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dc.contributor.authorWiberg, R. Axel W.
dc.contributor.authorGaggiotti, Oscar E.
dc.contributor.authorMorrissey, Michael B.
dc.contributor.authorRitchie, Michael G.
dc.identifier.citationWiberg , R A W , Gaggiotti , O E , Morrissey , M B & Ritchie , M G 2017 , ' Identifying consistent allele frequency differences in studies of stratified populations ' , Methods in Ecology and Evolution , vol. 8 , no. 12 , pp. 1899-1909 .
dc.identifier.otherPURE: 249951261
dc.identifier.otherPURE UUID: cc641379-a4d7-4391-9927-845c0fd60922
dc.identifier.otherScopus: 85020757564
dc.identifier.otherORCID: /0000-0001-7913-8675/work/46761125
dc.identifier.otherWOS: 000417239200026
dc.identifier.otherORCID: /0000-0003-1827-1493/work/61370086
dc.descriptionThis work was supported by a combined NERC and St Andrews 600th Anniversary PhD Studentship [grant NE/L501852/1] to RAWW and also supported by a NERC grant [grant NE/I027800/1] to MGR and Rhonda R. Snook. MBM was supported by The Royal Society. OEG was supported by MASTS (Marine Alliance for Science and Technology for Scotland). Computational analyses were supported by the University of St Andrews Bioinformatics Unit which is funded by a Wellcome Trust ISSF award [grant 105621/Z/14/Z].en
dc.description.abstract1. With increasing application of pooled-sequencing approaches to population genomics robust methods are needed to accurately quantify allele frequency differences between populations. Identifying consistent differences across stratified populations can allow us to detect genomic regions under selection and that differ between populations with different histories or attributes. Current popular statistical tests are easily implemented in widely available software tools which make them simple for researchers to apply. However, there are potential problems with the way such tests are used,which means that underlying assumptions about the data are frequently violated. 2. These problems are highlighted by simulation of simple but realistic population genetic models of neutral evolution and the performance of different tests are assessed. We present alternative tests (including GLMs with quasibinomial error structure) with attractive properties for the analysis of allele frequency differences and re-analyse a published dataset. 3. The simulations show that common statistical tests for consistent allele frequency differences perform poorly, with high false positive rates. Applying tests that do not confound heterogeneity and main effects significantly improves inference. Variation in sequencing coverage likely produces many false positives and re-scaling allele frequencies to counts out of a common value or an effective sample size reduces this effect. 4. Many researchers are interested in identifying allele frequencies that vary consistently across replicates to identify loci underlying phenotypic responses to selection or natural variation in phenotypes. Popular methods that have been suggested for this task perform poorly in simulations. Overall, quasibinomial GLMs perform better and also have the attractive feature of allowing correction for multiple testing by standard procedures and are easily extended to other designs.
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rights© 2017 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en
dc.subjectAllele frequency differencesen
dc.subjectQuaibinomial GLMen
dc.subjectExperimental evolutionen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectQH426 Geneticsen
dc.titleIdentifying consistent allele frequency differences in studies of stratified populationsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews.Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews.Institute of Behavioural and Neural Sciencesen
dc.description.statusPeer revieweden

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