The genetic basis and the diagnostic yield of genetic testing related to nonsyndromic hearing loss in Qatar
Bibliographic record
Abstract
Hearing loss is the most predominant sensory defect occurring in pediatrics, of which, 66% cases are attributed to genetic factors. The prevalence of hereditary hearing loss increases in consanguineous populations, and the prevalence of hearing loss in Qatar is 5.2%. We aimed to investigate the genetic basis of nonsyndromic hearing loss (NSHL) in Qatar and to evaluate the diagnostic yield of different genetic tests available. A retrospective chart review was conducted for 59 pediatric patients with NSHL referred to the Department of Adult and Pediatric Medical Genetics at Hamad Medical Corporation in Qatar, and who underwent at least one genetic test. Out of the 59 patients, 39 were solved cases due to 19 variants in 11 genes and two copy number variants that explained the NSHL phenotype. Of them 2 cases were initially uncertain and were reclassified using familial segregation. Around 36.8% of the single variants were in GJB2 gene and c.35delG was the most common recurrent variant seen in solved cases. We detected the c.283C > T variant in FGF3 that was seen in a Qatari patient and found to be associated with NSHL for the first time. The overall diagnostic yield was 30.7%, and the diagnostic yield was significantly associated with genetic testing using GJB2 sequencing and using the hearing loss (HL) gene panel. The diagnostic yield for targeted familial testing was 60% (n = 3 patients) and for gene panel was 50% (n = 5). Thus, we recommend using GJB2 gene sequencing as a first-tier genetic test and HL gene panel as a second-tier genetic test for NSHL. Our work provided new insights into the genetic pool of NSHL among Arabs and highlights its unique diversity, this is believed to help further in the diagnostic and management options for NSHL Arab patients.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".