Abstract

BackgroundThe explosive growth in known human gene variation presents enormous challenges to current approaches for variant classification that impact diagnosis and treatment of many genetic diseases. For disorders caused by mutations in cardiac ion channels, such as congenital long-QT syndrome (LQTS), in vitro electrophysiological evidence has high value in discriminating pathogenic from benign variants, but these data are often lacking because assays are cost-, time- and labor-intensive.\n\nMethods and ResultsWe implemented a strategy for performing high throughput, functional evaluations of ion channel variants that repurposed an automated electrophysiology platform developed previously for drug discovery. We demonstrated success of this approach by evaluating 78 variants in KCNQ1, a major LQTS gene. We benchmarked our results with traditional electrophysiological approaches and observed a high level of concordance. Our results provided functional data useful for classifying ~70% of previously unstudied KCNQ1 variants annotated with uninformative descriptions in the public database ClinVar. Further, we show that rare and ultra-rare KCNQ1 variants in the general population exhibit functional properties ranging from normal to severe loss-of-function indicating that allele frequency is not a reliable predictor of channel function.\n\nConclusionsOur results illustrate an efficient and high throughput paradigm linking genotype to function for a human cardiac channelopathy that will enable data-driven classification of large numbers of variants and create new opportunities for precision medicine.