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. 2016 Sep 12;12(9):e1005033.
doi: 10.1371/journal.pcbi.1005033. eCollection 2016 Sep.

Bipartite Community Structure of eQTLs

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Free PMC article

Bipartite Community Structure of eQTLs

John Platig et al. PLoS Comput Biol. .
Free PMC article

Abstract

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.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of the CONDOR algorithm.
All possible SNP-gene pairs from an appropriate data set are considered in an eQTL analysis. Both cis- and trans-acting eQTLs (FDR < 0.1) are used to construct a bipartite network linking SNPs and genes. The resulting network structure is then analyzed, first globally to understand its overall structure and to identify network “hubs.” Then the community structure of the bipartite network is determined, each community is subject to functional enrichment analysis, and a core score is calculated to identify those SNPs most likely to disrupt individual communities.
Fig 2
Fig 2. Quantile-quantile plot for 13,333,199 cis- and 17,228,062,483 trans-eQTL p-values.
Fig 3
Fig 3. SNPs and genes display broad-tailed degree distributions.
The degree distribution, with the frequency of node degree plotted on a log-log scale, is shown for SNPs (a) and genes (b) in all connected components with more than 5 SNPs and 5 genes in the bipartite eQTL network.
Fig 4
Fig 4. Degree distributions for NHGRI-GWAS (red) and all (black) SNPs.
NHGRI-GWAS SNPs tend not to be global network “hubs,” which are located in the far-right tail of the distribution. The highest degree NHGRI-GWAS SNP was connected to 10 genes.
Fig 5
Fig 5. eQTLs show strong community structure.
(a) Plot of the communities within the bipartite eQTL network. The nodes (genes and SNPs) in each community form a ring, with the link density within each ring visibly darker than links between communities. (b) Links within communities (colored points) are shown along the diagonal, with links that go between communities in black. Community IDs are plotted along the x-axis.
Fig 6
Fig 6. Communities comprise SNPs and genes from multiple chromosomes.
Number of different chromosomes in each community based on (a) SNP and (b) gene locations.
Fig 7
Fig 7. NHGRI-GWAS SNPs have higher core scores than non-GWAS SNPs based on Kolmogorov-Smirnov test statistics.
Histogram of Kolmogorov-Smirnov test statistics comparing the distribution of Qih scores for sets of randomly relabeled NHGRI-GWAS/non-GWAS SNPs. The KS test statistic for the true labeling is in red. The permutation p-value associated with the KS test is P < 10−5 given 105 permutations.
Fig 8
Fig 8. NHGRI-GWAS SNPs have higher core scores than non-GWAS SNPs based on Wilcoxon test statistics.
Histogram of Wilcoxon test statistics comparing the distribution of Qih scores for sets of randomly relabeled NHGRI-GWAS/non-GWAS SNPs. The Wilcoxon test statistic for the true labeling is in red. The permutation p-value associated with the Wilcoxon test is P < 10−5 given 105 permutations.
Fig 9
Fig 9. The majority of COPD Network GWAS SNPs are annotated for functional impact.
Of the 30 SNPs that are eQTLs in the LGRC network and also associated with COPD (FDR < 0.05), 15 are likely to affect transcription factor (TF) binding and linked to the expression of a target gene (a score of 1b, d, or f), 2 have evidence of TF binding or a DNase peak (a score of 5), and 11 are located in a motif hit (a score of 6) according to RegulomeDB [37].
Fig 10
Fig 10. The median core score for COPD Network GWAS SNPs is higher than for non-significant SNPs.
The median core score for the 30 FDR-significant COPD GWAS SNPs (FDR < 0.05, left) is 20.3 times higher than the median core score for the non-significant SNPs (FDR ≥ 0.05, right).

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