Robust forward simulations of recurrent positive selection

Robust forward simulations of recurrent positive selection
Lawrence H. Uricchio, Ryan D. Hernandez
(Submitted on 24 Jul 2013)

It is well known that recurrent positive selection reduces the amount of genetic variation at linked sites. In recent decades, analytical results have been proposed to quantify the magnitude of this reduction with simple Wright-Fisher models and diffusion approximations. However, extending these results to include interference between selected sites, arbitrary selection schemes, and complicated demographic processes has proved to be challenging. Forward simulation can provide insights into these processes, but few studies have examined recurrent positive selection in a forward simulation context due to computational constraints. Here, we extend the flexible forward simulator SFS_CODE to greatly improve the efficiency of simulations of recurrent positive selection. Forward simulations are computationally intensive and often necessitate rescaling of relevant parameters (e.g., population size and sequence length) to achieve computational feasibility. However, it is not obvious that parameter rescaling will maintain expected patterns of diversity in all parameter regimes. We develop a simple method for parameter rescaling that provides the best possible computational performance for a given error tolerance, and a detailed theoretical analysis of the robustness of rescaling across the parameter space. These results show that ad hoc approaches to parameter rescaling under the recurrent hitchhiking model may not always provide sufficiently accurate dynamics, potentially skewing patterns of diversity in simulated DNA sequences.


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