Improved diagnostic yield of neuromuscular disorders applying clinical exome sequencing in patients arising from a consanguineous population
Bibliographic record
Abstract
Neuromuscular diseases (NMDs) include a broad range of disorders affecting muscles, nerves and neuromuscular junctions. Their overlapping phenotypes and heterogeneous genetic nature have created challenges in diagnosis which calls for the implementation of massive parallel sequencing as a candidate strategy to increase the diagnostic yield. In this study, total of 45 patients, mostly offspring of consanguineous marriages were examined using whole exome sequencing. Data analysis was performed to identify the most probable pathogenic rare variants in known NMD genes which led to identification of causal variants for 33 out of 45 patients (73.3%) in the following known genes: CAPN3, Col6A1, Col6A3, DMD, DYSF, FHL1, GJB1, ISPD, LAMA2, LMNA, PLEC1, RYR1, SGCA, SGCB, SYNE1, TNNT1 and 22 novel pathogenic variants were detected. Today, the advantage of whole exome sequencing in clinical diagnostic strategies of heterogeneous disorders is clear. In this cohort, a diagnostic yield of 73.3% was achieved which is quite high compared to the overall reported diagnostic yield of 25% to 50%. This could be explained by the consanguineous background of these patients and is another strong advantage of offering clinical exome sequencing in diagnostic laboratories, especially in populations with high rate of consanguinity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".