IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v11y2012i1n9.html
   My bibliography  Save this article

Stopping-Time Resampling and Population Genetic Inference under Coalescent Models

Author

Listed:
  • Jenkins Paul A.

    (University of California, Berkeley)

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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1770
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1770?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Birkner, Matthias & Blath, Jochen & Steinrücken, Matthias, 2011. "Importance sampling for Lambda-coalescents in the infinitely many sites model," Theoretical Population Biology, Elsevier, vol. 79(4), pages 155-173.
    2. Uyenoyama, Marcy K. & Takebayashi, Naoki & Kumagai, Seiji, 2020. "Allele frequency spectra in structured populations: Novel-allele probabilities under the labelled coalescent," Theoretical Population Biology, Elsevier, vol. 133(C), pages 130-140.
    3. Merle, C. & Leblois, R. & Rousset, F. & Pudlo, P., 2017. "Resampling: An improvement of importance sampling in varying population size models," Theoretical Population Biology, Elsevier, vol. 114(C), pages 70-87.
    4. Tsukuda Koji & Mano Shuhei & Yamamoto Toshimichi, 2020. "Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-18, April.
    5. Griffiths, Robert C. & Tavaré, Simon, 2018. "Ancestral inference from haplotypes and mutations," Theoretical Population Biology, Elsevier, vol. 122(C), pages 12-21.
    6. Sainudiin, Raazesh & Véber, Amandine, 2018. "Full likelihood inference from the site frequency spectrum based on the optimal tree resolution," Theoretical Population Biology, Elsevier, vol. 124(C), pages 1-15.
    7. Steinrücken, Matthias & Paul, Joshua S. & Song, Yun S., 2013. "A sequentially Markov conditional sampling distribution for structured populations with migration and recombination," Theoretical Population Biology, Elsevier, vol. 87(C), pages 51-61.
    8. N. Englezos & X. Kartala & P. Koundouri & M. Tsionas & A. Alamanos, 2023. "A Novel HydroEconomic - Econometric Approach for Integrated Transboundary Water Management Under Uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 975-1030, April.
    9. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    10. Larribe Fabrice & Lessard Sabin, 2008. "A Composite-Conditional-Likelihood Approach for Gene Mapping Based on Linkage Disequilibrium in Windows of Marker Loci," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-33, August.
    11. 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.
    12. Mogens Bladt & Samuel Finch & Michael Sørensen, 2016. "Simulation of multivariate diffusion bridges," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 343-369, March.
    13. Hobolth, Asger & Wiuf, Carsten, 2009. "The genealogy, site frequency spectrum and ages of two nested mutant alleles," Theoretical Population Biology, Elsevier, vol. 75(4), pages 260-265.
    14. Ganapathy, Ganeshkumar & Uyenoyama, Marcy K., 2009. "Site frequency spectra from genomic SNP surveys," Theoretical Population Biology, Elsevier, vol. 75(4), pages 346-354.
    15. Heled, Joseph, 2012. "Sequence diversity under the multispecies coalescent with Yule process and constant population size," Theoretical Population Biology, Elsevier, vol. 81(2), pages 97-101.
    16. Kyoung-Kuk Kim & Sojung Kim, 2016. "Simulation of Tempered Stable Lévy Bridges and Its Applications," Operations Research, INFORMS, vol. 64(2), pages 495-509, April.
    17. Hössjer Ola & Hartman Linda & Humphreys Keith, 2009. "Ancestral Recombination Graphs under Non-Random Ascertainment, with Applications to Gene Mapping," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-44, September.
    18. Vogl, Claus & Mikula, Lynette C. & Burden, Conrad J., 2020. "Maximum likelihood estimators for scaled mutation rates in an equilibrium mutation–drift model," Theoretical Population Biology, Elsevier, vol. 134(C), pages 106-118.
    19. Johndrow, James E. & Palacios, Julia A., 2019. "Exact limits of inference in coalescent models," Theoretical Population Biology, Elsevier, vol. 125(C), pages 75-93.
    20. Noa Chapal-Ilani & Yosef E Maruvka & Adam Spiro & Yitzhak Reizel & Rivka Adar & Liran I Shlush & Ehud Shapiro, 2013. "Comparing Algorithms That Reconstruct Cell Lineage Trees Utilizing Information on Microsatellite Mutations," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-17, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:11:y:2012:i:1:n:9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.