Electronic health records (EHRs) are a powerful resource for studying individual outcomes via multiple longitudinal data elements, such as disease diagnoses, laboratory measures, medications, and other health-related information. EHR data have been useful in population health research; more importantly, linking EHR data with genomics data enables us to examine the genetic architecture of various disease outcomes and traits. PheWASs have been an effective tool to mine genetic associations for candidate SNPs or genome-wide variants;10 hence, PheWASs provide the ability to identify cross-phenotype associations in which one SNP is associated with multiple diseases or traits. While investigating such cross-phenotype associations at a genome-wide scale, researchers might uncover potential hidden connections between diseases, especially when two diseases share associations with two or more SNPs that are located in different regions of the genome (Figure 1). One way to examine these connections is by creating a network of diseases in which pairs of diseases are connected on the basis of their shared associations with one or more SNPs. The strength of the network approach is that it condenses the complex links between SNPs and diseases and reveals links between diseases that would be hard to identify by just looking at disease associations at a single locus, such as when one only considers cross-phenotype association with a SNP.
Figure 1 Overview of Network Construction
The cross-phenotype associations from a PheWAS analysis were used to construct the network of diseases. In the construction of the bipartite network, diseases (represented by yellow circles) and SNPs (represented by blue triangles) formed an edge if there was an association identified between them. Then, the bipartite network projection for the diseases was used for constructing a disease-disease network (DDN).