Allele Sails: launching traits and fates into wild populations with DNA sequence modifiers
Bibliographic record
Abstract
Population-scale genome editing can be used to alter the composition or fate of wild populations. One approach to achieving these aims utilizes a synthetic gene drive element-a multi-gene cassette-to bring about an increase in the frequency of an existing allele. However, the use of gene drives is complicated by the multiple scientific, regulatory, and social issues associated with transgene persistence and gene flow. Alternatives in which transgenes are not driven could potentially avoid some of these issues. Here we propose an approach to population scale gene editing using a system we refer to as an Allele Sail. An Allele Sail consists of a genome editor (the Wind) that introduces DNA sequence edits (the Sail) at one or more sites, resulting in progeny that are viable and fertile. The editor, such as a sequence-specific nuclease, or a prime- or base-editor, is inherited in a Mendelian fashion. Meanwhile, the edits it creates experience an arithmetic, Super-Mendelian increase in frequency. We explore this system using agent-based modeling, and identify contexts in which a single, low frequency release of an editor brings edits to a very high frequency. We also identify conditions in which manipulation of sex determination can be used to bring about population suppression. Current regulatory frameworks often distinguish between transgenics as genetically modified organisms (GMOs), and their edited non-transgenic progeny as non-GMO. In this context an Allele Sail provides a path to alter traits and fates of wild populations in ways that may be considered more acceptable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".