Decrypting Variants of Uncertain Significance in Long-QT Syndrome

基因检测已经成为许多遗传性疾病的标准护理，包括先天性长寿 QT间期综合征(LQTS)。然而，解释基因测试结果往往会被这一发现搞糊涂。 没有足够的数据来确定是否存在 特定的变异是良性的或致病性的。出现了广泛的临床基因检测和 下一代测序在研究中的使用导致了爆炸性的增长 已知的与疾病特征和种群相关的变异。这个项目的目标是开发 一种新的区分LQTS和LQTS良性变异致病突变的范例 相关遗传性心律失常综合征。我们将重点研究两个钾通道亚单位基因， KCNQ1和KCNE1与LQTS、短QT综合征和家族性心房 纤颤。能够可靠地辨别变种是否是真正的风险因素将是变革性的， 通过避免对携带者进行不必要或潜在有害的干预来改善患者护理 良性变异，指导真突变携带者的治疗，改善家庭咨询。 在之前的支持期间，我们实施并优化了高吞吐量实验 确定位于KCNQ1中的~110个KCNQ1变异的功能后果的策略 电压传感域(VSD)(目标1)。同时，我们还阐述了它的稳定性，结构性质， 和细胞表面表达的~50个KCNQ1室缺变异体，并推导出以前未被识别的 渠道中的功能域(S0段；目标2)。使用来自文献和AIMS的数据 1-2，我们开发、训练和测试了一个计算预测器，用于估计 KCNQ1变异导致的通道功能障碍，其预测效果好于其他变异 算法(目标3)。我们的工作共同提供了一种新的范式来解决新兴的 遗传变异分类的挑战。在下一个资助期，我们建议继续这样做 新的多学科范式评估约200个额外的KCNQ1变体 结构水平，重点是孔区和C-末端的变体，以研究 所有已知的KCNE1变体(~110)的功能和结构后果，检查 KCNQ1和KCNE1变异体关于亚基间结合，并开发了一种高级计算方法 致病性预测因子。我们的研究将产生一个史无前例的大型数据库， 数百种KCNQ1和KCNE1变异体的结构和生化特性 先进的数据训练计算预测算法，能够准确区分 来自良性变异的有害的。这些结果将有助于提高LQTS的精度 基因测试解释和改善LQTS的医疗决策。

Genetic testing has become standard-of-care for many heritable diseases including congenital long- QT syndrome (LQTS). However, interpreting genetic test results is often confounded by the discovery of ‘variants of unknown significance’ (VUS) for which there is insufficient data to determine whether a particular variant is benign or pathogenic. The emergence of widespread clinical genetic testing and the use of next-generation sequencing in research have caused explosive growth in the number of known variants associated with disease traits and in populations. The goal of this project is to develop a novel paradigm for distinguishing disease-causing mutations from benign variants in LQTS and related genetic arrhythmia syndromes. We will focus on two potassium channel subunit genes, KCNQ1 and KCNE1, which are associated with LQTS, short-QT syndrome and familial atrial fibrillation. The ability to discern reliably whether a variant is a true risk factor would be transformative, improving patient care by avoiding unnecessary or potentially harmful interventions in carriers of benign variants, guiding therapy of true mutation carriers and improving family counseling. During the prior period of support, we implemented and optimized a high throughput experimental strategy to determine the functional consequences of ~110 KCNQ1 variants located in the KCNQ1 voltage-sensing domain (VSD) (Aim 1). In parallel, we elucidated the stability, structural properties, and cell surface expression of ~50 KCNQ1 VSD variants and deduced a previously unrecognized functional domain in the channel (S0 segment; Aim 2). Using data from the literature and from Aims 1-2, we developed, trained and tested a computational predictor for estimating the likelihood of channel dysfunction caused by KCNQ1 variants that performs better than other variant prediction algorithms (Aim 3). Together our work provides a new paradigm for addressing the emerging challenge of genetic variant classification. In the next funding period, we propose to continue this novel multidisciplinary paradigm to evaluate ~200 additional KCNQ1 variants at the functional and structural levels with an emphasis on variants in the pore domain and C-terminus, to investigate the functional and structural consequences of all known KCNE1 variants (~110), examine the impact of KCNQ1 and KCNE1 variants on intersubunit binding, and to develop an advanced computational pathogenicity predictor. Our study will yield a large and unprecedented database of functional, structural and biochemical properties of hundreds of KCNQ1 and KCNE1 variants along with an advanced, data-trained computational prediction algorithm capable of accurately discriminating deleterious from benign variants. These results will contribute to improving the accuracy of LQTS genetic test interpretation and improve medical decision-making for LQTS.