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Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power

Authors

  • David Paetkau,

    1. Department of Zoology and Entomology, University of Queensland, St. Lucia, QLD 4072, Australia,
    2. Wildlife Genetics International, Box 274, Nelson, BC V1L 5P9, Canada,
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  • Robert Slade,

    1. Graduate Research College, Southern Cross University, Australia,
    2. Australian National Genomic Information Service, University of Sydney & NSW 2006, Australia,
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  • Michael Burden,

    1. Australian National Genomic Information Service, University of Sydney & NSW 2006, Australia,
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  • Arnaud Estoup

    Corresponding author
    1. Department of Zoology and Entomology, University of Queensland, St. Lucia, QLD 4072, Australia,
    2. INRA, Centre de Biologie et de Gestion des Populations, Campus International de Baillarguet, CS 30 016, 34900, Monferrier/Lez cedex, France
      A. Estoup, INRA-CBGP, Campus International de Baillarguet, CS 30 016, 34900, Monferrier/Lez cedex, France. E-mail: estoup@ensam.inra.fr
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A. Estoup, INRA-CBGP, Campus International de Baillarguet, CS 30 016, 34900, Monferrier/Lez cedex, France. E-mail: estoup@ensam.inra.fr

Abstract

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.

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