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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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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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| Registros recuperados : 103 | |
| 9. |  | FASIABEN, M. do C. R.; LIMA, H. P. de; OLIVEIRA, S. R. de M.; ALMEIDA, M. M. T. B.; OLIVEIRA, O. C. de; MORAES, A. S. Diferenciação e caracterização de municípios produtores de bovinos de corte do Bioma Pampa segundo o Censo Agropecuário 2017. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 14., 2023, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023. p. 135-142. SBIAgro 2023.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Soja. |
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| 10. | | FASIABEN, M. do C. R.; LIMA, H. P. de; OLIVEIRA, S. R. de M.; ALMEIDA, M. M. T. B.; OLIVEIRA, O. C. de; MORAES, A. S. Tipologia de municípios produtores de bovinos de corte no Bioma Amazônia a partir do Censo Agropecuário 2017. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 14., 2023, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023. p. 143-150. SBIAgro 2023.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Soja. |
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| 11. | | FASIABEN, M. do C. R.; OLIVEIRA, S. R. de M.; ALMEIDA, M. M. T. B.; LIMA, H. P. de. Padrões de produção da bovinocultura de corte no bioma cerrado segundo o Censo Agropecuário 2017. In: CONGRESSO DA SOCIEDADE BRASILEIRA DE ECONOMIA, ADMINISTRAÇÃO E SOCIOLOGIA RURAL, 61., 2023, Piracicaba. Agropecuária do futuro: tecnologia, sustentabilidade e a segurança alimentar: anais. Piracicaba: ESALQ/USP, 2023. 15 p. SOBER 2023. GT07.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 12. | | FASIABEN, M. do C. R.; OLIVEIRA, S. R. de M.; ALMEIDA, M. M. T. B.; OLIVEIRA, O. C. de; LIMA, H. P. de. Tipologia de municípios produtores de bovinos de corte no bioma Amazônia. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 12., 2019, Indaiatuba. Anais... Ponta Grossa: SBIAGRO, 2019. p. 392-401. Organizadores: Maria Fernanda Moura, Jayme Garcia Arnal Barbedo, Alaine Margarete Guimarães, Valter Castelhano de Oliveira. SBIAgro 2019.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 13. | | LAMPERT, V. do N.; FASIABEN, M. do C. R.; ABREU, U. G. P. de; OSMARI, E. K.; OLIVEIRA, S. R. de M.; ALMEIDA, M. M. T. B.; OLIVEIRA, O. C. de; LIMA, H. P. de. Identificação de padrões de produção e intensificação tecnológica de municípios produtores de bovinos de corte no bioma Pampa. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 12., 2019, Indaiatuba. Anais... Indaiatuba: FATEC; Campinas: Embrapa Informática Agropecuária; Ponta Grossa: UEPG, 2019. p. 447-457. SBIAGRO.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Pantanal. |
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| 14. | | LAMPERT, V. do N.; FASIABEN, M. do C. R.; ABREU, U. G. P. de; OSMARI, E. K.; OLIVEIRA, S. R. de M.; ALMEIDA, M. M. T. B.; OLIVEIRA, O. C. de; LIMA, H. P. de. Identificação de padrões de produção e intensificação tecnológica de municípios produtores de bovinos de corte no bioma Pampa. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 12., 2019, Indaiatuba. Anais... Ponta Grossa: SBIAGRO, 2019. p. 459-468. Organizadores: Maria Fernanda Moura, Jayme Garcia Arnal Barbedo, Alaine Margarete Guimarães, Valter Castelhano de Oliveira. SBIAgro 2019.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pecuária Sul. |
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| Registros recuperados : 103 | |
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Registro completo
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Biblioteca(s): |
Catálogo Coletivo de Periódicos Embrapa; Embrapa Amazônia Oriental. |
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Identificador: |
1266 |
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Data corrente: |
09/05/2002 |
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Data da última atualização: |
20/10/2015 |
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Código do título: |
4700007 |
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ISSN: |
0373-7896 |
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Código CCN: |
098511-2 |
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Título e Subtítulo: |
BERICHTE DES GEOBOTANISCHEN INSTITUTES DER EIBGENOESSISCHEN HOCHSCHULE STIFTUNG RUEBEL |
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Título anterior: |
BERICHT UBER DAS GEOBOTANISCHE FORCHUNGSINSTITUL RUBEL IN ZURICH |
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Entidade: |
Institut der Eide Techn. Hochschule |
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Local de publicação: |
Zurich, Suica |
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Periodicidade: |
irregular |
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Inicio de publicação: |
1960 |
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Coleções da unidade: |
Embrapa Amazônia Oriental
1960 31; 1961 32; 1962 33; 1963 34; 1965 36; 1967 37; 1968 38; 1969 39; 1970 40; 1972 41
Classificação: 581.05B31 |
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