Genetic and neuro-epigenetic effects of divergent artificial selection for feather pecking behaviour in chickens.

Feather pecking (FP) is a repetitive behaviour in chickens, influenced by genetic, epigenetic, and environmental factors, similar to behaviours seen in human developmental disorders (e.g., hyperactivity, autism).

The mitoepigenome responds to stress, suggesting novel mito-nuclear interactions in vertebrates.

The mitochondria are central in the cellular response to changing environmental conditions resulting from disease states, environmental exposures or normal physiological processes. Although the influences of environmental stressors upon the nuclear epigenome are well characterized, the existence and role of the mitochondrial epigenome remains contentious. Here, by quantifying the mitochondrial epigenomic response of pineal gland cells to circadian stress, we confirm the presence of extensive cytosine methylation within the mitochondrial genome.

An enriched maternal environment and stereotypies of sows differentially affect the neuro-epigenome of brain regions related to emotionality in their piglets.

Epigenetic mechanisms are important modulators of neurodevelopmental outcomes in the offspring of animals challenged during pregnancy. Pregnant sows living in a confined environment are challenged with stress and lack of stimulation which may result in the expression of stereotypies (repetitive behaviours without an apparent function). Little attention has been devoted to the postnatal effects of maternal stereotypies in the offspring.

Practical application of a Bayesian network approach to poultry epigenetics and stress.

Background: Relationships among genetic or epigenetic features can be explored by learning probabilistic networks and unravelling the dependencies among a set of given genetic/epigenetic features. Bayesian networks (BNs) consist of nodes that represent the variables and arcs that represent the probabilistic relationships between the variables. However, practical guidance on how to make choices among the wide array of possibilities in Bayesian network analysis is limited.