IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0018155.html
   My bibliography  Save this article

Estimating Parameters of Speciation Models Based on Refined Summaries of the Joint Site-Frequency Spectrum

Author

Listed:
  • Aurélien Tellier
  • Peter Pfaffelhuber
  • Bernhard Haubold
  • Lisha Naduvilezhath
  • Laura E Rose
  • Thomas Städler
  • Wolfgang Stephan
  • Dirk Metzler

Abstract

Understanding the processes and conditions under which populations diverge to give rise to distinct species is a central question in evolutionary biology. Since recently diverged populations have high levels of shared polymorphisms, it is challenging to distinguish between recent divergence with no (or very low) inter-population gene flow and older splitting events with subsequent gene flow. Recently published methods to infer speciation parameters under the isolation-migration framework are based on summarizing polymorphism data at multiple loci in two species using the joint site-frequency spectrum (JSFS). We have developed two improvements of these methods based on a more extensive use of the JSFS classes of polymorphisms for species with high intra-locus recombination rates. First, using a likelihood based method, we demonstrate that taking into account low-frequency polymorphisms shared between species significantly improves the joint estimation of the divergence time and gene flow between species. Second, we introduce a local linear regression algorithm that considerably reduces the computational time and allows for the estimation of unequal rates of gene flow between species. We also investigate which summary statistics from the JSFS allow the greatest estimation accuracy for divergence time and migration rates for low (around 10) and high (around 100) numbers of loci. Focusing on cases with low numbers of loci and high intra-locus recombination rates we show that our methods for the estimation of divergence time and migration rates are more precise than existing approaches.

Suggested Citation

  • Aurélien Tellier & Peter Pfaffelhuber & Bernhard Haubold & Lisha Naduvilezhath & Laura E Rose & Thomas Städler & Wolfgang Stephan & Dirk Metzler, 2011. "Estimating Parameters of Speciation Models Based on Refined Summaries of the Joint Site-Frequency Spectrum," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-13, May.
    • Handle: RePEc:plo:pone00:0018155
    DOI: 10.1371/journal.pone.0018155
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0018155
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0018155&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0018155?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ryan N Gutenkunst & Ryan D Hernandez & Scott H Williamson & Carlos D Bustamante, 2009. "Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data," PLOS Genetics, Public Library of Science, vol. 5(10), pages 1-11, October.
    Full references (including those not matched with items on IDEAS)

    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. D.T. Frazier & G.M. Martin & C.P. Robert & J. Rousseau, 2016. "Asymptotic Properties of Approximate Bayesian Computation," Monash Econometrics and Business Statistics Working Papers 18/16, Monash University, Department of Econometrics and Business Statistics.
    2. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    3. Kirk E Lohmueller & Anders Albrechtsen & Yingrui Li & Su Yeon Kim & Thorfinn Korneliussen & Nicolas Vinckenbosch & Geng Tian & Emilia Huerta-Sanchez & Alison F Feder & Niels Grarup & Torben Jørgensen , 2011. "Natural Selection Affects Multiple Aspects of Genetic Variation at Putatively Neutral Sites across the Human Genome," PLOS Genetics, Public Library of Science, vol. 7(10), pages 1-15, October.
    4. Felix M Key & Benjamin Peter & Megan Y Dennis & Emilia Huerta-Sánchez & Wei Tang & Ludmila Prokunina-Olsson & Rasmus Nielsen & Aida M Andrés, 2014. "Selection on a Variant Associated with Improved Viral Clearance Drives Local, Adaptive Pseudogenization of Interferon Lambda 4 (IFNL4)," PLOS Genetics, Public Library of Science, vol. 10(10), pages 1-12, October.
    5. Henri Pesonen & Umberto Simola & Alvaro Köhn‐Luque & Henri Vuollekoski & Xiaoran Lai & Arnoldo Frigessi & Samuel Kaski & David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Jukka Corander, 2023. "ABC of the future," International Statistical Review, International Statistical Institute, vol. 91(2), pages 243-268, August.
    6. Jung Hsuan & Marjoram Paul, 2011. "Choice of Summary Statistic Weights in Approximate Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-23, September.
    7. Melissa J Hubisz & Amy L Williams & Adam Siepel, 2020. "Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph," PLOS Genetics, Public Library of Science, vol. 16(8), pages 1-24, August.
    8. Maxime Lenormand & Franck Jabot & Guillaume Deffuant, 2013. "Adaptive approximate Bayesian computation for complex models," Computational Statistics, Springer, vol. 28(6), pages 2777-2796, December.
    9. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.
    10. 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.
    11. Baey, Charlotte & Smith, Henrik G. & Rundlöf, Maj & Olsson, Ola & Clough, Yann & Sahlin, Ullrika, 2023. "Calibration of a bumble bee foraging model using Approximate Bayesian Computation," Ecological Modelling, Elsevier, vol. 477(C).
    12. Jonathan U Harrison & Ruth E Baker, 2020. "An automatic adaptive method to combine summary statistics in approximate Bayesian computation," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    13. 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.
    14. repec:dau:papers:123456789/5724 is not listed on IDEAS
    15. Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019. "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, vol. 35(2), pages 521-539.
    16. Buzbas, Erkan O. & Rosenberg, Noah A., 2015. "AABC: Approximate approximate Bayesian computation for inference in population-genetic models," Theoretical Population Biology, Elsevier, vol. 99(C), pages 31-42.
    17. Béchaux Camille & Crépet Amélie & Clémençon Stéphan, 2014. "Improving Dietary Exposure Models by Imputing Biomonitoring Data through ABC Methods," The International Journal of Biostatistics, De Gruyter, vol. 10(2), pages 277-287, November.
    18. Nancy Reid, 2024. "On Sufficiency and Ancillarity," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 160-172, November.
    19. Creel, Michael & Kristensen, Dennis, 2015. "ABC of SV: Limited information likelihood inference in stochastic volatility jump-diffusion models," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 85-108.
    20. Soubeyrand Samuel & Carpentier Florence & Guiton François & Klein Etienne K., 2013. "Approximate Bayesian computation with functional statistics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(1), pages 17-37, March.
    21. Mikael Sunnåker & Alberto Giovanni Busetto & Elina Numminen & Jukka Corander & Matthieu Foll & Christophe Dessimoz, 2013. "Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-10, January.

    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:plo:pone00:0018155. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.