Haplotyping SNPs for allele-specific gene editing of the expanded huntingtin allele using long-read sequencing
Bibliographic record
Abstract
Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease caused by CAG trinucleotide repeat expansions in exon-1 of huntingtin (HTT). Currently, there is no cure for HD, and the clinical care of individuals with HD is focused on symptom management. Previously, we showed allele-specific deletion of the expanded HTT allele (mHTT) using CRISPR-Cas9 by targeting nearby (<10 kb) SNPs that created or eliminated a protospacer adjacent motif (PAM) near exon-1. Here, we comprehensively analyzed all potential PAM sites within a 10.4-kb genomic region flanking exon-1 of HTT in 983 individuals with HD using a multiplex targeted long-read sequencing approach on the Oxford Nanopore platform. We developed computational tools (NanoBinner and NanoRepeat) to de-multiplex the data, detect repeats, and phase the reads on the expanded or the wild-type HTT allele. One SNP common to 30% of individuals with HD of European ancestry emerged through this analysis, which was confirmed as a strong candidate for allele-specific deletion of the mHTT in human HD cell lines. In addition, up to 57% HD individuals may be candidates for allele-specific editing through combinatorial SNP targeting. Cumulatively, we provide a haplotype map of the region surrounding exon-1 of HTT in individuals affected with HD. Our workflow can be applied to other repeat expansion diseases to facilitate the design of guide RNAs for allele-specific gene editing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".