Hybrid adaptation is hampered by Haldane’s sieve
Bibliographic record
Abstract
Abstract Hybrids between species exhibit plastic genomic architectures that foster phenotypic diversity. Their genomic instability also incurs costs, potentially limiting adaptation. When challenged to evolve in an environment containing a UV mimetic drug, yeast hybrids have reduced adaptation rates compared to parents. We hypothesized that this reduction could result from a faster accumulation of genomic changes, but we found no such association. Alternatively, we proposed that hybrids might lack access to adaptive mutations occurring in the parents, yet, we identified mutations in the same genes ( PDR1 and YRR1 ), suggesting similar molecular adaptation mechanisms. However, mutations in these genes tended to be homozygous in the parents but heterozygous in the hybrids. We hypothesized that a lower rate of loss of heterozygosity (LOH) in hybrids could limit fitness gain. Using genome editing, we demonstrated that mutations display incomplete dominance, requiring homozygosity to show full impact and to circumvent Haldane’s sieve, which favors the fixation of dominant mutations. We used frozen ‘fossils’ to track genotype frequency dynamics and confirmed that LOH occurs at a slower pace in hybrids than in parents. Together, these findings show that Haldane’s sieve slows down adaptation in hybrids, revealing an intrinsic constraint of hybrid genomic architecture that can limit the role of hybridization in adaptive evolution.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".