Amiodarone and fluconazole both increase QTc interval. Pharmacodynamic synergy
Fluoxetine both increase QTc interval. Pharmacodynamic synergy
ARITMO-89KCNE1-c.253G>A-p. D85NPRA0.00810.012FlecainideKRn/an/aCitalopramKRn/an/aFlecainide and citalopram both increase QTc interval. Pharmacodynamic synergy
ARITMO-90No variantn/an/an/aAmiodaroneKRn/an/aDisopyramideKRn/an/aAmiodarone and disopyramide both increase QTc interval. Pharmacodynamic synergy
ARITMO-93KCNQ1-c.733G>A-p. G245RPRA00CisaprideKRn/an/aItraconazoleCRn/an/a1. Itraconazole will increase the level or effect of cisapride by affecting hepatic/intestinal enzyme CYP3A4 metabolism.
2. Cisapride and itraconazole both increase QTc interval. Pharmacodynamic synergy
ARITMO-98CYP2E1-c.377G>A-p. R126QPRA0.000060.00007PimozideKR1A2, 2C8, 2C9, 2C19, 2D6, 2E1, 2J2, 3A4, 3A5, 3A7yesHaloperidolKR1A1, 1A2, 2C8, 2C9, 2C19, 2D6, 2J2, 3A4, 3A5, 3A7noHaloperidol and pimozide both increase QTc interval. Pharmacodynamic synergy
ANK2-c.9854T>C-p. I3285TVUS0.00780.011
ARITMO-104No variantn/an/an/aMethadoneKRn/an/aPimozideKRn/an/aMethadone and pimozide both increase QTc interval. Pharmacodynamic synergy
ARITMO-108No variantn/an/an/aCiprofloxacinKRn/an/aHaloperidolKRn/an/aCiprofloxacin and haloperidol both increase QTc interval. Pharmacodynamic synergy
ARITMO-110No variantn/an/an/aPromethazinePRn/an/aTrimipramine+fluoexetine+quetiapinePR+CR+CRn/an/a1. Fluoxetine will increase the level or effect of promethazine by affecting hepatic enzyme CYP2D6 metabolism.
2. Quetiapine, fluoxetine. Either increases toxicity of the other by QTc interval.
3. Quetiapine, trimipramine. Either increases toxicity of the other by QTc interval.
4. Trimipramine, promethazine. Either increases levels of the other by decreasing metabolism. Minor/Significance Unknown.
5. Trimipramine, promethazine. Either increases levels of the other by pharmacodynamic synergy Minor/Significance Unknown.
6. Promethazine and trimipramine both increase QTc interval. Pharmacodynamic synergy
7. Promethazine and fluoxetine both increase QTc interval. Pharmacodynamic synergy
8. Trimipramine and fluoxetine both increase QTc interval. Pharmacodynamic synergy
ARITMO-113SCN5A-c.1715C>A-p. A572DVUS0.0070.003AmiodaroneKRn/an/aHydrochlorothiazideCRn/an/aAmiodarone will increase the level or effect of hydrochlorothiazide by basic (cationic) drug competition for renal tubular clearance.
ARITMO-115No variantn/an/an/aAmiodaroneKRn/an/aHydrochlorothiazideCRn/aN/AAmiodarone Will Increase The Level Or Effect Of Hydrochlorothiazide By Basic (Cationic) Drug Competition For Renal Tubular Clearance.
ARITMO-124No variantn/an/an/aAmiodaroneKRn/an/aFurosemide+ondansetronCR+KRn/an/aAmiodarone and ondansetron both increase QTc interval. Pharmacodynamic synergy
ARITMO-126KCNE1-c.253G>A-p. D85NPRA0.00810.012AmiodaroneKRn/an/aImipraminePRn/an/a1. Amiodarone will increase the level or effect of imipramine by affecting hepatic enzyme CYP2D6 metabolism.
2. Imipramine and amiodarone both increase QTc interval. Pharmacodynamic synergy
ARITMO-134CYP2B6-c.923G>A-p. R308HPRA0.000030.00002DomperidoneKR1A2, 2B6, 2C8, 2D6, 3A4, 3A5, 3A7yesHaloperidolKR1A1, 1A2, 2C8, 2C9, 2C19, 2D6, 2J2, 3A4, 3A5, 3A7non/a
ARITMO-135No variantn/an/an/aAmiodaroneKRn/an/aHydrochlorothiazideCRn/an/aAmiodarone will increase the level or effect of hydrochlorothiazide by basic (cationic) drug competition for renal tubular clearance.
ARITMO-136No variantn/an/an/aClarithromycinKRn/an/aAmiodarone+metoclopramideKR+CRn/an/a1. Clarithromycin will increase the level or effect of amiodarone by affecting hepatic/intestinal enzyme CYP3A4 metabolism.
2. Amiodarone and clarithromycin both increase QTc interval. Pharmacodynamic synergy
ARITMO-138No variantn/an/an/aLevofloxacinKRn/an/aDronedaroneKRn/an/aDronedarone and levofloxacin both increase QTc interval. Pharmacodynamic synergy
ARITMO-139CYP2B6-c.445G>A-p. E149KPRA0.000030.00009MethadoneKR1A2, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, 3A4, 3A5, 3A7yesn/an/an/an/an/a
ARITMO-150CYP1A2-c.1493C>A-p. T498NPRA0.000030AmiodaroneKR1A1, 1A2, 2C8, 2C9, 2C19, 2D6, 3A4, 3A5, 3A7yesn/an/an/an/an/a
ARITMO-153KCNE2-c.22A>G-p. T8APRA0.0040.006HydroxyzineCRn/an/aFluoxetineCRn/an/aHydroxyzine increases toxicity of fluoxetine by QTc interval.

