Citations

BackgroundProlongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has important prognostic and management implications. We aimed to investigate if quantitative T‐wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation.MethodsPatients were identified through an institution‐wide computer‐based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T‐wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10‐fold cross‐validation to statistically test the selected features.ResultsThe 12‐lead ECG of 38 patients with congenital LQTS and 114 patients with drug‐induced and/or electrolyte‐mediated QT prolongation were analyzed. In lead V5, patients with acquired QT prolongation had a shallower T wave right slope (−2,322 vs. −3,593 mV/s), greater T‐peak‐Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%).ConclusionT‐wave morphological analysis on lead V5 of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Noonan syndrome (NS; MIM 163950) is an autosomal dominant disorder and a member of a family of developmental disorders termed “RASopathies,” which are caused mainly by gain-of-function mutations in genes encoding RAS/MAPK signaling pathway proteins. Whole exome sequencing (WES) and trio-based genomic triangulation of a 15-year-old female with a clinical diagnosis of NS and concomitant cardiac hypertrophy and her unaffected parents identified a de novo variant in MRAS -encoded RAS-related protein 3 as the cause of her disease. Mutation analysis using in silico mutation prediction tools and molecular dynamics simulations predicted the identified variant, p.Gly23Val-MRAS, to be damaging to normal protein function and adversely affect effector interaction regions and the GTP-binding site. Subsequent ectopic expression experiments revealed a 40-fold increase in MRAS activation for p.Gly23Val-MRAS compared with WT-MRAS. Additional biochemical assays demonstrated enhanced activation of both RAS/MAPK pathway signaling and downstream gene expression in cells expressing p.Gly23Val-MRAS. Mutational analysis of MRAS in a cohort of 109 unrelated patients with phenotype-positive/genotype-negative NS and cardiac hypertrophy yielded another patient with a sporadic de novo MRAS variant (p.Thr68Ile, c.203C>T). Herein, we describe the discovery of mutations in MRAS in patients with NS and cardiac hypertrophy, establishing MRAS as the newest NS with cardiac hypertrophy-susceptibility gene.

Background Variable expressivity and incomplete penetrance between individuals with identical long QT syndrome (LQTS) causative mutations largely remain unexplained. Founder populations provide a unique opportunity to explore modifying genetic effects. We examined the role of a novel synonymous KCNQ1 p.L353L variant on the splicing of exon 8 and on heart rate corrected QT interval (QTc) in a population known to have a pathogenic LQTS type 1 (LQTS1) causative mutation, p.V205M, in KCNQ1-encoded Kv7.1. Methods 419 adults were genotyped for p.V205M, p.L353L and a previously described QTc modifier (KCNH2-p.K897T). Adjusted linear regression determined the effect of each variant on QTc, alone and in combination. In addition, peripheral blood RNA was extracted from three controls and three p.L353L-positive individuals. The mutant transcript levels were assessed via qPCR and normalised to overall KCNQ1 transcript levels to assess the effect on splicing. Results For women and men, respectively, p.L353L alone conferred a 10.0 (p=0.064) ms and 14.0 (p=0.014) ms increase in QTc and in men only a significant interaction effect in combination with the p.V205M (34.6 ms, p=0.003) resulting in a QTc of ∼500 ms. The mechanism of p.L353L's effect was attributed to approximately threefold increase in exon 8 exclusion resulting in ∼25% mutant transcripts of the total KCNQ1 transcript levels. Conclusions Our results provide the first evidence that synonymous variants outside the canonical splice sites in KCNQ1 can alter splicing and clinically impact phenotype. Through this mechanism, we identified that p.L353L can precipitate QT prolongation by itself and produce a clinically relevant interactive effect in conjunction with other LQTS variants.

Objective Sudden cardiac arrest (SCA) is the most devastating outcome in hypertrophic cardiomyopathy (HCM). We evaluated repolarisation features on the surface electrocardiogram (ECG) to identify the potential risk factors for SCA. Methods Data was collected from 52 patients with HCM who underwent implantable cardioverter defibrillator (ICD) implantation. Leads V2 and V5 from the ECG closest to the time of ICD implant were utilised for measuring the Tpeak-Tend interval (Tpe), QTc, Tpe/QTc, T-wave duration and T-wave amplitude. The presence of the five traditional SCA-associated risk factors was assessed, as well as the HCM risk-SCD score. Results 16 (30%) patients experienced aborted cardiac arrest over 8.5±4.1 years, with 9 receiving an ICD shock and 7 receiving ATP. On univariate analysis, T-wave amplitude was associated with appropriate ICD therapy (HR per 0.1 mV 0.79, 95% CI 0.56 to 0.96, p=0.02). Aborted SCA was not associated with a greater mean QTc duration, Tpeak-Tend interval, T-wave duration, or Tpe/QT ratio. Multivariate analysis (adjusting for cardinal HCM SCA-risk factors) showed T-wave amplitude in Lead V2 was an independent predictor of risk (adjusted HR per 0.1 mV 0.74, 95% CI 0.57 to 0.97, p=0.03). Addition of T-wave amplitude in Lead V2 to the traditional risk factors resulted in significant improvement in risk stratification (C-statistic from 0.65 to 0.75) but did not improve the performance of the HCM SCD-risk score. Conclusions T-wave amplitude is a novel marker of SCA in this high risk HCM population and may provide incremental predictive value to established risk factors. Further work is needed to define the role of repolarisation abnormalities in predicting SCA in HCM.

ABSTRACTNext‐generation sequencing technologies have afforded unprecedented characterization of low‐frequency and rare genetic variation. Due to low power for single‐variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel‐machine regression and adaptive testing methods for aggregative rare‐variant association testing have been demonstrated to be powerful approaches for pathway‐level analysis, although these methods tend to be computationally intensive at high‐variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare‐variant analysis using component gene‐level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family‐wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case‐control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open‐source R code for public use to facilitate easy application of our methods to existing rare‐variant analysis results.