Common Genetic Variant Risk Score Is Associated With Drug-Induced QT Prolongation and Torsade de Pointes Risk: A Pilot Study

Population-specific variations in KCNH2 predispose patients to delayed ventricular repolarization upon dihydroartemisinin-piperaquine therapy

Dihydroartemisinin-piperaquine is efficacious for the treatment of uncomplicated malaria and its use is increasing globally. Despite the positive results in fighting malaria, inhibition of the Kv11.1 channel (hERG; encoded by the KCNH2 gene) by piperaquine has raised concerns about cardiac safety. Whether genetic factors could modulate the risk of piperaquine-mediated QT prolongations remained unclear. Here, we first profiled the genetic landscape of KCNH2 variability using data from 141,614 individuals. Overall, we found 1,007 exonic variants distributed over the entire gene body, 555 of which were missense. By optimizing the gene-specific parametrization of 16 partly orthogonal computational algorithms, we developed a KCNH2-specific ensemble classifier that identified a total of 116 putatively deleterious missense variations. To evaluate the clinical relevance of KCNH2 variability, we then sequenced 293 Malian patients with uncomplicated malaria and identified 13 variations within the voltage sensing and pore domains of Kv11.1 that directly interact with channel blockers. Cross-referencing of genetic and electrocardiographic data before and after piperaquine exposure revealed that carriers of two common variants, rs1805121 and rs41314375, experienced significantly higher QT prolongations (ΔQTc of 41.8 ms and 61 ms, respectively, vs 14.4 ms in controls) with more than 50% of carriers having increases in QTc >30 ms. Furthermore, we identified three carriers of rare population-specific variations who experienced clinically relevant delayed ventricular repolarization. Combined, our results map population-scale genetic variability of KCNH2 and identify genetic biomarkers for piperaquine-induced QT prolongation that could help to flag at-risk patients and optimize efficacy and adherence to antimalarial therapy.

QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms

BACKGROUND

Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.

OBJECTIVES

This study sought to evaluate the use of a convolutional neural network (CNN) applied to electrocardiograms (ECGs) to predict diLQTS in an outpatient population.

METHODS

We identified all adult outpatients newly prescribed a QT-prolonging medication between January 1, 2003, and March 31, 2022, who had a 12-lead sinus ECG in the preceding 6 months. Using risk factor data and the ECG signal as inputs, the CNN QTNet was implemented in TensorFlow to predict diLQTS.

RESULTS

Models were evaluated in a held-out test dataset of 44,386 patients (57% female) with a median age of 62 years. Compared with 3 other models relying on risk factors or ECG signal or baseline QTc alone, QTNet achieved the best (P < 0.001) performance with a mean area under the curve of 0.802 (95% CI: 0.786-0.818). In a survival analysis, QTNet also had the highest inverse probability of censorship-weighted area under the receiver-operating characteristic curve at day 2 (0.875; 95% CI: 0.848-0.904) and up to 6 months. In a subgroup analysis, QTNet performed best among males and patients ≤50 years or with baseline QTc <450 ms. In an external validation cohort of solely suburban outpatient practices, QTNet similarly maintained the highest predictive performance.

CONCLUSIONS

An ECG-based CNN can accurately predict diLQTS in the outpatient setting while maintaining its predictive performance over time. In the outpatient setting, our model could identify higher-risk individuals who would benefit from closer monitoring.

Clinical Genetic Testing for Atrial Fibrillation: Are We There Yet?

Important progress has been made toward unravelling the complex genetics underlying atrial fibrillation (AF). Initial studies were aimed to identify monogenic causes; however, it has become increasingly clear that the most common predisposing genetic substrate for AF is polygenic. Despite intensive investigations, there is robust evidence for rare variants for only a limited number of genes and cases. Although the current yield for genetic testing in early onset AF might be modest, there is an increasing appreciation that genetic culprits for potentially life-threatening ventricular cardiomyopathies and channelopathies might initially present with AF. The potential clinical significance of this recognition is highlighted by evidence that suggests that identification of a pathogenic or likely pathogenic rare variant in a patient with early onset AF is associated with an increased risk of death. These findings suggest that it might be warranted to screen patients with early onset AF for these potentially more sinister cardiac conditions. Beyond facilitating the early identification of genetic culprits associated with potentially malignant phenotypes, insight into underlying AF genetic substrates might improve the selection of patients for existing therapies and guide the development of novel ones. Herein, we review the evidence that links genetic factors to AF, then discuss an approach to using genetic testing for early onset AF patients in the present context, and finally consider the potential value of genetic testing in the foreseeable future. Although further work might be necessary before recommending uniform integration of genetic testing in cases of early onset AF, ongoing research increasingly highlights its potential contributions to clinical care.

The electrophysiologic effects of KCNQ1 extend beyond expression of IKs: evidence from genetic and pharmacologic block

AIMS

While variants in KCNQ1 are the commonest cause of the congenital long QT syndrome, we and others find only a small IKs in cardiomyocytes from human induced pluripotent stem cells (iPSC-CMs) or human ventricular myocytes.

METHODS AND RESULTS

We studied population control iPSC-CMs and iPSC-CMs from a patient with Jervell and Lange-Nielsen (JLN) syndrome due to compound heterozygous loss of function KCNQ1 variants. We compared the effects of pharmacologic IKs block to those of genetic KCNQ1 ablation, using JLN cells, cells homozygous for the KCNQ1 loss of function allele G643S, or siRNAs reducing KCNQ1 expression. We also studied the effects of two blockers of IKr, the other major cardiac repolarizing current, in the setting of pharmacologic or genetic ablation of KCNQ1: moxifloxacin, associated with a very low risk of drug-induced long QT, and dofetilide, a high-risk drug.In control cells, a small IKs was readily recorded but pharmacologic IKs block produced no change in action potential duration at 90% repolarization (APD90). By contrast, in cells with genetic ablation of KCNQ1 (JLN), baseline APD90 was markedly prolonged compared with control cells (469 ± 20 vs. 310 ± 16 ms). JLN cells displayed increased sensitivity to acute IKr block: the concentration (μM) of moxifloxacin required to prolong APD90 100 msec was 237.4 (median, IQR 100.6-391.6, n = 7) in population cells versus 23.7 (17.3-28.7, n = 11) in JLN cells. In control cells, chronic moxifloxacin exposure (300μM) mildly prolonged APD90 (10%) and increased IKs, while chronic exposure to dofetilide (5 nM) produced greater prolongation (67%) and no increase in IKs. However, in the siRNA-treated cells, moxifloxacin did not increase IKs, and markedly prolonged APD90.

