Genetics of prelingual isolated deafness and Usher syndrome in the Maghreb and Jordan: Harnessing the potential of homozygosity
Bibliographic record
Abstract
The molecular genetic diagnosis of prelingual sensorineural hearing impairment (HI) is essential for genetic counseling and patient management. Effective diagnosis requires a knowledge of the genetic architecture of HI, which is often lacking. We established a cohort of 450 unrelated patients with familial (at least two affected relatives) severe-to-profound bilateral prelingual HI in five countries with high consanguinity rates: Tunisia, Jordan, Algeria, Morocco, and Mauritania (the TJAMM cohort). Recessive and dominant inheritance were observed in 92% and 8% of cases, respectively; 14% were syndromic. Genome analysis detected 211 different mutations (36% not reported before) in 49 deafness genes, and fully resolved 90% of cases of autosomal recessive isolated deafness (DFNB forms), 89% of the mutations being homozygous. The deafness genes involved were similar in different countries, but their mutations, except a few in GJB2 and LRTOMT , differed considerably, suggesting an overrepresentation of private mutations. Biallelic missense mutations in MYO7A , CDH23 , PCDH15 , USH1C cause either DFNB forms or Usher syndrome type 1 (USH1) ( USH1/DFNB genes). Such mutations were overrepresented (13% of patients), highlighting the importance of distinguishing between these two mutation classes. We hypothesized that current difficulties might stem from the misclassification of certain mutations. By studying the 65 USH1/DFNB missense mutations reported to cause DFNB in the homozygous state, we identified some that, when associated with a loss-of-function mutation, resulted in USH1, a characteristic pattern of some recessive hypomorphic mutations. This reappraised classification of USH1/DFNB mutations has the potential to improve molecular diagnosis and patient management significantly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".