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

• Published in: BMC bioinformatics 2022-07, Vol.23 (1), p.261-16, Article 261
• Main Authors: Videla Rodriguez, Emiliano A, Pértille, Fábio, Guerrero-Bosagna, Carlos, Mitchell, John B O, Jensen, Per, Smith, V Anne
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Animals; Bayes Theorem; Bayesian analysis; Bayesian networks; Bayesian statistical decision theory; Biomarkers; Chickens; Chickens - genetics; Datasets; Decision making; Differential methylation; Epigenesis, Genetic; Epigenetic inheritance; Epigenetics; Female; Genetic aspects; Heuristic; Intestine; Learning; Mathematical models; Network analysis; Open source software; Organs; Parents & parenting; Physiological aspects; Poultry; Social interactions; Statistical analysis; Stress; Stress (Physiology); Variables
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04800-0

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. Our study aimed to apply a BN approach, while clearly laying out our analysis choices as an example for future researchers, in order to provide further insights into the relationships among epigenetic features and a stressful condition in chickens (Gallus gallus). Chickens raised under control conditions (n = 22) and chickens exposed to a social isolation protocol (n = 24) were used to identify differentially methylated regions (DMRs). A total of 60 DMRs were selected by a threshold, after bioinformatic pre-processing and analysis. The treatment was included as a binary variable (control = 0; stress = 1). Thereafter, a BN approach was applied: initially, a pre-filtering test was used for identifying pairs of features that must not be included in the process of learning the structure of the network; then, the average probability values for each arc of being part of the network were calculated; and finally, the arcs that were part of the consensus network were selected. The structure of the BN consisted of 47 out of 61 features (60 DMRs and the stressful condition), displaying 43 functional relationships. The stress condition was connected to two DMRs, one of them playing a role in tight and adhesive intracellular junctions in organs such as ovary, intestine, and brain. We clearly explain our steps in making each analysis choice, from discrete BN models to final generation of a consensus network from multiple model averaging searches. The epigenetic BN unravelled functional relationships among the DMRs, as well as epigenetic features in close association with the stressful condition the chickens were exposed to. The DMRs interacting with the stress condition could be further explored in future studies as possible biomarkers of stress in poultry species.