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Predictions of single-nucleotide polymorphism differentiation between two populations in terms of mutual information

Authors

  • RODERICK C. DEWAR,

    1. Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia
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  • WILLIAM B. SHERWIN,

    1. Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia
    2. Evolution and Ecology Research Centre, School of Biological Earth and Environmental Science, University of New South Wales, Sydney, NSW 2052, Australia
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  • EMMA THOMAS,

    1. Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia
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  • CLARE E. HOLLELEY,

    1. Evolution and Ecology Research Centre, School of Biological Earth and Environmental Science, University of New South Wales, Sydney, NSW 2052, Australia
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  • RICHARD A. NICHOLS

    1. Evolution and Ecology Research Centre, School of Biological Earth and Environmental Science, University of New South Wales, Sydney, NSW 2052, Australia
    2. School of Biological Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK
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Roderick C. Dewar, Fax: +61 2 6125 4919; E-mail: roderick.dewar@anu.edu.au

Abstract

Mutual information (I) provides a robust measure of genetic differentiation for the purposes of estimating dispersal between populations. At present, however, there is little predictive theory for I. The growing importance in population biology of analyses of single-nucleotide and other single-feature polymorphisms (SFPs) is a potent reason for developing an analytic theory for I with respect to a single locus. This study represents a first step towards such a theory. We present theoretical predictions of I between two populations with respect to a single haploid biallelic locus. Dynamical and steady-state forecasts of I are derived from a Wright–Fisher model with symmetrical mutation between alleles and symmetrical dispersal between populations. Analytical predictions of a simple Taylor approximation to I are in good agreement with numerical simulations of I and with data on I from SFP analyses of dispersal experiments on Drosophila fly populations. The theory presented here also provides a basis for the future inclusion of selection effects and extension to multiallelic loci.

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