Effects of new mutations on fitness: insights from models and data

Bataillon, Thomas; Bailey, Susan F.

doi:10.1111/nyas.12460

Cited by 89 publications

(88 citation statements)

References 83 publications

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“…A number of experimental studies have made strides towards characterizing the effect and size of new mutations affecting fitness. Mutation accumulation and directed mutagenesis experiments, now in combination with genome sequencing, have given us much insight into the distribution of effects of all arising mutations in a range of model organisms (Halligan & Keightley ), while experimental evolution studies have given us insight into the effects of mutations directly responsible for increases in fitness (recent review by Bataillon & Bailey ). In general, these experiments suggest that while the majority of mutations appear to have nearly neutral effects, it is mutations of intermediate effects that tend to drive adaptive evolution.…”

Section: What Kinds Of Mutations Enable Adaptation?mentioning

confidence: 99%

Can the experimental evolution programme help us elucidate the genetic basis of adaptation in nature?

Bailey

Bataillon

2015

Molecular Ecology

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There have been a variety of approaches taken to try to characterize and identify the genetic basis of adaptation in nature, spanning theoretical models, experimental evolution studies and direct tests of natural populations. Theoretical models can provide formalized and detailed hypotheses regarding evolutionary processes and patterns, from which experimental evolution studies can then provide important proofs of concepts and characterize what is biologically reasonable. Genetic and genomic data from natural populations then allow for the identification of the particular factors that have and continue to play an important role in shaping adaptive evolution in the natural world. Further to this, experimental evolution studies allow for tests of theories that may be difficult or impossible to test in natural populations for logistical and methodological reasons and can even generate new insights, suggesting further refinement of existing theories. However, as experimental evolution studies often take place in a very particular set of controlled conditions – that is simple environments, a small range of usually asexual species, relatively short timescales – the question remains as to how applicable these experimental results are to natural populations. In this review, we discuss important insights coming from experimental evolution, focusing on four key topics tied to the evolutionary genetics of adaptation, and within those topics, we discuss the extent to which the experimental work compliments and informs natural population studies. We finish by making suggestions for future work in particular a need for natural population genomic time series data, as well as the necessity for studies that combine both experimental evolution and natural population approaches.

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Section: What Kinds Of Mutations Enable Adaptation?mentioning

confidence: 99%

Can the experimental evolution programme help us elucidate the genetic basis of adaptation in nature?

Bailey

Bataillon

2015

Molecular Ecology

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“…For example, the genetic basis of resistance to a variety of drugs is known in many species of bacteria (reviewed in MacLean et al 2010), fungi (reviewed in Robbins et al 2017), and viruses (reviewed in Yilmaz et al 2016). This abundance of data reflects both the applied need to prevent drug resistance and the relative ease of isolating the genotypes that survive (hereafter “rescue genotypes”), e.g., in a Luria-Delbrück fluctuation assay (reviewed in Bataillon and Bailey 2014). Assaying fitness in the environment used to isolate mutants (e.g., in the drug) then provides the distribution of fitness effects of potential rescue genotypes.…”

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Genetic paths to evolutionary rescue and the distribution of fitness effects along them

Osmond

Otto

Martin

2019

Preprint

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1The past century has seen substantial theoretical and empirical progress on the genetic basis of adaptation. Over this same period a pressing need to prevent the evolution of drug resistance has uncovered much about the potential genetic basis of persistence in declining populations. However, we have little theory to predict and generalize how persistence -by sufficiently rapid adaptation -might be realized in this explicitly demographic scenario. Here we use Fisher's geometric model with absolute fitness to begin a line of theoretical inquiry into the genetic basis of evolutionary rescue, focusing here on asexual populations that adapt through de novo mutations. We show how the dominant genetic path to rescue switches from a single mutation to multiple as mutation rates and the severity of the environmental change increase. In multi-step rescue, intermediate genotypes that themselves go extinct provide a 'springboard' to rescue genotypes. Comparing to a scenario where persistence is assured, our approach allows us to quantify how a race between evolution and extinction leads to a genetic basis of adaptation that is composed of fewer loci of larger effect. We hope this work brings awareness to the impact of demography on the genetic basis of adaptation. 2 3 4 5 6 7 8 9 10 11 12

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“…http://dx.doi.org/10.1101/062216 doi: bioRxiv preprint first posted online Jul. 5, 2016;Bataillon and Bailey 2014). The DFE has been modeled 334 using a wide range of functional continuous forms (Boyko et al 335 2008; Kousathanas and Keightley 2013;Galtier 2016), but also 336 as a discrete distribution (Gronau et al 2013;Kousathanas and comes from an exponential distribution with mean S b > 0 (Fig-345 ure 2A).…”

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Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data

Tataru

Mollion

Glémin

et al. 2016

Preprint

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The distribution of fitness effects (DFE) encompasses deleterious, neutral and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inference of DFE and α from patterns of polymorphism (SFS) and divergence data has been a longstanding goal of evolutionary genetics. A widespread assumption shared by numerous methods developed so far to infer DFE and α from such data is that beneficial mutations contribute only negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is only predicted by contrasting the SFS with divergence data from an outgroup. Here, we develop a hierarchical probabilistic framework that extends on previous methods and also can infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. We show that both a full DFE, comprising both deleterious and beneficial mutations, and α can be inferred without resorting to divergence data. We demonstrate that inference of DFE from polymorphism data alone can in fact provide more reliable estimates, as it does not rely on strong assumptions about a shared DFE between the outgroup and ingroup species used to obtain the SFS and divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We illustrate these points using our newly developed framework, while also comparing to one of the most widely used inference methods available.KEYWORDS distribution of fitness effects, rate of adaptive molecular evolution, beneficial mutations, polymorphism and divergence data, Poisson random field 1 New mutations are the ultimate source of heritable variation. 2The fitness properties of new mutations determine the possible 3

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Effects of new mutations on fitness: insights from models and data

Cited by 89 publications

References 83 publications

Can the experimental evolution programme help us elucidate the genetic basis of adaptation in nature?

Can the experimental evolution programme help us elucidate the genetic basis of adaptation in nature?

Genetic paths to evolutionary rescue and the distribution of fitness effects along them

Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data

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