Fig. 1

Steps taken and decisions made to build a consensus Bayesian network. The starting point was methylation data from 46 chickens under two treatment conditions (22 control, 24 stress). Bioinformatic analyses were performed as described in [54, 57]. Thereafter, a set of 60 differentially methylated regions (DMRs) were selected. The corresponding methylation values of each DMR were counts (ranging 0–39). Considering that the most frequent value was 0, binary discretization was implemented, leading us to explore discrete Bayesian network (BN) algorithms: we used the bnlearn package in R, exploring the search space with a score-and-search algorithm and the BDe score. Considering that the data had imbalances between binary states that could lead to the discovery of artefactual arcs, a contingency test (chi-square) was applied to all possible pairs of variables to create a list of arcs to avoid. Test searches and the software BayesPiles showed that the search space was complex and building the consensus Bayesian network required a strategic and iterative approach: the combination of a phylogenetic model averaging, plus further selection of arcs common to all searches into a consensus weighted Bayesian network