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Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study

Authors

  • CHIBIAO CHEN,

    1. INRIA Rhône-Alpes, avenue De l’Europe, 38334 Montbonnot, Saint Ismier cedex, France,
    2. TIMC, Université Joseph Fourier, Institut National Polytechnique de Grenoble, avenue Felix Viallet, 38031 Grenoble cedex 1, France
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  • ERIC DURAND,

    1. TIMC, Université Joseph Fourier, Institut National Polytechnique de Grenoble, avenue Felix Viallet, 38031 Grenoble cedex 1, France
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  • FLORENCE FORBES,

    1. INRIA Rhône-Alpes, avenue De l’Europe, 38334 Montbonnot, Saint Ismier cedex, France,
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  • OLIVIER FRANÇOIS

    1. TIMC, Université Joseph Fourier, Institut National Polytechnique de Grenoble, avenue Felix Viallet, 38031 Grenoble cedex 1, France
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O. François, TIMB, TIMC, Faculté de Médecine de Grenoble, F38706 La Tronche cedex, France. Fax: +33 456520 044; E-mail: olivier.francois@imag.fr

Abstract

On the basis of simulated data, this study compares the relative performances of the Bayesian clustering computer programs structure, geneland, geneclust and a new program named tess. While these four programs can detect population genetic structure from multilocus genotypes, only the last three ones include simultaneous analysis from geographical data. The programs are compared with respect to their abilities to infer the number of populations, to estimate membership probabilities, and to detect genetic discontinuities and clinal variation. The results suggest that combining analyses using tess and structure offers a convenient way to address inference of spatial population structure.

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