SimOutbreakSelection: a simulation-based tool to optimise sampling design and analysis strategies for detecting epidemic-driven selection.

Abstract

Throughout history, populations across species have been decimated by epidemic outbreaks. Recent studies have raised the enticing idea that such outbreaks have led to strong natural selection acting on disease-protective genetic variants in the host population. However, so far few, if any, clear examples of such selection exist. This could be because previous studies were underpowered to detect the type of selection an outbreak must induce: extremely short-term selection on standing variation. Here we present a simulation-based framework that allows users to explore under what circumstances selection scan methods like F_ST have power to detect epidemic-driven selection on a variant. Using two examples, we illustrate how the framework can be used. The examples also show that comparing those who died from an outbreak to survivors has the potential to render higher power than more commonly used sampling schemes. And importantly, they show that even for severe outbreaks, like the Black Death (≈50% mortality), selection may have led to only a modest increase in allele frequency, suggesting large sample sizes are required to obtain appropriate power. We hope this framework can help in designing well-powered future studies and thus help clarify the evolutionary role epidemic-driven selection has played in different species.