Inference for one-step beneficial mutations using next generation sequencing

Wojtowicz Andrzej J. (), Miller Craig R. and Joyce Paul
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Wojtowicz Andrzej J.: Department of Animal Sciences, Washington State University, Pullman, WA 99164, USA Department of Mathematics, University of Idaho, Moscow, ID 83844, USA Department of Statistics, University of Idaho, Moscow, ID 83844, USA Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, USA
Miller Craig R.: Department of Mathematics, University of Idaho, Moscow, ID 83844, USA Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, USA Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA
Joyce Paul: Department of Mathematics, University of Idaho, Moscow, ID 83844, USA Department of Statistics, University of Idaho, Moscow, ID 83844, USA Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, USA

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 1, 65-81

Abstract: Experimental evolution is an important research method that allows for the study of evolutionary processes occurring in microorganisms. Here we present a novel approach to experimental evolution that is based on application of next generation sequencing. Under this approach population level sequencing is applied to an evolving population in which multiple first-step beneficial mutations occur concurrently. As a result, frequencies of multiple beneficial mutations are observed in each replicate of an experiment. For this new type of data we develop methods of statistical inference. In particular, we propose a method for imputing selection coefficients of first-step beneficial mutations. The imputed selection coefficient are then used for testing the distribution of first-step beneficial mutations and for estimation of mean selection coefficient. In the case when selection coefficients are uniformly distributed, collected data may also be used to estimate the total number of available first-step beneficial mutations.

Keywords: adaptive evolution; experimental evolution; next generation sequencing; one-step benefical mutations; selection coefficients (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0030

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