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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
29/05/2022 |
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Data da última atualização: |
12/04/2024 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
CROSSA, J.; MONTESINOS-LÓPEZ, O. A.; PÉREZ-RODRÍGUEZ, P.; COSTA-NETO, G.; FRITSCHE-NETO, R.; ORTIZ, R.; MARTINI, J. W. R.; LILLEMO, M.; MONTESINOS-LÓPEZ, A.; JARQUIN, D.; BRESEGHELLO, F.; CUEVAS, J.; RINCENT, R. |
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Afiliação: |
JOSE CROSSA, CIMMYT; OSVAL ANTONIO MONTESINOS-LOPEZ, UNIVERSIDAD DE COLIMA, México; PAULINO PEREZ-RODRIGUEZ, COLEGIO DE POSTGRADUADOS, Montecillos-Mexico; GERMANO COSTA-NETO, ESALQ; ROBERTO FRITSCHE-NETO, ESALQ; RODOMIRO ORTIZ, SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES, Alnarp-Sweden; JOHANNES W. R. MARTINI, CIMMYT; MORTEN LILLEMO, NORWEGIAN UNIVERSITY OF LIFE SCIENCES, Norway; ABELARDO MONTESINOS-LOPEZ, CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS, Guanajuato-Mexico; DIEGO JARQUIN, UNIVERSITY OF NEBRASKA, Lincoln-NE; FLAVIO BRESEGHELLO, CNPAF; JAIME CUEVAS, UNIVERSIDAD DE QUINTANA ROO, Quintana Roo-Mexico; RENAUD RINCENT, INRAE, Clermont-Ferrand-France. |
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Título: |
Genome and environment based prediction models and methods of complex traits incorporating genotype × environment interaction. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: AHMADI, N.; BARTHOLOME, J. (ed.). Genomic prediction of complex traits: methods and protocols. New York: Humana Press, 2022. |
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Páginas: |
p. 245-283. |
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Série: |
(Methods in Molecular Biology). |
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ISBN: |
978-1-0716-2205-6 |
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DOI: |
https://doi.org/10.1007/978-1-0716-2205-6_9 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E. MenosGenomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and... Mostrar Tudo |
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Palavras-Chave: |
Genome-enabled prediction; Genomic selection; Models with G x E interaction. |
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Thesagro: |
Genótipo; Interação Genética; Melhoramento Genético Vegetal. |
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Thesaurus Nal: |
Genome; Genomics; Genotype-environment interaction; Plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143533/1/cap9-2022.pdf
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Marc: |
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020 $a978-1-0716-2205-6
024 7 $ahttps://doi.org/10.1007/978-1-0716-2205-6_9$2DOI
100 1 $aCROSSA, J.
245 $aGenome and environment based prediction models and methods of complex traits incorporating genotype × environment interaction.$h[electronic resource]
260 $c2022
300 $ap. 245-283.
490 $a(Methods in Molecular Biology).
520 $aGenomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
650 $aGenome
650 $aGenomics
650 $aGenotype-environment interaction
650 $aPlant breeding
650 $aGenótipo
650 $aInteração Genética
650 $aMelhoramento Genético Vegetal
653 $aGenome-enabled prediction
653 $aGenomic selection
653 $aModels with G x E interaction
700 1 $aMONTESINOS-LÓPEZ, O. A.
700 1 $aPÉREZ-RODRÍGUEZ, P.
700 1 $aCOSTA-NETO, G.
700 1 $aFRITSCHE-NETO, R.
700 1 $aORTIZ, R.
700 1 $aMARTINI, J. W. R.
700 1 $aLILLEMO, M.
700 1 $aMONTESINOS-LÓPEZ, A.
700 1 $aJARQUIN, D.
700 1 $aBRESEGHELLO, F.
700 1 $aCUEVAS, J.
700 1 $aRINCENT, R.
773 $tIn: AHMADI, N.; BARTHOLOME, J. (ed.). Genomic prediction of complex traits: methods and protocols. New York: Humana Press, 2022.
