Complexity, Pleiotropy, and the Fitness Effect of Mutations

Lourenço, João; Galtier, Nicolas; Glémin, Sylvain

doi:10.1111/j.1558-5646.2011.01237.x

Cited by 61 publications

(117 citation statements)

References 54 publications

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“…This negative relationship with fitness is expected given that most mutational changes are deleterious so that the more characters are affected by a mutation, the more likely the net effect on fitness is harmful, even if the mutation is beneficial for a subset of characters. This claim has been verified in theoretical studies based on Fisher's geometrical model (Chevin et al 2010;Lourenço et al 2011). Pleiotropy is consequently seen as a constraint on evolution because it reduces the adaptive capacity of an organism (Orr 2000;Welch and Waxman 2003).…”

mentioning

confidence: 64%

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Guillaume¹,

Otto

2012

Genetics

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Pleiotropy is the property of genes affecting multiple functions or characters of an organism. Genes vary widely in their degree of pleiotropy, but this variation is often considered a by-product of their evolutionary history. We present a functional theory of how pleiotropy may itself evolve. We consider genes that contribute to two functions, where contributing more to one function detracts from allocation to the second function. We show that whether genes become pleiotropic or specialize on a single function depends on the nature of trade-offs as gene activities contribute to different traits and on how the functionality of these traits affects fitness. In general, when a gene product can perform well at two functions, it evolves to do so, but not when pleiotropy would greatly disrupt each function. Consequently, reduced pleiotropy should often evolve, with genes specializing on the trait that is currently more important to fitness. Even when pleiotropy does evolve, not all genes are expected to become equally pleiotropic; genes with higher levels of expression are more likely to evolve greater pleiotropy. For the case of gene duplicates, we find that perfect subfunctionalization evolves only under stringent conditions. More often, duplicates are expected to maintain a certain degree of functional redundancy, with the gene contributing more to trait functionality evolving the highest degree of pleiotropy. Gene product interactions can facilitate subfunctionalization, but whether they do so depends on the curvature of the fitness surface. Finally, we find that stochastic gene expression favors pleiotropy by selecting for robustness in fitness components. PLEIOTROPY is the property whereby a gene affects more than one function or phenotypic character of an organism. Gene-knockout studies in yeast indicate that deleting genes with higher degrees of pleiotropy has, on average, a more harmful effect on fitness (Salathé et al. 2006; Cooper et al. 2007). This negative relationship with fitness is expected given that most mutational changes are deleterious so that the more characters are affected by a mutation, the more likely the net effect on fitness is harmful, even if the mutation is beneficial for a subset of characters. This claim has been verified in theoretical studies based on Fisher's geometrical model (Chevin et al. 2010;Lourenço et al. 2011). Pleiotropy is consequently seen as a constraint on evolution because it reduces the adaptive capacity of an organism (Orr 2000;Welch and Waxman 2003).Recent observations in a variety of species have found that the extent of pleiotropy varies among genes and is often limited, with a majority of genes influencing a small set of traits while a few genes affect many traits (Dudley et al. 2005; Albert et al. 2008;Wagner et al. 2008;Wang et al. 2010;Wagner and Zhang 2011). This is in direct opposition to the historical assumption of universal pleiotropy underlying most population and quantitative genetics approaches to the joint evolution of multiple characters (...

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confidence: 64%

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Guillaume¹,

Otto

2012

Genetics

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“…The second prediction of FGM is that smaller populations are predicted to have a larger proportion of beneficial mutations due to increased fixation of deleterious mutations in smaller populations when populations are in equilibrium [drift load (33)]. Note that population size here refers to long-term effective population size; thus, it could be affected by background selection and selective sweeps as well as demographic processes.…”

Section: Resultsmentioning

confidence: 99%

Determining the factors driving selective effects of new nonsynonymous mutations

Huber

Kim

Marsden

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

129

157

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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...

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“…Most progress in this regard comes from phenotype fitness-landscape models such as Fisher’s geometric model (FGM) (Martin and Lenormand 2006; Chevin et al 2010; Lourenço et al 2011; Tenaillon 2014; Huber et al 2016) and biophysical models of protein stability (Cherry 1998; Goldstein 2013; Serohijos and Shakhnovich 2014). Under fairly general assumptions, the predicted DFE under these models for a perfectly adapted population is gamma distributed (Martin and Lenormand 2006; Martin 2014; Serohijos and Shakhnovich 2014), and a strongly leptokurtic shape would suggest that most mutations have low pleiotropy (Martin and Lenormand 2006; Lourenço et al 2011). However, our finding of a neutral+gamma distribution suggests that the general FGM is inadequate, since it does not predict the neutral point mass.…”

Section: Discussionmentioning

confidence: 99%

Inference of the Distribution of Selection Coefficients for New Nonsynonymous Mutations Using Large Samples

2017

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The distribution of fitness effects (DFE) has considerable importance in population genetics. To date, estimates of the DFE come from studies using a small number of individuals. Thus, estimates of the proportion of moderately to strongly deleterious new mutations may be unreliable because such variants are unlikely to be segregating in the data. Additionally, the true functional form of the DFE is unknown, and estimates of the DFE differ significantly between studies. Here we present a flexible and computationally tractable method, called Fit∂a∂i, to estimate the DFE of new mutations using the site frequency spectrum from a large number of individuals. We apply our approach to the frequency spectrum of 1300 Europeans from the Exome Sequencing Project ESP6400 data set, 1298 Danes from the LuCamp data set, and 432 Europeans from the 1000 Genomes Project to estimate the DFE of deleterious nonsynonymous mutations. We infer significantly fewer (0.38–0.84 fold) strongly deleterious mutations with selection coefficient |s| > 0.01 and more (1.24–1.43 fold) weakly deleterious mutations with selection coefficient |s| < 0.001 compared to previous estimates. Furthermore, a DFE that is a mixture distribution of a point mass at neutrality plus a gamma distribution fits better than a gamma distribution in two of the three data sets. Our results suggest that nearly neutral forces play a larger role in human evolution than previously thought.

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Complexity, Pleiotropy, and the Fitness Effect of Mutations

Cited by 61 publications

References 54 publications

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Determining the factors driving selective effects of new nonsynonymous mutations

Inference of the Distribution of Selection Coefficients for New Nonsynonymous Mutations Using Large Samples

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