Visible traits demonstrate that crispant founder mice can be used for phenotypic assessment
Bibliographic record
Abstract
Abstract Genes can be knocked out in model organisms by introducing a single guide RNA and Cas9 into one cell zygotes. Recently, the zebrafish and Xenopus communities have employed this method in genetic screening pipelines that assess phenotypes in founders (F0), referred to as “crispants”. In contrast, phenotyping crispant mice has been avoided as results are believed to be confounded by genetic mosaicism, requiring that only established mouse lines undergo phenotypic assessment. Here, we targeted seven genes associated with visible recessive phenotypes. We observed the expected null phenotype in up to 100% founders per gene. Crucially, we achieved 100% editing efficiency in all but two animals. Genetic mosaicism was common, but did not confound an animal’s phenotype when comprised of mutations that all disrupted the targeted gene. Mosaicism included short in-frame mutations, but these were sufficient to disrupt function of five genes. Several founders were compound heterozygotes carrying a null and a non-null allele (short in-frame mutation or late truncation), enabling functional assessment of the non-null allele to dissect protein function. Our results set the stage for using crispant founders for initial phenotypic assessment in genetic screening, before selecting candidates for further study. This will dramatically reduce animal numbers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".