Huntingtin Haplotypes Provide Prioritized Target Panels for Allele-specific Silencing in Huntington Disease Patients of European Ancestry
Bibliographic record
Abstract
Huntington disease (HD) is a dominant neurodegenerative disorder caused by a CAG repeat expansion in the Huntingtin gene (HTT). Heterozygous polymorphisms in cis with the mutation allow for allele-specific suppression of the pathogenic HTT transcript as a therapeutic strategy. To prioritize target selection, precise heterozygosity estimates are needed across diverse HD patient populations. Here we present the first comprehensive investigation of all common target alleles across the HTT gene, using 738 reference haplotypes from the 1000 Genomes Project and 2364 haplotypes from HD patients and relatives in Canada, Sweden, France, and Italy. The most common HD haplotypes (A1, A2, and A3a) define mutually exclusive sets of polymorphisms for allele-specific therapy in the greatest number of patients. Across all four populations, a maximum of 80% are treatable using these three target haplotypes. We identify a novel deletion found exclusively on the A1 haplotype, enabling potent and selective silencing of mutant HTT in approximately 40% of the patients. Antisense oligonucleotides complementary to the deletion reduce mutant A1 HTT mRNA by 78% in patient cells while sparing wild-type HTT expression. By suppressing specific haplotypes on which expanded CAG occurs, we demonstrate a rational approach to the development of allele-specific therapy for a monogenic disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".