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dc.contributor.authorde Villemereuil, Pierre
dc.contributor.authorGaggiotti, Oscar Eduardo
dc.date.accessioned2016-07-18T23:30:57Z
dc.date.available2016-07-18T23:30:57Z
dc.date.issued2015-11
dc.identifier.citationde Villemereuil , P & Gaggiotti , O E 2015 , ' A new F ST  method to uncover local adaptation using environmental variables ' Methods in Ecology and Evolution , vol. 6 , no. 11 , pp. 1248-1258 . https://doi.org/10.1111/2041-210X.12418en
dc.identifier.issn2041-210X
dc.identifier.otherPURE: 192095619
dc.identifier.otherPURE UUID: f6c1c632-882e-4ebd-9485-51d903ea6ef7
dc.identifier.otherScopus: 84955175412
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1111/mec.13360/abstracten
dc.descriptionFunding: Marine Alliance for Science and Technology for Scotland (MASTS) (OEG) Date of Acceptance: 25/05/2015en
dc.description.abstractGenome-scan methods are used for screening genome-wide patterns of DNA polymorphism to detect signatures of positive selection. There are two main types of methods: (i) "outlier'' detection methods based on FST that detect loci with high differentiation compared to the rest of the genome, and (ii) environmental association methods that test the association between allele frequencies and environmental variables. We present a new FST-based genome-scan method, BayeScEnv, which incorporates environmental information in the form of "environmental differentiation''. It is based on the F-model, but, as opposed to existing approaches, it considers two locus-specific effects; one due to divergent selection, and another due to various other processes different from local adaptation (e.g. range expansions, differences in mutation rates across loci or background selection). The method was developped in C++ and is avaible at http://github.com/devillemereuil/bayescenv. A simulation study shows that our method has a much lower false positive rate than an existing FST-based method, BayeScan, under a wide range of demographic scenarios. Although it has lower power, it leads to a better compromise between power and false positive rate. We apply our method to a human dataset and show that it can be used successfully to study local adaptation. We discuss its scope and compare it to other existing methods.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rightsThis is the peer reviewed version of the following article: A new FST method to uncover local adaptation using environmental variables de Villemereuil, P. & Gaggiotti, O. E. 2015 In : Methods in Ecology and Evolution, which has been published in final form at http://dx.doi.org/10.1111/2041-210X.12418. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.en
dc.subjectLocal adaptationen
dc.subjectGenetic differentiationen
dc.subjectBayesian approachen
dc.subjectQH301 Biologyen
dc.subject.lccQH301en
dc.titleA new FST method to uncover local adaptation using environmental variablesen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.identifier.doihttps://doi.org/10.1111/2041-210X.12418
dc.description.statusPeer revieweden
dc.date.embargoedUntil2016-07-19


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