ArticleProceedings of the National Academy of Sciences of the United States of America2023
A null model for the distribution of fitness effects of mutations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Weakest link epistasis and the geometry of genetic load.bioRxiv : the preprint server for biology · 2026Article
- Pervasive but biome-dependent relationship between fragmentation and resilience in forests.Nature ecology & evolution · 2025Article
- A fitness distribution law for amino-acid replacements.bioRxiv : the preprint server for biology · 2024Article
- Fisher's Geometric Model as a Tool to Study Speciation.Cold Spring Harbor perspectives in biology · 2024Review
- Gene birth in a model of non-genic adaptation.BMC biology · 2023Article
- Genetic Architecture of Flowering Time Differs Between Populations With Contrasting Demographic and Selective Histories.Molecular biology and evolution · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
The distribution of fitness effects (DFE) of new mutations is key to our understanding of many evolutionary processes. Theoreticians have developed several models to help understand the patterns seen in empirical DFEs. Many such models reproduce the broad patterns seen in empirical DFEs but these models often rely on structural assumptions that cannot be tested empirically. Here, we investigate how much of the underlying "microscopic" biological processes involved in the mapping of new mutations to fitness can be inferred from "macroscopic" observations of the DFE. We develop a null model by generating random genotype-to-fitness maps and show that the null DFE is that with the largest possible information entropy. We further show that, subject to one simple constraint, this null DFE is a Gompertz distribution. Finally, we illustrate how the predictions of this null DFE match empirically measured DFEs from several datasets, as well as DFEs simulated from Fisher's geometric model. This suggests that a match between models and empirical data is often not a very strong indication of the mechanisms underlying the mapping of mutation to fitness.
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Registered trials
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