Deleterious mutation/epimutation–selection balance with and without inbreeding: a population (epi)genetics model
Bibliographic record
Abstract
Epigenetics in the form of DNA methylation and other processes is an established property of genotypes and a focus of empirical research. Yet, there remain fundamental gaps in the evolutionary theory of epigenetics. To support a comprehensive understanding of epigenetics, this paper investigates theoretically the combined effects of deleterious mutation and epimutation with and without inbreeding. Both spontaneous epimutation and paramutation are considered to cover a broader range of epigenetic phenomena. We find that inbreeding generally reduces the amount of segregating deleterious genetic and epigenetic variation at equilibrium, although interestingly inbreeding can also increase the amount of deleterious genetic or epigenetic variation. Furthermore, we also demonstrate that epimutation indirectly can cause increased or decreased deleterious genetic variation at equilibrium relative to classic expectations, which is particularly evident when paramutation is occurring. With the addition of deleterious epimutation, there may be significantly increased purging of deleterious variation in more inbred populations and a significantly increased amount of segregating deleterious variation in more outbred populations, with notable exceptions.
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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".