A new eigenfunction spatial analysis describing population genetic structure

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dc.creatorDiniz Filho, Jose Alexandre Felizola
dc.creatorDiniz, João Vitor Barnez Pignata Leal
dc.creatorRangel, Thiago Fernando Lopes Valle de Britto
dc.creatorSoares, Thannya Nascimento
dc.creatorTelles, Mariana Pires de Campos
dc.creatorCollevatti, Rosane Garcia
dc.creatorBini, Luis Mauricio
dc.date.accessioned2020-11-23T16:45:08Z
dc.date.available2020-11-23T16:45:08Z
dc.date.issued2013
dc.description.abstractSeveral methods of spatial analyses have been proposed to infer the relative importance of evolutionary processes on genetic population structure. Here we show how a new eigenfunction spatial analysis can be used to model spatial patterns in genetic data. Considering a sample of n local populations, the method starts by modeling the response variable (allele frequencies or phenotypic variation) against the eigenvectors sequentially extracted from a geographic distance matrix (n 9 n). The relationship between the coefficient of determination (R2) of the models and the cumulative eigenvalues, which we named the spatial signal-representation (SSR) curve, can be more efficient than Moran’s I correlograms in describing different patterns. The SSR curve was also applied to simulated data (under distinct scenarios of population differentiation) and to analyze spatial patterns in alleles from microsatellite data for 25 local populations of Dipteryx alata, a tree species endemic to the Brazilian Cerrado. The SSR curves are consistent with previous phylogeographical patterns of the species, revealing combined effects of isolation-by-distance and range expansion. Our analyses demonstrate that the SSR curve is a useful exploratory tool for describing spatial patterns of genetic variability and for selecting spatial eigenvectors for models aiming to explain spatial responses to environmental variables and landscape features.pt_BR
dc.identifier.citationDINIZ-FILHO, José Alexandre Felizola et al. A new eigenfunction spatial analysis describing population genetic structure. Genetica, Heidelberg, v. 141, p. 479-489, 2013.pt_BR
dc.identifier.doi10.1007/s10709-013-9747-0
dc.identifier.issn0016-6707
dc.identifier.issne- 1573-6857
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/19268
dc.language.isoengpt_BR
dc.publisher.countryAlemanhapt_BR
dc.publisher.departmentInstituto de Ciências Biológicas - ICB (RG)pt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCerradopt_BR
dc.subjectDipteryx alatapt_BR
dc.subjectEigenfunction analysespt_BR
dc.subjectSpatial genetic structurept_BR
dc.subjectMicrosatellitespt_BR
dc.subjectSpatial autocorrelationpt_BR
dc.titleA new eigenfunction spatial analysis describing population genetic structurept_BR
dc.typeArtigopt_BR

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