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Registros recuperados : 27 | |
21. | | FERREIRA, R. C. U.; LARA, L. A. de C.; CHIARI, L.; BARRIOS, S. C. L.; VALLE, C. B. do; VALERIO, J. R.; TORRES, F. Z. V.; GARCIA, A. A. F.; SOUZA, A. P. de. Genetic Mapping With Allele Dosage Information in Tetraploid Urochloa decumbens (Stapf) R. D. Webster Reveals Insights Into Spittlebug (Notozulia entreriana Berg) Resistance. Frontiers in Plant Science, v. 10, February, 2019. Biblioteca(s): Embrapa Gado de Corte. |
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22. | | DEO, T. G.; FERREIRA, R. C. U.; LARA, L. A. C.; MORAES, A. C. L.; ALVES-PEREIRA, A.; OLIVEIRA, F. A. de; GARCIA, A. A. F.; SANTOS, M. F.; JANK, L.; SOUZA, A. P. de. High-Resolution Linkage Map With Allele Dosage Allows the Identification of Regions Governing Complex Traits and Apospory in Guinea Grass (Megathyrsus maximus). Frontiers in Plant Science, 26 fev.. 2020. Biblioteca(s): Embrapa Gado de Corte. |
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23. | | MARTINS, F. B.; MORAES, A. C. L.; AONO, A. H.; FERREIRA, R. C. U.; CHIARI, L.; SIMEÃO, R. M.; BARRIOS, S. C. L.; SANTOS, M. F.; JANK, L.; VALLE, C. B. do; VIGNA, B. B. Z.; SOUZA, A. P. DE. A semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses. Frontiers in Plant Science, v.12, article 737919, 2021. 19 p. Biblioteca(s): Embrapa Gado de Corte; Embrapa Pecuária Sudeste. |
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24. | | SOUZA, A. P.; MORAES, A. C. L.; LARA, L. A. C.; FERREIRA, R. C. U.; DÉO, T. G.; MARTIN, F. B.; VALLE, C. B.; GARCIA, A. A. F.; VIGNA, B. B. Z. Development of single nucleotide polymorphisms (snp) markers for genetic map saturation of hexaploid urochloa humidicola. In: INTERNATIONAL FORAGE TURF BREEDING CONFERENCE, 2019, Lake Buena Vista, Florida. Proceedings... Lake Buena Vista, Florida: University of Florida, 2019. 50 Biblioteca(s): Embrapa Pecuária Sudeste. |
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25. | | MORAES, A. da C. L.; MOLLINARI, M.; FERREIRA, R. C. U.; AONO, A.; LARA, L. A. de C.; PESSOA FILHO, M. A. C. de P.; BARRIOS, S. C. L.; GARCIA, A. A. F.; VALLE, C. B. do; SOUZA, A. P. de; VIGNA, B. B. Z. Advances in genomic characterization of Urochloa humidicola: exploring polyploid inheritance and apomixis. Theoretical and Applied Genetics, v. 136, n. 11, 2023. 17 p. Biblioteca(s): Embrapa Cerrados; Embrapa Gado de Corte; Embrapa Pecuária Sudeste. |
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26. | | RESENDE, R. M. S.; VALLE, C. B. do; ALVES, G. F.; MOREIRA, D. A. L.; SILVA, D. R. da; ARAÚJO, D. de F.; FERREIRA, R. C. U.; BARRIOS, S. C. L.; JANK, L.; CARAMALAC, G. R.; NAKA, I. M.; CALIXTO, S.; CARVALHO, J. de. Melhoramento de Brachiaria ruziziensis tetraploide sexual na Embrapa: métodos e avanços. Campo Grande, MS: Embrapa Gado de Corte, 2012. 28 p. (Embrapa Gado de Corte. Documentos, 194). Biblioteca(s): Embrapa Gado de Corte. |
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27. | | AONO, A. H.; FERREIRA, R. C. U.; MORAES, A. da C. L.; LARA, L. A. de C.; PIMENTA, R. J. G.; COSTA, E. A.; PINTO, L. R.; LANDELL, M. G. de A.; SANTOS, M. F.; JANK, L.; BARRIOS, S. C. L.; VALLE, C. B.; CHIARI, L.; GARCIA, A. A. F.; KUROSHU, R. M.; LORENA, A. C.; GORJANC, G.; SOUZA, A. P. de. A joint learning approach for genomic prediction in polyploid grasses. Scientific Reports, 12, article 12499, 2022. 17 p. Biblioteca(s): Embrapa Gado de Corte. |
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Registros recuperados : 27 | |
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Registro Completo
Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pecuária Sudeste. |
Data corrente: |
07/12/2021 |
Data da última atualização: |
20/12/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
MARTINS, F. B.; MORAES, A. C. L.; AONO, A. H.; FERREIRA, R. C. U.; CHIARI, L.; SIMEÃO, R. M.; BARRIOS, S. C. L.; SANTOS, M. F.; JANK, L.; VALLE, C. B. do; VIGNA, B. B. Z.; SOUZA, A. P. DE. |
Afiliação: |
FELIPE BITENCOURT MARTINS, Center for Molecular Biology and Genetic Engineering; ALINE COSTA LIMA MORAES, Center for Molecular Biology and Genetic Engineering; ALEXANDRE HILD AONO, Center for Molecular Biology and Genetic Engineering; REBECCA CAROLINE ULBRICHT FERREIRA, Center for Molecular Biology and Genetic Engineering; LUCIMARA CHIARI, CNPGC; ROSANGELA MARIA SIMEAO, CNPGC; SANZIO CARVALHO LIMA BARRIOS, CNPGC; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; CACILDA BORGES DO VALLE, CNPGC; BIANCA BACCILI ZANOTTO VIGNA, CPPSE; ANETE PEREIRA DE SOUZA, Center for Molecular Biology and Genetic Engineering; UNICAMP. |
Título: |
A semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Frontiers in Plant Science, v.12, article 737919, 2021. |
Páginas: |
19 p. |
DOI: |
https://doi.org/10.3389/fpls.2021.737919 |
Idioma: |
Inglês |
Conteúdo: |
Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs. MenosArtificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made ava... Mostrar Tudo |
Palavras-Chave: |
Allele dosage; Apomictic clones; Clustering analysis; GBS; Half sibling; Self fertilization; Shiny. |
Thesaurus NAL: |
Principal component analysis. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228605/1/SemiAutomatedSNP.pdf
|
Marc: |
LEADER 02794naa a2200373 a 4500 001 2138071 005 2021-12-20 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3389/fpls.2021.737919$2DOI 100 1 $aMARTINS, F. B. 245 $aA semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses.$h[electronic resource] 260 $c2021 300 $a19 p. 520 $aArtificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs. 650 $aPrincipal component analysis 653 $aAllele dosage 653 $aApomictic clones 653 $aClustering analysis 653 $aGBS 653 $aHalf sibling 653 $aSelf fertilization 653 $aShiny 700 1 $aMORAES, A. C. L. 700 1 $aAONO, A. H. 700 1 $aFERREIRA, R. C. U. 700 1 $aCHIARI, L. 700 1 $aSIMEÃO, R. M. 700 1 $aBARRIOS, S. C. L. 700 1 $aSANTOS, M. F. 700 1 $aJANK, L. 700 1 $aVALLE, C. B. do 700 1 $aVIGNA, B. B. Z. 700 1 $aSOUZA, A. P. DE 773 $tFrontiers in Plant Science$gv.12, article 737919, 2021.
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Embrapa Gado de Corte (CNPGC) |
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