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Updated: Sep 27, 2026
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Likelihood ratio calibration aligns MAVE and automated patch-clamp functional evidence for KCNH2 variants in long QT
Qianyi Shen1, Joanne G Ma1, Brett M Kroncke2
1Mark Cowley Lidwill Research Program in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia.
Background:
Many people with long QT syndrome carry missense variants that are classified as variants of uncertain significance. Functional evidence from laboratory experiments can help improve genetic diagnosis, but it requires proper validation before use in a clinical interpretation framework.
Objective:
To compare two functional datasets for KCNH2 variants: a high-throughput assay that measures how much KV11.1 channel reaches the cell surface, and automated patch clamp, which measures the level of potassium current in HEK293 cells.
Methods:
Automated patch clamp results were converted into likelihood ratios using reference sets of pathogenic and benign KCNH2 variants. These values were then mapped to ACMG/AMP functional evidence categories and compared with previously calibrated MAVE evidence for 495 single-nucleotide KCNH2 variants found in individuals with long QT syndrome.
Results:
Overall, 392 of 495 variants (79%) had concordant interpretations between MAVE and automated patch clamp. The remaining 103 variants differed between assays: 49 shifted between normal and abnormal functional evidence, and 54 shifted between a functional evidence category and an indeterminate result. Among variants assigned strong abnormal evidence by both assays, 54 were absent from ClinVar. Of 111 unresolved or conflicting ClinVar variants, 92 would meet evidence combinations sufficient for likely pathogenic classification when strong functional evidence was combined with supporting computational and population-frequency evidence.
Conclusion:
Likelihood ratio calibration allows MAVE and automated patch clamp data to be compared within the same clinical interpretation framework. Together, these functional datasets can support more transparent interpretation of KCNH2 variants in long QT syndrome.
