Boosting predictive ability of tropical maize hybrids via genotype‐by‐environment interaction under multivariate GBLUP models

Autor: Antonio Augusto Franco Garcia, Matheus Dalsente Krause, Kaio Olímpio das Graças Dias, Maria Marta Pastina, Gabriel Rodrigues Alves Margarido, Jhonathan P. R. dos Santos, Lauro José Moreira Guimarães, Amanda Avelar de Oliveira
Přispěvatelé: 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'.
Rok vydání: 2020
Předmět:
Zdroj: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice)
Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
ISSN: 1435-0653
0011-183X
Popis: 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. Made available in DSpace on 2020-12-22T09:02:13Z (GMT). No. of bitstreams: 1 Boosting-predictive.pdf: 1139997 bytes, checksum: 641636e15f14edc0b42f567b080ea4de (MD5) Previous issue date: 2020
Databáze: OpenAIRE
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