Data/hora: 23/09/2024 10:34:12

Biblioteca(s): 	Embrapa Milho e Sorgo.	
Data corrente: 	06/08/2020	
Data da última atualização: 	07/05/2021	
Autoria: 	OLIVEIRA, A. A. de; RESENDE JÚNIOR, M. F. R.; FERRÃO, L. F. V.; AMADEU, R. R.; GUIMARAES, L. J. M.; GUIMARÃES, C. T.; PASTINA, M. M.; MARGARIDO, G. R. A.	
Afiliação: 	Amanda Avelar de Oliveira, Escola Superior de Agricultura "Luiz de Queiroz"; Marcio F. R. Resende Júnior, University of Florida; Luís Felipe Ventorim Ferrão, University of Florida; Rodrigo Rampazo Amadeu, University of Florida; LAURO JOSE MOREIRA GUIMARAES, CNPMS; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS; Gabriel Rodrigues Alves Margarido, Escola Superior de Agricultura "Luiz de Queiroz'.	
Título: 	Genomic prediction applied to multiple traits and environments in second season maize hybrids.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Heredity, v. 125, n. 1/2, p. 60-72, 2020.	
DOI: 	https://doi.org/10.1038/s41437-020-0321-0	
Idioma: 	Inglês	
Conteúdo: 	Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.	
Thesagro: 	Genética; Milho; Seleção Genética.	
Categoria do assunto: 	G Melhoramento Genético	
Registro original: 	Embrapa Milho e Sorgo (CNPMS)	
Exemplares: 	CNPMS - (29314-1) - UPC - AP - DD -  -  -  - DIS