A novel copy number variant in the murine <i>Cdh23</i> gene gives rise to profound deafness and vestibular dysfunction
Bibliographic record
Abstract
Hearing loss is the most common congenital sensory deficit worldwide and exhibits high genetic heterogeneity, making molecular diagnoses elusive for most individuals. Detecting novel mutations that contribute to hearing loss is crucial to providing accurate personalized diagnoses, tailored interventions, and improving prognosis. Copy number variants (CNVs) are structural mutations that are understudied, potential contributors to hearing loss. Here, we present the Abnormal Wobbly Gait (AWG) mouse, the first documented mutant exhibiting waltzer-like locomotor dysfunction, hyperactivity, circling behaviour, and profound deafness caused by a spontaneous CNV deletion in cadherin 23 (Cdh23). We were unable to identify the causative mutation through a conventional whole-genome sequencing (WGS) and variant detection pipeline, but instead found a linked variant in hexokinase 1 (Hk1) that was insufficient to recapitulate the AWG phenotype when introduced into C57BL/6J mice using CRISPR-Cas9. Investigating nearby deafness-associated genes revealed a pronounced downregulation of Cdh23 mRNA and a complete absence of full-length CDH23 protein, which is critical for the development and maintenance of inner ear hair cells, in whole head extracts from AWG neonates. Manual inspection of WGS read depth plots of the Cdh23 locus revealed a putative 10.4 kb genomic deletion of exons 11 and 12 that was validated by PCR and Sanger sequencing. This study underscores the imperative to refine variant detection strategies to permit identification of pathogenic CNVs easily missed by conventional variant calling to enhance diagnostic precision and ultimately improve clinical outcomes for individuals with genetically heterogenous disorders such as hearing loss.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".