Drug-to-drug interactions were analyzed according to CredibleMeds (https://crediblemeds.org) and to the Drug Interaction Checker from Medscape (http://reference.medscape.com/drug-interactionchecker). CR indicates conditional risk of torsades de pointes; gnomAD, genome aggregation database; KR, known risk of torsades de pointes; LP, likely pathogenic; LQTS, long QT syndrome; MAF, minor allele frequency; n/a, not applicable; PR, possible risk of torsades de pointes; PRA, putative risk allele; QTc, corrected QT; TA, drug to avoid in long QT syndrome; and VUS, variant of uncertain significance.

Figure 1.

Figure 1. Complex genetic and environmental background leading to drug-induced cardiac event.

LQTS indicates long QT syndrome; QTc, corrected QT; TdP, torsades de pointes; and VF, ventricular fibrillation.

Rare Variant Burden Analyses

Primary Cohort

Rare variant association studies found a significantly higher burden of nonsynonymous rare variants (MAF<1%) in cases compared with controls for CYP and main LQTS gene sets with Combined Multivariate and Collapsing (CMC) test (P=0.004 and P=0.002 respectively) but not with Optimised Sequence Kernel Association (SKAT-O) test.

Replication Cohort

Rare variant association studies confirmed an increased burden of nonsynonymous rare variants (MAF<1%) in the CYP genes in cases compared with controls (SKAT-O P=0.02, CMC P=0.01). When analyzing for main LQTS genes, there was no significant burden identified (CMC P=0.2, SKAT-O P=0.16).

Synonymous Rare Variants

In both datasets, no gene showed significant differences in burden of rare synonymous variants with CMC or SKAT-O.

Case Characteristics

After annotation, filtering for MAF<1.0%, and Sanger confirmation, 46 variants from 39 cases (25.5%) were included (Figure 2). These variants were from two groups: LQTS genes (34 variants, 22.2% patients) and CYP genes (12 variants, 7.8% patients). Six patients (3.9% of cases) carried >1 variant (Table S3 in the Supplemental Material). There were no significant baseline clinical differences between carriers and noncarriers (Table 1). In 11 of the 12 (92%) patients who carried CYP rare variants, at least one culprit drug was metabolized by the CYP450 enzyme in which the variant was identified (Table 2, Figure 1). Three patients were found to harbor both an LQTS variant and a CYP variant (Supplemental Results and Table S3 in the Supplemental Material).

Figure 2.

Figure 2. Flow diagram of study.

aLQTS indicates acquired LQTS; gnomAD, genome aggregation database; LQTS, long QT syndrome; and MAF, minor allele frequency.

There were 2 ultrarare/novel variants identified in 2 noncardiac ion channel genes (GRIN3A and SCN2A). These genes are largely expressed in brain tissue with very low expression in heart tissue, and neither gene has prior functional or clinical data associated with aLQTS. There were no statistical, functional, or clinical data associated with these variants. They were, therefore, considered variants of uncertain significance (Table S4 in the Supplemental Material).

Variant Susceptibility

Of the 46 rare variants identified within the cohort, 6 (3.9%) were classified as pathogenic/likely pathogenic; 25 were classified as putative risk allele variants (13 [8.4%] in LQTS genes and 12 [7.8%] in CYP genes); and 15 (9.8%) were classified as variants of uncertain significance (Figure 2). The 4 pathogenic and 2 likely pathogenic variants were all found in KCNQ1. Six distinct CYP variants were classified as putative risk alleles as they have either previously been associated with decreased enzyme activity (n=2) or decreased enzyme expression in vivo (n=1) or were very rare or novel (MAF <0.001%) variants with supportive in silico data (Table S4 in the Supplemental Material). There were 13 (9 distinct) rare (MAF<1%) risk alleles in LQTS genes with either functional or statistical data, as well as 15 (6 distinct) variants of uncertain significance, all of which were in LQTS genes. The classification of 9 rare variants with conflicting interpretations was determined by consensus (Supplemental Results).

Discussion

The unpredictable and serious nature of an adverse drug reaction, such as TdP, has important implications. By employing a novel approach to in silico target profiling in a large cohort of aLQTS cases, we demonstrate the important and novel finding of rare variation affecting CYP genes that cause loss of function and likely increased bioavailability of drugs culpable for aLQTS. We hypothesized that this would lead to further reduction of repolarization reserve and increase the susceptibility to aLQTS. This was partially supported by a subsequent unbiased case-control burden analysis for rare variation. We then tested this hypothesis and replicated our findings in an independent case-control dataset showing a statistically robust excess of rare CYP variants in cases. In addition, nongenetic pharmacokinetic susceptibility was postulated in the form of impairment of culprit drug metabolism due to drug-drug interactions. We also confirmed previous findings of a small but significant yield of pathogenic or likely pathogenic, rare, or ultrarare variation in LQTS genes.2,5,8 Burden testing of rare variation in LQTS genes was, however, inconsistent across both cohorts.

