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resumen

Resumen
Advances in genotyping technology, such as molecular markers, have noticeably improved our capacity to characterize genomes at multiple loci. Concomitantly, the methodological framework to analyze genetic data has expanded, and keeping abreast with the latest statistical developments to analyze molecular marker data in the context of spatial genetics has become a difficult task. Most methods in spatial statistics are devoted to univariate data whereas the [ver mas...]
dc.contributor.authorTeich, Ingrid
dc.contributor.authorVerga, Anibal Ramón
dc.contributor.authorBalzarini, Mónica Graciela
dc.date.accessioned2019-01-02T19:05:39Z
dc.date.available2019-01-02T19:05:39Z
dc.date.issued2014-01
dc.identifier.issn2156-8456
dc.identifier.issn2156-8502 (Online)
dc.identifier.other10.4236/abb.2014.52013
dc.identifier.urihttp://hdl.handle.net/20.500.12123/4194
dc.description.abstractAdvances in genotyping technology, such as molecular markers, have noticeably improved our capacity to characterize genomes at multiple loci. Concomitantly, the methodological framework to analyze genetic data has expanded, and keeping abreast with the latest statistical developments to analyze molecular marker data in the context of spatial genetics has become a difficult task. Most methods in spatial statistics are devoted to univariate data whereas the nature of molecular marker data is highly dimensional. Multivariate methods are aimed at finding proximities between entities characterized by multiple variables by summarizing information in few synthetic variables. In particular, Principal Component analysis (PCA) has been used to study genetic structure of geo-referenced allele frequency profiles, incorporating spatial information with a posteriori analysis. Conversely, the recently developed spatially restricted PCA (sPCA) explicitly includes spatial data in the optimization criterion. In this work, we compared the results of the application of PCA and sPCA in the study of the spatial genetic structure at fine scale of a Prosopis flexuosa and P. chilensis hybrid swarm. Data consisted in the genetic characterization of 87 trees sampled in Córdoba, Argentina and genotyped at six microsatellites, which yielded 72 alleles. As expected, principal components explained more variance than sPCA components, but were less spatially autocorrelated. The maps obtained by the interpolation of sPC1 values allowed a better visualization of a patchy spatial pattern of genetic variability than the PC1 synthetic map. We also proposed a PC-sPC scatter plot of allele loadings to better understand the allele contributions to spatial genetic variability.eng
dc.formatapplication/pdfeng
dc.language.isoeng
dc.publisherScientific Research Publishingeng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceAdvances in bioscience and biotechnology 5 (2) : 89-99. (January 2014)eng
dc.subjectProsopises_AR
dc.subjectMarcadores Genéticoses_AR
dc.subjectGenetic Markerseng
dc.subjectAnálisis Multivariantees_AR
dc.subjectMultivariate Analysiseng
dc.subjectBosqueses_AR
dc.subjectForestseng
dc.subject.othersPCAeng
dc.titleAssessing spatial genetic structure from molecular marker data via principal component analyses: a case study in a Prosopis sp. foreseng
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.description.origenInstituto de Fitopatologíaes_AR
dc.description.filFil: Teich, Ingrid. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentinaes_AR
dc.description.filFil: Verga, Anibal Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Fitopatología y Fisiología Vegetal; Argentinaes_AR
dc.description.filFil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentinaes_AR
dc.subtypecientifico


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