An Elevated Birth Prevalence of Fraser Syndrome in Quebec Linked To A Founder Pathogenic Variant
Bibliographic record
Abstract
ABSTRACT Purpose Fraser syndrome (FS) is an autosomal recessive disorder, characterized by cryptophthalmos, syndactyly, and anomalies of the respiratory and urogenital tracts. Here we estimate the birth prevalence of FS in the French-Canadian founder population of Quebec, where no prevalence has been reported to date. We also describe the phenotype of probands with FS. Methods Pathogenic allele frequency was estimated in the Quebec IBD cohort and the population-based cohort CARTaGENE (CaG), from exome sequencing ( n =2,323), short-read genome sequencing ( n =2,173) and genotyping array data ( n =29,330). Phenotypic data was collected for FS probands at CHU Ste-Justine (between 2013 and 2023). Results FRAS1 p.(Arg124Ter) was the most frequent pathogenic variant in the Quebec IBD and CaG cohorts, with frequencies 17-fold and 21-fold higher than Non-Finnish Europeans. Four French-Canadian probands were diagnosed with FS at CHU Ste-Justine, three of whom were homozygous for the variant. Birth prevalence was 0.23 per 100,000 births when predicted from the Quebec IBD cohort, 0.98 from CaG, and 1.34 from reported cases. Conclusion FS shows an elevated birth prevalence in the French-Canadian population of Quebec. We propose FRAS1 p.(Arg124Ter) as a candidate founder pathogenic variant, informing clinical practices in this population.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".