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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
26/07/2023 |
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Data da última atualização: |
02/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MORAIS JUNIOR, O. P.; MÜLLER, B. S. F.; VALDISSER, P. A. M. R.; BRONDANI, C.; VIANELLO, R. P. |
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Afiliação: |
ODILON PEIXOTO MORAIS JUNIOR, UNIVERSIDADE FEDERAL DE GOIÁS; BÁRBARA S. F. MÜLLER, UNIVERSITY OF FLORIDA, Gainesville, FL; PAULA ARIELLE M RIBEIRO VALDISSER, CNPAF; CLAUDIO BRONDANI, CNPAF; ROSANA PEREIRA VIANELLO, CNPAF. |
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Título: |
Genomic prediction for drought tolerance using multienvironment data in a common bean (Phaseolus vulgaris) breeding program. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Crop Science, v. 63, n. 4, p. 2145-2161, July/Aug. 2023. |
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ISSN: |
0011-183X |
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DOI: |
https://doi.org/10.1002/csc2.21000 |
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Idioma: |
Inglês |
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Conteúdo: |
This work evaluated the efficiency of different genomic prediction (GP) methods in a diverse Mesoamerican panel of 339 common bean accessions, genotyped with 3398 SNP markers. Field experiments were carried out for three consecutive years, with adequate water supply (non-stress?NS) and water restriction imposition (water-stress?WS), analyzing seed weight (SW) and grain yield (GY). Two methods to predict the accuracies (r?g) were adopted (GBLUP and Bayes) and also considered the environmental variation (GBLUP-based reaction norm model). Similar accuracies were observed for both methods. For GY, the highest r?g were detected under NS (rgg = 0.49) in 2016 (r?g = 0.49) and in the joint analysis for the WS condition (rgg = 0.33), both for models using local landraces. For SW under NS, the rgg was higher for the elite lines (rgg = 0.72), whereas for WS, the rgg dropped considerably, ranging from 0.45 to 0.61 for the joint analysis, considering the landraces and all samples, respectively. For GY and SW, under NS, the rgg using both models increased with increasing number of SNPs, until reaching a plateau of 800 and 300 SNPs, respectively. Increasing the training population (TP) size resulted in greater accuracy. Taking in account the Genotype × Environment, the multienvironment model performed better especially for more complex traits (GY/NS: rgg = 0.32). The GP approach has great potential to help commercial bean breeding programs improving the performance of target quantitative traits. MenosThis work evaluated the efficiency of different genomic prediction (GP) methods in a diverse Mesoamerican panel of 339 common bean accessions, genotyped with 3398 SNP markers. Field experiments were carried out for three consecutive years, with adequate water supply (non-stress?NS) and water restriction imposition (water-stress?WS), analyzing seed weight (SW) and grain yield (GY). Two methods to predict the accuracies (r?g) were adopted (GBLUP and Bayes) and also considered the environmental variation (GBLUP-based reaction norm model). Similar accuracies were observed for both methods. For GY, the highest r?g were detected under NS (rgg = 0.49) in 2016 (r?g = 0.49) and in the joint analysis for the WS condition (rgg = 0.33), both for models using local landraces. For SW under NS, the rgg was higher for the elite lines (rgg = 0.72), whereas for WS, the rgg dropped considerably, ranging from 0.45 to 0.61 for the joint analysis, considering the landraces and all samples, respectively. For GY and SW, under NS, the rgg using both models increased with increasing number of SNPs, until reaching a plateau of 800 and 300 SNPs, respectively. Increasing the training population (TP) size resulted in greater accuracy. Taking in account the Genotype × Environment, the multienvironment model performed better especially for more complex traits (GY/NS: rgg = 0.32). The GP approach has great potential to help commercial bean breeding programs improving the performance of target quantitative t... Mostrar Tudo |
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Thesagro: |
Feijão; Melhoramento Genético Vegetal; Phaseolus Vulgaris; Resistência a Seca. |
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Thesaurus Nal: |
Beans; Breeding and Genetic Improvement; Drought tolerance; Genomics. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02428naa a2200289 a 4500
001 2155303
005 2023-08-02
008 2023 bl uuuu u00u1 u #d
022 $a0011-183X
024 7 $ahttps://doi.org/10.1002/csc2.21000$2DOI
100 1 $aMORAIS JUNIOR, O. P.
245 $aGenomic prediction for drought tolerance using multienvironment data in a common bean (Phaseolus vulgaris) breeding program.$h[electronic resource]
260 $c2023
520 $aThis work evaluated the efficiency of different genomic prediction (GP) methods in a diverse Mesoamerican panel of 339 common bean accessions, genotyped with 3398 SNP markers. Field experiments were carried out for three consecutive years, with adequate water supply (non-stress?NS) and water restriction imposition (water-stress?WS), analyzing seed weight (SW) and grain yield (GY). Two methods to predict the accuracies (r?g) were adopted (GBLUP and Bayes) and also considered the environmental variation (GBLUP-based reaction norm model). Similar accuracies were observed for both methods. For GY, the highest r?g were detected under NS (rgg = 0.49) in 2016 (r?g = 0.49) and in the joint analysis for the WS condition (rgg = 0.33), both for models using local landraces. For SW under NS, the rgg was higher for the elite lines (rgg = 0.72), whereas for WS, the rgg dropped considerably, ranging from 0.45 to 0.61 for the joint analysis, considering the landraces and all samples, respectively. For GY and SW, under NS, the rgg using both models increased with increasing number of SNPs, until reaching a plateau of 800 and 300 SNPs, respectively. Increasing the training population (TP) size resulted in greater accuracy. Taking in account the Genotype × Environment, the multienvironment model performed better especially for more complex traits (GY/NS: rgg = 0.32). The GP approach has great potential to help commercial bean breeding programs improving the performance of target quantitative traits.
650 $aBeans
650 $aBreeding and Genetic Improvement
650 $aDrought tolerance
650 $aGenomics
650 $aFeijão
650 $aMelhoramento Genético Vegetal
650 $aPhaseolus Vulgaris
650 $aResistência a Seca
700 1 $aMÜLLER, B. S. F.
700 1 $aVALDISSER, P. A. M. R.
700 1 $aBRONDANI, C.
700 1 $aVIANELLO, R. P.
773 $tCrop Science$gv. 63, n. 4, p. 2145-2161, July/Aug. 2023.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
20/01/2009 |
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Data da última atualização: |
21/07/2014 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
SHIMIZU, J. Y.; SEBBENN, A. M. |
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Afiliação: |
Jarbas Yukio Shimizu, Pesquisador aposentado da Embrapa Florestas; Alexandre Magno Sebbenn, Instituto Florestal de São Paulo. |
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Título: |
Espécies de Pinus na silvicultura brasileira. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
In: SHIMIZU, J. Y. (Ed.). Pínus na silvicultura brasileira. Colombo: Embrapa Florestas, 2008. |
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Páginas: |
p. 49-74 |
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Idioma: |
Português |
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Thesagro: |
Espécie; Madeira; Silvicultura. |
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Thesaurus NAL: |
Pinus. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00504naa a2200181 a 4500
001 1315394
005 2014-07-21
008 2008 bl uuuu u00u1 u #d
100 1 $aSHIMIZU, J. Y.
245 $aEspécies de Pinus na silvicultura brasileira.
260 $c2008
300 $ap. 49-74
650 $aPinus
650 $aEspécie
650 $aMadeira
650 $aSilvicultura
700 1 $aSEBBENN, A. M.
773 $tIn: SHIMIZU, J. Y. (Ed.). Pínus na silvicultura brasileira. Colombo: Embrapa Florestas, 2008.
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