CONCLUSION

Our data strongly suggest that KCNQ1 expression modulates baseline cardiac repolarization, and the response to IKr block, through mechanisms beyond simply generating IKs.

TRANSLATIONAL PERSPECTIVE

Mutations in KCNQ1 - whose expression generates IKs - are the major cause of long QT syndrome. We report here that while pharmacologic IKs block in human cardiomyocytes generates minimal change in repolarization, suppressing KCNQ1 expression markedly increases both baseline repolarization duration and sensitivity to some (but not all) specific IKr blockers. Thus, beyond simply generating IKs, KCNQ1 subserves critical additional role(s) in repolarization control at baseline and in response to IKr block. Our findings imply that assessment of arrhythmic risk in individual patients and by drugs requires a framework that extends beyond a simple one gene-one ion current paradigm.

Genetic risk factors for drug-induced long QT syndrome: findings from a large real-world case-control study

Aim: Drug-induced long QT syndrome (diLQTS), an adverse effect of many drugs, can lead to sudden cardiac death. Candidate genetic variants in cardiac ion channels have been associated with diLQTS, but several limitations of previous studies hamper clinical utility. Materials & methods: Thus, the purpose of this study was to assess the associations of KCNE1-D85N, KCNE2-I57T and SCN5A-G615E with diLQTS in a large observational case-control study (6,083 self-reported white patients treated with 27 different high-risk QT-prolonging medications; 12.0% with diLQTS). Results: KCNE1-D85N significantly associated with diLQTS (adjusted odds ratio: 2.24 [95% CI: 1.35-3.58]; p = 0.001). Given low minor allele frequencies, the study had insufficient power to analyze KCNE2-I57T and SCN5A-G615E. Conclusion: KCNE1-D85N is a risk factor for diLQTS that should be considered in future clinical practice guidelines.

Genetic QT Score and Sleep Apnea as Predictors of Sudden Cardiac Death in the UK Biobank

Introduction

The goal of this study was to evaluate the association between a polygenic risk score (PRS) for QT prolongation (QTc-PRS), QTc intervals and mortality in patients enrolled in the UK Biobank with and without sleep apnea.

Methods

The QTc-PRS was calculated using allele copy number and previously reported effect estimates for each single nuclear polymorphism SNP. Competing-risk regression models adjusting for age, sex, BMI, QT prolonging medication, race, and comorbid cardiovascular conditions were used for sudden cardiac death (SCD) analyses.

Results

500,584 participants were evaluated (56.5 ±8 years, 54% women, 1.4% diagnosed with sleep apnea). A higher QTc-PRS was independently associated with the increased QTc interval duration (p<0.0001). The mean QTc for the top QTc-PRS quintile was 15 msec longer than the bottom quintile (p<0.001). Sleep apnea was found to be an effect modifier in the relationship between QTc-PRS and SCD. The adjusted HR per 5-unit change in QTc-PRS for SCD was 1.64 (95% CI 1.16 - 2.31, p=0.005) among those with sleep apnea and 1.04 (95% CI 0.95 - 1.14, p=0.44) among those without sleep apnea (p for interaction =0.01). Black participants with sleep apnea had significantly elevated adjusted risk of SCD compared to White participants (HR=9.6, 95% CI 1.24 - 74, p=0.03).

Conclusion

In the UK Biobank population, the QTc-PRS was associated with SCD among participants with sleep apnea but not among those without sleep apnea, indicating that sleep apnea is a significant modifier of the genetic risk. Black participants with sleep apnea had a particularly high risk of SCD.

Cardiovascular precision medicine - A pharmacogenomic perspective

Precision medicine envisages the integration of an individual's clinical and biological features obtained from laboratory tests, imaging, high-throughput omics and health records, to drive a personalised approach to diagnosis and treatment with a higher chance of success. As only up to half of patients respond to medication prescribed following the current one-size-fits-all treatment strategy, the need for a more personalised approach is evident. One of the routes to transforming healthcare through precision medicine is pharmacogenomics (PGx). Around 95% of the population is estimated to carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association. Whilst there are compelling examples of pharmacogenomic implementation in clinical practice, the case for cardiovascular PGx is still evolving. In this review, we shall summarise the current status of PGx in cardiovascular diseases and look at the key enablers and barriers to PGx implementation in clinical practice.

Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review

Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.

4D Thermo-Responsive Smart hiPSC-CM Cardiac Construct for Myocardial Cell Therapy

Purpose

4D fabrication techniques have been utilized for advanced biomedical therapeutics due to their ability to create dynamic constructs that can transform into desired shapes on demand. The internal structure of the human cardiovascular system is complex, where the contracting heart has a highly curved surface that changes shape with the heart's dynamic beating motion. Hence, 4D architectures that adjust their shapes as required are a good candidate to readily deliver cardiac cells into the damaged heart and/or to serve as self-morphing tissue scaffolds/patches for healing cardiac diseases. In this proof-of-concept in vitro study, a two-in-one 4D smart cardiac construct that integrates the functions of minimally invasive cell vehicles and in situ tissue patches was developed for repairing damaged myocardial tissue.

