Classification models for KCNQ1 variants distinguish functional and trafficking effects to enhance pathogenicity interpretation

Abstract

Missense variants in the potassium channel KCNQ1 underlie most cases of congenital long QT syndrome (LQTS), one of the most common genetic arrhythmias. Variants affect protein stability, trafficking, and function, which are measurable properties that support variant interpretation. Leveraging the extensive experimental data generated by our laboratories, we developed random forest classifiers that predict seven KCNQ1 metrics: four electrophysiology and three trafficking measurements. The features for our classifiers integrate predictions from large machine learning models with protein-specific biophysical values, outperforming using either set of features alone. We applied our classifiers to interpret ClinVar variants of uncertain significance and AlphaMissense-ambiguous variants and developed global dysfunction and mistrafficking scores which distinguished benign from pathogenic variants. Global scores complemented AlphaMissense predictions, linking variants with LQTS-causing mechanisms. While effective for KCNQ1, our approach to variant prediction is generalizable to other ion channels and we recommend systematic benchmarking as done in this work to fully assess performance of future variant effect predictors.