Selection leads to false inferences of introgression using popular methods

Abstract

AbstractDetecting introgression between closely related populations or species is a fundamental objective in evolutionary biology. Existing methods for detecting migration and inferring migration rates from population genetic data often assume a neutral model of evolution. Growing evidence of the pervasive impact of selection on large portions of the genome across diverse taxa suggests that this assumption is unrealistic in most empirical systems. Further, ignoring selection has previously been shown to negatively impact demographic inferences (e.g., of population size histories). However, the impacts of biologically realistic selection on inferences of migration remain poorly explored. Here, we simulate data under models of background selection, selective sweeps, and adaptive introgression. We show that ignoring selection leads to false inferences of migration in three popularly used methods (fastsimcoal, ∂a∂i, and BPP). Selection results in the rejection of isolation-only models in favor of isolation-with-migration models and leads to elevated estimates of migration rates across methods. Our results suggest that such methods may be unreliable in many empirical systems, such that new methods that are robust to selection will need to be developed.Article SummaryDetecting migration between closely related populations is a central objective in many evolutionary biology studies. However, popular methods for detecting migration assume a simplified model of evolution. Here, we evaluate the impacts of biologically realistic natural selection, recombination, and mutation on three methods for detecting migration. We find that biological complexity leads to false inferences of migration, suggesting that results should be interpreted with caution and that new methods are needed to make robust inferences of migration across empirical systems.