Data/hora: 14/06/2024 00:39:02

Biblioteca(s): 	Embrapa Milho e Sorgo.	
Data corrente: 	20/08/2020	
Data da última atualização: 	20/12/2020	
Autoria: 	KRAUSE, M. D.; DIAS, K. O. das G.; SANTOS, J. P. R. dos; OLIVEIRA, A. A. de; GUIMARAES, L. J. M.; PASTINA, M. M.; MARGARIDO, G. R. A.; GARCIA, A. A. F.	
Afiliação: 	Matheus Dalsente Krause, Iowa State University; Kaio Olímpio das Graças Dias, Escola Superior de Agricultura "Luiz de Queiroz"; Jhonathan Pedroso Rigal dos Santos, Escola Superior de Agricultura "Luiz de Queiroz"; Amanda Avelar de Oliveira, Escola Superior de Agricultura "Luiz de Queiroz"; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; Gabriel Rodrigues Alves Margarido, Escola Superior de Agricultura "Luiz de Queiroz"; Antonio Augusto Franco Garcia, Escola Superior de Agricultura "Luiz de Queiroz".	
Título: 	Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Crop Science, v. 60, n. 6, p. 3049-3065, 2020.	
DOI: 	10.1002/csc2.20253	
Idioma: 	Inglês	
Conteúdo: 	Genomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield.	
Thesagro: 	Genótipo; Melhoramento Genético Vegetal; Milho.	
Categoria do assunto: 	G Melhoramento Genético	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/219490/1/Boosting-predictive.pdf	
Registro original: 	Embrapa Milho e Sorgo (CNPMS)	
Exemplares: 	CNPMS - (29332-1) - UPC - AP - DD -  -  -  - DIS