Rich dependency structures are often formed in genetic association studies between
the phenotypic, clinical, and environmental descriptors. These descriptors may not
be standardized, and may encompass various disease definitions and clinical endpoints
which are only weakly influenced by various (e.g., genetic) factors. Such loosely
defined complex intermediate clinical phenotypes are typically used in follow-up candidate
gene association studies, e.g., after genome-wide analysis, to deepen the understanding
of the associations and to estimate effect strength. This chapter discusses a solid
methodology, which is useful in such a scenario, by using probabilistic graphical
models, namely, Bayesian networks in the Bayesian statistical framework. This method
offers systematically scalable, comprehensive hierarchical hypotheses about multivariate
relevance. We discuss its workflow: from data engineering to semantic publication
of the results. We overview the construction, visualization, and interpretation of
complex hypotheses related to the structural analysis of relevance. Furthermore, we
illustrate the use of a dependency model-based relevance measure, which takes into
account the structural properties of the model, for quantifying the effect strength.
Finally, we discuss the "interpretational" or translational challenge of a genetic
association study, with a focus on the fusion of heterogeneous omic knowledge to reintegrate
the results into a genome-wide context.
