Likely Pathogenicity of Uncharacterized KCNQ1 and KCNE1 Variants
Bibliographic record
Abstract
Background: Voltage-gated potassium channels Kv7.1 encoded by gene KCNQ1 play critical roles in various physiological processes. In cardiomyocytes, complex Kv7.1-KCNE1 mediates the slow component of delayed rectifier potassium current that is essential for the action potential repolarization. Over 1,000 KCNQ1 missense variants, many of which are associated with long QT syndrome, are reported in ClinVar and other databases. However, over 600 variants are of uncertain clinical significance (VUS), have conflicting interpretations of pathogenicity or lack germline information. Computational prediction of damaging potential of such variants is important for diagnostics and treatment of cardiac disease. Methods and Results: We collected 1,750 benign and pathogenic missense variants of Kv channels from databases ClinVar, Humsavar and Ensembl Variation and tested 26 bioinformatics tools in their ability to identify known damaging variants. The best-performing tool, AlphaMissense, correctly predicted pathogenicity of 195 VUSs in Kv7.1. Among these, 79 variants of 66 wildtype residues (WTRs) are also reported as pathogenic or likely pathogenic (P/LP) in sequentially matching positions of at least one paralogue of hKv7.1. In available cryoEM structures of Kv7.1 with activated and deactivated voltage sensing domains, 52 WTRs form intersegment contacts with WTRs of ClinVar-listed variants, including 21 WTRs with P/LP variants. Analysis of state-dependent contacts suggests atomic mechanisms of dysfunction for some variants. ClinPred and paralogues annotation methods consensually predicted that 21 WTRs of KCNE1 have 34 VUSs with damaging potential. Among these, eight WTRs are contacting 23 Kv7.1 WTRs with 13 ClinVar-listed variants in AplhaFold3 model. Conclusions: Bioinformatics tools, including the paralogue annotation method, predicted likely pathogenicity of 79 VUSs in Kv7.1 and 34 VUSs in KCNE1. Analysis of intersegment contacts in CryoEM and AplhaFold3 structures suggests atomic mechanism of dysfunction for some VUSs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".