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• W2950668898 abstract "In systems biology and genomics, epistasis characterizes 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 individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes 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." @default.
• W2950668898 created "2019-06-27" @default.
• W2950668898 creator A5007658568 @default.
• W2950668898 creator A5009846007 @default.
• W2950668898 creator A5068557724 @default.
• W2950668898 date "2017-01-01" @default.
• W2950668898 modified "2023-10-16" @default.
• W2950668898 title "Widespread Historical Contingency in Influenza Viruses" @default.
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• W2950668898 doi "https://doi.org/10.1534/genetics.116.193979" @default.
• W2950668898 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5223518" @default.
• W2950668898 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28049709" @default.
• W2950668898 hasPublicationYear "2017" @default.
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