Thus, over one-third of cases had at least one predisposing factor identified, including rare variation in aLQTS genes, rare variation in CYP450 genes, pharmacodynamic or pharmacokinetic susceptibility with a small number having multifactorial risk. The role of other recognized intrinsic and extrinsic risk factors especially hypokalemia as predisposing or triggering factors also appeared important.

CYP Variation

Common variants in drug metabolism genes, including the CYP genes, account for variability in pharmacokinetics of liver metabolized drugs, including drugs that cause QT prolongation.7 There are many CYP genes, with the most important genes involved in drug metabolism identified as CYP3A4, CYP2C19, CYP2D6, CYP1A2, CYP2A6, and CYP2B6; however, there has been little investigation of their role in susceptibility to aLQTS.16 The most interesting and novel finding from our data was the increased burden of rare variants in CYP450 genes among patients with aLQTS suggesting that such variants may alter metabolism of QT-prolonging drugs in certain individuals and thereby play a role in adverse events.17–19 In 11 of the 12 (92%) patients who carried CYP rare variants, at least one culprit drug was metabolized by the CYP450 enzyme in which the variant was identified. CYP2B6-c.499C>G, found in one case, was associated with decreased enzyme activity in vivo17,18, whereas 2 other rare CYP variants found in 5 cases, CYP2B6-c.1172T>A and CYP2B6-c.415A>G, were associated with decreased enzyme expression in vivo.19CYP2B6-c.1172T>A (present in 4 [3%] of cases) does have a relatively high MAF in non-Finnish Europeans (0.8%) and may represent an important pharmacogenomic risk factor requiring further evaluation in larger population sets. The other CYP putative risk alleles had not been functionally characterized but in silico tools supported likely dysfunction. We then found an excess of CYP rare variation in aLQTS cases compared to healthy controls on statistical burden testing that was more robust in an independent case-control cohort. This supported our hypothesis that rare CYP variants play an important role in a subset of aLQTS.

In this respect, case reports of drug-drug interactions may serve as an indirect validation of this conclusion. For example, a drug-drug interaction between methadone and voriconazole has been attributed to CYP2B6 inhibition by voriconazole causing increased concentrations of methadone leading to TdP.20 Therefore, voriconazole administration could be regarded as a chemical knockout of CYP2B6, similar to the putative effect of the novel variant CYP2B6-c.445G>A identified in a patient in our cohort presenting with TdP while receiving methadone.

In a study of the Ontario Drug Benefit Claims Database, strong or moderately strong CYP3A4 inhibitors were coprescribed in up to 10.7% of 122 233 patients receiving domperidone, a known risk of TdP drug. Further known risk of TdP or possible risk of TdP medications were coprescribed to 18.3% and 18.8% of this cohort, respectively, leading to an increase in the proarrhythmic potential of domperidone.21 Our findings complement these data by emphasizing that pharmacogenetic risk may also be involved in the pharmacokinetic interactions leading to drug-induced adverse drug reactions. Indeed, a risk allele CYP3A4-c.1000G>T was found in a 58-year-old male in our series who presented with TdP while receiving amiodarone, a known risk of TdP drug metabolized by CYP3A4. And among the 6 patients being treated with domperidone at presentation, 4 carried at least one rare CYP risk allele, 3 in a CYP gene whose product is known to metabolize domperidone. This was, however, not a consistent observation. Fluoroquinolones are liver metabolized yet none of our aLQTS cases due to fluoroquinolones had an associated CYP risk allele. This may be a chance finding or other proarrhythmic factors may have been at play in these cases. Sotalol is renally excreted in general, and as expected, no CYP risk alleles were associated.

Marked variation has been detected in the CYP genes including novel variants and variation with presumed functional effects (nonsense, frameshift truncations, and splice site variants). This variability is over three times more pronounced in Black populations compared with White participants. Up to 7.6% of White individuals studies carried at least one potentially deleterious allele in a major drug-metabolizing CYP gene compared with 11.7% of Black individuals.22 One strength of our study is that 99% of the cohort was of White ethnicity, permitting a more consistent case-control analysis.

Implications for Personalized Medicine

Understanding the significance of this variability in CYP genes is a challenge for the personalized medicine initiative. It has already been proposed that pharmacogenomic testing for rare variation in the LQTS genes may help prevent aLQTS.2 Although further functional analyses addressing pharmacokinetic impact of these variants are required to reinforce our novel findings, these suggest that rare variation in CYP genes is also an important new avenue for identifying individualized risk to prevent the risk of proarrhythmia due to drugs that are liver metabolized. This will require sequencing of a panel of candidate pharmacogenomic genes to identify rare variation before drug prescription. Focusing on common variation alone will miss this potential risk. The timing and strategy for delivering this personalized approach remain to be determined.

Drug-Drug Interactions

The high prevalence of drug interactions caused by polypharmacy serves as a further warning over the importance of simple pharmacokinetic considerations when prescribing for patients. Prevention will require more robust systems for flagging up interactions, changing prescriptions, or otherwise monitoring patients with an elevated risk profile.

