A founder mutation in the PEX6 gene is responsible for increased incidence of Zellweger syndrome in a French Canadian population
Bibliographic record
Abstract
BACKGROUND: Zellweger syndrome (ZS) is a peroxisome biogenesis disorder due to mutations in any one of 13 PEX genes. Increased incidence of ZS has been suspected in French-Canadians of the Saguenay-Lac-St-Jean region (SLSJ) of Quebec, but this remains unsolved. METHODS: We identified 5 ZS patients from SLSJ diagnosed by peroxisome dysfunction between 1990-2010 and sequenced all coding exons of known PEX genes in one patient using Next Generation Sequencing (NGS) for diagnostic confirmation. RESULTS: A homozygous mutation (c.802_815del, p.[Val207_Gln294del, Val76_Gln294del]) in PEX6 was identified and then shown in 4 other patients. Parental heterozygosity was confirmed in all. Incidence of ZS was estimated to 1 in 12,191 live births, with a carrier frequency of 1 in 55. In addition, we present data suggesting that this mutation abolishes a SF2/ASF splice enhancer binding site, resulting in the use of two alternative cryptic donor splice sites and predicted to encode an internally deleted in-frame protein. CONCLUSION: We report increased incidence of ZS in French-Canadians of SLSJ caused by a PEX6 founder mutation. To our knowledge, this is the highest reported incidence of ZS worldwide. These findings have implications for carrier screening and support the utility of NGS for molecular confirmation of peroxisomal disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".