  Genome Wide Association Studies (GWAS) and eQTL analyses have produced a
large and growing number of genetic associations linked to a wide range of
human phenotypes. As of 2013, there were more than 11,000 SNPs associated with
a trait as reported in the NHGRI GWAS Catalog. However, interpreting the
functional roles played by these SNPs remains a challenge. Here we describe an
approach that uses the inherent bipartite structure of eQTL networks to place
SNPs into a functional context.
  Using genotyping and gene expression data from 163 lung tissue samples in a
study of Chronic Obstructive Pulmonary Disease (COPD) we calculated eQTL
associations between SNPs and genes and cast significant associations (FDR $<
0.1$) as links in a bipartite network. To our surprise, we discovered that the
highly-connected "hub" SNPs within the network were devoid of
disease-associations. However, within the network we identified 35 highly
modular communities, which comprise groups of SNPs associated with groups of
genes; 13 of these communities were significantly enriched for distinct
biological functions (P $ < 5 \times 10^{-4}$) including COPD-related
functions. Further, we found that GWAS-significant SNPs were enriched at the
cores of these communities, including previously identified GWAS associations
for COPD, asthma, and pulmonary function, among others. 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.