Methods

For this purpose, a series of thermo-responsive 4D structures with different shapes and sizes were fabricated via the combination of fused deposition modeling (FDM)-printing and stamping molding. The thermo-responsive 4D constructs were firstly optimized to exhibit their shape transformation behavior at the designated temperature for convenient control. After which, the mechanical properties, shape recovery rate, and shape recovery speed of the 4D constructs at different temperatures were thoroughly evaluated. Also, the proliferation and functional prototype of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) on the 4D constructs were quantified and evaluated using F-actin staining and immunostaining.

Results

Our results showed that the 4D constructs possessed the desirable capability of shape-changing from spherical carriers to unfolded patches at human body temperature and exhibited excellent biocompatibility. Moreover, myocardial maturation in vitro with a uniform and printing pattern-specific cell distribution was observed on the surface of the unfolded 4D constructs.

Conclusion

We successfully developed a 4D smart cardiac construct that integrates the functions of minimally invasive cell vehicles and in situ tissue patches for repairing damaged myocardial tissue.

Precision medicine for long QT syndrome: patient-specific iPSCs take the lead

Long QT syndrome (LQTS) is a detrimental arrhythmia syndrome mainly caused by dysregulated expression or aberrant function of ion channels. The major clinical symptoms of ventricular arrhythmia, palpitations and syncope vary among LQTS subtypes. Susceptibility to malignant arrhythmia is a result of delayed repolarisation of the cardiomyocyte action potential (AP). There are 17 distinct subtypes of LQTS linked to 15 autosomal dominant genes with monogenic mutations. However, due to the presence of modifier genes, the identical mutation may result in completely different clinical manifestations in different carriers. In this review, we describe the roles of various ion channels in orchestrating APs and discuss molecular aetiologies of various types of LQTS. We highlight the usage of patient-specific induced pluripotent stem cell (iPSC) models in characterising fundamental mechanisms associated with LQTS. To mitigate the outcomes of LQTS, treatment strategies are initially focused on small molecules targeting ion channel activities. Next-generation treatments will reap the benefits from development of LQTS patient-specific iPSC platform, which is bolstered by the state-of-the-art technologies including whole-genome sequencing, CRISPR genome editing and machine learning. Deep phenotyping and high-throughput drug testing using LQTS patient-specific cardiomyocytes herald the upcoming precision medicine in LQTS.

Cardiac Repolarization in Health and Disease

Abnormal cardiac repolarization is at the basis of life-threatening arrhythmias in various congenital and acquired cardiac diseases. Dysfunction of ion channels involved in repolarization at the cellular level are often the underlying cause of the repolarization abnormality. The expression pattern of the gene encoding the affected ion channel dictates its impact on the shape of the T-wave and duration of the QT interval, thereby setting the stage for both the occurrence of the trigger and the substrate for maintenance of the arrhythmia. Here we discuss how research into the genetic and electrophysiological basis of repolarization has provided us with insights into cardiac repolarization in health and disease and how this in turn may provide the basis for future improved patient-specific management.

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

BACKGROUND

Drug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability.

OBJECTIVE

In this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk.

METHODS

We examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters.

RESULTS

Among the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration.

CONCLUSIONS

In summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.

Genetics of congenital arrhythmia syndromes: the challenge of variant interpretation

Congenital arrhythmia syndromes are rare genetic disorders that can cause a high risk of sudden cardiac death. Expert panels have affirmed 15 genes that are linked to congenital arrhythmias. These genes mostly encode cardiac ion channel proteins or associated regulatory proteins that generate the cardiac action potential. Common genetic variation modulates the risk of rare variants and partially explains the incomplete penetrance of these disorders. As genetic testing becomes more prevalent, a major challenge is that most detected variants are annotated as variants of uncertain significance. This review will highlight emerging methods that are refining our understanding of arrhythmia genetics, including phenotype risk scores, large cohorts, in vitro functional assays, structural models, and computational predictions.

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

INTRODUCTION

QTc prolongation is key in diagnosing long QT syndrome (LQTS), however 25%-50% with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) is unclear.

METHODS

Electronic databases were searched using the terms "LQTS," "long QT syndrome," "QTc prolongation," "prolonged QT," and "T wave," "T wave morphology," "T wave pattern," "T wave biomarkers." Whole text articles assessing TWM, independent of QTc, were included.

RESULTS

Seventeen studies met criteria. TWM measurements included T-wave amplitude, duration, magnitude, Tpeak-Tend, QTpeak, left and right slope, center of gravity (COG), sigmoidal and polynomial classifiers, repolarizing integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak, Tpeak-Tend, left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J-Tpeak; and Tpeak-Tend correlated with QTc in aLQTS. Multichannel block in aLQTS was identified by early repolarization (ERD_30% ) and late repolarization (LRD_30% ), with ERD reflecting hERG-specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V₅ , left slope in lead V₆ ; and COG last 25% in lead I. T wave right slope in lead I and T-roundness achieved this in aLQTS.

CONCLUSION

Numerous TWM biomarkers which supplement QTc assessment were identified. Their diagnostic capabilities include differentiation of genotypes, identification of concealed LQTS, differentiating aLQTS from cLQTS; and determining multichannel versus hERG channel blockade.