LQTS Variation

The proportion of cases carrying pathogenic or likely pathogenic LQTS variants (3.9%) likely to have a severe impact on repolarization reserve and able to cause monogenic LQTS in their own right is lower than yields from previous Sanger sequencing studies (average 10%).2 These early studies tended to focus on the 3 major LQTS genes (KCNQ1, KCNH2, SCN5A) as well as the minor genes KCNE1 and KCNE2. The additional yield of rare variants from including other minor genes was, however, only minimal (3.3%) as SCN4B, AKAP9, and CAV3 have not been associated with aLQTS previously and are now considered to be uncertain in causing monogenic LQTS.23 Thus, all were classed as variants of uncertain significances. Our overall yield was relatively similar to that of the 188 aLQTS cases by Itoh et al, where 28% of patients harbored rare variants in these five principal LQTS genes.10 In our cohort, the combined yield of rare aLQTS variation was 22.2%. There are important ethnic differences between the two cohorts, with Itoh et al having a majority (78%) of Japanese individuals in their cohort, whereas our population was overwhelmingly (98%) White. Otherwise, the other characteristics of the 2 cohorts (age, gender, QTc interval postdrug) were all similar. It is worth noting that individuals with Japanese ancestry are expected to have more variants of uncertain significances on genetic testing because the reference gene sequences are defined based on European and Black genes. Other reasons for the slight differences in yield may be due to different genotyping platforms, the slightly more symptomatic nature of their population, and the inherent differences in potency of effect on cardiac repolarisation of the drugs to which the individual is exposed (eg, cardiac drugs have more potent effects than antiemetics). Ramirez et al12 had also reported a substantially higher yield of 36% of variants carriers while screening 79 candidate cardiac genes in 31 aLQTS patients. However, their population was composed only of patients with TdP and pathogenicity was based upon only 2 in silico tools (Polyphen-2 and sorting intolerant from tolerant) which would now only score a single supporting (PP3) evidence criterion in the American College of Medical Genetics and Genomics guidelines. Other features would be required before pathogenicity can be attributed.24 Interestingly, only 10 rare variants were identified in 132 cases sequenced for the main LQTS genes and included in a large study of White cases submitted for genome wide association study.13

Other rare variants were identified, not expected to cause monogenic disease, but still demonstrating increased susceptibility to aLQTS by modifying repolarization reserve. For example, the KCNE1-D85N variant was present in four cases (3% of White subjects) and, therefore, seems to be overrepresented in our aLQTS population. This variant has previously been associated with an increased risk of both LQTS25 and aLQTS,10,26 as well as with drug-induced TdP.27 A minority of the population reported here (n=57) was included in the latter publication.27 Although its MAF in genome aggregation database overall is <1%, in non-Finnish Europeans its MAF is higher (1.2%) suggesting a pharmacogenomic risk factor that requires prospective evaluation in large-scale population datasets.

The contribution to the aLQTS phenotype by some variants, however, remained unclear, and these were classified as variants of uncertain significance. For instance, the CAV3 variants c.233C>T and c.216C>G were found in five of our cases and have been previously reported in LQTS28 and sudden infant death syndrome cohorts.29,30 Their role may be questionable however as both have been identified with a significant prevalence in exome data from population studies31 and functional data are inconsistent.32

When the burden of rare variation in LQTS genes was tested in the primary and replication datasets a statistically significant association was not confirmed. This does not exclude LQTS rare variation as a risk factor but suggests that the importance of rare LQTS variants in predisposing to aLQTS may be less than previously anticipated in White participants. Larger series from different ethnic groups with both genome wide association study and rare variant data will be required to determine the genomic architecture of aLQTS from the perspective of repolarisation reserve.

Limitations

We were not able to evaluate QT normalization following removal of drug for every case as ECGs fully off drug and were not always available. Thus a formal cutoff for QTc change was not applied and inclusion relied upon expert evaluation of the circumstances and timing of exposure to the putative culpable drug(s), QTc changes, and onset and offset of TdP. The primary cohort and control were sequenced using different capture systems and platforms due to unavoidable logistical issues. Burden of nonsynomyous variants was however similar for all genes. To overcome this limitation further, we also sought a replication cohort where cases and controls had been sequenced with the same capture systems and platform. Obtaining a large enough replication cohort for this rare phenotype was challenging but ultimately successful. Although we were able to replicate increased burden of rare CYP gene variation in aLQTS cases compared with controls, we were not able to identify specific functionally relevant variants. Rare variants reported previously in public databases but without any functional data were present. These may have functional effects that are important contributors to the risk of TdP but assessing these was beyond the scope of our current project. The controls for the primary analysis were population controls and may have harbored relevant variants. However, this risk was mitigated by the rarity of aLQTS and the use of a drug-tolerant control for the replication cohort. Finally, 2 variants identified in the cohort (including KCNE1-D85N) have an overall genome aggregation database MAF<1%. Their MAF was slightly above 1% in the European non-Finnish subpopulation. However, given that we did not know the particular subpopulation ethnicities of our study subjects, and that prior data show the relevance of KCNE1-D85N in aLQTS, we maintained the use of overall genome aggregation database MAF<1% as a filter for this study.

