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

Anders P Charmouh, Greta Bocedi, Matthew Hartfield

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 (p_lth) to capture the proportion of strongly deleterious mutations. We show that p_lth 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 p_lth 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.
Original languageEnglish
Article numberjkad140
Number of pages11
JournalG3: Genes | Genomes | Genetics
Issue number9
Early online date20 Jun 2023
Publication statusPublished - 18 Jul 2023

Keywords / Materials (for Non-textual outputs)

  • distribution of fitness effects
  • mutational effect inference
  • site frequency spectrum
  • wright-fisher simulations
  • theoretical population genetics
  • poisson random field theory


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