Genetic factors contribute to medication-induced QT prolongation: A review

QT prolongation is a heart rhythm condition that impacts the lives of many people and when severe can be life-threatening. QT prolongation has been linked to variations in several genes, but it can also arise in the course of treatments with medications such as certain antipsychotics and antidepressants. However, it is unclear whether the risk of medication-induced QT prolongation (MIQTP) depends on specific genetic vulnerability. Here, we review the available literature on the interplay between genetic risk and medication exposure in the context of psychiatric treatment. A review was conducted on the genetic contribution to MIQTP in psychiatric patients. A literature search was conducted on the PubMed platform with 8 papers meeting criteria for review. A total of 3,838 patients from 8 studies meeting criteria for a psychotic or mood disorder were included in this review. All studies found evidence for the genetic contribution to MIQTP. The specific genes identified in these studies included the NOS1AP, ABCB1, KCNH2, SLC22A23, EPB41L4A, LEP, CACNA1C, CERKL, SLCO3A1, BRUNOL4, NRG3, NUBPL, PALLD, NDRG4 and PLN genes. The findings highlight both the importance of monitoring heart parameters in psychiatry and the possible role for genetic profiling to increase the treatment safety.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

The genetics of drug-induced QT prolongation: evaluating the evidence for pharmacodynamic variants

Drug-induced long QT syndrome (diLQTS) is an adverse effect of many commonly prescribed drugs, and it can increase the risk for lethal ventricular arrhythmias. Genetic variants in pharmacodynamic genes have been associated with diLQTS, but the strength of the evidence for each of those variants has not yet been evaluated. Therefore, the purpose of this review was to evaluate the strength of the evidence for pharmacodynamic genetic variants associated with diLQTS using a novel, semiquantitative scoring system modified from the approach used for congenital LQTS. KCNE1-D85N and KCNE2-T8A had definitive and strong evidence for diLQTS, respectively. The high level of evidence for these variants supports current consideration as risk factors for patients that will be prescribed a QT-prolonging drug.

Monogenic and Polygenic Contributions to QTc Prolongation in the Population

BACKGROUND

Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population.

METHODS

We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed.

RESULTS

Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/decile of PRS=1.4 ms [95% CI, 1.3 to 1.5]; P=1.1×10-196). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS).

CONCLUSIONS

QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

Polygenic risk scores: An overview from bench to bedside for personalised medicine

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.

Pharmacogenetics to Avoid Adverse Reactions in Cardiology: Ready for Implementation?

Cardiovascular Diseases (CVs) are one of the main causes of mortality and disability around the world. Advances in drug treatment have greatly improved survival and quality of life in the past decades, but associated adverse events remain a relevant problem. Pharmacogenetics can help individualize cardiovascular treatment, reducing associated toxicities and improving outcomes. Several scientific societies and working groups periodically review available studies and provide consensus recommendations for those gene-drug pairs with a sufficient level of evidence. However, these recommendations are rarely mandatory, and the indications on how to adjust treatment can vary between different guidelines, which limits their clinical applicability. The aim of this review is to compile, compare and discuss available guidelines and recommendations by the main Pharmacogenetics Consortiums (Clinical Pharmacogenetics Implementation Consortium (CPIC); Dutch Pharmacogenetics Working Group (DPWG); the French Network of Pharmacogenetics (Réseau national de pharmacogénétique (RNPGx) and The Canadian Pharmacogenomics Network for Drug Safety (CPNDS) regarding how to apply pharmacogenetic results to optimize pharmacotherapy in cardiology. Pharmacogenetic recommendations included in European or American drug labels, as well as those included in the European Society of Cardiology (ESC) and the American College of Cardiology (ACC) and the American Heart Association (AHA) treatment guidelines are also discussed.

Genetics and genomics of arrhythmic risk: current and future strategies to prevent sudden cardiac death

A genetic risk of sudden cardiac arrest and sudden death due to an arrhythmic cause, known as sudden cardiac death (SCD), has become apparent from epidemiological studies in the general population and in patients with ischaemic heart disease. However, genetic susceptibility to sudden death is greatest in young people and is associated with uncommon, monogenic forms of heart disease. Despite comprehensive pathology and genetic evaluations, SCD remains unexplained in a proportion of young people and is termed sudden arrhythmic death syndrome, which poses challenges to the identification of relatives from affected families who might be at risk of SCD. In this Review, we assess the current understanding of the epidemiology and causes of SCD and evaluate both the monogenic and the polygenic contributions to the risk of SCD in the young and SCD associated with drug therapy. Finally, we analyse the potential clinical role of genomic testing in the prevention of SCD in the general population.

Pharmacogenetics to guide cardiovascular drug therapy

Over the past decade, pharmacogenetic testing has emerged in clinical practice to guide selected cardiovascular therapies. The most common implementation in practice is CYP2C19 genotyping to predict clopidogrel response and assist in selecting antiplatelet therapy after percutaneous coronary intervention. Additional examples include genotyping to guide warfarin dosing and statin prescribing. Increasing evidence exists on outcomes with genotype-guided cardiovascular therapies from multiple randomized controlled trials and observational studies. Pharmacogenetic evidence is accumulating for additional cardiovascular medications. However, data for many of these medications are not yet sufficient to support the use of genotyping for drug prescribing. Ultimately, pharmacogenetics might provide a means to individualize drug regimens for complex diseases such as heart failure, in which the treatment armamentarium includes a growing list of medications shown to reduce morbidity and mortality. However, sophisticated analytical approaches are likely to be necessary to dissect the genetic underpinnings of responses to drug combinations. In this Review, we examine the evidence supporting pharmacogenetic testing in cardiovascular medicine, including that available from several clinical trials. In addition, we describe guidelines that support the use of cardiovascular pharmacogenetics, provide examples of clinical implementation of genotype-guided cardiovascular therapies and discuss opportunities for future growth of the field.

Antipsychotics in routine treatment are minor contributors to QT prolongation compared to genetics and age

BACKGROUND

Drug-induced prolongation of cardiac repolarization limits the treatment with many psychotropic drugs. Recently, the contribution of polygenic variation to the individual duration of the QT interval was identified.

AIMS

To explore the interaction between antipsychotic drugs and the individual polygenic influence on the QT interval.

METHODS

Retrospective analysis of clinical and genotype data of 804 psychiatric inpatients diagnosed with a psychotic disorder. The individual polygenic influence on the QT interval was calculated according to the method of Arking et al.