Conclusions

Our findings support the role for rare genetic susceptibility in both pharmacodynamic and pharmacokinetic risk for aLQTS and illustrate the potential of unbiased computational methods at uncovering new genetic biomarkers of adverse drug reactions. Although rare variants in cLQTS genes predispose to risk by reducing repolarization reserve, rare variation in the CYP450 enzyme genes may be an important new pathophysiological pathway. Indeed, burden testing suggests a more statistically significant role for susceptibility variants in CYP genes than cLQTS genes among White participants. Together with drug interactions, this finding may have a role in the personalized medicine initiatives of the future, if we are to reduce the risk of this important adverse drug reaction.

Article Information

Acknowledgments

We acknowledge the contribution of Dr Soren Fanoe (Copenhagen, Denmark), Dr Vijaya Ramachandran (London, United Kingdom), and the contributors to the ARITMO Consortium. Dr Behr conceived the study. Dr Baruteau and Gray wrote the first and successive drafts of the article. Drs Antolin, Gray, Pittman, and Baruteau modeled and analyzed the data. Drs Sarganas, Shakir, Mestres, Tfelt-Hansen, Tan, Garbe, Camm, and Behr contributed to study conception and design. Drs Behr, Baruteau, Gray, and Pittman contributed to data analysis. Drs Sarganas, Molokhia, Bastiaenen, Blom, Bardai, Priori, Napolitano, Haverkamp, Winkel collected the data. All authors revised the article for important intellectual content.

Supplemental Materials

Supplemental Methods

Supplemental Results

Tables S1–S4

References 30,33–50

Nonstandard Abbreviations and Acronyms

aLQTS

acquired LQTS

cLQTS

congenital LQTS

LQTS

long QT syndrome

MAF

minor allele frequency

QTc

corrected QT

TdP

torsades de pointes

VF

ventricular fibrillation

Disclosures Dr Garbe is running a department that occasionally performs studies for pharmaceutical industries. The companies include Mundipharma, Bayer-Schering, Stada, Sanofi-Aventis, Sanofi-Pasteur, Novartis, Celgene, and GlaxoSmithKline (GSK). Dr Garbe has been consultant to Bayer-Schering, Nycomed, Teva, and Novartis in the past. Dr Sturkenboom is running a group that occasionally performs studies for pharmaceutical industries with the full freedom to publish. The companies include Pfizer, Eli Lilly, AstraZeneca. Prof. Behr has received research funding from Biotronik, the International Serious Adverse Events Consortium, and St Jude Medical. Drs Gray, Baruteau, Pittman, Blom, Bastiaenen, Bardai, Priori, Napolitano, Winkel, Witney, Chis-Ster, Sangaralingam, Camm, Tfelt-Hansen, Tan, and Behr are members of the European Reference Network for rare, low prevalence and complex diseases of the heart—ERN GUARD-Heart. The other authors report no conflicts.

Footnotes

For Sources of Funding and Disclosures, see page 66.

*B. Gray and A.-E. Baruteau contributed equally as co-first authors.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCGEN.121.003391.

Correspondence to: Elijah R. Behr, MD, Cardiology Clinical Academic Group, Molecular and Clinical Sciences Research Institute, St George’s University of London, Cranmer Terrace, SW17 0RE, London, United Kingdom. Email

