Decrypting Variants of Uncertain Significance in Long-QT Syndrome

解密长QT综合征中不确定意义的变异

• 批准号: 10004933
• 负责人: Alfred L. George
• 金额: $ 4.42万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10004933
• 关键词: Address; Algorithms; Arrhythmia; Benign; Binding; Biochemical; Biochemistry; Biophysics; Cell surface; Cells; Child; Classification; Complex; Counseling; Data; Databases; Decision Making; Disease; Electrophysiology (science); Evaluation; Familial atrial fibrillation; Family; Functional disorder; Funding; Genes; Genetic; Goals; Growth; Guidelines; Heritability; Hospitals; Impairment; Intervention; Investigation; Laboratories; Literature; Long QT Syndrome; Medical; Medical Genetics; Methods; Modeling; Mutation; Network-based; Online Systems; Other Genetics; Pathogenicity; Patient Care; Population; Potassium Channel; Property; Research; Risk Factors; Short QT syndrome; Structure; Syndrome; Test Result; Testing; Thermodynamics; Training; Variant; Work; artificial neural network; base; computerized tools; disease-causing mutation; genetic testing; genetic variant; improved; innovation; loss of function; molecular dynamics; multidisciplinary; mutation carrier; next generation sequencing; novel; prediction algorithm; rare variant; standard of care; structural biology; structured data; sudden cardiac death; trafficking; trait; variant of unknown significance; voltage; young adult

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.

基因检测已成为许多遗传性疾病的标准护理，包括先天性长- QT综合征（LQTS）。然而，解释基因测试结果往往是混淆的发现 未知意义的变异（VUS），没有足够的数据来确定是否存在 特定变体是良性的或致病的。广泛的临床基因检测的出现， 下一代测序技术在研究中的应用导致了基因组数量的爆炸性增长， 与疾病特征和人群相关的已知变异。该项目的目标是开发 一种区分LQTS中致病突变与良性变异的新范式， 相关遗传性心律失常综合征我们将重点关注两个钾通道亚基基因， KCNQ 1和KCNE 1与LQTS、短QT综合征和家族性心房颤动相关 纤维性颤动可靠地辨别一种变异是否是真正的风险因素的能力将是变革性的， 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和改善家庭咨询。 在前期支持期间，我们实施并优化了高通量实验 确定位于KCNQ 1中的~110个KCNQ 1变体的功能后果的策略 电压敏感域（VSD）（目标1）。同时，我们阐明了稳定性，结构特性， 和细胞表面表达的~50 KCNQ 1 VSD变异体，并推导出一个以前未被认识到的 通道中的功能域（S 0段; Aim 2）。使用文献和Aims中的数据 1-2，我们开发，训练和测试了一个计算预测器，用于估计 由KCNQ 1变体引起的通道功能障碍，其表现优于其他变体预测 算法（目标3）。我们的工作为解决新出现的 遗传变异分类的挑战。在下一个拨款期内，我们建议继续进行这项工作， 一种新的多学科范式，用于评估约200种额外的KCNQ 1变体的功能和 结构水平，重点是孔结构域和C-末端的变体，以研究 所有已知KCNE 1变体的功能和结构后果（~110），检查 KCNQ 1和KCNE 1变体对亚基间结合的影响，并开发一种先进的计算 致病性预测因子我们的研究将产生一个巨大的和前所未有的数据库的功能， 数百种KCNQ 1和KCNE 1变体的结构和生化特性沿着 先进的数据训练计算预测算法，能够准确区分 从良性变异有害。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。