Integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modeling familial omics data, for example, to study fetal programming of related maternal-offspring phenotypes. Efforts to find 'genetically determined metabotypes' using classic GWAS approaches have proven useful for characterizing complex disease, but conclusions are often limited to a disjointed series variant-metabolite associations. We adapted Bayesian network models to integrate metabotypes with maternal-fetal genetic dependencies and metabolic profile correlations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study, we demonstrate that strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, spinglass clustering of metabolites and conditional linear Gaussian methods clarify fetal programming of newborn adiposity related to maternal glycemia. Exploration of network growth over a range of penalty parameters, coupled with interactive plotting, facilitate interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.