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dc.contributor.authorLauterbur, M.E.
dc.contributor.authorMunch, K.
dc.contributor.authorEnard, D.
dc.date.accessioned2024-08-12T01:36:30Z
dc.date.available2024-08-12T01:36:30Z
dc.date.issued2023-06-12
dc.identifier.citationM Elise Lauterbur, Kasper Munch, David Enard, Versatile Detection of Diverse Selective Sweeps with Flex-Sweep, Molecular Biology and Evolution, Volume 40, Issue 6, June 2023, msad139, https://doi.org/10.1093/molbev/msad139
dc.identifier.issn0737-4038
dc.identifier.pmid37307561
dc.identifier.doi10.1093/molbev/msad139
dc.identifier.urihttp://hdl.handle.net/10150/674144
dc.description.abstractUnderstanding the impacts of selection pressures influencing modern-day genomic diversity is a major goal of evolutionary genomics. In particular, the contribution of selective sweeps to adaptation remains an open question, with persistent statistical limitations on the power and specificity of sweep detection methods. Sweeps with subtle genomic signals have been particularly challenging to detect. Although many existing methods powerfully detect specific types of sweeps and/or those with strong signals, their power comes at the expense of versatility. We present Flex-sweep, a machine learning-based tool designed to detect sweeps with a variety of subtle signals, including those thousands of generations old. It is especially valuable for nonmodel organisms, for which we have neither expectations about the overall characteristics of sweeps nor outgroups with population-level sequencing to otherwise facilitate detecting very old sweeps. We show that Flex-sweep has the power to detect sweeps with subtle signals, even in the face of demographic model misspecification, recombination rate heterogeneity, and background selection. Flex-sweep detects sweeps up to 0.125∗4Ne generations old, including those that are weak, soft, and/or incomplete; it can also detect strong, complete sweeps up to 0.25∗4Ne generations old. We apply Flex-sweep to the 1000 Genomes Yoruba data set and, in addition to recovering previously identified sweeps, show that sweeps disproportionately occur within genic regions and are close to regulatory regions. In addition, we show that virus-interacting proteins (VIPs) are strongly enriched for selective sweeps, recapitulating previous results that demonstrate the importance of viruses as a driver of adaptive evolution in humans. © 2023 The Author(s). Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.
dc.language.isoen
dc.publisherOxford University Press
dc.rights© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/ licenses/by/4.0/
dc.titleVersatile Detection of Diverse Selective Sweeps with Flex-Sweep
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Ecology and Evolutionary Biology, University of Arizona
dc.identifier.journalMolecular Biology and Evolution
dc.description.noteOpen access article
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal Published Version
dc.source.journaltitleMolecular Biology and Evolution
refterms.dateFOA2024-08-12T01:36:30Z


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© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).