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Coalescent simulation in continuous space: Algorithms for large neighbourhood size

Author

Listed:
  • Kelleher, J.
  • Etheridge, A.M.
  • Barton, N.H.

Abstract

Many species have an essentially continuous distribution in space, in which there are no natural divisions between randomly mating subpopulations. Yet, the standard approach to modelling these populations is to impose an arbitrary grid of demes, adjusting deme sizes and migration rates in an attempt to capture the important features of the population. Such indirect methods are required because of the failure of the classical models of isolation by distance, which have been shown to have major technical flaws. A recently introduced model of extinction and recolonisation in two dimensions solves these technical problems, and provides a rigorous technical foundation for the study of populations evolving in a spatial continuum. The coalescent process for this model is simply stated, but direct simulation is very inefficient for large neighbourhood sizes. We present efficient and exact algorithms to simulate this coalescent process for arbitrary sample sizes and numbers of loci, and analyse these algorithms in detail.

Suggested Citation

  • Kelleher, J. & Etheridge, A.M. & Barton, N.H., 2014. "Coalescent simulation in continuous space: Algorithms for large neighbourhood size," Theoretical Population Biology, Elsevier, vol. 95(C), pages 13-23.
    • Handle: RePEc:eee:thpobi:v:95:y:2014:i:c:p:13-23
    DOI: 10.1016/j.tpb.2014.05.001
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    References listed on IDEAS

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    1. Barton, N.H. & Etheridge, A.M. & Kelleher, J. & Véber, A., 2013. "Inference in two dimensions: Allele frequencies versus lengths of shared sequence blocks," Theoretical Population Biology, Elsevier, vol. 87(C), pages 105-119.
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    Cited by:

    1. Jerome Kelleher & Alison M Etheridge & Gilean McVean, 2016. "Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-22, May.
    2. Jerome Kelleher & Kevin R Thornton & Jaime Ashander & Peter L Ralph, 2018. "Efficient pedigree recording for fast population genetics simulation," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-21, November.
    3. Engen, Steinar & Sæther, Bernt-Erik, 2016. "Phenotypic evolution by distance in fluctuating environments: The contribution of dispersal, selection and random genetic drift," Theoretical Population Biology, Elsevier, vol. 109(C), pages 16-27.
    4. Kelleher, J. & Etheridge, A.M. & Véber, A. & Barton, N.H., 2016. "Spread of pedigree versus genetic ancestry in spatially distributed populations," Theoretical Population Biology, Elsevier, vol. 108(C), pages 1-12.

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