Evaluating genetic drift in time-series evolutionary analysis

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dc.contributor.author Nene, Nuno R.
dc.contributor.author Mustonen, Ville
dc.contributor.author Illingworth, Christopher J. R.
dc.date.accessioned 2017-12-21T08:41:01Z
dc.date.available 2017-12-21T08:41:01Z
dc.date.issued 2018-01-21
dc.identifier.citation Nene , N R , Mustonen , V & Illingworth , C J R 2018 , ' Evaluating genetic drift in time-series evolutionary analysis ' , Journal of Theoretical Biology , vol. 437 , pp. 51-57 . https://doi.org/10.1016/j.jtbi.2017.09.021
dc.identifier.other PURE: 95598924
dc.identifier.other PURE UUID: da93887c-eaeb-4c21-bada-07b45495d975
dc.identifier.other WOS: 000417228400007
dc.identifier.other Scopus: 85032866875
dc.identifier.other ORCID: /0000-0002-7270-1792/work/52697412
dc.identifier.uri http://hdl.handle.net/10138/229854
dc.description.abstract The Wright-Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite population size. Approximations have commonly been used but the model itself has rarely been tested against time-resolved genomic data. Here, we evaluate the extent to which it can be inferred as the correct model under a likelihood framework. Given genome-wide data from an evolutionary experiment, we validate the Wright-Fisher drift model as the better option for describing evolutionary trajectories in a finite population. This was found by evaluating its performance against a Gaussian model of allele frequency propagation. However, we note a range of circumstances under which standard Wright-Fisher drift cannot be correctly identified. (C) 2017 The Author(s). Published by Elsevier Ltd. en
dc.format.extent 7
dc.language.iso eng
dc.relation.ispartof Journal of Theoretical Biology
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject Genetic drift
dc.subject Time-resolved genome sequence data
dc.subject Wright-Fisher model
dc.subject Experimental evolution
dc.subject FREQUENCY
dc.subject INFERENCE
dc.subject DYNAMICS
dc.subject LINKAGE
dc.subject TRANSITION
dc.subject 1182 Biochemistry, cell and molecular biology
dc.title Evaluating genetic drift in time-series evolutionary analysis en
dc.type Article
dc.contributor.organization Biosciences
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1016/j.jtbi.2017.09.021
dc.relation.issn 0022-5193
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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