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Genome Wide Association Studies (GWAS) and eQTL analyses have produced alarge and growing number of genetic associations linked to a wide range ofhuman phenotypes. As of 2013, there were more than 11,000 SNPs associated witha trait as reported in the NHGRI GWAS Catalog. However, interpreting thefunctional roles played by these SNPs remains a challenge. Here we describe anapproach that uses the inherent bipartite structure of eQTL networks to placeSNPs into a functional context.  Using genotyping and gene expression data from 163 lung tissue samples in astudy of Chronic Obstructive Pulmonary Disease (COPD) we calculated eQTLassociations between SNPs and genes and cast significant associations (FDR $<0.1$) as links in a bipartite network. To our surprise, we discovered that thehighly-connected "hub" SNPs within the network were devoid ofdisease-associations. However, within the network we identified 35 highlymodular communities, which comprise groups of SNPs associated with groups ofgenes; 13 of these communities were significantly enriched for distinctbiological functions (P $ < 5 \times 10^{-4}$) including COPD-relatedfunctions. Further, we found that GWAS-significant SNPs were enriched at thecores of these communities, including previously identified GWAS associationsfor COPD, asthma, and pulmonary function, among others. These results speak toour intuition: rather than single SNPs influencing single genes, we see groupsof SNPs associated with the expression of families of functionally relatedgenes and that disease SNPs are associated with the perturbation of thosefunctions. These methods are not limited in their application to COPD and canbe used in the analysis of a wide variety of disease processes and otherphenotypic traits.

Bipartite Community Structure of eQTLs