Environment and genotype predict the genomic nature of domestication of salmonids as revealed by gene expression
Bibliographic record
Abstract
Billions of salmonids are produced annually by artificial reproduction for harvest and conservation. Morphologically, behaviourally and physiologically these fish differ from wild-born fish, including in ways consistent with domestication. Unlike most studied domesticates, which diverged from wild ancestors millennia ago, salmonids offer a tractable model for early-stage domestication. Here, we review a fundamental mechanism for domestication-driven differences in early-stage domestication, differentially expressed genes (DEGs), in salmonids. We found 34 publications examining DEGs under domestication driven by environment and genotype, covering six species, over a range of life-history stages and tissues. Three trends emerged. First, domesticated genotypes have increased expression of growth hormone and related metabolic genes, with differences magnified under artificial environments with increased food. Regulatory consequences of these DEGs potentially drive overall DEG patterns. Second, immune genes are often DEGs under domestication and not simply owing to release from growth-immune trade-offs under increased food. Third, domesticated genotypes exhibit reduced gene expression plasticity, with plasticity further reduced in low-complexity environments typical of production systems. Recommendations for experimental design improvements, coupled with tissue-specific expression and emerging analytical approaches for DEGs present tractable avenues to understand the evolution of domestication in salmonids and other species.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".