O'Neill Matthew J, Ng Chai-Ann, Aizawa Takanori, Sala Luca, Bains Sahej, Winbo Annika, Ullah Rizwan, Shen Qianyi, Tan Chek-Ying, Kozek Krystian, Vanags Loren R, Mitchell Devyn W, Shen Alex, Wada Yuko, Kashiwa Asami, Crotti Lia, Dagradi Federica, Musu Giulia, Spazzolini Carla, Neves Raquel, Bos J Martijn, Giudicessi John R, Bledsoe Xavier, Gamazon Eric R, Lancaster Megan C, Glazer Andrew M, Knollmann Bjorn C, Roden Dan M, Weile Jochen, Roth Frederick, Salem Joe-Elie, Earle Nikki, Stiles Rachael, Agee Taylor, Johnson Christopher N, Horie Minoru, Skinner Jonathan R, Ackerman Michael J, Schwartz Peter J, Ohno Seiko, Vandenberg Jamie I, Kroncke Brett M
Medical Scientist Training Program, Vanderbilt University School of Medicine, Nashville, TN (M.J.O., X.B.).
Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, Australia (C.-A.N., Q.S., C.-Y.T., J.I.V.).
Circulation. 2024 Dec 3;150(23):1869-1881. doi: 10.1161/CIRCULATIONAHA.124.069828. Epub 2024 Sep 24.
Long QT syndrome is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by . 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 missense variant heterozygotes.
We quantified cell-surface trafficking of 18 796 variants in using a multiplexed assay of variant effect (MAVE). We recorded 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 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.
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]).
High-throughput 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 missense variants.
长QT综合征是一种致死性心律失常综合征，通常由编码钾通道的罕见功能丧失变异引起。变异分类困难，常常是因为缺乏功能数据。此外，基于变异的风险分层也因临床数据的异质性和不完全外显率而变得复杂。在此，我们试图检验主要来自高通量功能检测的变异特异性信息是否能改善一个大型、统一队列中错义变异杂合子的分类和心脏事件风险分层。
我们使用变异效应多重检测（MAVE）对18796个变异的细胞表面转运进行了量化。我们通过自动膜片钳记录了533个变异的电流密度。我们根据临床基因组学指南校准了MAVE数据的证据强度。我们对1458例有错义变异的患者进行了深入表型分析，包括QTc、心脏事件史和死亡率。我们将变异功能数据和贝叶斯长QT综合征外显率估计值与队列表型进行关联，并评估心脏事件的风险比。
变异MAVE转运评分与自动膜片钳记录的峰尾电流高度相关（斯皮尔曼等级相关系数ρ=0.69；n=433）。发现MAVE数据为严重功能丧失变异提供了高达致病性的非常有力的证据。在该队列中，当与患者性别和校正QT间期（QTc）独立建模时，功能检测和贝叶斯长QT综合征外显率估计值均能显著预测心脏事件；然而，当也有峰尾电流和外显率估计值时，MAVE数据变得不显著。基于患者特异性性别和QTc的20年事件结局的受试者操作特征曲线下面积（曲线下面积，0.80[0.76-0.83]），在根据MAVE得出的前瞻性可用外显率评分（曲线下面积，0.86[0.83-0.89]）或可获得的自动膜片钳峰尾电流数据（曲线下面积，0.84[0.81-0.88]）的条件下得到了改善。
高通量钾通道变异MAVE数据对大规模变异分类有重要贡献，而长QT综合征外显率估计值和自动膜片钳峰尾电流测量对杂合性钾通道错义变异患者的心脏事件风险分层有重要贡献。
