ArticleCirculation. Cardiovascular genetics2017
Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.
Article in Circulation. Cardiovascular genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Article
- Functional Profiling ofCirculation. Genomic and precision medicine · 2025Article
- Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation.bioRxiv : the preprint server for biology · 2025Article
- Ligand distances as key predictors of pathogenicity and function in NMDA receptors.Human molecular genetics · 2025Article
- Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Towards generalizable predictions for G protein-coupled receptor variant expression.Biophysical journal · 2022Article
- Mutation-Specific Differences in Kv7.1 (International journal of molecular sciences · 2022Review
- Predicting the functional effects of voltage-gated potassium channel missense variants with multi-task learning.EBioMedicine · 2022Article
- Structural and electrophysiological basis for the modulation of KCNQ1 channel currents by ML277.Nature communications · 2022Article
- GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes.Circulation. Arrhythmia and electrophysiology · 2022Article
- Predicting the functional impact of KCNQ1 variants with artificial neural networks.PLoS computational biology · 2022Article
- How Functional Genomics Can Keep Pace With VUS Identification.Frontiers in cardiovascular medicine · 2022Review
- Mutation location and IKs regulation in the arrhythmic risk of long QT syndrome type 1: the importance of the KCNQ1 S6 region.European heart journal · 2021Article
- Opportunities and challenges for the computational interpretation of rare variation in clinically important genes.American journal of human genetics · 2021Review
- Compound HeterozygousFrontiers in cardiovascular medicine · 2021Article
- High-Throughput Reclassification of SCN5A Variants.American journal of human genetics · 2020Article
- A Bayesian method to estimate variant-induced disease penetrance.PLoS genetics · 2020Article
- Structures Illuminate Cardiac Ion Channel Functions in Health and in Long QT Syndrome.Frontiers in pharmacology · 2020Review
- Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking.Computational and structural biotechnology journal · 2019Article
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11 authors.
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Abstract
backgroundAn emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity. METHODS AND
resultsIn this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred.
conclusionsAlthough a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools.
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