In vivo CRISPR–Cas9 genome editing in mice identifies genetic modifiers of somatic CAG repeat instability in Huntington’s disease
Bibliographic record
Abstract
Huntington’s disease, one of more than 50 inherited repeat expansion disorders1, is a dominantly inherited neurodegenerative disease caused by a CAG expansion in HTT2. Inherited CAG repeat length is the primary determinant of age of onset, with human genetic studies underscoring that the disease is driven by the CAG length-dependent propensity of the repeat to further expand in the brain3–9. Routes to slowing somatic CAG expansion, therefore, hold promise for disease-modifying therapies. Several DNA repair genes, notably in the mismatch repair pathway, modify somatic expansion in Huntington’s disease mouse models10. To identify novel modifiers of somatic expansion, we used CRISPR–Cas9 editing in Huntington’s disease knock-in mice to enable in vivo screening of expansion-modifier candidates at scale. This included testing of Huntington’s disease onset modifier genes emerging from human genome-wide association studies as well as interactions between modifier genes, providing insight into pathways underlying CAG expansion and potential therapeutic targets. A novel in vivo screening strategy identifies new modifiers of somatic CAG repeat expansion that contribute to age of onset in Huntington’s disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".