Literature DB >> 24151997

Selection biases the prevalence and type of epistasis along adaptive trajectories.

Jeremy A Draghi1, Joshua B Plotkin.   

Abstract

The contribution to an organism's phenotype from one genetic locus may depend upon the status of other loci. Such epistatic interactions among loci are now recognized as fundamental to shaping the process of adaptation in evolving populations. Although little is known about the structure of epistasis in most organisms, recent experiments with bacterial populations have concluded that antagonistic interactions abound and tend to deaccelerate the pace of adaptation over time. Here, we use the NK model of fitness landscapes to examine how natural selection biases the mutations that substitute during evolution based on their epistatic interactions. We find that, even when beneficial mutations are rare, these biases are strong and change substantially throughout the course of adaptation. In particular, epistasis is less prevalent than the neutral expectation early in adaptation and much more prevalent later, with a concomitant shift from predominantly antagonistic interactions early in adaptation to synergistic and sign epistasis later in adaptation. We observe the same patterns when reanalyzing data from a recent microbial evolution experiment. These results show that when the order of substitutions is not known, standard methods of analysis may suggest that epistasis retards adaptation when in fact it accelerates it.
© 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

Entities:  

Keywords:  Adaptation; epistasis; models/simulations; molecular evolution; population genetics

Mesh:

Year:  2013        PMID: 24151997     DOI: 10.1111/evo.12192

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  37 in total

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5.  Patterns and Mechanisms of Diminishing Returns from Beneficial Mutations.

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6.  On the (un)predictability of a large intragenic fitness landscape.

Authors:  Claudia Bank; Sebastian Matuszewski; Ryan T Hietpas; Jeffrey D Jensen
Journal:  Proc Natl Acad Sci U S A       Date:  2016-11-18       Impact factor: 11.205

7.  Inferring fitness landscapes by regression produces biased estimates of epistasis.

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Journal:  Proc Natl Acad Sci U S A       Date:  2014-05-19       Impact factor: 11.205

Review 8.  Empirical fitness landscapes and the predictability of evolution.

Authors:  J Arjan G M de Visser; Joachim Krug
Journal:  Nat Rev Genet       Date:  2014-06-10       Impact factor: 53.242

9.  Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint.

Authors:  Stephanie J Spielman; Claus O Wilke
Journal:  Mol Biol Evol       Date:  2016-08-10       Impact factor: 16.240

10.  Epistasis and the Dynamics of Reversion in Molecular Evolution.

Authors:  David M McCandlish; Premal Shah; Joshua B Plotkin
Journal:  Genetics       Date:  2016-05-18       Impact factor: 4.562

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