Novel pathogenic variants and genes for myopathies identified by whole exome sequencing
Bibliographic record
Abstract
Neuromuscular diseases (NMD) account for a significant proportion of infant and childhood mortality and devastating chronic disease. Determining the specific diagnosis of NMD is challenging due to thousands of unique or rare genetic variants that result in overlapping phenotypes. We present four unique childhood myopathy cases characterized by relatively mild muscle weakness, slowly progressing course, mildly elevated creatine phosphokinase (CPK), and contractures. We also present two additional cases characterized by severe prenatal/neonatal myopathy. Prior extensive genetic testing and histology of these cases did not reveal the genetic etiology of disease. Here, we applied whole exome sequencing (WES) and bioinformatics to identify likely causal pathogenic variants in each pedigree. In two cases, we identified novel pathogenic variants in COL6A3. In a third case, we identified novel likely pathogenic variants in COL6A6 and COL6A3. We identified a novel splice variant in EMD in a fourth case. Finally, we classify two cases as calcium channelopathies with identification of novel pathogenic variants in RYR1 and CACNA1S. These are the first cases of myopathies reported to be caused by variants in COL6A6 and CACNA1S. Our results demonstrate the utility and genetic diagnostic value of WES in the broad class of NMD phenotypes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".