Characterizing cancer risk SNPs using expression quantitative trait loci bipartite networks

Speaker: Maud Fagny, Postdoctoral fellow, Le Moulon, French National Institute for Agricultural Research (INRA), Paris-Sud University, France and French National Center for Scientific Research (CNRS), AgroParisTech, University of Paris-Saclay, France


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 affect  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 tissue-specific eQTL network is organized into communities that group sets of SNPs and functionally-related genes. When mapping cancer-risk SNPs to these networks, we find that, in each tissue, these SNPs are significantly over-represented in communities enriched for immune response processes as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be "cores" of their communities, influencing 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 these key cancer 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.


Published Aug. 19, 2019 3:53 PM - Last modified Aug. 29, 2019 10:52 AM