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Stopping-Time Resampling and Population Genetic Inference under Coalescent Models

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  • Jenkins Paul A.

Abstract

To extract full information from samples of DNA sequence data, it is necessary to use sophisticated model-based techniques such as importance sampling under the coalescent. However, these are limited in the size of datasets they can handle efficiently. Chen and Liu (2000) introduced the idea of stopping-time resampling and showed that it can dramatically improve the efficiency of importance sampling methods under a finite-alleles coalescent model. In this paper, a new framework is developed for designing stopping-time resampling schemes under more general models. It is implemented on data both from infinite sites and stepwise models of mutation, and extended to incorporate crossover recombination. A simulation study shows that this new framework offers a substantial improvement in the accuracy of likelihood estimation over a range of parameters, while a direct application of the scheme of Chen and Liu (2000) can actually diminish the estimate. The method imposes no additional computational burden and is robust to the choice of parameters.

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  • Jenkins Paul A., 2012. "Stopping-Time Resampling and Population Genetic Inference under Coalescent Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-20, January.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:1:n:9
    DOI: 10.2202/1544-6115.1770
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    References listed on IDEAS

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    1. Hobolth Asger & Uyenoyama Marcy K & Wiuf Carsten, 2008. "Importance Sampling for the Infinite Sites Model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-26, October.
    2. Ian J. Wilson & Michael E. Weale & David J. Balding, 2003. "Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(2), pages 155-188, June.
    3. Lin, Ming & Chen, Rong & Mykland, Per, 2010. "On Generating Monte Carlo Samples of Continuous Diffusion Bridges," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 820-838.
    4. Matthew Stephens & Peter Donnelly, 2000. "Inference in molecular population genetics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 605-635.
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    Cited by:

    1. Blath, Jochen & Buzzoni, Eugenio & Koskela, Jere & Wilke Berenguer, Maite, 2020. "Statistical tools for seed bank detection," Theoretical Population Biology, Elsevier, vol. 132(C), pages 1-15.

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