Bipartite Community Structure of eQTLs

TL;DR

This study uses bipartite network analysis of eQTL data from lung tissue to identify gene modules associated with COPD, revealing that disease-linked SNPs tend to cluster in functional communities rather than act as network hubs.

Contribution

It introduces a bipartite community detection approach to interpret eQTL networks, uncovering functionally enriched SNP-gene modules related to disease.

Findings

Identified 35 modular communities with biological functions.

GWAS SNPs enriched in community cores, linked to diseases.

Highly connected SNPs lacked disease associations.

Abstract

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.