Data from: Variation in juvenile Chinook salmon (Oncorhynchus tshawytscha) transcription among and within eight population crosses from British Columbia, Canada
Bibliographic record
Abstract
Phenotypic differences among populations within a species have been reported for a variety of traits, ranging from life history to physiology to gene transcription. Population-level phenotypic variation has been attributed to genetic differences resulting from genetic drift and/or local adaptation as well as environmental differences resulting from plasticity. We studied population- and family-level variation in gene transcription for 22 fitness-related genes, comprising immune, growth, metabolic, and stress processes in Chinook salmon (Oncorhynchus tshawytscha). We created hybrid Chinook salmon families from eight populations and treated them with an immune stimulus, a handling stress challenge, and held some as a no-treatment control group. Population effects, sire effects, and narrow-sense heritability (h2) were calculated for each candidate gene within each treatment group. We expected population to have a significant effect on gene transcription for many of our genes; however, we found a population effect for transcription at only one immune gene at rest. The limited number of significant population effects on gene transcription, combined with significant additive genetic variance within each population does not support the expectation of past strong selection pressures acting on heritable transcription profiles among populations. Instead, our results indicate that Chinook salmon likely adapt to their local environment through transcriptional plasticity rather than fixed differences. The expectation for fixed population-level differences in gene transcription at fitness-related genes, reflecting accepted models of local adaptation is high; however, comparisons among multiple populations using half-sib breeding designs are rare. Our work fills an important gap in our growing understanding of the process of among and within-population divergence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".