Please use this identifier to cite or link to this item: https://doi.org/10.2202/1544-6115.1482
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dc.titleThe apportionment of total genetic variation by categorical analysis of variance
dc.contributor.authorKhang, T.F.
dc.contributor.authorYap, V.B.
dc.date.accessioned2014-10-28T05:15:43Z
dc.date.available2014-10-28T05:15:43Z
dc.date.issued2010
dc.identifier.citationKhang, T.F., Yap, V.B. (2010). The apportionment of total genetic variation by categorical analysis of variance. Statistical Applications in Genetics and Molecular Biology 9 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.2202/1544-6115.1482
dc.identifier.issn15446115
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105406
dc.description.abstractWe wish to suggest the categorical analysis of variance as a means of quantifying the proportion of total genetic variation attributed to different sources of variation. This method potentially challenges researchers to rethink conclusions derived from a well-known method known as the analysis of molecular variance (AMOVA). The CATANOVA framework allows explicit definition, and estimation, of two measures of genetic differentiation. These parameters form the subject of interest in many research programmes, but are often confused with the correlation measures defined in AMOVA, which cannot be interpreted as relative contributions of particular sources of variation. Through a simulation approach, we show that under certain conditions, researchers who use AMOVA to estimate these measures of genetic differentiation may attribute more than justified amounts of total variation to population labels. Moreover, the two measures can also lead to incongruent conclusions regarding the genetic structure of the populations of interest. Fortunately, one of the two measures seems robust to variations in relative sample sizes used. Its merits are illustrated in this paper using mitochondrial haplotype and amplified fragment length polymorphism (AFLP) data. © 2010 The Berkeley Electronic Press. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.2202/1544-6115.1482
dc.sourceScopus
dc.subjectAFLP
dc.subjectAMOVA
dc.subjectANOVA
dc.subjectBinary data
dc.subjectCATANOVA
dc.subjectCategorical data
dc.subjectMeasure of genetic differentiation
dc.subjectMitochondrial haplotype
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.2202/1544-6115.1482
dc.description.sourcetitleStatistical Applications in Genetics and Molecular Biology
dc.description.volume9
dc.description.issue1
dc.description.page-
dc.identifier.isiut000274198200006
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