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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 proba...

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Autores principales: Videla Rodriguez, Emiliano A., Pértille, Fábio, Guerrero-Bosagna, Carlos, Mitchell, John B. O., Jensen, Per, Smith, V. Anne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250184/
https://www.ncbi.nlm.nih.gov/pubmed/35778683
http://dx.doi.org/10.1186/s12859-022-04800-0
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author Videla Rodriguez, Emiliano A.
Pértille, Fábio
Guerrero-Bosagna, Carlos
Mitchell, John B. O.
Jensen, Per
Smith, V. Anne
author_facet Videla Rodriguez, Emiliano A.
Pértille, Fábio
Guerrero-Bosagna, Carlos
Mitchell, John B. O.
Jensen, Per
Smith, V. Anne
author_sort Videla Rodriguez, Emiliano A.
collection PubMed
description 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. 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). RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04800-0.
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spelling pubmed-92501842022-07-03 Practical application of a Bayesian network approach to poultry epigenetics and stress Videla Rodriguez, Emiliano A. Pértille, Fábio Guerrero-Bosagna, Carlos Mitchell, John B. O. Jensen, Per Smith, V. Anne BMC Bioinformatics Research 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. 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). RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04800-0. BioMed Central 2022-07-01 /pmc/articles/PMC9250184/ /pubmed/35778683 http://dx.doi.org/10.1186/s12859-022-04800-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Videla Rodriguez, Emiliano A.
Pértille, Fábio
Guerrero-Bosagna, Carlos
Mitchell, John B. O.
Jensen, Per
Smith, V. Anne
Practical application of a Bayesian network approach to poultry epigenetics and stress
title Practical application of a Bayesian network approach to poultry epigenetics and stress
title_full Practical application of a Bayesian network approach to poultry epigenetics and stress
title_fullStr Practical application of a Bayesian network approach to poultry epigenetics and stress
title_full_unstemmed Practical application of a Bayesian network approach to poultry epigenetics and stress
title_short Practical application of a Bayesian network approach to poultry epigenetics and stress
title_sort practical application of a bayesian network approach to poultry epigenetics and stress
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250184/
https://www.ncbi.nlm.nih.gov/pubmed/35778683
http://dx.doi.org/10.1186/s12859-022-04800-0
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