Winbo, Annika

BACKGROUND: Long QT syndrome is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2 . Variant classification is difficult, often because of lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes. METHODS: We quantified cell-surface trafficking of 18 796 variants in KCNH2 using a multiplexed assay of variant effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping. We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian long QT syndrome penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events. RESULTS: Variant MAVE trafficking scores and automated patch clamping peak tail currents were highly correlated (Spearman rank-order ρ=0.69; n=433). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian long QT syndrome penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and corrected QT interval (QTc); however, MAVE data became nonsignificant when peak tail current and penetrance estimates were also available. The area under the receiver operator characteristic curve for 20-year event outcomes based on patient-specific sex and QTc (area under the curve, 0.80 [0.76–0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (area under the curve, 0.86 [0.83–0.89]) or attainable automated patch clamping peak tail current data (area under the curve, 0.84 [0.81–0.88]). CONCLUSIONS: High-throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale, whereas long QT syndrome penetrance estimates and automated patch clamping peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants.

Abstract Aims In long QT syndrome (LQTS), primary prevention improves outcome; thus, early identification is key. The most common LQTS phenotype is a foetal heart rate (FHR) < 3rd percentile for gestational age (GA) but the effects of cohort, genotype, variant, and maternal β-blocker therapy on FHR are unknown. We assessed the influence of these factors on FHR in pregnancies with familial LQTS and developed a FHR/GA threshold for LQTS. Methods and results In an international cohort of pregnancies in which one parent had LQTS, LQTS genotype, familial variant, and maternal β-blocker effects on FHR were assessed. We developed a testing algorithm for LQTS using FHR and GA as continuous predictors. Data included 1966 FHRs at 7–42 weeks’ GA from 267 pregnancies/164 LQTS families [220 LQTS type 1 (LQT1), 35 LQTS type 2 (LQT2), and 12 LQTS type 3 (LQT3)]. The FHRs were significantly lower in LQT1 and LQT2 but not LQT3 or LQTS negative. The LQT1 variants with non-nonsense and severe function loss (current density or β-adrenergic response) had lower FHR. Maternal β-blockers potentiated bradycardia in LQT1 and LQT2 but did not affect FHR in LQTS negative. A FHR/GA threshold predicted LQT1 and LQT2 with 74.9% accuracy, 71% sensitivity, and 81% specificity. Conclusion Genotype, LQT1 variant, and maternal β-blocker therapy affect FHR. A predictive threshold of FHR/GA significantly improves the accuracy, sensitivity, and specificity for LQT1 and LQT2, above the infant’s a priori 50% probability. We speculate this model may be useful in screening for LQTS in perinatal subjects without a known LQTS family history.

Background: 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. Methods: We conducted genome-wide association studies followed by transethnic meta-analysis in 1656 unrelated patients with LQTS of European or Japanese ancestry and 9890 controls to identify susceptibility single nucleotide polymorphisms. We estimated the common variant heritability of LQTS and tested the genetic correlation between LQTS susceptibility and other cardiac traits. Furthermore, we tested the aggregate effect of the 68 single nucleotide polymorphisms previously associated with the QT-interval in the general population using a polygenic risk score. Results: Genome-wide association analysis identified 3 loci associated with LQTS at genome-wide statistical significance ( P <5×10 −8 ) near NOS1AP , KCNQ1 , and KLF12 , and 1 missense variant in KCNE1 (p.Asp85Asn) at the suggestive threshold ( P <10 −6 ). Heritability analyses showed that ≈15% of variance in overall LQTS susceptibility was attributable to common genetic variation ( h2SNP 0.148; standard error 0.019). LQTS susceptibility showed a strong genome-wide genetic correlation with the QT-interval in the general population (r g =0.40; P =3.2×10 −3 ). The polygenic risk score comprising common variants previously associated with the QT-interval in the general population was greater in LQTS cases compared with controls ( P <10−13), and it is notable that, among patients with LQTS, this polygenic risk score was greater in patients who were genotype negative compared with those who were genotype positive ( P <0.005). Conclusions: This work establishes an important role for common genetic variation in susceptibility to LQTS. We demonstrate overlap between genetic control of the QT-interval in the general population and genetic factors contributing to LQTS susceptibility. Using polygenic risk score analyses aggregating common genetic variants that modulate the QT-interval in the general population, we provide evidence for a polygenic architecture in genotype negative LQTS.