DNA Methylation Profiles Suggest Intergenerational Transfer of Maternal Effects
Bibliographic record
Abstract
The view of maternal effects (nongenetic maternal environmental influence on offspring phenotype) has changed from one of distracting complications in evolutionary genetics to an important evolutionary mechanism for improving offspring fitness. Recent studies have shown that maternal effects act as an adaptive mechanism to prepare offspring for stressful environments. Although research into the magnitude of maternal effects is abundant, the molecular mechanisms of maternal influences on offspring phenotypic variation are not fully understood. Despite recent work identifying DNA methylation as a potential mechanism of nongenetic inheritance, currently proposed links between DNA methylation and parental effects are indirect and primarily involve genomic imprinting. We combined a factorial breeding design and gene-targeted sequencing methods to assess inheritance of methylation during early life stages at 14 genes involved in growth, development, metabolism, stress response, and immune function of Chinook salmon (Oncorhynchus tshawytscha). We found little evidence for additive or nonadditive genetic effects acting on methylation levels during early development; however, we detected significant maternal effects. Consistent with conventional maternal effect data, maternal effects on methylation declined through development and were replaced with nonadditive effects when offspring began exogenous feeding. We mapped methylation at individual CpG sites across the selected candidate genes to test for variation in site-specific methylation profiles and found significant maternal effects at selected CpG sites that also declined with development stage. While intergenerational inheritance of methylated DNA is controversial, we show that CpG-specific methylation may function as an underlying molecular mechanism for maternal effects, with important implications for offspring fitness.
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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".