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| Registros recuperados : 643 | |
| 12. |  | RESENDE, M. D. V. de. Matemática. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 7-55.| Biblioteca(s): Embrapa Florestas. |
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| 16. | | RESENDE, M. D. V. de. Modelos BLUP univariados multiefeitos. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 226-256.| Biblioteca(s): Embrapa Florestas. |
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| 17. | | RESENDE, M. D. V. de. Modelos computacionais BLUP. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 206-225.| Biblioteca(s): Embrapa Florestas. |
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| 19. | | RESENDE, M. D. V. de. Modelos lineares. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 130-205.| Biblioteca(s): Embrapa Florestas. |
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| 20. | | RESENDE, M. D. V. de. Modelos multivariados. In: RESENDE, M. D. V. de; SILVA, F. F. e; AZEVEDO, C. F. Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), inferência bayesiana, regressão, aleatória, seleção genômica, QTL, GWAS, estatística espacial e temporal, competição, sobrevivência. Viçosa, MG: UFV, 2014. p. 257-311.| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
06/12/2023 |
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Data da última atualização: |
06/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SILVA, C. M. e; MEZZOMO, H. C.; ALVES, R. S.; RESENDE, M. D. V. de; NARDINO, M. |
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Afiliação: |
CAIQUE MACHADO E SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA; HENRIQUE CALETTI MEZZOMO, UNIVERSIDADE FEDERAL DE VIÇOSA; RODRIGO SILVA ALVES, INSTITUTO NACIONAL DE CIÊNCIA E TECNOLOGIADO CAFÉ; MARCOS DEON VILELA DE RESENDE, CNPCa; MAICON NARDINO, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Optimizing selection of wheat genotypes through simulatedindividual BLUP and modified simulated individual BLUP. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Agronomy Journal, v. 115, n. 3, p. 1237-1247, 2023. |
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Idioma: |
Inglês |
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Conteúdo: |
The early stages of wheat breeding programs often contain large number of plants in segregating populations that must be selected. Selecting the best populations and plants in those stages is not an easy task, making it necessary to use robust methodologies that facilitate the selection process. The goals of this work were to evaluate different methods for the selection of individual plants in early wheat breeding stages, compare the efficiency of each method, and suggest the most suitable one for implementation in wheat breeding programs. An experiment with 56 F2 segregant populations derived from a complete diallel, together with eight parents, was conducted and evaluated for grain yield (g pL−1). Analysis of variance and mixed model analysis were applied to obtain the genetic parameters and predict genotypic values. Four selection strategies were designed: mass selection and other three mixed model-based methods (individual best linear unbiased prediction [BLUPI]; simulated individual best linear unbiased prediction [BLUPIS]; and modified simulated individual best linear unbiased [BLPISM]). The genotypic variability among and within populations allowed the selection of superior genotypes. Simple linear regression analysis and Spearman correlation analysis allowed to conclude that BLUPIS and BLUPISM are equivalent to BLUPI. The BLUPIS and BLUPISM should be used because they easily indicate for the selection of plants in populations with positive genetic effects, dismissing the evaluation of individual plants. MenosThe early stages of wheat breeding programs often contain large number of plants in segregating populations that must be selected. Selecting the best populations and plants in those stages is not an easy task, making it necessary to use robust methodologies that facilitate the selection process. The goals of this work were to evaluate different methods for the selection of individual plants in early wheat breeding stages, compare the efficiency of each method, and suggest the most suitable one for implementation in wheat breeding programs. An experiment with 56 F2 segregant populations derived from a complete diallel, together with eight parents, was conducted and evaluated for grain yield (g pL−1). Analysis of variance and mixed model analysis were applied to obtain the genetic parameters and predict genotypic values. Four selection strategies were designed: mass selection and other three mixed model-based methods (individual best linear unbiased prediction [BLUPI]; simulated individual best linear unbiased prediction [BLUPIS]; and modified simulated individual best linear unbiased [BLPISM]). The genotypic variability among and within populations allowed the selection of superior genotypes. Simple linear regression analysis and Spearman correlation analysis allowed to conclude that BLUPIS and BLUPISM are equivalent to BLUPI. The BLUPIS and BLUPISM should be used because they easily indicate for the selection of plants in populations with positive genetic effects, dismissing... Mostrar Tudo |
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Thesaurus NAL: |
Genotype; Plant breeding; Wheat. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02152naa a2200205 a 4500
001 2159224
005 2023-12-06
008 2023 bl uuuu u00u1 u #d
100 1 $aSILVA, C. M. e
245 $aOptimizing selection of wheat genotypes through simulatedindividual BLUP and modified simulated individual BLUP.$h[electronic resource]
260 $c2023
520 $aThe early stages of wheat breeding programs often contain large number of plants in segregating populations that must be selected. Selecting the best populations and plants in those stages is not an easy task, making it necessary to use robust methodologies that facilitate the selection process. The goals of this work were to evaluate different methods for the selection of individual plants in early wheat breeding stages, compare the efficiency of each method, and suggest the most suitable one for implementation in wheat breeding programs. An experiment with 56 F2 segregant populations derived from a complete diallel, together with eight parents, was conducted and evaluated for grain yield (g pL−1). Analysis of variance and mixed model analysis were applied to obtain the genetic parameters and predict genotypic values. Four selection strategies were designed: mass selection and other three mixed model-based methods (individual best linear unbiased prediction [BLUPI]; simulated individual best linear unbiased prediction [BLUPIS]; and modified simulated individual best linear unbiased [BLPISM]). The genotypic variability among and within populations allowed the selection of superior genotypes. Simple linear regression analysis and Spearman correlation analysis allowed to conclude that BLUPIS and BLUPISM are equivalent to BLUPI. The BLUPIS and BLUPISM should be used because they easily indicate for the selection of plants in populations with positive genetic effects, dismissing the evaluation of individual plants.
650 $aGenotype
650 $aPlant breeding
650 $aWheat
700 1 $aMEZZOMO, H. C.
700 1 $aALVES, R. S.
700 1 $aRESENDE, M. D. V. de
700 1 $aNARDINO, M.
773 $tAgronomy Journal$gv. 115, n. 3, p. 1237-1247, 2023.
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