References

  • 1. Roden DM. Predicting drug-induced QT prolongation and torsades de pointes.J Physiol. 2016; 594:2459–2468. doi: 10.1113/JP270526CrossrefMedlineGoogle Scholar
  • 2. Behr ER, January C, Schulze-Bahr E, Grace AA, Kääb S, Fiszman M, Gathers S, Buckman S, Youssef A, Pirmohamed M, et al. The International Serious Adverse Events Consortium (iSAEC) phenotype standardization project for drug-induced torsades de pointes.Eur Heart J. 2013; 34:1958–1963. doi: 10.1093/eurheartj/ehs172CrossrefMedlineGoogle Scholar
  • 3. Behr ER, Roden D. Drug-induced arrhythmia: pharmacogenomic prescribing?Eur Heart J. 2013; 34:89–95. doi: 10.1093/eurheartj/ehs351CrossrefMedlineGoogle Scholar
  • 4. Woosley RL, Romero K. Assessing cardiovascular drug safety for clinical decision-making.Nat Rev Cardiol. 2013; 10:330–337. doi: 10.1038/nrcardio.2013.57CrossrefMedlineGoogle Scholar
  • 5. Schwartz PJ, Woosley RL. Predicting the unpredictable: drug-induced QT prolongation and torsades de pointes.J Am Coll Cardiol. 2016; 67:1639–1650. doi: 10.1016/j.jacc.2015.12.063CrossrefMedlineGoogle Scholar
  • 6. Veerman CC, Verkerk AO, Blom MT, Klemens CA, Langendijk PN, van Ginneken AC, Wilders R, Tan HL. Slow delayed rectifier potassium current blockade contributes importantly to drug-induced long QT syndrome.Circ Arrhythm Electrophysiol. 2013; 6:1002–1009. doi: 10.1161/CIRCEP.113.000239LinkGoogle Scholar
  • 7. Niemeijer MN, van den Berg ME, Eijgelsheim M, Rijnbeek PR, Stricker BH. Pharmacogenetics of drug-induced QT interval prolongation: an update.Drug Saf. 2015; 38:855–867. doi: 10.1007/s40264-015-0316-6CrossrefMedlineGoogle Scholar
  • 8. Makita N, Horie M, Nakamura T, Ai T, Sasaki K, Yokoi H, Sakurai M, Sakuma I, Otani H, Sawa H, et al. Drug-induced long-QT syndrome associated with a subclinical SCN5A mutation.Circulation. 2002; 106:1269–1274. doi: 10.1161/01.cir.0000027139.42087.b6LinkGoogle Scholar
  • 9. Napolitano C, Schwartz PJ, Brown AM, Ronchetti E, Bianchi L, Pinnavaia A, Acquaro G, Priori SG. Evidence for a cardiac ion channel mutation underlying drug-induced QT prolongation and life-threatening arrhythmias.J Cardiovasc Electrophysiol. 2000; 11:691–696. doi: 10.1111/j.1540-8167.2000.tb00033.xCrossrefMedlineGoogle Scholar
  • 10. Itoh H, Crotti L, Aiba T, Spazzolini C, Denjoy I, Fressart V, Hayashi K, Nakajima T, Ohno S, Makiyama T, et al. The genetics underlying acquired long QT syndrome: impact for genetic screening.Eur Heart J. 2016; 37:1456–1464. doi: 10.1093/eurheartj/ehv695CrossrefMedlineGoogle Scholar
  • 11. Yang P, Kanki H, Drolet B, Yang T, Wei J, Viswanathan PC, Hohnloser SH, Shimizu W, Schwartz PJ, Stanton M, et al. Allelic variants in long-QT disease genes in patients with drug-associated torsades de pointes.Circulation. 2002; 105:1943–1948. doi: 10.1161/01.cir.0000014448.19052.4cLinkGoogle Scholar
  • 12. Ramirez AH, Shaffer CM, Delaney JT, Sexton DP, Levy SE, Rieder MJ, Nickerson DA, George AL, Roden DM. Novel rare variants in congenital cardiac arrhythmia genes are frequent in drug-induced torsades de pointes.Pharmacogenomics J. 2013; 13:325–329. doi: 10.1038/tpj.2012.14CrossrefMedlineGoogle Scholar
  • 13. Behr ER, Ritchie MD, Tanaka T, Kääb S, Crawford DC, Nicoletti P, Floratos A, Sinner MF, Kannankeril PJ, Wilde AA, et al. Genome wide analysis of drug-induced torsades de pointes: lack of common variants with large effect sizes.PLoS One. 2013; 8:e78511. doi: 10.1371/journal.pone.0078511CrossrefMedlineGoogle Scholar
  • 14. Kannankeril PJ, Roden DM, Norris KJ, Whalen SP, George AL, Murray KT. Genetic susceptibility to acquired long QT syndrome: pharmacologic challenge in first-degree relatives.Heart Rhythm. 2005; 2:134–140. doi: 10.1016/j.hrthm.2004.10.039CrossrefMedlineGoogle Scholar
  • 15. Manikandan P, Nagini S. Cytochrome P450 structure, function and clinical significance: a review.Curr Drug Targets. 2018; 19:38–54. doi: 10.2174/1389450118666170125144557CrossrefMedlineGoogle Scholar
  • 16. Sim SC, Ingelman-Sundberg M. The Human Cytochrome P450 (CYP) Allele Nomenclature website: a peer-reviewed database of CYP variants and their associated effects.Hum Genomics. 2010; 4:278–281. doi: 10.1186/1479-7364-4-4-278CrossrefMedlineGoogle Scholar
  • 17. Gatanaga H, Hayashida T, Tsuchiya K, Yoshino M, Kuwahara T, Tsukada H, Fujimoto K, Sato I, Ueda M, Horiba M, et al. Successful efavirenz dose reduction in HIV type 1-infected individuals with cytochrome P450 2B6 *6 and *26.Clin Infect Dis. 2007; 45:1230–1237. doi: 10.1086/522175CrossrefMedlineGoogle Scholar
