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

Anders Charmouh* (Corresponding Author), Greta Bocedi, Matthew Hartfield

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Number of pages11
JournalG3: Genes, Genomes, Genetics Mission
Early online date20 Jun 2023
Publication statusE-pub ahead of print - 20 Jun 2023

Bibliographical note

Open Access via the OUP Agreement
We thank Adam Eyre–Walker, Brian Charlesworth, Thomas Bataillon, Jane M. Reid, Roslyn Henry, and Max Tschol for many helpful ideas, comments, and suggestions. We also thank Nicolas Galtier for reading an earlier draft and providing many useful comments and much valuable feedback.

Anders P. Charmouh was supported by the University of Aberdeen. Greta Bocedi was supported by a Royal Society University Research Fellowship (UF160614). Matthew Hartfield is supported by a NERC Independent Research Fellowship (NE/R015686/1) and a UKRI Frontier Research Guarantee Grant (EP/X027570/1).

Data Availability Statement

The simulation software was implemented in C++, and the full source code is available at

Supplemental material available at G3 online.


  • Distribution of fitness effects
  • mutational effect inference
  • site frequency spectrum
  • Wright-Fisher simulations
  • theoretical population genetics
  • Poisson random field theory


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