Bipartite Community Structure of eQTLs

Genome Wide Association Studies (GWAS) and expression quantitative trait locus (eQTL) analyses have identified genetic associations with a wide range of human phenotypes. However, many of these variants have weak effects and understanding their combined effect remains a challenge. One hypothesis is that multiple SNPs interact in complex networks to influence functional processes that ultimately lead to complex phenotypes, including disease states. Here we present CONDOR, a method that represents both cis- and trans-acting SNPs and the genes with which they are associated as a bipartite graph and then uses the modular structure of that graph to place SNPs into a functional context. In applying CONDOR to eQTLs in chronic obstructive pulmonary disease (COPD), we found the global network “hub” SNPs were devoid of disease associations through GWAS. However, the network was organized into 52 communities of SNPs and genes, many of which were enriched for genes in specific functional classes. We identified local hubs within each community (“core SNPs”) and these were enriched for GWAS SNPs for COPD and many other diseases. These results speak to our intuition: rather than single SNPs influencing single genes, we see groups of SNPs associated with the expression of families of functionally related genes and that disease SNPs are associated with the perturbation of those functions. These methods are not limited in their application to COPD and can be used in the analysis of a wide variety of disease processes and other phenotypic traits.Author Summary: Large-scale studies have identified thousands of genetic variants associated with different phenotypes without explaining their function. Expression quantitative trait locus analysis associates the compendium of genetic variants with expression levels of individual genes, providing the opportunity to link those variants to functions. But the complexity of those associations has caused most analyses to focus solely on genetic variants immediately adjacent to the genes they may influence. We describe a method that embraces the complexity, representing all variant-gene associations as a bipartite graph. The graph contains highly modular, functional communities in which disease-associated variants emerge as those likely to perturb the structure of the network and the function of the genes in these communities.