  • 18. Fukushima-Uesaka H, Saito Y, Maekawa K, Ozawa S, Hasegawa R, Kajio H, Kuzuya N, Yasuda K, Kawamoto M, Kamatani N, et al. Genetic variations and haplotypes of CYP2C19 in a Japanese population.Drug Metab Pharmacokinet. 2005; 20:300–307. doi: 10.2133/dmpk.20.300CrossrefMedlineGoogle Scholar
  • 19. Lang T, Klein K, Richter T, Zibat A, Kerb R, Eichelbaum M, Schwab M, Zanger UM. Multiple novel nonsynonymous CYP2B6 gene polymorphisms in Caucasians: demonstration of phenotypic null alleles.J Pharmacol Exp Ther. 2004; 311:34–43. doi: 10.1124/jpet.104.068973CrossrefMedlineGoogle Scholar
  • 20. Reinhold JA, Sanoski CA, Russo AM, Cooper JM, Spinler SA. Torsades de pointes associated with methadone and voriconazole.BMJ Case Rep. 2009; 2009:bcr07.2009.2119. doi: 10.1136/bcr.07.2009.2119CrossrefMedlineGoogle Scholar
  • 21. Rojas-Fernandez C, Stephenson AL, Fischer HD, Wang X, Mestre T, Hutson JR, Pondal M, Lee DS, Rochon PA, Marras C. Current use of domperidone and co-prescribing of medications that increase its arrhythmogenic potential among older adults: a population-based cohort study in Ontario, Canada.Drugs Aging. 2014; 31:805–813. doi: 10.1007/s40266-014-0215-zCrossrefMedlineGoogle Scholar
  • 22. Gordon AS, Tabor HK, Johnson AD, Snively BM, Assimes TL, Auer PL, Ioannidis JP, Peters U, Robinson JG, Sucheston LE, et al; NHLBI GO Exome Sequencing Project. Quantifying rare, deleterious variation in 12 human cytochrome P450 drug-metabolism genes in a large-scale exome dataset.Hum Mol Genet. 2014; 23:1957–1963. doi: 10.1093/hmg/ddt588CrossrefMedlineGoogle Scholar
  • 23. Giudicessi JR, Wilde AAM, Ackerman MJ. The genetic architecture of long QT syndrome: A critical reappraisal.Trends Cardiovasc Med. 2018; 28:453–464. doi: 10.1016/j.tcm.2018.03.003CrossrefMedlineGoogle Scholar
  • 24. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, et al; ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet Med. 2015; 17:405–424. doi: 10.1038/gim.2015.30CrossrefMedlineGoogle Scholar
  • 25. Nishio Y, Makiyama T, Itoh H, Sakaguchi T, Ohno S, Gong YZ, Yamamoto S, Ozawa T, Ding WG, Toyoda F, et al. D85N, a KCNE1 polymorphism, is a disease-causing gene variant in long QT syndrome.J Am Coll Cardiol. 2009; 54:812–819. doi: 10.1016/j.jacc.2009.06.005CrossrefMedlineGoogle Scholar
  • 26. Weeke P, Mosley JD, Hanna D, Delaney JT, Shaffer C, Wells QS, Van Driest S, Karnes JH, Ingram C, Guo Y, et al. Exome sequencing implicates an increased burden of rare potassium channel variants in the risk of drug-induced long QT interval syndrome.J Am Coll Cardiol. 2014; 63:1430–1437. doi: 10.1016/j.jacc.2014.01.031CrossrefMedlineGoogle Scholar
  • 27. Kääb S, Crawford DC, Sinner MF, Behr ER, Kannankeril PJ, Wilde AA, Bezzina CR, Schulze-Bahr E, Guicheney P, Bishopric NH, et al. A large candidate gene survey identifies the KCNE1 D85N polymorphism as a possible modulator of drug-induced torsades de pointes.Circ Cardiovasc Genet. 2012; 5:91–99. doi: 10.1161/CIRCGENETICS.111.960930LinkGoogle Scholar
  • 28. Vatta M, Ackerman MJ, Ye B, Makielski JC, Ughanze EE, Taylor EW, Tester DJ, Balijepalli RC, Foell JD, Li Z, et al. Mutant caveolin-3 induces persistent late sodium current and is associated with long-QT syndrome.Circulation. 2006; 114:2104–2112. doi: 10.1161/CIRCULATIONAHA.106.635268LinkGoogle Scholar
  • 29. Cronk LB, Ye B, Kaku T, Tester DJ, Vatta M, Makielski JC, Ackerman MJ. Novel mechanism for sudden infant death syndrome: persistent late sodium current secondary to mutations in caveolin-3.Heart Rhythm. 2007; 4:161–166. doi: 10.1016/j.hrthm.2006.11.030CrossrefMedlineGoogle Scholar
  • 30. Arnestad M, Crotti L, Rognum TO, Insolia R, Pedrazzini M, Ferrandi C, Vege A, Wang DW, Rhodes TE, George AL, et al. Prevalence of long-QT syndrome gene variants in sudden infant death syndrome.Circulation. 2007; 115:361–367. doi: 10.1161/CIRCULATIONAHA.106.658021LinkGoogle Scholar
  • 31. Andreasen L, Nielsen JB, Christophersen IE, Holst AG, Sajadieh A, Tveit A, Haunsø S, Svendsen JH, Schmitt N, Olesen MS. Genetic modifier of the QTc interval associated with early-onset atrial fibrillation.Can J Cardiol. 2013; 29:1234–1240. doi: 10.1016/j.cjca.2013.06.009CrossrefMedlineGoogle Scholar
  • 32. Hedley PL, Kanters JK, Dembic M, Jespersen T, Skibsbye L, Aidt FH, Eschen O, Graff C, Behr ER, Schlamowitz S, et al. The role of CAV3 in long-QT syndrome: clinical and functional assessment of a caveolin-3/Kv11.1 double heterozygote versus caveolin-3 single heterozygote.Circ Cardiovasc Genet. 2013; 6:452–461. doi: 10.1161/CIRCGENETICS.113.000137LinkGoogle Scholar
