Robust forward simulations of recurrent hitchhiking

TL;DR

This paper develops an extended recurrent hitchhiking model and a rescaling method to enable accurate, efficient forward simulations of genetic diversity under complex evolutionary scenarios, especially for large populations.

Contribution

It introduces a new model extension and a rescaling approach that improve simulation accuracy and efficiency for complex evolutionary parameters.

Findings

Rescaling approaches can introduce inaccuracies in simulations.

The new model extension improves accuracy for small populations.

Application to Drosophila parameters demonstrates practical utility.

Abstract

Evolutionary forces shape patterns of genetic diversity within populations and contribute to phenotypic variation. In particular, recurrent positive selection has attracted significant interest in both theoretical and empirical studies. However, most existing theoretical models of recurrent positive selection cannot easily incorporate realistic confounding effects such as interference between selected sites, arbitrary selection schemes, and complicated demographic processes. It is possible to quantify the effects of arbitrarily complex evolutionary models by performing forward population genetic simulations, but forward simulations can be computationally prohibitive for large population sizes ($> 10^5$). A common approach for overcoming these computational limitations is rescaling of the most computationally expensive parameters, especially population size. Here, we show that ad hoc approaches to parameter rescaling under the recurrent hitchhiking model do not always provide sufficiently accurate dynamics, potentially skewing patterns of diversity in simulated DNA sequences. We derive an extension of the recurrent hitchhiking model that is appropriate for strong selection in small population sizes, and use it to develop a method for parameter rescaling that provides the best possible computational performance for a given error tolerance. We perform a detailed theoretical analysis of the robustness of rescaling across the parameter space. Finally, we apply our rescaling algorithms to parameters that were previously inferred for Drosophila, and discuss practical considerations such as interference between selected sites.