A Complex <scp> <i>FGF14</i> </scp> ( <scp>TTC</scp> )/( <scp>TGC</scp> ) Repeat Expansion in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Repeat expansions have been reported as genetic causes/risk factors of Parkinson's disease (PD). As a novel repeat expansion locus, the FGF14-SCA27B (GAA)•(TTC) repeat locus is unexplored in PD. METHODS: Utilizing genetic sequencing and various polymerase chain reaction (PCR) methodologies, pure and complex repeat expansions in FGF14 were detected in Asian PD patients. Targeted long-read sequencing was performed to investigate the detailed sequence composition of these repeat expansions. Case-control studies were further performed. RESULTS: repeat expansion was detected as the main expanded genotype in our discovery cluster. Using targeted long-read sequencing, these complex (TTC)/(TGC) repeat expansions were characterized as (TTC)exp(TGCTTC)exp(TGCTTCTTCTTCTTC)n(TTC)n alleles with four segments (Seg 1-4), and further classified into four genotypic patterns. Pattern 1 was mainly characterized by a (CTC) interruption in the Seg 1-(TTC)exp. Patterns 2-4 were characterized by different repeat length of Seg 1-(TTC)exp and Seg 3-(TGCTTCTTCTTCTTC)n. Case-control analysis revealed a significant enrichment of Pattern 4 (TTC)/(TGC) repeat expansion in PD compared with controls (P = 0.024, OR: 2.60, 95% CI: 1.07-7.23) in the discovery cluster. This significant association between Pattern 4 (TTC)/(TGC) repeat expansion and PD was confirmed in one of two replication clusters (P = 0.035, OR: 2.18, 95% CI: 0.95-4.53) and the meta-analysis across all three clusters (P = 0.015, OR: 1.75, 95% CI: 1.10-2.79). INTERPRETATION: We identified a unique complex (TTC)/(TGC) repeat expansion in FGF14 as a novel genetic risk factor of PD in the Asian population. © 2025 International Parkinson and Movement Disorder Society.
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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".