Studies in other human cohorts and mice have used directed Bayesian networks or undirected weighted gene coexpression networks to incorporate existing marker and phenotype data into models that have made biologically validated predictions [11-16]. These network-based methods and their alternatives (e.g. [17-21]) show great promise but often have high computational complexity, making them most practical for smaller datasets with limited numbers of traits. Furthermore, prior information is generally not readily available in humans. For example, most TF binding sites remain unknown and even within the same tissue, the vast majority of TFBS appear divergent between human and mouse [22]. This suggests that relying on sequence conservation or the presence of conserved TF binding motifs may miss some key associations and that agnostic, complementary methods should be developed.
