Population differences in Chinook salmon (<i>Oncorhynchus tshawytscha</i>) DNA methylation: Genetic drift and environmental factors
Bibliographic record
Abstract
Abstract Local adaptation and phenotypic differences among populations have been reported in many species, though most studies focus on either neutral or adaptive genetic differentiation. With the discovery of DNA methylation, questions have arisen about its contribution to individual variation in and among natural populations. Previous studies have identified differences in methylation among populations of organisms, although most to date have been in plants and model animal species. Here we obtained eyed eggs from eight populations of Chinook salmon (Oncorhynchus tshawytscha) and assayed DNA methylation at 23 genes involved in development, immune function, stress response, and metabolism using a gene‐targeted PCR‐based assay for next‐generation sequencing. Evidence for population differences in methylation was found at eight out of 23 gene loci after controlling for developmental timing in each individual. However, we found no correlation between freshwater environmental parameters and methylation variation among populations at those eight genes. A weak correlation was identified between pairwise DNA methylation dissimilarity among populations and pairwise FST based on 15 microsatellite loci, indicating weak effects of genetic drift or geographic distance on methylation. The weak correlation was primarily driven by two genes, GTIIBS and Nkef. However, single‐gene Mantel tests comparing methylation and pairwise FST were not significant after Bonferroni correction. Thus, population differences in DNA methylation are more likely related to unmeasured oceanic environmental conditions, local adaptation, and/or genetic drift. DNA methylation is an additional mechanism that contributes to among population variation, with potential influences on organism phenotype, adaptive potential, and population resilience.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".