A null model for the distribution of fitness effects of mutations

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.SignificanceThe distribution of fitness effects (DFE) of new mutations plays a fundamental role in how evolution by natural selection occurs. A key research goal is therefore to infer properties of the underlying genotype-fitness map from empirically observed DFEs. Here, we show that such an inference is extremely difficult because many different genotype-fitness maps produce the very same DFE. Indeed, we demonstrate that if a genotype-fitness map is chosen at random, then it will almost certainly result in a DFE that has the largest possible information entropy. Subject to certain constraints this null DFE is shown to be a Gompertz distribution. We also demonstrate that this null DFE matches empirically measured DFEs from several datasets very well.

Automated summary

This study reveals that inferring the underlying genotype-fitness map from observed DFEs is challenging, as many different maps can produce the same DFE. The research also suggests that a random genotype-fitness map would result in the DFE with the largest information entropy, which matches empirical DFEs well.