Abstract
The effect of a mutation on fitness may differ between populations depending on environmental and genetic context, but little is known about the factors that underlie such differences. To quantify genome-wide correlations in mutation fitness effects, we developed a novel concept called a joint distribution of fitness effects (DFE) between populations. We then proposed a new statistic w to measure the DFE correlation between populations. Using simulation, we showed that inferring the DFE correlation from the joint allele frequency spectrum is statistically precise and robust. Using population genomic data, we inferred DFE correlations of populations in humans, Drosophila melanogaster, and wild tomatoes. In these species, we found that the overall correlation of the joint DFE was inversely related to genetic differentiation. In humans and D. melanogaster, deleterious mutations had a lower DFE correlation than tolerated mutations, indicating a complex joint DFE. Altogether, the DFE correlation can be reliably inferred, and it offers extensive insight into the genetics of population divergence.
Highlights
New mutations that alter fitness are the key input into the evolutionary process
We refer to the joint probability distribution for (s1, s2) as the joint distribution of fitness effects (DFE), and we refer to the marginal probability distributions for s1 or s2 as the marginal DFEs for population 1 or population 2, respectively
We introduced the concept of a joint DFE between pairs of populations, and we developed and applied an approach for inferring it
Summary
New mutations that alter fitness are the key input into the evolutionary process. Typically, the majority of new mutations are deleterious or nearly neutral, and only a small minority are adaptive. A linear regression method can be used to infer the DFE from nucleotide diversity (James et al 2017) These approaches has been applied to numerous organisms, including plants (Chen et al 2017; Huber et al 2018; Chen et al 2020), Drosophila melanogaster (Keightley and Eyre-Walker 2007; Castellano et al 2017; Huber et al 2017; Barton and Zeng 2018; Johri et al 2020), and primates (Boyko et al 2008; Huber et al 2017; Kim et al 2017; Ma et al 2013; Castellano et al 2019)
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