Identification of novel mutations causing pediatric cataract in Bhutan, Cambodia, and Sri Lanka
Bibliographic record
Abstract
BACKGROUND: Pediatric cataract is an important cause of blindness and visual impairment in children. A large proportion of pediatric cataracts are inherited, and many genes have been described for this heterogeneous Mendelian disease. Surveys of schools for the blind in Bhutan, Cambodia, and Sri Lanka have identified many children with this condition and we aimed to identify the genetic causes of inherited cataract in these populations. METHODS: We screened, in parallel, 51 causative genes for inherited cataracts in 33 probands by Ampliseq enrichment and sequencing on an Ion Torrent PGM. Rare novel protein coding variants were assessed for segregation in family members, where possible, by Sanger sequencing. RESULTS: We identified 24 rare (frequency <1% in public databases) or novel protein coding variants in 12 probands and confirmed segregation of variants with disease in the extended family where possible. Of these, six are predicted to be the cause of disease in the patient, with four other variants also highly likely to be pathogenic. CONCLUSION: This study found that 20%-30% of patients in these countries have a mutation in a known cataract causing gene, which is considerably lower than the 60%-70% reported in Caucasian cohorts. This suggests that additional cataract genes remain to be discovered in this cohort of Asian pediatric cataract 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".