RESULTS

Linear regression modelling showed a significant association of the individual polygenic QT interval score (ß_std = 0.176, p < 0.001) and age (ß_std = 0.139, p < 0.001) with the QTc interval corrected according to Fridericia's formula. Sex showed a nominal trend towards significance (ß_std = 0.064, p = 0.064). No association was observed for the number of QT prolonging drugs according to AZCERT taken. Subsample analysis (n = 588) showed a significant association of potassium serum concentrations with the QTc interval (ß_std = -0.104, p = 0.010). Haloperidol serum concentrations were associated with the QTc interval only in single medication analysis (n = 26, ß_std = 0.101, p = 0.004), but not in multivariate regression analysis. No association was observed for aripiprazole, clozapine, quetiapine and perazine, while olanzapine and the sum of risperidone and its metabolite showed a negative association.

CONCLUSIONS

Individual genetic factors and age are main determinants of the QT interval. Antipsychotic drug serum concentrations within the therapeutic range contribute to QTc prolongation on an individual level.

Genome-wide association studies of cardiac electrical phenotypes

The genetic basis of cardiac electrical phenotypes has in the last 25 years been the subject of intense investigation. While in the first years, such efforts were dominated by the study of familial arrhythmia syndromes, in recent years, large consortia of investigators have successfully pursued genome-wide association studies (GWAS) for the identification of single-nucleotide polymorphisms that govern inter-individual variability in electrocardiographic parameters in the general population. We here provide a review of GWAS conducted on cardiac electrical phenotypes in the last 14 years and discuss the implications of these discoveries for our understanding of the genetic basis of disease susceptibility and variability in disease severity. Furthermore, we review functional follow-up studies that have been conducted on GWAS loci associated with cardiac electrical phenotypes and highlight the challenges and opportunities offered by such studies.

Genetic and Molecular Aspects of Drug-Induced QT Interval Prolongation

Long QT syndromes can be either acquired or congenital. Drugs are one of the many etiologies that may induce acquired long QT syndrome. In fact, many drugs frequently used in the clinical setting are a known risk factor for a prolonged QT interval, thus increasing the chances of developing torsade de pointes. The molecular mechanisms involved in the prolongation of the QT interval are common to most medications. However, there is considerable inter-individual variability in drug response, thus making the application of personalized medicine a relevant aspect in long QT syndrome, in order to evaluate the risk of every individual from a pharmacogenetic standpoint.

Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data

BACKGROUND

Drug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools utilized to predict which individuals are at greatest risk. Machine learning (ML) algorithms may provide a method for identifying these individuals, and could be automated to directly alert providers in real time.

OBJECTIVE

This study applies ML techniques to electronic health record (EHR) data to identify an integrated risk-prediction model that can be deployed to predict risk of drug-induced QT prolongation.

METHODS

We examined harmonized data from the UCHealth EHR and identified inpatients who had received a medication known to prolong the QT interval. Using a binary outcome of the development of a QTc interval >500 ms within 24 hours of medication initiation or no ECG with a QTc interval >500 ms, we compared multiple machine learning methods by classification accuracy and performed calibration and rescaling of the final model.

RESULTS

We identified 35,639 inpatients who received a known QT-prolonging medication and an ECG performed within 24 hours of administration. Of those, 4,558 patients developed a QTc > 500 ms and 31,081 patients did not. A deep neural network with random oversampling of controls was found to provide superior classification accuracy (F1 score 0.404; AUC 0.71) for the development of a long QT interval compared with other methods. The optimal cutpoint for prediction was determined and was reasonably accurate (sensitivity 71%; specificity 73%).

CONCLUSIONS

We found that deep neural networks applied to EHR data provide reasonable prediction of which individuals are most susceptible to drug-induced QT prolongation. Future studies are needed to validate this model in novel EHRs and within the physician order entry system to assess the ability to improve patient safety.

The Role of Pharmacogenomics in Contemporary Cardiovascular Therapy: A position statement from the European Society of Cardiology Working Group on Cardiovascular Pharmacotherapy

There is a strong and ever-growing body of evidence regarding the use of pharmacogenomics to inform cardiovascular pharmacology. However, there is no common position taken by international cardiovascular societies to unite diverse availability, interpretation and application of such data, nor is there recognition of the challenges of variation in clinical practice between countries within Europe. Aside from the considerable barriers to implementing pharmacogenomic testing and the complexities of clinically actioning results, there are differences in the availability of resources and expertise internationally within Europe. Diverse legal and ethical approaches to genomic testing and clinical therapeutic application also require serious thought. As direct-to-consumer genomic testing becomes more common, it can be anticipated that data may be brought in by patients themselves, which will require critical assessment by the clinical cardiovascular prescriber. In a modern, pluralistic and multi-ethnic Europe, self-identified race/ethnicity may not be concordant with genetically detected ancestry and thus may not accurately convey polymorphism prevalence. Given the broad relevance of pharmacogenomics to areas such as thrombosis and coagulation, interventional cardiology, heart failure, arrhythmias, clinical trials, and policy/regulatory activity within cardiovascular medicine, as well as to genomic and pharmacology subspecialists, this position statement attempts to address these issues at a wide-ranging level.

The Interface of Therapeutics and Genomics in Cardiovascular Medicine

Pharmacogenomics has a burgeoning role in cardiovascular medicine, from warfarin dosing to antiplatelet choice, with recent developments in sequencing bringing the promise of personalised medicine ever closer to the bedside. Further scientific evidence, real-world clinical trials, and economic modelling are needed to fully realise this potential. Additionally, tools such as polygenic risk scores, and results from Mendelian randomisation analyses, are only in the early stages of clinical translation and merit further investigation. Genetically targeted rational drug design has a strong evidence base and, due to the nature of genetic data, academia, direct-to-consumer companies, healthcare systems, and industry may meet in an unprecedented manner. Data sharing navigation may prove problematic. The present manuscript addresses these issues and concludes a need for further guidance to be provided to prescribers by professional bodies to aid in the consideration of such complexities and guide translation of scientific knowledge to personalised clinical action, thereby striving to improve patient care. Additionally, technologic infrastructure equipped to handle such large complex data must be adapted to pharmacogenomics and made user friendly for prescribers and patients alike.

