Inbreeding effects on gene‐specific <scp>DNA</scp> methylation among tissues of Chinook salmon
Bibliographic record
Abstract
Inbreeding depression is the loss of fitness resulting from the mating of genetically related individuals. Traditionally, the study of inbreeding depression focused on genetic effects, although recent research has identified DNA methylation as also having a role in inbreeding effects. Since inbreeding depression and DNA methylation change with age and environmental stress, DNA methylation is a likely candidate for the regulation of genes associated with inbreeding depression. Here, we use a targeted, multigene approach to assess methylation at 22 growth-, metabolic-, immune- and stress-related genes. We developed PCR-based DNA methylation assays to test the effects of intense inbreeding on intragenic gene-specific methylation in inbred and outbred Chinook salmon. Inbred fish had altered methylation at three genes, CK-1, GTIIBS and hsp70, suggesting that methylation changes associated with inbreeding depression are targeted to specific genes and are not whole-genome effects. While we did not find a significant inbreeding by age interaction, we found that DNA methylation generally increases with age, although methylation decreased with age in five genes, CK-1, IFN-ɣ, HNRNPL, hsc71 and FSHb, potentially due to environmental context and sexual maturation. As expected, we found methylation patterns differed among tissue types, highlighting the need for careful selection of target tissue for methylation studies. This study provides insight into the role of epigenetic effects on ageing, environmental response and tissue function in Chinook salmon and shows that methylation is a targeted and regulated cellular process. We provide the first evidence of epigenetically based inbreeding depression in vertebrates.
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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 | 0.000 | 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) | 0.001 | 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".