Abstract
We present a method to infer the role of selection acting during the within-host evolution of the influenza virus from short-read genome sequence data. Linkage disequilibrium between loci is accounted for by treating short-read sequences as noisy multilocus emissions from an underlying model of haplotype evolution. A hierarchical model-selection procedure is used to infer the underlying fitness landscape of the virus insofar as that landscape is explored by the viral population. In a first application of our method, we analyze data from an evolutionary experiment describing the growth of a reassortant H5N1 virus in ferrets. Across two sets of replica experiments we infer multiple alleles to be under selection, including variants associated with receptor binding specificity, glycosylation, and with the increased transmissibility of the virus. We identify epistasis as an important component of the within-host fitness landscape, and show that adaptation can proceed through multiple genetic pathways.
Highlights
The manner in which selection acts upon a population can be characterized in terms of a fitness landscape, which describes the fitness of each genotype in the population (Wright 1932)
Evidence for significant changes in frequency was found at 12 loci in the HA genome, with selection being inferred to act upon the 10 variants G496T, A557T, A642C, A736C, G738A, T741A, G754T, G788A, C789A, and G1580A
We have developed a method for inferring the within-host fitness landscape of influenza based upon error-prone shortread sequence data
Summary
The manner in which selection acts upon a population can be characterized in terms of a fitness landscape, which describes the fitness of each genotype in the population (Wright 1932). Phylogenetic models of the past development of the virus have been combined with sequence-based fitness models, or analyzed in their own right (Bush 1999; Luksza and La€ssig 2014; Neher et al 2014), to predict the strain most likely to be prevalent in the following influenza season. In this case, efficient strain prediction has the potential to improve the selection of candidates for vaccine development, conveying a significant benefit to human health
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