An evolution-based high-fidelity method of epistasis measurement: theory and application to influenza

Abstract

AbstractLinkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the speed of evolution and the statistics of phylogeny. However, predicting the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult both to predict theoretically and detect experimentally in sequence data. A large number of false interactions arise from stochastic linkage effects and indirect interactions, which mask true interactions. Here we develop a method to filter out false-positive interactions. We start by demonstrating that the averaging of the two-way haplotype frequencies over a multiple independent populations is necessary but not sufficient, because it still leaves high numbers of false interactions. To compensate for this residual stochastic noise, we develop a triple-way haplotype method isolating true interactions. The fidelity of the method is confirmed using simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large database sequences of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure site-site interactions from sequence data.Author’s summaryInteraction of genomic sites creating “fitness landscape” is very important for predicting the escape of viruses from drugs and immune response and for passing through fitness valleys. Many efforts have been invested into measuring these interactions from DNA sequence sets. Unfortunately, reproducibility of the results remains low, due partly to a very small fraction of interaction pairs, and partly to stochastic noise intrinsic for evolution masking true interactions. Here we propose a method based on analysis of genetic sequences at three genomic sites to clean stochastic linkage and apply it to influenza virus sequence data.