Editorial: Epigenetic Variation Influences on Livestock Production and Disease Traits
Bibliographic record
Abstract
Epigenetic Variation Influences on Livestock Production and Disease TraitsThe 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.This Research Topic collates four review articles and 19 original research articles covering major topics in epigenetics (DNA methylation, RNA methylation, and histone modification) in relation to livestock health, reproduction, production and response to stress and environmental perturbations in many livestock species, including cattle (seven articles), chicken (five articles), sheep (three articles), pig (two articles), yak (one article), and pearl oyster (one article).Although, many livestock species and a broad range of epigenomics alterations are covered, there is a bias towards DNA methylation alterations and more concentration on cattle, highlighting the need to widen/intensify research efforts to include all livestock species as well as address more issues of health and productivity, which is vital for
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".