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...

Is adaptation limited by mutation? A timescale-dependent effect of genetic diversity on the adaptive substitution rate in animals

“…Groups of species evolving under small long-term N a are further away from their optimum, compared to larger-N e groups, due to an increased rate of fixation of deleterious mutations at equilibrium, so they are predicted to undergo a larger proportion of beneficial, compensatory mutations. Empirical analyses of SFS based on large samples in humans and flies are consistent with the hypothesis that humans are on average more distant to their optimum than flies (11).…”

“…In particular, one key assumption of the MK approach is that the long-term N e , which determines ω, is equal to the short-term N e and can therefore be estimated from polymorphism data.This appears unlikely to be generally true, and ancient fluctuations in N e could in principle fault the MK rationale (12,23,24). Eyre-Walker (24) theoretically considered the problem of a single ancient change in N e and showed that an expansion in population size, even if old, could lead to overestimation of the adaptive substitution rate, a bias that could create spurious positive correlation between ω a and N e and that we need to keep in mind when interpreting this type of estimates.The first applications of the MK method to large-scale data sets indicated that the adaptive rate is higher in Drosophila than in humans (11,13,14), consistent with the prediction of more efficient adaptation in large populations and with the hypothesis that mutation limits adaptation. These studies, however, were focusing on the α=ω a /(ω a +ω na ) statistics, i.e., the proportion of amino-acid substitutions that result from adaptation.…”

“…The first applications of the MK method to large-scale data sets indicated that the adaptive rate is higher in Drosophila than in humans (11,13,14), consistent with the prediction of more efficient adaptation in large populations and with the hypothesis that mutation limits adaptation. These studies, however, were focusing on the α=ω a /(ω a +ω na ) statistics, i.e., the proportion of amino-acid substitutions that result from adaptation.…”

“…In the short term, an increase in N e is expected to boost the adaptive substitution rate if the supply of new mutations is limiting. In the long run, differences in N e could also lead to changes in the DFE, and particularly the proportion of beneficial mutations, due to small-N e species being pulled away from their fitness optimum via genetic drift (11,18,32). How these two opposing forces interact and combine to determine the relationship between ω a and θ is still unknown, in absence of a multi-scale study.…”

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, 2017;. Here, we classify mutations into one of two classes: "deleterious" or "neutral."…”