Experimental evolution of hybrid populations to identify Dobzhansky-Muller incompatibility loci
Bibliographic record
Abstract
Abstract Epistatic interactions between loci that reduce fitness in inter-species hybrids, Dobzhansky-Muller incompatibilities (DMIs), contribute genetically to the inviability and infertility within hybrid populations. It remains a challenge, however, to identify the loci that contribute to DMIs as causes of reproductive isolation between species. Here, we assess through forward simulation the power of evolve and resequence experimental evolution of hybrid populations to map DMI loci. We document conditions under which such a mapping strategy may be most feasible and demonstrate how mapping power is sensitive to biologically relevant parameters such as one-way versus two-way incompatibility type, selection strength, recombination rate, and dominance interactions. We also assess the influence of parameters under direct control of an experimenter, including duration of experimental evolution and number of replicate populations. We conclude that an evolve and re-sequence strategy for mapping DMI loci, and other cases of epistasis, can be a viable option under some circumstances for study systems with short generation times like Caenorhabditis nematodes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".