  • 33. Jamshidi Y, Nolte IM, Dalageorgou C, Zheng D, Johnson T, Bastiaenen R, Ruddy S, Talbott D, Norris KJ, Snieder H, et al. Common variation in the NOS1AP gene is associated with drug-induced QT prolongation and ventricular arrhythmia.J Am Coll Cardiol. 2012; 60:841–850. doi: 10.1016/j.jacc.2012.03.031CrossrefMedlineGoogle Scholar
  • 34. Coughtrie AL, Behr ER, Layton D, Marshall V, Camm AJ, Shakir SAW. Drugs and life-threatening ventricular arrhythmia risk: results from the DARE study cohort.BMJ Open. 2017; 7:e016627. doi: 10.1136/bmjopen-2017-016627CrossrefMedlineGoogle Scholar
  • 35. Sarganas G, Garbe E, Klimpel A, Hering RC, Bronder E, Haverkamp W. Epidemiology of symptomatic drug-induced long QT syndrome and Torsade de Pointes in Germany.Europace. 2014; 16:101–108. doi: 10.1093/europace/eut214CrossrefMedlineGoogle Scholar
  • 36. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibrián-Uhalte E, et al. The ChEMBL database in 2017.Nucleic Acids Res. 2017; 45(D1):D945–D954. doi: 10.1093/nar/gkw1074CrossrefMedlineGoogle Scholar
  • 37. Garcia-Serna R, Vidal D, Remez N, Mestres J. Large-scale predictive drug safety: from structural alerts to biological mechanisms.Chem Res Toxicol. 2015; 28:1875–1887. doi: 10.1021/acs.chemrestox.5b00260CrossrefMedlineGoogle Scholar
  • 38. Ruark E, Münz M, Renwick A, Clarke M, Ramsay E, Hanks S, Mahamdallie S, Elliott A, Seal S, Strydom A, et al. The ICR1000 UK exome series: a resource of gene variation in an outbred population.F1000Res. 2015; 4:883. doi: 10.12688/f1000research.7049.1CrossrefMedlineGoogle Scholar
  • 39. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test.Am J Hum Genet. 2011; 89:82–93. doi: 10.1016/j.ajhg.2011.05.029CrossrefMedlineGoogle Scholar
  • 40. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data.Am J Hum Genet. 2008; 83:311–321. doi: 10.1016/j.ajhg.2008.06.024CrossrefMedlineGoogle Scholar
  • 41. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ. RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data.Bioinformatics. 2016; 32:1423–1426. doi: 10.1093/bioinformatics/btw079CrossrefMedlineGoogle Scholar
  • 42. Giudicessi JR, Roden DM, Wilde AAM, Ackerman MJ. Classification and reporting of potentially proarrhythmic common genetic variation in long qt syndrome genetic testing.Circulation. 2018; 137:619–630. doi: 10.1161/CIRCULATIONAHA.117.030142LinkGoogle Scholar
  • 43. Grunnet M, Behr ER, Calloe K, Hofman-Bang J, Till J, Christiansen M, McKenna WJ, Olesen SP, Schmitt N. Functional assessment of compound mutations in the KCNQ1 and KCNH2 genes associated with long QT syndrome.Heart Rhythm. 2005; 2:1238–1249. doi: 10.1016/j.hrthm.2005.07.025CrossrefMedlineGoogle Scholar
  • 44. Mank-Seymour AR, Richmond JL, Wood LS, Reynolds JM, Fan YT, Warnes GR, Milos PM, Thompson JF. Association of torsades de pointes with novel and known single nucleotide polymorphisms in long QT syndrome genes.Am Heart J. 2006; 152:1116–1122. doi: 10.1016/j.ahj.2006.08.020CrossrefMedlineGoogle Scholar
  • 45. Albert CM, Nam EG, Rimm EB, Jin HW, Hajjar RJ, Hunter DJ, MacRae CA, Ellinor PT. Cardiac sodium channel gene variants and sudden cardiac death in women.Circulation. 2008; 117:16–23. doi: 10.1161/CIRCULATIONAHA.107.736330LinkGoogle Scholar
  • 46. Tester DJ, Valdivia C, Harris-Kerr C, Alders M, Salisbury BA, Wilde AA, Makielski JC, Ackerman MJ. Epidemiologic, molecular, and functional evidence suggest A572D-SCN5A should not be considered an independent LQT3-susceptibility mutation.Heart Rhythm. 2010; 7:912–919. doi: 10.1016/j.hrthm.2010.04.014CrossrefMedlineGoogle Scholar
  • 47. Jongbloed R, Marcelis C, Velter C, Doevendans P, Geraedts J, Smeets H. DHPLC analysis of potassium ion channel genes in congenital long QT syndrome.Hum Mutat. 2002; 20:382–391. doi: 10.1002/humu.10131CrossrefMedlineGoogle Scholar
  • 48. Paulussen AD, Gilissen RA, Armstrong M, Doevendans PA, Verhasselt P, Smeets HJ, Schulze-Bahr E, Haverkamp W, Breithardt G, Cohen N, et al. Genetic variations of KCNQ1, KCNH2, SCN5A, KCNE1, and KCNE2 in drug-induced long QT syndrome patients.J Mol Med (Berl). 2004; 82:182–188. doi: 10.1007/s00109-003-0522-zCrossrefMedlineGoogle Scholar
  • 49. Ware JS, Walsh R, Cunningham F, Birney E, Cook SA. Paralogous annotation of disease-causing variants in long QT syndrome genes.Hum Mutat. 2012; 33:1188–1191. doi: 10.1002/humu.22114CrossrefMedlineGoogle Scholar
  • 50. Sesti F, Abbott GW, Wei J, Murray KT, Saksena S, Schwartz PJ, Priori SG, Roden DM, George AL, Goldstein SA. A common polymorphism associated with antibiotic-induced cardiac arrhythmia.Proc Natl Acad Sci U S A. 2000; 97:10613–10618. doi: 10.1073/pnas.180223197CrossrefMedlineGoogle Scholar

eLetters(0)

eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.