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THURSDAY Biostatistics Seminar: Maud Fagny

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

Title: Characterizing cancer risk SNPs using expression quantitative trait loci bipartite networks

Location: Domus Medica, new lunch area at dept. of Biostatistics
Coffee and tea will be served.

Abstract: 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.

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Data Science, Dept. of Mathematics, UiO and Big Insight

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Earlier Event: October 16
Data Science Day 2019