Human genetic variation alters CRISPR-Cas9 on- and off-targeting specificity at therapeutically implicated loci
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The CRISPR-Cas9 nuclease system holds enormous potential for therapeutic genome editing of a wide spectrum of diseases. Large efforts have been made to further understanding of on- and off-target activity to assist the design of CRISPR-based therapies with optimized efficacy and safety. However, current efforts have largely focused on the reference genome or the genome of cell lines to evaluate guide RNA (gRNA) efficiency, safety, and toxicity. Here, we examine the effect of human genetic variation on both on- and off-target specificity. Specifically, we utilize 7,444 whole-genome sequences to examine the effect of variants on the targeting specificity of ∼3,000 gRNAs across 30 therapeutically implicated loci. We demonstrate that human genetic variation can alter the off-target landscape genome-wide including creating and destroying protospacer adjacent motifs (PAMs). Furthermore, single-nucleotide polymorphisms (SNPs) and insertions/deletions (indels) can result in altered on-target sites and novel potent off-target sites, which can predispose patients to treatment failure and adverse effects, respectively; however, these events are rare. Taken together, these data highlight the importance of considering individual genomes for therapeutic genome-editing applications for the design and evaluation of CRISPR-based therapies to minimize risk of treatment failure and/or adverse outcomes.
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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 | 0.000 | 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) | 0.000 | 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 it