Sequence Variants in Small CAG Repeat Expansions of the <i>HTT</i> Gene and Disease Onset and Progression in Huntington Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: ) expansions beyond onset. METHODS: repeats. We used either clonal Sanger sequencing (in Vancouver) or short read sequencing (in Paris) to detect sequence variants. We compared age at onset (AO) and the ratio between reported and the Langbehn-predicted AO depending on the DNA sequence. We assessed the longitudinal progression of cognitive, motor, and functional scales over disease duration using linear mixed models and compared progression slopes according to the DNA sequence. RESULTS: < 0.001). Motor progression and cognitive decline were significantly faster in patients with a loss of the CAA and CCA interruptions (CAG-CCG LOI) compared with those harboring the canonical sequence. In addition, we identified 1 novel variant (CAG LOI-LO CCG) in 5 patients, leading to underestimation of 3 CAG repeats. DISCUSSION: In this large cohort, including DNA sequence and phenotypical data, the LOI variant showed a significant modifying effect on AO, motor, and cognitive disease progression. These findings, along with the identification of a novel variant, have important implications for genetic testing and counseling, especially for individuals with expansions close to cutoff ranges. In addition, they underscore the need to integrate the DNA sequence in the diagnostic process and revisit current onset prediction models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".