Predicting cardiac electrical response to sodium-channel blockade and Brugada syndrome using polygenic risk scores

Aims

Sodium-channel blockers (SCBs) are associated with arrhythmia, but variability of cardiac electrical response remains unexplained. We sought to identify predictors of ajmaline-induced PR and QRS changes and Type I Brugada syndrome (BrS) electrocardiogram (ECG).

Methods and results

In 1368 patients that underwent ajmaline infusion for suspected BrS, we performed measurements of 26 721 ECGs, dose–response mixed modelling and genotyping. We calculated polygenic risk scores (PRS) for PR interval (PRS_PR), QRS duration (PRS_QRS), and Brugada syndrome (PRS_BrS) derived from published genome-wide association studies and used regression analysis to identify predictors of ajmaline dose related PR change (slope) and QRS slope. We derived and validated using bootstrapping a predictive model for ajmaline-induced Type I BrS ECG. Higher PRS_PR, baseline PR, and female sex are associated with more pronounced PR slope, while PRS_QRS and age are positively associated with QRS slope (P < 0.01 for all). PRS_BrS, baseline QRS duration, presence of Type II or III BrS ECG at baseline, and family history of BrS are independently associated with the occurrence of a Type I BrS ECG, with good predictive accuracy (optimism-corrected C-statistic 0.74).

Conclusion

We show for the first time that genetic factors underlie the variability of cardiac electrical response to SCB. PRS_BrS, family history, and a baseline ECG can predict the development of a diagnostic drug-induced Type I BrS ECG with clinically relevant accuracy. These findings could lead to the use of PRS in the diagnosis of BrS and, if confirmed in population studies, to identify patients at risk for toxicity when given SCB.

Polygenic Score for β-Blocker Survival Benefit in European Ancestry Patients With Reduced Ejection Fraction Heart Failure

BACKGROUND

β-Blockers (BBs) are mainstay therapy for heart failure with reduced ejection fraction. However, individual patient responses to BB vary, which may be partially due to genetic variation. The goal of this study was to derive and validate the first polygenic response predictor (PRP) for BB survival benefit in heart failure with reduced ejection fraction patients.

METHODS

Derivation and validation analyses were performed in n=1436 total HF patients of European descent and with ejection fraction <50%. The PRP was derived in a random subset of the Henry Ford Heart Failure Pharmacogenomic Registry (n=248) and then validated in a meta-analysis of the remaining patients from Henry Ford Heart Failure Pharmacogenomic Registry (n=247), the TIME-CHF (Trial of Intensified Versus Standard Medical Therapy in Elderly Patients With Congestive Heart Failure; n=431), and HF-ACTION trial (Heart Failure: a Controlled Trial Investigating Outcomes of Exercise Training; n=510). The PRP was constructed from a genome-wide analysis of BB×genotype interaction predicting time to all-cause mortality, adjusted for Meta-Analysis Global Group in Chronic Heart Failure score, genotype, level of BB exposure, and BB propensity score.

RESULTS

Five-fold cross-validation summaries out to 1000 single-nucleotide polymorphisms identified optimal prediction with a 44 single-nucleotide polymorphism score and cutoff at the 30th percentile. In validation testing (n=1188), greater BB exposure was associated with reduced all-cause mortality in patients with low PRP score (n=251; hazard ratio, 0.19 [95% CI, 0.04-0.51]; P=0.0075) but not high PRP score (n=937; hazard ratio, 0.84 [95% CI, 0.53-1.3]; P=0.448)-a difference that was statistically significant (P interaction, 0.0235). Results were consistent regardless of atrial fibrillation, ejection fraction (≤40% versus 41%-50%), or when examining cardiovascular death.

CONCLUSIONS

Among patients of European ancestry with heart failure with reduced ejection fraction, a PRP distinguished patients who derived substantial survival benefit from BB exposure from a larger group that did not. Additional work is needed to prospectively test clinical utility and to develop PRPs for other population groups and other medications.

Influence of multiple apolipoprotein A-I and B genetic variations on insulin resistance and metabolic syndrome in obstructive sleep apnea

Background

The relationships between apolipoprotein A-I (APOA-I), apolipoprotein B (APOB) with insulin resistance, metabolic syndrome (MetS) are unclear in OSA. We aimed to evaluate whether the multiple single nucleotide polymorphism (SNP) variants of APOA-I and APOB exert a collaborative effect on insulin resistance and MetS in OSA.

Methods

Initially, 12 APOA-I SNPs and 30 APOB SNPs in 5259 subjects were examined. After strict screening, four APOA-I SNPs and five APOB SNPs in 4007 participants were included. For each participant, the genetic risk score (GRS) was calculated based on the cumulative effect of multiple genetic variants of APOA-I and APOB. Logistic regression analyses were used to evaluate the relationships between APOA-I/APOB genetic polymorphisms, insulin resistance, and MetS in OSA.

Results

Serum APOB levels increased the risk of insulin resistance and MetS adjusting for age, gender and BMI [odds ratio (OR = 3.168, P < 0.001; OR = 6.098, P < 0.001, respectively]. APOA-I GRS decreased the risk of insulin resistance and MetS after adjustments (OR = 0.917, P = 0.001; OR = 0.870, P < 0.001, respectively). APOB GRS had no association with insulin resistance (OR = 1.364, P = 0.610), and had weak association with MetS after adjustments (OR = 1.072, P = 0.042). In addition, individuals in the top quintile of the APOA-I genetic score distribution had a lower risk of insulin resistance and MetS after adjustments (OR = 0.761, P = 0.007; OR = 0.637, P < 0.001, respectively).

Conclusions

In patients with OSA, cumulative effects of APOA-I genetic variations decreased the risk of insulin resistance and MetS, whereas multiple APOB genetic variations had no associations with insulin resistance and weak association with MetS.

