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The impact of genetic diversity statistics on model selection between coalescents

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  • Freund, Fabian
  • Siri-Jégousse, Arno

Abstract

Modeling genetic diversity needs an underlying genealogy model. To choose a fitting model based on genetic data, one can perform model selection between classes of genealogical trees, e.g. Kingman’s coalescent with exponential growth or multiple merger coalescents. Such selection can be based on many different statistics measuring genetic diversity. A random forest based Approximate Bayesian Computation is used to disentangle the effects of different statistics on distinguishing between various classes of genealogy models. For the specific question of inferring whether genealogies feature multiple mergers, a new statistic, the minimal observable clade size, is introduced. When combined with classical site frequency based statistics, it reduces classification errors considerably.

Suggested Citation

  • Freund, Fabian & Siri-Jégousse, Arno, 2021. "The impact of genetic diversity statistics on model selection between coalescents," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:csdana:v:156:y:2021:i:c:s0167947320301468
    DOI: 10.1016/j.csda.2020.107055
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    References listed on IDEAS

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    1. Miró Pina, Verónica & Joly, Émilien & Siri-Jégousse, Arno, 2023. "Estimating the Lambda measure in multiple-merger coalescents," Theoretical Population Biology, Elsevier, vol. 154(C), pages 94-101.
    2. Hobolth, Asger & Rivas-González, Iker & Bladt, Mogens & Futschik, Andreas, 2024. "Phase-type distributions in mathematical population genetics: An emerging framework," Theoretical Population Biology, Elsevier, vol. 157(C), pages 14-32.

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