A study about the evolutionary dynamics and repeatability in time-varying fitness landscapes

The distribution of epistasis on simple fitness landscapes

10.1101/490573 ◽

2018 ◽

Author(s):

Christelle Fraïsse ◽

John J. Welch

Keyword(s):

Evolutionary Dynamics ◽

Fitness Landscape ◽

Null Model ◽

Large Data ◽

Fitness Landscapes ◽

Mutational Bias ◽

Data Set ◽

Epistatic Interactions ◽

Standing Variation ◽

Mean And Variance

AbstractFitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information about the overall distribution is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effects sizes, mutational bias, and maladaptation of the wild-type. We illustrate our approach by reanalysing a large data set of mutant effects in a yeast snoRNA. Though characterized by some strong epistatic interactions, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have little effect on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape, and the distribution of mutations, and so it is expected to vary in consistent ways between new mutations, standing variation, and fixed mutations.

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The distribution of epistasis on simple fitness landscapes

Biology Letters ◽

10.1098/rsbl.2018.0881 ◽

2019 ◽

Vol 15 (4) ◽

pp. 20180881 ◽

Cited By ~ 5

Author(s):

Christelle Fraïsse ◽

John J. Welch

Keyword(s):

Evolutionary Dynamics ◽

Fitness Landscape ◽

Null Model ◽

Fitness Landscapes ◽

Mutational Bias ◽

Variable Effect ◽

Epistatic Effects ◽

Standing Variation ◽

Mean And Variance ◽

Nucleolar Rna

Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA (small nucleolar RNA). Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.

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Epigenetically Inherited Mutation Rates Predicted to Maximize Adaptation Rates

10.1101/2021.07.14.452333 ◽

2021 ◽

Author(s):

Yoav Ram ◽

Yitzhak Tzachi Pilpel ◽

Gabriela Aleksandra Lobinska

Keyword(s):

Mutation Rate ◽

Adaptive Evolution ◽

Evolutionary Dynamics ◽

Epigenetic Inheritance ◽

Fitness Landscapes ◽

Mutation Rates ◽

Switching Rate ◽

Asexual Population ◽

Parental Phenotype ◽

Rugged Fitness Landscapes

The mutation rate is an important determinant of evolutionary dynamics. Because the mutation rate determines the rate of appearance of beneficial and deleterious mutations, it is subject to second-order selection. The mutation rate varies between and within species and populations, increases under stress, and is genetically controlled by mutator alleles. The mutation rate may also vary among genetically identical individuals: empirical evidence from bacteria suggests that the mutation rate may be affected by translation errors and expression noise in various proteins (1). Importantly, this non-genetic variation may be heritable via transgenerational epigenetic inheritance. Here we investigate how the inheritance mode of the mutation rate affects the rate of adaptive evolution on rugged fitness landscapes. We model an asexual population with two mutation rate phenotypes, non-mutator and mutator. An offspring may switch from its parental phenotype to the other phenotype. The rate of switching between the mutation rate phenotypes is allowed to span a range of values. Thus, the mutation rate can be interpreted as a genetically inherited trait when the switching rate is low, as an epigenetically inherited trait when the switching rate is intermediate, or as a randomly determined trait when the switching rate is high. We find that epigenetically inherited mutation rates result in the highest rates of adaptation on rugged fitness landscapes for most realistic parameter sets. This is because an intermediate switching rate can maintain the association between a mutator phenotype and pre-existing mutations, which facilitates the crossing of fitness valleys. Our results provide a rationale for the evolution of epigenetic inheritance of the mutation rate, suggesting that it could have been selected because it facilitates adaptive evolution.

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Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics

10.1101/435669 ◽

2018 ◽

Cited By ~ 1

Author(s):

Atish Agarwala ◽

Daniel S. Fisher

Keyword(s):

Evolutionary Dynamics ◽

Fitness Landscape ◽

Broad Class ◽

Fitness Landscapes ◽

High Dimensional ◽

General Function ◽

Long Distance ◽

Future Evolution ◽

Adaptive Mutations ◽

Adaptive Walks

AbstractThe dynamics of evolution is intimately shaped by epistasis — interactions between genetic elements which cause the fitness-effect of combinations of mutations to be non-additive. Analyzing evolutionary dynamics that involves large numbers of epistatic mutations is intrinsically difficult. A crucial feature is that the fitness landscape in the vicinity of the current genome depends on the evolutionary history. A key step is thus developing models that enable study of the effects of past evolution on future evolution. In this work, we introduce a broad class of high-dimensional random fitness landscapes for which the correlations between fitnesses of genomes are a general function of genetic distance. Their Gaussian character allows for tractable computational as well as analytic understanding. We study the properties of these landscapes focusing on the simplest evolutionary process: random adaptive (uphill) walks. Conventional measures of “ruggedness” are shown to not much affect such adaptive walks. Instead, the long-distance statistics of epistasis cause all properties to be highly conditional on past evolution, determining the statistics of the local landscape (the distribution of fitness-effects of available mutations and combinations of these), as well as the global geometry of evolutionary trajectories. In order to further explore the effects of conditioning on past evolution, we model the effects of slowly changing environments. At long times, such fitness “seascapes” cause a statistical steady state with highly intermittent evolutionary dynamics: populations undergo bursts of rapid adaptation, interspersed with periods in which adaptive mutations are rare and the population waits for more new directions to be opened up by changes in the environment. Finally, we discuss prospects for studying more complex evolutionary dynamics and on broader classes of high-dimensional landscapes and seascapes.

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Effect of mutators on adaptability in time-varying fitness landscapes

Physical Review E ◽

10.1103/physreve.77.042901 ◽

2008 ◽

Vol 77 (4) ◽

Cited By ~ 5

Author(s):

Pavel Gorodetsky ◽

Emmanuel Tannenbaum

Keyword(s):

Fitness Landscapes ◽

Time Varying

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Fitness Landscapes and Evolutionary Dynamics

Advances in Intelligent Systems and Computing - Nostradamus: Modern Methods of Prediction, Modeling and Analysis of Nonlinear Systems ◽

10.1007/978-3-642-33227-2_2 ◽

2013 ◽

pp. 5-8

Author(s):

Hendrik Richter

Keyword(s):

Evolutionary Dynamics ◽

Fitness Landscapes

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Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1902731116 ◽

2019 ◽

Vol 116 (36) ◽

pp. 17890-17899 ◽

Cited By ~ 11

Author(s):

Mark A. Taylor ◽

Amity M. Wilczek ◽

Judith L. Roe ◽

Stephen M. Welch ◽

Daniel E. Runcie ◽

...

Keyword(s):

Arabidopsis Thaliana ◽

Flowering Time ◽

Environmental Conditions ◽

Regulatory Networks ◽

Evolutionary Dynamics ◽

Natural Populations ◽

Fitness Landscapes ◽

Natural Environments ◽

Multiple Phenotypes ◽

Mutant Phenotypes

Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments.

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