| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Milho e Sorgo. |
|
Data corrente: |
24/07/2018 |
|
Data da última atualização: |
05/02/2019 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. |
|
Afiliação: |
Kaio Olímpio das Graças Dias, Universidade Federal de Lavras; Salvador Alejandro Gezan, School of Forest Resources & Conservation, University of Florida, Gainesville.; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; Alireza Nazarian, School of Forest Resources & Conservation, University of Florida, Gainesville.; Luciano da Costa e Silva, JMP Division, SAS Institute Inc., Cary.; SIDNEY NETTO PARENTONI, CNPMS; PAULO EVARISTO DE O GUIMARAES, CNPMS; Carina de Oliveira Anoni, Escola Superior de Agricultura “Luiz de Queiroz”; José Maria Villela Pádua, Universidade Federal de Lavras; MARCOS DE OLIVEIRA PINTO, CNPMS; ROBERTO WILLIANS NODA, CNPMS; Carlos Alexandre Gomes Ribeiro, Universidade Federal de Viçosa; JURANDIR VIEIRA DE MAGALHAES, CNPMS; Antonio Augusto Franco Garcia, Escola Superior de Agricultura “Luiz de Queiroz”; João Cândido de Souza, Universidade Federal de Lavras; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS. |
|
Título: |
Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. |
|
Ano de publicação: |
2018 |
|
Fonte/Imprenta: |
Heredity, London, v. 121, n. 1, p. 24-37, 2018. |
|
DOI: |
10.1038/s41437-018-0053-6 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids. MenosBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS a... Mostrar Tudo |
|
Thesagro: |
Milho; Resistência a Seca. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 03081naa a2200349 a 4500
001 2093500
005 2019-02-05
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1038/s41437-018-0053-6$2DOI
100 1 $aDIAS, K. O. das G.
245 $aImproving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.$h[electronic resource]
260 $c2018
520 $aBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.
650 $aMilho
650 $aResistência a Seca
700 1 $aGEZAN, S. A.
700 1 $aGUIMARÃES, C. T.
700 1 $aNAZARIAN, A.
700 1 $aSILVA, L. da C. e
700 1 $aPARENTONI, S. N.
700 1 $aGUIMARAES, P. E. de O.
700 1 $aANONI, C. de O.
700 1 $aPÁDUA, J. M. V.
700 1 $aPINTO, M. de O.
700 1 $aNODA, R. W.
700 1 $aRIBEIRO, C. A. G.
700 1 $aMAGALHAES, J. V. de
700 1 $aGARCIA, A. A. F.
700 1 $aSOUZA, J. C. de
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
773 $tHeredity, London$gv. 121, n. 1, p. 24-37, 2018.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 127 | |
| 11. |  | SOUZA, J. C. de; WADA, A.; FREITAS, H.; SANTOS, S. A.; FREITAS, J. A. de. Comportamento. In: SANTOS, S. A.; SALIS, S. M. de; COMASTRI FILHO, J. A. (Ed.). Cavalo pantaneiro: rústico por natureza. Brasília, DF: Embrapa, 2016. p. 279-311.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Pantanal. |
|   |
| 12. | | CARDOSO, G. A.; SENA, M. R.; SOUZA, J. C. de; PACHECO, C. A. P. Comportamento de variedades de milho em Lavras - MG. In: CONGRESSO NACIONAL DE MILHO E SORGO, 27.; SIMPÓSIO BRASILEIRO SOBRE A LAGARTA-DO-CARTUCHO, SPODOPTERA FRUGIPERDA, 3.; WORKSHOP SOBRE MANEJO E ETIOLOGIA DA MANCHA BRANCA DO MILHO, 2008, Londrina. Agroenergia, produção de alimentos e mudanças climáticas: desafios para milho e sorgo: trabalhos e palestras. [Londrina]: IAPAR; [Sete Lagoas]: Embrapa Milho e Sorgo, 2008. 4 p.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
| |
| 13. | | CARDOSO, G. A.; SENA, M. R.; SOUZA, J. C. de; PACHECO, C. A. P. Comportamento de variedades de milho em Lavras-MG. In: CONGRESSO NACIONAL DE MILHO E SORGO, 27.; SIMPOSIO BRASILEIRO SOBRE A LAGARTA-DO-CARTUCHO, SPODOPTERA FRUGIPERDA, 3.; WORKSHOP SOBRE MANEJO E ETIOLOGIA DA MANCHA BRANCA DO MILHO, 2008, Londrina. Agroenergia, produção de alimentos e mudanças climáticas: desafios para milho e sorgo: trabalhos e palestras. [Londrina]: IAPAR; [Sete Lagoas]: Embrapa Milho e Sorgo, 2008. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Milho e Sorgo. |
|  |
| Registros recuperados : 127 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
