The effects of population size histories on estimates of selection coefficients from time-series genetic data

Abstract

AO_SCPCAPBSTRACTC_SCPCAPMany approaches have been developed for inferring selection coefficients from time series data while accounting for genetic drift. However, the improvement in inference accuracy that can be attained by modeling drift is unknown. Here, by comparing maximum likelihood estimates of selection coefficients that account for the true population size history with estimates that ignore drift, we address the following questions: how much can modeling the population size history improve estimates of selection coefficients? How much can mis-inferred population sizes hurt inferences of selection coefficients? We conduct our analysis under the discrete Wright-Fisher model by deriving the exact probability of an allele frequency trajectory in a population of time-varying size and we replicate our results under the diffusion model by extending the exact probability of a frequency trajectory derived by Steinrucken et al. (2014) to the case of a piecewise constant population. For both the discrete Wright-Fisher and diffusion models, we find that ignoring drift leads to estimates of selection coefficients that are nearly as accurate as estimates that account for the true population history, even when population sizes are small and drift is high. In populations of time-varying size, estimates of selection coefficients that ignore drift are similar in accuracy to estimates that rely on crude, yet reasonable, estimates of the population history. These results are of interest because inference methods that ignore drift are widely used in evolutionary studies and can be many orders of magnitude faster than methods that account for population sizes.