An emerging idea is to use Bayesian network analysis119-121 or similar techniques to discover novel pathways. Bayesian networks have been widely used in the analysis of gene co-expression data to discover cliques of interacting loci. The starting point is usually a matrix of gene-gene correlations across multiple experimental conditions (e.g., time series of synchronized cell cultures or different environmental stressors), which can be used to derive a parsimonious graphical representation of the important interactions. Unlike co-expression data, GWA data provides only a single estimate of the association between genotype and phenotype, but no information about gene-gene connections. G×G interaction analyses do, however, yield information about pairs of genes that could be mined in a similar way, as could G×E interactions. Sebastiani et al.10 applied the technique to modeling the posterior probability of genotypes and exposures given disease status, yielding graphical models that can be interpreted in terms of interactions. However, these probabilities depend on both the risk of disease given G and E (and their interactions) and the correlations among these factors, so do not represent a pure interactome122 model.
