Many genes, including KCNH2, contain ‘hotspot’ domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function.  Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of KCNH2, a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death.  Forty-two percent of KCNH2 exon 2 variants caused at least 50 % reduction in protein trafficking and 65% of these trafficking defective variants exerted a dominant-negative effect when co-expressed with a WT KCNH2 allele as assessed using a calibrated patch clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this ‘hotspot’ domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.

The parallel trafficking dataset was collected by sequencing flow-sorted cells that express a pool of different KCNH2 homozygous variants. The dataset contains the sequencing files and its associated barcoded key for identifying different KCNH2 variants.

An excel sheet ('Tile 1 sequencing details.xlsx') describing all of the parallel trafficking experiments is available with the barcode-key and list of sorted cell big fastq files. In house Python and R scripts to associate barcodes and variants across the tile for the parallel trafficking are available at https://github.com/kroncke-lab/KCNH2_DMS.