Epigenetic variation in the Egfr gene generates quantitative variation in a complex trait in ants
Bibliographic record
Abstract
Complex quantitative traits, like size and behaviour, are a pervasive feature of natural populations. Quantitative trait variation is the product of both genetic and environmental factors, yet little is known about the mechanisms through which their interaction generates this variation. Epigenetic processes, such as DNA methylation, can mediate gene-by-environment interactions during development to generate discrete phenotypic variation. We therefore investigated the developmental role of DNA methylation in generating continuous size variation of workers in an ant colony, a key trait associated with division of labour. Here we show that, in the carpenter ant Camponotus floridanus, global (genome-wide) DNA methylation indirectly regulates quantitative methylation of the conserved cell-signalling gene Epidermal growth factor receptor to generate continuous size variation of workers. DNA methylation can therefore generate quantitative variation in a complex trait by quantitatively regulating the transcription of a gene. This mechanism, alongside genetic variation, may determine the phenotypic possibilities of loci for generating quantitative trait variation in natural populations. Variation in complex traits is generated by the interaction of genetic and environmental factors. Here, the authors show that genome-wide DNA methylation indirectly regulates quantitative methylation of the Egfrgene to generate continuous size variation of larvae workers in the carpenter ant.
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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".