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.
The goal of this project is to develop a method to reliably predict the consequences of genetic variants of unknown significance discovered in the course of genetic testing in the congenital long-QT syndrome (LQTS), a cause of sudden cardiac death in children and young adults. We propose a multidisciplinary experimental approach that uses electrophysiology, biochemistry and structural biology deployed on a large scale to generate information on ~300 genetic variants in two genes (KCNQ1, KCNE1) responsible for most cases of LQTS. Our final product will be a data-trained computational strategy that will outperform existing methods for accurately predicting the functional consequences of novel KCNQ1 and KCNE1 variants, enhance the value of genetic testing in LQTS and provide for more informed medical decisions.
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