Whole‐exome sequencing is a valuable diagnostic tool for inherited peripheral neuropathies: Outcomes from a cohort of 50 families
Bibliographic record
Abstract
The inherited peripheral neuropathies (IPNs) are characterized by marked clinical and genetic heterogeneity and include relatively frequent presentations such as Charcot-Marie-Tooth disease and hereditary motor neuropathy, as well as more rare conditions where peripheral neuropathy is associated with additional features. There are over 250 genes known to cause IPN-related disorders but it is estimated that in approximately 50% of affected individuals a molecular diagnosis is not achieved. In this study, we examine the diagnostic utility of whole-exome sequencing (WES) in a cohort of 50 families with 1 or more affected individuals with a molecularly undiagnosed IPN with or without additional features. Pathogenic or likely pathogenic variants in genes known to cause IPN were identified in 24% (12/50) of the families. A further 22% (11/50) of families carried sequence variants in IPN genes in which the significance remains unclear. An additional 12% (6/50) of families had variants in novel IPN candidate genes, 3 of which have been published thus far as novel discoveries (KIF1A, TBCK, and MCM3AP). This study highlights the use of WES in the molecular diagnostic approach of highly heterogeneous disorders, such as IPNs, places it in context of other published neuropathy cohorts, while further highlighting associated benefits for discovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".