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Drosophila-virus genotype interactions dominate transmission, virulence and load, eroding additive fitness variance

Belyi, A.; Du, Y.; Wilson, A. J.; Longdon, B.; Jiggins, F. M.

2026-05-24 evolutionary biology
10.64898/2026.05.21.726842 bioRxiv
Show abstract

Host-parasite coevolution is expected to generate strong selection for susceptibility and infectivity that has the potential to erode genetic variation. Despite this, natural populations often retain extensive genetic variation in these traits. Negative frequency-dependent selection could explain the maintenance of variation since it results in genotype-by-genotype interactions on fitness that, unlike additive genetic variance among hosts and pathogens, is masked from selection and so can remain cryptic. Here we combine coevolutionary modelling with large-scale experimental infection assays to quantify how host-pathogen interactions structure fitness variance in a vertically transmitted virus system, Drosophila melanogaster and sigma virus. Simulations show that coevolution typically erodes additive genetic variance in host and pathogen fitness, concentrating variance in host-virus interaction terms. Consistent with these predictions, experiments spanning 90 host-virus genotype combinations reveal that transmission, viral load and virulence are overwhelmingly governed by host-virus genetic interactions rather than host or virus main (i.e., additive) effects. As a result, neither host resistance alleles nor viral genotypes confer consistently higher fitness across genetic backgrounds. Interactions tend to be sex-specific, further limiting heritable fitness variation. Our results demonstrate that coevolution can substantially mask heritable genetic variation from selection by rendering fitness context dependent. This extensive cryptic genetic variation may be revealed either when ecological or evolutionary conditions shift, or when the process of coevolution itself alters the direction of selection. This demonstrates the need to account for genotype-specific interactions when forecasting evolutionary responses.

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