Pathogenic variation underlying rare diseases in an Arab population: Implications for screening programs
Bibliographic record
Abstract
Abstract Background Genetic variation underlying rare diseases in Arab populations is poorly understood, limiting effective carrier screening for recessive disorders which are prevalent due to high consanguineous rates. Methods Using the ACMG/AMP guidelines, we curated pathogenic and likely pathogenic variants in 1,333 Arab Emirati families (346 internal cohort and 987 from the literature). We also analyzed coding pathogenic variants in 1,194 Emirati exomes, calculated allele frequencies, and estimated carrier rates for the associated recessive conditions. Results Among the 1,333 families, 701 out of 855 variants met the ACMG/AMP criteria for pathogenicity, with 52% and 30% being absent from the gnomAD and ClinVar databases, respectively. Independently, we determined the frequency of coding pathogenic variants in 1,194 Emirati exomes as well as cumulative gene-disease carrier rates. The CYP21A2 gene showed the highest carrier rate (10.6%) followed by HBB (9.6%), MEFV (5.9%) and ABCA4 (4.3%). Using a provisional gene list for carrier screening, based on our analysis, we estimate the rate of at-risk couples (4–21%) which varies across different screening gene lists recommended in other populations. Conclusion Our findings emphasize the necessity of identifying prevalent diseases in underrepresented populations to develop effective and equitable preventive public health measures, including premarital screening programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".