Epigenetic marks: regulators of livestock phenotypes and conceivable sources of missing variation in livestock improvement programs
Bibliographic record
Abstract
Improvement in animal productivity has been achieved over the years through careful breeding and selection programs. Today, variations in the genome are gaining increasing importance in livestock improvement strategies. Genomic information alone, however, explains only a part of the phenotypic variance in traits. It is likely that a portion of the unaccounted variance is embedded in the epigenome. The epigenome encompasses epigenetic marks such as DNA methylation, histone tail modifications, chromatin remodeling, and other molecules that can transmit epigenetic information such as non-coding RNA species. Epigenetic factors respond to external or internal environmental cues such as nutrition, pathogens, and climate, and have the ability to change gene expression leading to emergence of specific phenotypes. Accumulating evidence shows that epigenetic marks influence gene expression and phenotypic outcome in livestock species. This review examines available evidence of the influence of epigenetic marks on livestock (cattle, sheep, goat, and pig) traits and discusses the potential for consideration of epigenetic markers in livestock improvement programs. However, epigenetic research activities on farm animal species are currently limited partly due to lack of recognition, funding and a global network of researchers. Therefore, considerable less attention has been given to epigenetic research in livestock species in comparison to extensive work in humans and model organisms. Elucidating therefore the epigenetic determinants of animal diseases and complex traits may represent one of the principal challenges to use epigenetic markers for further improvement of animal productivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".