Molecular Genetics of the Usher Syndrome in Lebanon: Identification of 11 Novel Protein Truncating Mutations by Whole Exome Sequencing
Bibliographic record
Abstract
BACKGROUND: Usher syndrome (USH) is a genetically heterogeneous condition with ten disease-causing genes. The spectrum of genes and mutations causing USH in the Lebanese and Middle Eastern populations has not been described. Consequently, diagnostic approaches designed to screen for previously reported mutations were unlikely to identify the mutations in 11 unrelated families, eight of Lebanese and three of Middle Eastern origins. In addition, six of the ten USH genes consist of more than 20 exons, each, which made mutational analysis by Sanger sequencing of PCR-amplified exons from genomic DNA tedious and costly. The study was aimed at the identification of USH causing genes and mutations in 11 unrelated families with USH type I or II. METHODS: Whole exome sequencing followed by expanded familial validation by Sanger sequencing. RESULTS: We identified disease-causing mutations in all the analyzed patients in four USH genes, MYO7A, USH2A, GPR98 and CDH23. Eleven of the mutations were novel and protein truncating, including a complex rearrangement in GPR98. CONCLUSION: Our data highlight the genetic diversity of Usher syndrome in the Lebanese population and the time and cost-effectiveness of whole exome sequencing approach for mutation analysis of genetically heterogeneous conditions caused by large genes.
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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 | 0.000 | 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".