Editorial: Epigenetic Variation Influences on Livestock Production and Disease Traits
Bibliographic record
Abstract
The epigenome source of variation modulates gene expression without involving changes to the DNA sequence and is an underutilized source of information that may contribute to improved disease management, reproduction, productivity, and environmental adaptation of livestock. The epigenome which comprises DNA methylation, histone tail modifications, chromatin remodeling, and non-coding RNA species that can transmit epigenetic information (e.g. microRNAs and long non-coding RNAs, etc.) responds to environmental factors (nutrition, pathogens, and climate, etc.) to influence the expression of genes and the emergence of specific phenotypes. Increasing evidence indicates that phenotypic expression results from multi-level interactions between the genome, epigenome, environmental factors, and other non-genetic factors. Furthermore, numerous lines of evidence suggest the influence of epigenome variation on livestock production (e.g. milk production), disease traits, reproduction, and environmental adaptation. Given that livestock breeding depends heavily on the interaction between host genetics, environment (e.g. cold or warm climate, nutrition, pathogens, etc.), physiology (e.g. age) and management practices (e.g. type of feed material, form/ quantity of feed), many of which impact the epigenome, it is imperative that attention should be given to epigenome characterization and application in livestock breeding and disease management. Therefore, the aim of this Research Topic is to collect research data on the influences of epigenome alterations (DNA methylation, RNA methylation, histone tail modifications and chromatin remodeling) on livestock production traits, to facilitate gainful use of this important source of variation to support continued improvement in livestock traits and disease management. Non-coding RNA data on livestock is already covered by other Research Topics and is excluded from this topic.
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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 | 0.001 |
| 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 | 0.001 | 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".