Misrepresenting biases in arrival: a comment on Svensson (2022)

Cano, Alejandro; Couce, Alejandro; Masel, Joanna; Payne, Joshua L.; Stoltzfus, Arlin; Storz, Jay F.

doi:10.32942/x2sg6v

Cited by 3 publications

(2 citation statements)

References 35 publications

(65 reference statements)

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“…This largely ignores biases in the origin of genetic variation, which may play a major role in guiding evolution [1][2][3][4][5][6]. Although it is debated in general [7][8][9][10], the importance of such biases is obvious when there are multiple genetic pathways to an adaptive phenotype with similar fitness. Practical consequences are likely on short time scales as well, for example in pathogen evolution, colonization of new ecological niches or in weak mutation, strong selection contexts in experimental evolution.…”

Section: Introductionmentioning

confidence: 99%

Distribution of mutation rates challenges evolutionary predictability

Sun

Lind

2023

Microbiology

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Natural selection is commonly assumed to act on extensive standing genetic variation. Yet, accumulating evidence highlights the role of mutational processes creating this genetic variation: to become evolutionarily successful, adaptive mutants must not only reach fixation, but also emerge in the first place, i.e. have a high enough mutation rate. Here, we use numerical simulations to investigate how mutational biases impact our ability to observe rare mutational pathways in the laboratory and to predict outcomes in experimental evolution. We show that unevenness in the rates at which mutational pathways produce adaptive mutants means that most experimental studies lack power to directly observe the full range of adaptive mutations. Modelling mutation rates as a distribution, we show that a substantially larger target size ensures that a pathway mutates more commonly. Therefore, we predict that commonly mutated pathways are conserved between closely related species, but not rarely mutated pathways. This approach formalizes our proposal that most mutations have a lower mutation rate than the average mutation rate measured experimentally. We suggest that the extent of genetic variation is overestimated when based on the average mutation rate.

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Section: Introductionmentioning

confidence: 99%

Distribution of mutation rates challenges evolutionary predictability

Sun

Lind

2023

Microbiology

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“…This largely ignores biases in the origin of genetic variation, which may play a major role in guiding evolution (Yampolsky and Stoltzfus, 2001; Stoltzfus and McCandlish, 2017; Stoltzfus, 2021; Cano et al, 2022b,c; Monroe et al, 2022). Although it is debated in general (Svensson and Berger, 2019; Gomez et al, 2020; Svensson, 2022; Cano et al, 2022a), the importance of such biases is obvious when there are multiple genetic pathways to an adaptive phenotype with similar fitness. Practical consequences are likely on short time scales as well, for example in pathogen evolution, colonization of new ecological niches or in weak mutation, strong selection contexts in experimental evolution.…”

Section: Introductionmentioning

confidence: 99%

Distribution of mutation rates challenges evolutionary predictability

Sun

Lind

2022

Preprint

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Natural selection is commonly assumed to act on extensive standing genetic variation. Yet, accumulating evidence highlights the role of mutational processes creating this genetic variation: to become evolutionarily successful, adaptive mutants must not only reach fixation, but also emerge in the first place, i.e. have a high enough mutation rate relative to the population size. Here, we use numerical simulations to investigate how mutational biases impact our ability to observe rare mutational pathways in the laboratory and to predict outcomes in experimental evolution. We find the number of available mutations and the unevenness of their mutation rates to increase, sometimes impractically, the experimental effort required to discover all of them. Thus, most experimental studies lack power to directly observe the full range of adaptive mutations. Modelling mutation rates as a distribution, we show that a substantially larger target size ensures that a pathway mutates more commonly. Therefore, we predict that commonly mutated pathways are conserved between closely related species, contrary to rare pathways. This approach formalizes our proposal that most mutations have a lower mutation rate than the average mutation rate measured experimentally. We suggest that the extent of genetic variation is overestimated when based on the average mutation rate.

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Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’ biomorphs

Martin

Camargo

Louis

2023

Preprint

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Biomorphs, Richard Dawkins' iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong Occam's-razor-like preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations will fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems, which would have implications for longstanding debates about internal versus external causes in evolution.

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Misrepresenting biases in arrival: a comment on Svensson (2022)

Cited by 3 publications

References 35 publications

Distribution of mutation rates challenges evolutionary predictability

Distribution of mutation rates challenges evolutionary predictability

Distribution of mutation rates challenges evolutionary predictability

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’ biomorphs

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