The Transcriptional Landscape of Cross-Specific Hybrids and Its Possible Link With Growth in Brook Charr (<i>Salvelinus fontinalis</i>Mitchill)
Bibliographic record
Abstract
The genetic mechanisms underlying hybridization are poorly understood despite their potentially important roles in speciation processes, adaptative evolution, and agronomical innovation. In this study, transcription profiles were compared among three populations of brook charr and their hybrids using microarrays to assess the influence of hybrid origin on modes of transcription regulation inheritance and on the mechanisms underlying growth. We found that twice as many transcripts were differently expressed between the domestic population and the two wild populations (Rupert and Laval) than between wild ones, despite their deeper genetic distance. This could reflect the consequence of artificial selection during domestication. We detected that hybrids exhibited strikingly different patterns of mode of transcription regulation, being mostly additive (94%) for domestic × Rupert, and nonadditive for Laval × domestic (45.7%) and Rupert × Laval hybrids (37.5%). Both heterosis and outbreeding depression for growth were observed among the crosses. Our results indicated that prevalence of dominance in transcription regulation seems related to growth heterosis, while prevalence of transgressive transcription regulation may be more related to outbreeding depression. Our study clearly shows, for the first time in vertebrates, that the consequences of hybridization on both the transcriptome level and the phenotype are highly dependent on the specific genetic architectures of crossed populations and therefore hardly predictable.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".