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ABC inference of multi-population divergence with admixture from unphased population genomic data

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  • John D. Robinson
  • Lynsey Bunnefeld
  • Jack Hearn
  • Graham N. Stone
  • Michael J. Hickerson

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    Rights statement: © 2014 The Authors. Molecular Ecology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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http://doi.wiley.com/10.1111/mec.12881
Original languageEnglish
Pages (from-to)4458-4471
JournalMolecular Ecology
Volume23
Issue number18
DOIs
Publication statusPublished - 1 Sep 2014

Abstract

Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in nonmodel systems. Inferential tools for historical demography given these data sets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large data sets that are now attainable from nonmodel taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation, given histories of three-population divergence with admixture. We then explore different sampling regimes to illustrate how sampling more loci, longer loci or more individuals affects the quality of model selection and parameter estimation in this ABC framework. Our results show that inferences improved substantially with increases in the number and/or length of sequenced loci, while less benefit was gained by sampling large numbers of individuals. Optimal sampling strategies given our inferential models included at least 2000 loci, each approximately 2 kb in length, sampled from five diploid individuals per population, although specific strategies are model and question dependent. We tested our ABC approach through simulation-based cross-validations and illustrate its application using previously analysed data from the oak gall wasp, Biorhiza pallida.

    Research areas

  • approximate Bayesian computation, Biorhiza pallida, gene flow

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