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

The distribution of fitness effects is a key property in evolutionary genetics as it has implications for several evolutionary phenomena including the evolution of sex and mating systems, the rate of adaptive evolution, and the prevalence of deleterious mutations. 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 genetic data 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 that plth can 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. As plth and 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.

Automated summary

The distribution of fitness effects is a crucial factor in evolutionary genetics, impacting various evolutionary phenomena. However, inferring the effects of strongly deleterious mutations is challenging due to their rarity in genetic data. Recent research has proposed a method to address this issue by incorporating a parameter (plth) to explicitly account for strongly deleterious mutations. Our simulations demonstrate that plth can improve model fit for individual species but underestimates the true proportion of strongly deleterious mutations. Additionally, it can artificially maximize likelihood when inferring fitness effects across multiple species. These findings are relevant for improving inference algorithms and tools related to the distribution of fitness effects.