Targeted Re-Sequencing Emulsion PCR Panel for Myopathies: Results in 94 Cases
Bibliographic record
Abstract
BACKGROUND: Molecular diagnostics in the genetic myopathies often requires testing of the largest and most complex transcript units in the human genome (DMD, TTN, NEB). Iteratively targeting single genes for sequencing has traditionally entailed high costs and long turnaround times. Exome sequencing has begun to supplant single targeted genes, but there are concerns regarding coverage and needed depth of the very large and complex genes that frequently cause myopathies. OBJECTIVE: To evaluate efficiency of next-generation sequencing technologies to provide molecular diagnostics for patients with previously undiagnosed myopathies. METHODS: We tested a targeted re-sequencing approach, using a 45 gene emulsion PCR myopathy panel, with subsequent sequencing on the Illumina platform in 94 undiagnosed patients. We compared the targeted re-sequencing approach to exome sequencing for 10 of these patients studied. RESULTS: We detected likely pathogenic mutations in 33 out of 94 patients with a molecular diagnostic rate of approximately 35%. The remaining patients showed variants of unknown significance (35/94 patients) or no mutations detected in the 45 genes tested (26/94 patients). Mutation detection rates for targeted re-sequencing vs. whole exome were similar in both methods; however exome sequencing showed better distribution of reads and fewer exon dropouts. CONCLUSIONS: Given that costs of highly parallel re-sequencing and whole exome sequencing are similar, and that exome sequencing now takes considerably less laboratory processing time than targeted re-sequencing, we recommend exome sequencing as the standard approach for molecular diagnostics of myopathies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".