IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007426.html
   My bibliography  Save this article

Adaptation in structured populations and fuzzy boundaries between hard and soft sweeps

Author

Listed:
  • Yichen Zheng
  • Thomas Wiehe

Abstract

Selective sweeps, the genetic footprint of positive selection, have been extensively studied in the past decades, with dozens of methods developed to identify swept regions. However, these methods suffer from both false positive and false negative reports, and the candidates identified with different methods are often inconsistent with each other. We propose that a biological cause of this problem can be population subdivision, and a technical cause can be incomplete, or inaccurate, modeling of the dynamic process associated with sweeps. Here we used simulations to show how these effects interact and potentially cause bias. In particular, we show that sweeps maybe misclassified as either hard or soft, when the true time stage of a sweep and that implied, or pre-supposed, by the model do not match. We call this “temporal misclassification”. Similarly, “spatial misclassification (softening)” can occur when hard sweeps, which are imported by migration into a new subpopulation, are falsely identified as soft. This can easily happen in case of local adaptation, i.e. when the sweeping allele is not under positive selection in the new subpopulation, and the underlying model assumes panmixis instead of substructure. The claim that most sweeps in the evolutionary history of humans were soft, may have to be reconsidered in the light of these findings.Author summary: Identifying the traces of adaptive evolution is still difficult, in particular when populations are not in equilibrium. Using forward-in-time simulations, we studied adaptation by selective sweeps in populations that are divided into demes with limited migration among them. We applied different sweep tests, whose sensitivities are found to vary widely across demographic scenarios and temporal stages. First, the temporal stage of a sweep (ongoing vs completed) significantly affects detection, especially when machine learning algorithms are used and training and test stages do not match. Second, imported alleles from a neighboring deme with local adaptation can result in spurious sweep signals. In both cases, signals are often detected as “soft sweeps” (adaptation from standing variation) while in fact they are “hard sweeps” (adaptation from single mutation), originating in the same subpopulation in the former case and in some other subpopulation in the latter case. We call these phenomena “temporal” and “spatial softening”. Finally, under low migration, the time window in which a sweep can be detected becomes very narrow, and power tends to be low. Generally, however, haplotype-based methods seem to be less affected than frequency-spectrum-based tests.

Suggested Citation

  • Yichen Zheng & Thomas Wiehe, 2019. "Adaptation in structured populations and fuzzy boundaries between hard and soft sweeps," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-32, November.
  • Handle: RePEc:plo:pcbi00:1007426
    DOI: 10.1371/journal.pcbi.1007426
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007426
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007426&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1007426?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. Pleuni S Pennings & Joachim Hermisson, 2006. "Soft Sweeps III: The Signature of Positive Selection from Recurrent Mutation," PLOS Genetics, Public Library of Science, vol. 2(12), pages 1-15, December.
    2. Haipeng Li & Thomas Wiehe, 2013. "Coalescent Tree Imbalance and a Simple Test for Selective Sweeps Based on Microsatellite Variation," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-14, May.
    3. Pardis C. Sabeti & Patrick Varilly & Ben Fry & Jason Lohmueller & Elizabeth Hostetter & Chris Cotsapas & Xiaohui Xie & Elizabeth H. Byrne & Steven A. McCarroll & Rachelle Gaudet & Stephen F. Schaffner, 2007. "Genome-wide detection and characterization of positive selection in human populations," Nature, Nature, vol. 449(7164), pages 913-918, October.
    4. Rafajlović, M. & Klassmann, A. & Eriksson, A. & Wiehe, T. & Mehlig, B., 2014. "Demography-adjusted tests of neutrality based on genome-wide SNP data," Theoretical Population Biology, Elsevier, vol. 95(C), pages 1-12.
    5. Jeffrey D Jensen, 2014. "On the unfounded enthusiasm for soft selective sweeps," Nature Communications, Nature, vol. 5(1), pages 1-10, December.
    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. Michael DeGiorgio & Zachary A Szpiech, 2022. "A spatially aware likelihood test to detect sweeps from haplotype distributions," PLOS Genetics, Public Library of Science, vol. 18(4), pages 1-37, April.
    2. Garud, Nandita R. & Rosenberg, Noah A., 2015. "Enhancing the mathematical properties of new haplotype homozygosity statistics for the detection of selective sweeps," Theoretical Population Biology, Elsevier, vol. 102(C), pages 94-101.
    3. Chen, Hua & Hey, Jody & Slatkin, Montgomery, 2015. "A hidden Markov model for investigating recent positive selection through haplotype structure," Theoretical Population Biology, Elsevier, vol. 99(C), pages 18-30.
    4. Mohammad Hossein Olyaee & Alireza Khanteymoori & Khosrow Khalifeh, 2020. "A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-19, October.
    5. Wirtz, Johannes & Rauscher, Martina & Wiehe, Thomas, 2018. "Topological linkage disequilibrium calculated from coalescent genealogies," Theoretical Population Biology, Elsevier, vol. 124(C), pages 41-50.
    6. Ferretti, Luca & Klassmann, Alexander & Raineri, Emanuele & Ramos-Onsins, Sebastián E. & Wiehe, Thomas & Achaz, Guillaume, 2018. "The neutral frequency spectrum of linked sites," Theoretical Population Biology, Elsevier, vol. 123(C), pages 70-79.
    7. Benger, Etam & Sella, Guy, 2013. "Modeling the effect of changing selective pressures on polymorphism and divergence," Theoretical Population Biology, Elsevier, vol. 85(C), pages 73-85.
    8. Lauren A. Choate & Gilad Barshad & Pierce W. McMahon & Iskander Said & Edward J. Rice & Paul R. Munn & James J. Lewis & Charles G. Danko, 2021. "Multiple stages of evolutionary change in anthrax toxin receptor expression in humans," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    9. Parul Johri & Wolfgang Stephan & Jeffrey D Jensen, 2022. "Soft selective sweeps: Addressing new definitions, evaluating competing models, and interpreting empirical outliers," PLOS Genetics, Public Library of Science, vol. 18(2), pages 1-12, February.
    10. Rouzine, Igor M. & Coffin, John M., 2010. "Multi-site adaptation in the presence of infrequent recombination," Theoretical Population Biology, Elsevier, vol. 77(3), pages 189-204.
    11. Klassmann, A. & Ferretti, L., 2018. "The third moments of the site frequency spectrum," Theoretical Population Biology, Elsevier, vol. 120(C), pages 16-28.
    12. Hyeongmin Kim & Ki Duk Song & Hyeon Jeong Kim & WonCheoul Park & Jaemin Kim & Taeheon Lee & Dong-Hyun Shin & Woori Kwak & Young-jun Kwon & Samsun Sung & Sunjin Moon & Kyung-Tai Lee & Namshin Kim & Joo, 2015. "Exploring the Genetic Signature of Body Size in Yucatan Miniature Pig," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-16, April.
    13. 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.
    14. Smadi, Charline, 2015. "An eco-evolutionary approach of adaptation and recombination in a large population of varying size," Stochastic Processes and their Applications, Elsevier, vol. 125(5), pages 2054-2095.
    15. Hakhamanesh Mostafavi & Tomaz Berisa & Felix R Day & John R B Perry & Molly Przeworski & Joseph K Pickrell, 2017. "Identifying genetic variants that affect viability in large cohorts," PLOS Biology, Public Library of Science, vol. 15(9), pages 1-29, September.

    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:pcbi00:1007426. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.