Drug Development and the Use of Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Disease Modeling and Drug Toxicity Screening

: Over the years, numerous groups have employed human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a superb human-compatible model for investigating the function and dysfunction of cardiomyocytes, drug screening and toxicity, disease modeling and for the development of novel drugs for heart diseases. In this review, we discuss the broad use of iPSC-CMs for drug development and disease modeling, in two related themes. In the first theme-drug development, adverse drug reactions, mechanisms of cardiotoxicity and the need for efficient drug screening protocols-we discuss the critical need to screen old and new drugs, the process of drug development, marketing and Adverse Drug reactions (ADRs), drug-induced cardiotoxicity, safety screening during drug development, drug development and patient-specific effect and different mechanisms of ADRs. In the second theme-using iPSC-CMs for disease modeling and developing novel drugs for heart diseases-we discuss the rationale for using iPSC-CMs and modeling acquired and inherited heart diseases with iPSC-CMs.

Drug-Induced Arrhythmias: A Scientific Statement From the American Heart Association

Many widely used medications may cause or exacerbate a variety of arrhythmias. Numerous antiarrhythmic agents, antimicrobial drugs, psychotropic medications, and methadone, as well as a growing list of drugs from other therapeutic classes (neurological drugs, anticancer agents, and many others), can prolong the QT interval and provoke torsades de pointes. Perhaps less familiar to clinicians is the fact that drugs can also trigger other arrhythmias, including bradyarrhythmias, atrial fibrillation/atrial flutter, atrial tachycardia, atrioventricular nodal reentrant tachycardia, monomorphic ventricular tachycardia, and Brugada syndrome. Some drug-induced arrhythmias (bradyarrhythmias, atrial tachycardia, atrioventricular node reentrant tachycardia) are significant predominantly because of their symptoms; others (monomorphic ventricular tachycardia, Brugada syndrome, torsades de pointes) may result in serious consequences, including sudden cardiac death. Mechanisms of arrhythmias are well known for some medications but, in other instances, remain poorly understood. For some drug-induced arrhythmias, particularly torsades de pointes, risk factors are well defined. Modification of risk factors, when possible, is important for prevention and risk reduction. In patients with nonmodifiable risk factors who require a potentially arrhythmia-inducing drug, enhanced electrocardiographic and other monitoring strategies may be beneficial for early detection and treatment. Management of drug-induced arrhythmias includes discontinuation of the offending medication and following treatment guidelines for the specific arrhythmia. In overdose situations, targeted detoxification strategies may be needed. Awareness of drugs that may cause arrhythmias and knowledge of distinct arrhythmias that may be drug-induced are essential for clinicians. Consideration of the possibility that a patient's arrythmia could be drug-induced is important.

Corrected QT Interval–Polygenic Risk Score and Its Contribution to Type 1, Type 2, and Type 3 Long-QT Syndrome in Probands and Genotype-Positive Family Members

Background:

Long-QT syndrome (LQTS) is characterized by a prolonged heart rate–corrected QT interval (QTc). Genome-wide association studies identified common genetic variants that collectively explain ≈8% to 10% of QTc variation in the general population.

Methods:

Overall, 423 patients with LQT1, LQT2, or LQT3 were genotyped for 61 QTc-associated genetic variants used in a prototype QTc–polygenic risk score (QTc-PRS). A weighted QTc-PRS (range, 0–154.8 ms) was calculated for each patient, and the FHS (Framingham Heart Study) population-based reference cohort (n=853).

Results:

The average QTc-PRS in LQTS was 88.0±7.2 and explained only ≈2.0% of the QTc variability. The QTc-PRS in LQTS probands (n=137; 89.3±6.8) was significantly greater than both FHS controls (87.2±7.4, difference-in-means±SE: 2.1±0.7, P <0.002) and LQTS genotype-positive family members (87.5±7.4, difference-in-mean, 1.8±.7, P <0.009). There was no difference in QTc-PRS between symptomatic (n=156, 88.6±7.3) and asymptomatic patients (n=267; 87.7±7.2, difference-in-mean, 0.9±0.7, P=0.15). LQTS patients with a QTc≥480 ms (n=120) had a significantly higher QTc-PRS (89.3±6.7) than patients with a QTc<480 ms (n=303, 87.6±7.4, difference-in-mean, 1.7±0.8, P <0.05). There was no difference in QTc-PRS or QTc between genotypes.

Conclusions:

The QTc-PRS explained <2% of the QTc variability in our LQT1, LQT2, and LQT3 cohort, contributing 5× less to their QTc value than in the general population. This prototype QTc-PRS does not distinguish/predict the clinical outcomes of individuals with LQTS.

The contribution of non-coding regulatory elements to cardiovascular disease

Cardiovascular disease collectively accounts for a quarter of deaths worldwide. Genome-wide association studies across a range of cardiovascular traits and pathologies have highlighted the prevalence of common non-coding genetic variants within candidate loci. Here, we review genetic, epigenomic and molecular approaches to investigate the contribution of non-coding regulatory elements in cardiovascular biology. We then discuss recent insights on the emerging role of non-coding variation in predisposition to cardiovascular disease, with a focus on novel mechanistic examples from functional genomics studies. Lastly, we consider the clinical significance of these findings at present, and some of the current challenges facing the field.

Pharmacogenomics of anticancer drugs: Personalising the choice and dose to manage drug response

The field of pharmacogenomics has made great strides in oncology over the last 20 years and indeed a significant number of pre-emptive genetic tests are now routinely undertaken prior to anticancer drug administration. Many of these gene-drug interactions are the fruits of candidate gene and genome-wide association studies, which have largely focused on common genetic variants (allele frequency>1%). Examples where there is clinical utility include genotyping or phenotyping for G6PD to prevent rasburicase-induced RBC haemolysis, and TPMT to prevent thiopurine-induced bone marrow suppression. Other associations such as CYP2D6 status in determining the efficacy of tamoxifen are more controversial because of contradictory evidence from different sources, which has led to variability in the implementation of testing. As genomic technology becomes ever cheaper and more accessible, we must look to the additional data our genome can provide to explain interindividual variability in anticancer drug response. Clearly genes do not act on their own and it is therefore important to investigate genetic factors in conjunction with clinical factors, interacting concomitant drug therapies and other factors such as the microbiome, which can all affect drug disposition. Taking account of all of these factors, in conjunction with the somatic genome, is more likely to provide better predictive accuracy in determining anticancer drug response, both efficacy and safety. This review summarises the existing knowledge related to the pharmacogenomics of anticancer drugs and discusses areas of opportunity for further advances in personalisation of therapy in order to improve both drug safety and efficacy.

