Variant Curation Expert Panel Recommendations for RYR1 Pathogenicity Assertions in Malignant Hyperthermia Susceptibility
Bibliographic record
Abstract
ABSTRACT Purpose Prevention of malignant hyperthermia (MH) requires an understanding of RYR1 variant pathogenicity to assess the risk of exposure to triggering agents. Personalized medicine, especially secondary findings and eventually genomic screening, will contribute toward this goal. Methods We specified ACMG/AMP criteria for variant interpretation for RYR1 and MH. Proposed rules were piloted on 84 variants. We applied quantitative evidence calibration for several criteria using likelihood ratios based on the Bayesian framework. Results Seven ACMG/AMP criteria were adopted without changes, ten were adopted with RYR1 -specific modifications, and nine were dropped. The in silico (PP3 and BP4) and hot spot criteria (PM1) were evaluated quantitatively. REVEL gave an OR of 23:1 for PP3 and 16:1 for BP4 using trichotomized cut-offs of >0.85 (pathogenic) and <0.5 (benign). The PM1 hotspot criterion had an OR of 24:1. PP3 and PM1 were implemented at moderate strength. Applying the revised ACMG criteria to 44 recognized MH variants, 30 were assessed as pathogenic, 12 as likely pathogenic, and two as VUS. Conclusion Curation of these variants will facilitate interpretation of RYR1 /MH genomic testing results, which is especially important for secondary findings analyses. Our approach to quantitatively calibrating criteria are generalizable to other variant curation expert panels.
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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 | 0.124 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".