Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models

Statistical estimation of parameters in large models of evolutionary processes using SNP data is often too computationally inefficient to pursue using exact model likelihoods. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We use forward-in-time simulations of a mechanistic model of divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles, and investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. We evaluate usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and we answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection and make recommendations which summary statistics perform well in estimating selection.