Understanding the genomic basis of type 2 diabetes mellitus is a major challenge. Simple genome-wide association studies (GWAS) have identified ~250 loci that link to the phenotype; however, the great majority have tiny effect size of uncertain mechanistic significance. Polygenic risk score strategies do nothing more than integrate these statistical association into a single scalar parameter, again offering limited mechanistic insight. The new discipline of network medicine offers an approach by which to provide useful mechanistic information from GWAS and other omic data sets. To understand disease in the network context requires using a predefined comprehensive network-in our case the protein-protein interaction network, or interactome-as a template upon which to map loci from GWAS or other data sources. These loci have been shown to cluster in a subnetwork in the interactome (as is the case for most diseases), exploration of which identifies novel pathways that regulate disease pathogenesis and uncovers novel targets for therapeutic intervention. Such an approach is essential for utilizing the growing pool of omic data in a mechanistically rational way as we move increasingly towards precision medicine for this highly prevalent disorder.
Keywords: Complex systems; Genomics; Interactome; Metabolism; Network medicine; Systems biology.