A network-based approach to eQTL interpretation and SNP functional characterization

Expression quantitative trait loci (eQTLs) analysis is commonly used to identify genetic variants affecting gene regulation. While such studies provide insight into genetic associations, they generally consider only cis-acting SNPs, and fail to address tissue-specific effects that might influence eQTL associations. Here we applied a meta-analysis approach that jointly analyzes cis- and trans-eQTLs. Using data from twelve tissue types from the Genotype-Tissue Expression (GTEx) project V6, we identified hundreds of thousands significant eQTLs in each tissue (FDR < 10%), among which 12% to 42% were trans-eQTLs. By representing the associations between SNPs and genes as links in a bipartite graph, we discovered that these eQTL networks organize into dense, highly modular communities, driven by biological processes. While some communities are enriched for processes relevant to the tissue of origin, other communities can be retrieved in several tissues and are enriched for general functions. We found that SNPs with high degree of centrality, representing global hubs in the networks, were completely depleted in GWAS association with traits and diseases. Contrary to the global network hubs, we found that local, community-specific network hubs are enriched for association with traits and diseases. These “core-SNPs” are also preferentially located in active chromatin regions, such as DNase hypersensitive regions with active transcription factor binding sites, further implicating a regulatory role. Our results lead to new hypotheses about how large numbers of weak- effect SNPs may work together to alter function and phenotype.

Please cite:

Maud Fagny, Joseph N. Paulson, Marieke L. Kuijjer, Abhijeet R. Sonawane, Cho-Yi Chen, Camila M. Lopes-Ramos, Kimberly Glass, John Quackenbush, and John Platig (2017). Exploring regulation in tissues with eQTL networks. PNAS published ahead of print August 29, 2017, doi:10.1073/pnas.1707375114

Below we provide the full tissue network provided as an R rds file.*

Nongenic cancer-risk SNPss affect oncogenes, tumor suppressor genes, and immune function

Genome-wide associations studies (GWASes) have identified many non-coding germline single nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing cancer. However, how these SNPs impact cancer risk is still largely unknown. We used a systems biology approach to analyze the regulatory role of cancer-risk SNPs in thirteen tissues. Using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis. We represented both significant cis- and trans-eQTLs as edges in tissue-specific eQTL bipartite networks. Each network is organized into communities that group sets of SNPs and functionally-related genes. When mapping cancer-risk SNPs to eQTL networks, we find that in each tissue, they are significantly overrepresented in communities enriched for immune response processes as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be "cores" of communities and influence the expression of many genes within the same biological processes. Finally, cancer-risk SNPs preferentially target oncogenes and tumor suppressor genes, suggesting they may alter the expression of key cancer-associated genes. This approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

Please cite:

Maud Fagny, John Platig, Marieke L. Kuijjer, Xihong Lin and John Quackenbush (2019). Nongenic cancer-risk SNPs affect oncogenes, tumor suppressor genes, and immune function. bioRXiv, December 2018

Below we provide the cancer-risk SNP eQTL associations provided as an R rds file.*