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Esconder MarcMostrar Marc Completo |
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Biblioteca |
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Tipo/Formato |
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Registro |
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| Registros recuperados : 265 | |
| 161. |  | TOMAS, W. M.; CAMILO, A. R.; RIBAS, C.; LEUCHTENBERGER, C; BORGES, P. A. L; MOURAO, G.; PELLEGRIN, L. A. Distribution and conservation status of giant otter Pteronura brasiliensis in the Pantanal wetland, Brazil. The Latin American Journal of Aquatic Mammals, v. 10, n. 2, p. 107-114, dez. 2015.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 4 |
| Biblioteca(s): Embrapa Pantanal. |
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| 162. | | SILVEIRA, M.; MUNIN, R. L.; TOMAS, W. M.; FISCHER, E.; BORDIGNON, M. O.; SILVEIRA, G. de A. The distribution of the spectral bat, Vampyrum Spectrum, reaches the southern Pantanal. Biota Neotropica, v. 11, n. 1, p. 1-3, jan./mar., 2011. Publicado somente online.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
| Biblioteca(s): Embrapa Pantanal. |
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| 164. | | SEMEDO, T. B. F; LIBARDI, G. S.; STRÜSSMANN, C.; BERLINCK, C. N.; TOMAS, W. M.; GARBINO, G. S. T. Discovery of underground shelters occupied by the Chacoan Marsh Rat after massive wildfires in Pantanal. Therya Notes, v. 3, n.1, p. 30-35, 2022.| Tipo: Nota Técnica/Nota Científica |
| Biblioteca(s): Embrapa Pantanal. |
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| 165. | | ANTUNES, P. C.; SANTOS, L. G. R. O.; TOMAS, W. M.; FORESTER, J. D.; FERNANDEZ, F. A. S. Disentangling the effects of habitat, food, and intraspecific competition on resource selection by the spiny rat, Thrichomys fosteri. Journal of Mammalogy, v. 97, n. 6, p. 1738-1744, 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Pantanal. |
|  |
| 166. | | AMANCIO, C. O. da G.; CRAWSHAW JUNIOR, P. G.; TOMAS, W. M.; RODRIGUES, R. B.; SILVA, M. V. da. A dimensão humana e sua influência na conservação de carnívoros no Brasil: o exemplo do Pantanal. In: CAVALCANTI, S. M. C.; PAULA, R. C. de; GASPARINI-MORATO, R. L. (Org.). Conflitos com mamíferos carnívoros: referência para o manejo e a convivência Brasília, DF: Instituto Chico Mendes de Conservação da Biodiversidade, ICMBio, 2015. 143 p. p. 101-108| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Agrobiologia. |
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| 170. | | MARTINS, C. de A.; ROQUE, F. de O.; SANTOS, B. A.; FERREIRA, V. L.; STRUSSMANN, C.; TOMAS, W. M. What shapes the phylogenetic structure of anuran communities in a seasonal environment? The influence of determinism at regional scale to stochasticity or antagonistic forces at local scale. Plos One, v. 10, n. 6, p. 1-14, 2015.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Pantanal. |
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| 171. | | PÉRES, I. A. H. F. S.; OLIVEIRA, C. E. de; ROSINHA, G. M. S.; SOARES, C. O.; TOMAS, W. M.; PELLEGRIN, A. O. Association between brucellosis prevalence and taxes of pampas deer (Ozotoceros bezoarticus) found without fawns in southwestern Nhecolândia region, Pantanal wetland, Brazil. In: BRUCELLOSIS 2011- INTERNATIONAL RESEARCH CONFERENCE; BRUCELLOSIS RESEARCH CONFERENCE, 64., 2011, Buenos Aires. [Anales]. Buenos Aires: Asociación Argentina de Microbiologia, 2011. p. 106 P 103.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Corte. |
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| 172. | | TOMAS, W. M.; GARCIA, L. C.; ROQUE, F. de O.; LOURIVAL, R.; DIAS, F.; SALIS, S. M. de; MOURAO, G. de M. Análise dos conceitos de 'mesma identidade ecológica', 'equivalência ecológica' e 'offsetting' para compensação de Reserva Legal. Corumbá: Embrapa Pantanal, 2018. 23 p. (Embrapa Pantanal. Documentos, 159)| Biblioteca(s): Embrapa Pantanal. |
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| 174. | | OLIVEIRA, M. D. de; SANTOS, S. A.; NOGUEIRA, M.; PALHARES, J. C. P.; COMASTRI FILHO, J. A.; NOGUEIRA, E.; SALES, R. dos S.; CAMPOS, Z.; TOMAS, W. M. Captação e armazenamento de água para consumo animal durante a estação de seca na Planície Pantaneira. Corumbá: Embrapa Pantanal, 2020. 26 p. (Embrapa Pantanal. Documentos, 167).| Biblioteca(s): Embrapa Pantanal. |
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| 176. | | SANTOS, S. A.; RODELA, L. G.; TOMAS, W. M.; CUNHA, C. N. da; RAVAGLIA, A. G.; ARAUJO, M. T. B. D.; BUENO SOBRINHO, A. A. An method to define and classify native pastures of the Northern Pantanal wetlands using satellite images. In: INTECOL INTERNATIONAL WETLANDS CONFERENCE, 8., Cuiabá, 2008. Big wetlands, big concerns: abstracts. [Sl.: s.n], 2008. p.196| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Pantanal. |
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| 178. | | ANTUNES, P.; SANTOS, L. G. R. O.; SANTOS, T. M. R. dos; MENEZES, F. S. de; TOMAS, W. M.; FORESTER, J. D.; FERNANDEZ, F. A. S. Mating system of Thrichomys fosteri in the Brazilian Pantanal: spatial patterns indicate promiscuity. Mammalian Biology, v. 100, p. 365-375, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Pantanal. |
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| 179. | | ASADA, M.; TAKEDA, M.; TOMAS, W. M.; PELLEGRIN, A. O.; OLIVEIRA, C. H. S. de; BARBOSA, J. D.; SILVEIRA, J. A. G. da; BRAGA, E. M.; KANEKO, O. Close relationship of Plasmodium sequences detected from South American pampas deer (Ozotoceros bezoarticus) to Plasmodium spp. in North American white-tailed deer. International Journal For Parasitology: parasites and wildlife, v. 7, p. 44-47, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Pantanal. |
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| 180. | | GOMES, E. G.; SANTOS, S. A.; PAULA, E. S.; NOGUEIRA, M. A.; OLIVEIRA, M. D. de; SALIS, S. M.; SORIANO, B. M. A.; TOMAS, W. M. Multidimensional performance assessment of a sample of beef cattle ranches in the Pantanal from a data envelopment analysis perspective. Ciência Rural, v. 53, n. 12, 2023. Título em português: Avaliação multidimensional do desempenho de uma amostra de fazendas de pecuária do Pantanal sob a ótica de modelos de análise envoltória de dados.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 4 |
| Biblioteca(s): Embrapa Pantanal; Embrapa Unidades Centrais. |
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| Registros recuperados : 265 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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