Literature DB >> 25908222

Populations adapt to fluctuating selection using derived and ancestral allelic diversity.

Wei-Hsiang Lin1, Mark J Rocco1, Amelia Bertozzi-Villa1, Edo Kussell1,2.   

Abstract

Populations can adapt to changing environments by using allelic diversity, yet whether diversity is recently derived or ancestral is often debated. Although evolution could productively use both types of diversity in a changing environment, their relative frequency has not been quantified. We address this question experimentally using budding yeast strains that harbor a tandem repeat containing URA3 gene, which we expose to cyclical selection and counterselection. We characterize and quantify the dynamics of frameshift events in the URA3 gene in eight populations over 12 cycles of selection and find that ancestral alleles account for 10-20% of all adaptive events. Using a general model of fluctuating selection, we determine how these results depend on mutation rates, population sizes, and fluctuation timescales. We quantify the contribution of derived alleles to the adaptation process using the de novo mutation rate along the population's ancestral lineage, a novel measure that is applicable in a wide range of settings. We find that the adaptive dynamics undergoes a sharp transition from selection on ancestral alleles to selection on derived alleles as fluctuation timescales increase. Our results demonstrate that fluctuations can select between different modes of adaptation over evolutionary timescales.
© 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Entities:  

Keywords:  Adaptation; experimental; fitness; genetic variation; models/simulations; mutations; selection

Mesh:

Year:  2015        PMID: 25908222      PMCID: PMC4478211          DOI: 10.1111/evo.12665

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


  38 in total

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5.  Quantitative evolutionary dynamics using high-resolution lineage tracking.

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6.  Genome-wide analysis of a long-term evolution experiment with Drosophila.

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Journal:  Nature       Date:  2010-09-15       Impact factor: 49.962

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Authors:  Selwyn Quan; J Christian J Ray; Zakari Kwota; Trang Duong; Gábor Balázsi; Tim F Cooper; Russell D Monds
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Authors:  Benjamin M Peter; Emilia Huerta-Sanchez; Rasmus Nielsen
Journal:  PLoS Genet       Date:  2012-10-11       Impact factor: 5.917

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  4 in total

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Journal:  Evolution       Date:  2017-10-30       Impact factor: 3.694

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  4 in total

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