In systems biology and genomics, epistasis characters the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular ‘disease-causing’ mutations do not have the same outcome in all in- dividuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, es- pecially in fast-evolving organisms. Here, we present a novel statistical approach that takes inspiration from a model developed in ecology and that we adapt to analyze genetic data in a typically fast-evolving system: the influenza A virus. We validate the approach using experimentally-validated data: known interactions are recovered. We further evaluate the ability of our approach to detect epistasis during antigenic shifts or at the emergence of drug resistance. We show that in all cases, epistasis is prevalent in influenza A viruses, involving many pairs of sites linked together in chains, a hallmark of historical contingency. Strikingly, interacting sites are sepa- rated by large physical distances, which entail either long-range structural effects or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/33417 |
| Date | January 2015 |
| Creators | Nshogozabahizi, Jean Claude |
| Contributors | Aris-Brosou, Stéphane |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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