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| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.provenance | CONICET | - |
| dc.creator | Peña Malavera, Andrea Natalia | - |
| dc.creator | Fernandez, Elmer Andres | - |
| dc.creator | Bruno, Cecilia Ines | - |
| dc.creator | Balzarini, Monica Graciela | - |
| dc.date | 2018-01-23T15:20:42Z | - |
| dc.date | 2018-01-23T15:20:42Z | - |
| dc.date | 2014-06 | - |
| dc.date | 2018-01-22T14:18:31Z | - |
| dc.date.accessioned | 2019-04-29T15:34:08Z | - |
| dc.date.available | 2019-04-29T15:34:08Z | - |
| dc.date.issued | 2014-06 | - |
| dc.identifier | Peña Malavera, Andrea Natalia; Fernandez, Elmer Andres; Bruno, Cecilia Ines; Balzarini, Monica Graciela; Comparison of algorithms to infer genetic population structure from unlinked molecular markers; Berkeley Electronic Press; Statistical Applications In Genetics And Molecular Biology; 13; 4; 6-2014; 391-402 | - |
| dc.identifier | 1544-6115 | - |
| dc.identifier | http://hdl.handle.net/11336/34261 | - |
| dc.identifier | CONICET Digital | - |
| dc.identifier | CONICET | - |
| dc.identifier.uri | http://rodna.bn.gov.ar:8080/jspui/handle/bnmm/296754 | - |
| dc.description | Identifying population genetic structure (PGS) is crucial for breeding and conservation. Several clustering algorithms are available to identify the underlying PGS to be used with genetic data of maize genotypes. In this work, six methods to identify PGS from unlinked molecular marker data were compared using simulated and experimental data consisting of multilocus-biallelic genotypes. Datasets were delineated under different biological scenarios characterized by three levels of genetic divergence among populations (low, medium, and high FST) and two numbers of sub-populations (K=3 and K=5). The relative performance of hierarchical and non-hierarchical clustering, as well as model-based clustering (STRUCTURE) and clustering from neural networks (SOM-RP-Q). We use the clustering error rate of genotypes into discrete sub-populations as comparison criterion. In scenarios with great level of divergence among genotype groups all methods performed well. With moderate level of genetic divergence (FST=0.2), the algorithms SOM-RP-Q and STRUCTURE performed better than hierarchical and non-hierarchical clustering. In all simulated scenarios with low genetic divergence and in the experimental SNP maize panel (largely unlinked), SOM-RP-Q achieved the lowest clustering error rate. The SOM algorithm used here is more effective than other evaluated methods for sparse unlinked genetic data. | - |
| dc.description | Fil: Peña Malavera, Andrea Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina | - |
| dc.description | Fil: Fernandez, Elmer Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina | - |
| dc.description | Fil: Bruno, Cecilia Ines. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Catolica de Córdoba. Facultad de Ingeniería; Argentina | - |
| dc.description | Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.language | eng | - |
| dc.publisher | Berkeley Electronic Press | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1515/sagmb-2013-0006 | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/url/https://www.degruyter.com/view/j/sagmb.2014.13.issue-4/sagmb-2013-0006/sagmb-2013-0006.xml | - |
| dc.rights | info:eu-repo/semantics/restrictedAccess | - |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | - |
| dc.source | reponame:CONICET Digital (CONICET) | - |
| dc.source | instname:Consejo Nacional de Investigaciones Científicas y Técnicas | - |
| dc.source | instacron:CONICET | - |
| dc.source.uri | http://hdl.handle.net/11336/34261 | - |
| dc.subject | CLUSTER ANALYSIS | - |
| dc.subject | MULTILOCUS-BIALLELIC GENOTYPES | - |
| dc.subject | PLANT BREEDING | - |
| dc.subject | SELF-ORGANIZING MAPS | - |
| dc.subject | Ciencias de la Computación | - |
| dc.subject | Ciencias de la Computación e Información | - |
| dc.subject | CIENCIAS NATURALES Y EXACTAS | - |
| dc.title | Comparison of algorithms to infer genetic population structure from unlinked molecular markers | - |
| dc.type | info:eu-repo/semantics/article | - |
| dc.type | info:eu-repo/semantics/publishedVersion | - |
| dc.type | info:ar-repo/semantics/articulo | - |
| Aparece en las colecciones: | CONICET | |
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