Bayesian causal phenotype network incorporating genetic variation and biological knowledge

A Bayesian network has often been modeled to infer a gene regulatory network from expression data. Genotypes along with gene expression can further reveal the regulatory relations and genetic architectures. Biological knowledge can also be incorporated to improve the reconstruction of a gene network. In this work, we propose a Bayesian framework to jointly infer a gene network and weights of prior knowledge by integrating expression data, genetic variations, and prior biological knowledge. The proposed method encodes biological knowledge such as transcription factor and DNA binding, gene ontology annotation, and protein-protein interaction into a prior distribution of the network structures. A simulation study shows that the incorporation of genetic variation information and biological knowledge improves the reconstruction of gene network as long as biological knowledge is consistent with expression data.