Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals

Abstract

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.

Keywords: DiscovEHR study; EHR-based population study; PheWAS; electronic health record; human disease network.

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).

Figure 2

Disease-Disease Network Using the cross-phenotype associations from an EHR-based PheWAS, we generated the disease-disease network (DDN). In this network, nodes represent the diseases, and the edges (lines) between the nodes represent shared genetic associations between pairs of diseases. The color of the node represents the broader disease category to which it belongs. The size of the node indicates the importance of the node in the network; importance was based on the betweenness centrality measure. The bigger nodes have higher betweenness centrality, and these nodes are referred to as hub nodes. The width of the edges (lines) represents the number of shared variants or variants in an LD block.

Figure 3

Disease Neighbors In a network, the degree property is the number of direct connections between one node and other nodes. This plot presents the distribution of degrees observed in the DDN.

Figure 4

Diseases with Shared Enhancers in Adipose Tissue The highlighting of disease nodes in the network indicates that the shared SNPs between these diseases are located in the enhancer region of the nearby gene.

Figure 5

Disease Communities The plot shows the distribution of community disease connections, which were identified by community detection. The x axis shows the total number of communities identified, and the y axis shows the number of disease nodes in each community.

Figure 6

Comparison of Disease-Disease Network Construction through Two Orthogonal Approaches The figure illustrates the similarities between the disease network that was constructed on the basis of genetic associations (the DDN) (A) and the probabilistic model created from longitudinal EHR data (the Ising model) (B).