Inferring the distributions of fitness effects and proportions of strongly deleterious mutations

Abstract

AbstractThe distribution of fitness effects is a key property in evolutionary genetics as it has implications for several evolutionary phenomena such as mating system evolution and the evolution of sex, the rate of adaptive evolution, and the magnitude of the genetic load. Despite the distribution of fitness effects being extensively studied, the effects of strongly deleterious mutations are difficult to infer since such mutations are unlikely to be present in a sample of haplotypes, so the dataset on which inference is based may contain very little information about them. Recent work has attempted to correct for this issue by expanding the classic gamma-distributed model to explicitly account for strongly deleterious mutations. Here, we use simulations to investigate one such method, adding a parameter (plth) to capture the proportion of strongly deleterious mutations. We show thatplthcan improve the model fit when applied to individual species but underestimates the true proportion of strongly deleterious mutations. The parameter can also artificially maximize the likelihood when used to jointly infer a distribution of fitness effects from multiple species. Asplthand related parameters are used in current inference algorithms, our results are relevant with respect to avoiding model artifacts and improving future tools for inferring the distribution of fitness effects.