Transethnic Genome-Wide Association Study Provides Insights in the Genetic Architecture and Heritability of Long QT Syndrome

Supplemental Digital Content is available in the text.

Long QT syndrome (LQTS) is a rare genetic disorder and a major preventable cause of sudden cardiac death in the young. A causal rare genetic variant with large effect size is identified in up to 80% of probands (genotype positive) and cascade family screening shows incomplete penetrance of genetic variants. Furthermore, a proportion of cases meeting diagnostic criteria for LQTS remain genetically elusive despite genetic testing of established genes (genotype negative). These observations raise the possibility that common genetic variants with small effect size contribute to the clinical picture of LQTS. This study aimed to characterize and quantify the contribution of common genetic variation to LQTS disease susceptibility.

A current understanding of drug-induced QT prolongation and its implications for anticancer therapy

The QT interval, a global index of ventricular repolarization, varies among individuals and is influenced by diverse physiologic and pathophysiologic stimuli such as gender, age, heart rate, electrolyte concentrations, concomitant cardiac disease, and other diseases such as diabetes. Many drugs produce a small but reproducible effect on QT interval but in rare instances this is exaggerated and marked QT prolongation can provoke the polymorphic ventricular tachycardia 'torsades de pointes', which can cause syncope or sudden cardiac death. The generally accepted common mechanism whereby drugs prolong QT is block of a key repolarizing potassium current in heart, IKr, generated by expression of KCNH2, also known as HERG. Thus, evaluation of the potential that a new drug entity may cause torsades de pointes has relied on exposure of normal volunteers or patients to drug at usual and high concentrations, and on assessment of IKr block in vitro. More recent work, focusing on anticancer drugs with QT prolonging liability, is defining new pathways whereby drugs can prolong QT. Notably, the in vitro effects of some tyrosine kinase inhibitors to prolong cardiac action potentials (the cellular correlate of QT) can be rescued by intracellular phosphatidylinositol 3,4,5-trisphosphate, the downstream effector of phosphoinositide 3-kinase. This finding supports a role for inhibition of this enzyme, either directly or by inhibition of upstream kinases, to prolong QT through mechanisms that are being worked out, but include enhanced inward 'late' sodium current during the plateau of the action potential. The definition of non-IKr-dependent pathways to QT prolongation will be important for assessing risk, not only with anticancer therapies but also with other QT prolonging drugs and for generating a refined understanding how variable activity of intracellular signalling systems can modulate QT and associated arrhythmia risk.

The evolution of gene-guided management of inherited arrhythmia syndromes: Peering beyond monogenic paradigms towards comprehensive genomic risk scores

Inherited arrhythmia syndromes have traditionally been viewed as monogenic forms of disease whose pathophysiology is driven by a single highly penetrant rare genetic variant. Although an accurate depiction of a proportion of genetic variants, the variable penetrance frequently noted in genotype positive families and the presence of sporadic genotype negative cases have long highlighted a more nuanced truth being operative. Coupled with our more recent recognition that many rare variants implicated in inherited arrhythmia syndromes possess unexpectedly high allele frequencies within the general population, these observations have contributed to the realization that a spectrum of pathogenicity exists among clinically relevant genetic variants. Notably, variable mutation pathogenicity and corresponding variable degrees of penetrance emphasize a limitation of contemporary guidelines, which attempt to dichotomize genetic variants as pathogenic or benign. Recognition of the existence of low and intermediate penetrant variants insufficient to be causative for disease in isolation has served to emphasize the importance of additional genetic, clinical, and environmental factors in the pathogenesis of rare inherited arrhythmia syndromes. Despite being rare, it has also become increasingly evident that common genetic variants play critical roles in both heritable channelopathies and cardiomyopathies and in aggregate may even be the primary drivers in certain instances, such as genotype negative Brugada syndrome. Our growing realization that the genetic substrates of inherited arrhythmia syndromes have intricacies that extend beyond traditionally perceived monogenic paradigms has highlighted a potential value of leveraging more comprehensive genomic risk scores for predicting disease development and arrhythmic risk.

EPB41L4A and LEP gene polymorphisms are associated with antipsychotic-induced QTc interval prolongation in Han Chinese

To identify the genetic factors related to antipsychotic-induced QTc interval prolongation (AIQTIP), we analyzed the associations between single nucleotide polymorphisms (SNPs) of candidate genes and quantitative traits of AIQTIP in a Han Chinese population. In total, we collected 112 hospitalized patients suffered from schizophrenia meeting the entry criteria, including 34 first-episode drug-naïve patients (FENP). All patients were treated with a single atypical antipsychotic drug (AAPD) for 4 weeks. We analyzed the quantitative genetic association between 10 SNPs in 8 candidate genes and AIQTIP using PLINK software. After 4 weeks of treatment, QTc interval of all patients was significantly prolonged and QTc interval of female patients was significantly longer compared with baseline. Antipsychotics have different effects on the prolongation of QTc. Quetiapine had the most distinct effect on AIQTIP. In all subjects, we found a significant association between the EPB41L4A gene SNP rs7732687 and AIQTIP. In male patients, we also found a significant association between the EPB41L4A gene SNP rs7732687 and AIQTIP. In female patients, we found the LEP gene SNP rs7799039 was significantly associated with AIQTIP. Our results provide preliminary evidence to support the genetic role of EPB41L4A and LEP in AIQTIP.