Determining the factors driving selective effects of new nonsynonymous mutations

Abstract

The distribution of fitness effects (DFE) of new mutations plays a fundamental role in evolutionary genetics. However, the extent to which the DFE differs across species has yet to be systematically investigated. Furthermore, the biological mechanisms determining the DFE in natural populations remain unclear. Here, we show that theoretical models emphasizing different biological factors at determining the DFE, such as protein stability, back-mutations, species complexity, and mutational robustness make distinct predictions about how the DFE will differ between species. Analyzing amino acid-changing variants from natural populations in a comparative population genomic framework, we find that humans have a higher proportion of strongly deleterious mutations than Drosophila melanogaster. Furthermore, when comparing the DFE across yeast, Drosophila, mice, and humans, the average selection coefficient becomes more deleterious with increasing species complexity. Last, pleiotropic genes have a DFE that is less variable than that of nonpleiotropic genes. Comparing four categories of theoretical models, only Fisher's geometrical model (FGM) is consistent with our findings. FGM assumes that multiple phenotypes are under stabilizing selection, with the number of phenotypes defining the complexity of the organism. Our results suggest that long-term population size and cost of complexity drive the evolution of the DFE, with many implications for evolutionary and medical genomics. T he distribution of fitness effects (DFE) represents the distribution of selection coefficients, s, of random mutations in the genome. Here, s quantifies the relative change in fitness due to the mutation. The DFE plays a fundamental role in evolutionary genetics because it quantifies the amount of deleterious, neutral, and adaptive mutations entering a population (1). Despite the importance and considerable study of the DFE (1-3), the extent to which the DFE in terms of s differs across species has yet to be systematically quantified. Furthermore, the biological factors determining the DFE in different species remain elusive. Several theoretical models propose different mechanisms for the evolution of the DFE (Fig. 1) (4-8). Although each of these models has a reasonable theoretical basis as well as some support from experimental evolution studies or microbial studies, which model best explains differences in the DFE between species has not yet been determined. Nor have these models been tested with genetic variation data from natural populations in higher organisms. Because experimental evolution studies in laboratory organisms often use a homogeneous environment and genetically homogeneous organisms, they may better satisfy some of the assumptions of these theoretical models. However, natural populations may provide different qualitative results due to increased resolution to measure weakly deleterious mutations and the unnatural selection pressure in the laboratory (2, 9). Importantly, five theoretical models for the evolution of the DFE predic...

Strongly deleterious mutations are a primary determinant of extinction risk due to inbreeding depression

“…However, these dynamics may prove to be most relevant to mammals and other vertebrates with high levels of “organismal complexity,” which has been demonstrated to result in a higher fraction of strongly deleterious mutations (Huber et al. 2017).…”

“…These genes accumulate neutral and deleterious mutations at a rate of 1 × 10 −8 per site, with the ratio of deleterious to neutral mutations set to 2.31:1 (Huber et al. 2017; Kim et al. 2017).…”

Inferring genome-wide correlations of mutation fitness effects between populations

“…We assumed the mutation rate was 1.5 × 10 − 8 per nucleotide per generation (Ségurel et al 2014). We further assumed the ratio of the nonsynonymous to synonymous mutations in humans was 2.31 (Huber et al 2017). In our simulation, we used the human exome based on the reference genome hg19 from UCSC Genome Browser and the deCODE human genetic map (Kong et al 2010).…”

“…Moreover, 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; Huber et al 2017; Castellano 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).…”

Prediction of deleterious mutations in coding regions of mammals with transfer learning

“…Nearly all deleterious mutations in nonhuman mammals are found in the coding part of the genome and are typically missense mutations, that is, those which cause amino acid changes in the corresponding protein. However, many missense mutations do not cause a disease (Huber, Kim, Marsden, & Lohmueller, ; Kim, Huber, & Lohmueller, ). Here, we classify mutations into one of two classes: “deleterious” or “neutral.” To accomplish this task, there are a number of characteristics that need to be known about the mutation, such as whether it is a transition or a transversion (Stoltzfus et al., ), and the frequency of a particular mutation in the population (a detailed description of these characteristics is available in the Methods section).…”