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Amount of Information Needed for Model Choice in Approximate Bayesian Computation

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  • Michael Stocks
  • Mathieu Siol
  • Martin Lascoux
  • Stéphane De Mita

Abstract

Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its outcome depends heavily on the amount of information in the dataset, whether that be the level of genetic variation or the number of samples and loci. Here we look at the power to reject a simple constant population size coalescent model in favor of a bottleneck model in datasets of varying quality. Not only is this power dependent on the number of samples and loci, but it also depends strongly on the level of nucleotide diversity in the observed dataset. Whilst overall model choice in an ABC setting is fairly powerful and quite conservative with regard to false positives, detecting weaker bottlenecks is problematic in smaller or less genetically diverse datasets and limits the inferences possible in non-model organism where the amount of information regarding the two models is often limited. Our results show it is important to consider these limitations when performing an ABC analysis and that studies should perform simulations based on the size and nature of the dataset in order to fully assess the power of the study.

Suggested Citation

  • Michael Stocks & Mathieu Siol & Martin Lascoux & Stéphane De Mita, 2014. "Amount of Information Needed for Model Choice in Approximate Bayesian Computation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.
  • Handle: RePEc:plo:pone00:0099581
    DOI: 10.1371/journal.pone.0099581
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    References listed on IDEAS

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    1. Prangle Dennis & Fearnhead Paul & Cox Murray P. & Biggs Patrick J. & French Nigel P., 2014. "Semi-automatic selection of summary statistics for ABC model choice," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 67-82, February.
    2. Joyce Paul & Marjoram Paul, 2008. "Approximately Sufficient Statistics and Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-18, August.
    3. repec:dau:papers:123456789/6334 is not listed on IDEAS
    4. Nunes Matthew A & Balding David J, 2010. "On Optimal Selection of Summary Statistics for Approximate Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-16, September.
    5. Paul Fearnhead & Dennis Prangle, 2012. "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 419-474, June.
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