GR.2 A deep intronic FGF14 GAA repeat expansion causes late-onset cerebellar ataxia
Bibliographic record
Abstract
Background: The late-onset cerebellar ataxias (LOCAs) have until recently resisted molecular diagnosis. Contributing to this diagnostic gap is that non-coding structural variations, such as repeat expansions, are not fully accessible to standard short-read sequencing analysis. Methods: We combined bioinformatics analysis of whole-genome sequencing and long-read sequencing to search for repeat expansions in patients with LOCA. We enrolled 66 French-Canadian, 228 German, 20 Australian and 31 Indian patients. Pathogenic mechanisms were studied in post-mortem cerebellum and induced pluripotent stem cell (iPSC)-derived motor neurons from 2 patients. Results: We identified 128 patients who carried an autosomal dominant GAA repeat expansion in the first intron of the FGF14 gene. The expansion was present in 61%, 18%, 15% and 10% of patients in the French-Canadian, German, Australian and Indian cohorts, respectively. The pathogenic threshold was determined to be (GAA)≥250, although incomplete penetrance was observed in the (GAA)250-300 range. Patients developed a slowly progressive cerebellar syndrome at an average age of 59 years. Patient-derived post-mortem cerebellum and induced motor neurons both showed reduction in FGF14 RNA and protein expression compared to controls. Conclusions: This intronic, dominantly inherited GAA repeat expansion in FGF14 represents one of the most common genetic causes of LOCA uncovered to date.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".