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Genomic selection implementation in maize using a statistical-genetics model that accounts for genotype-by-environment interaction, additive and non-additive genetic effects

Grant number: 16/12977-7
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): November 01, 2016
Effective date (End): June 25, 2020
Field of knowledge:Agronomical Sciences - Agronomy
Principal Investigator:Antonio Augusto Franco Garcia
Grantee:Kaio Olimpio das Graças Dias
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Associated scholarship(s):18/00634-3 - Genomic prediction using multi-year data: a case study in a hybrid maize breeding program, BE.EP.PD

Abstract

Once reduction of the costs of genotyping technologies have been occurred, the use of genomic selection for the improvement of quantitative traits, is a technology that has been applied to increase the genetic gains and reduce costs of phenotyping in breeding programs. In genomic selection, phenotypic data arising from breeding programs in multiple environments trials maybe used for the composition of training populations. To this end, statistical-genetics model that accounts for genotype-by-environment interaction need to be used. Furthermore, in species with high level of heterosis, such as maize, it is appropriate that the genomic selection models consider not only additive genetic effects, but also the non-additive genetic effects (dominance and epistatic) to the prediction of genotypic values of hybrids. In this context, this project goal is to implement genomic selection strategies in a maize breeding program using data from single hybrids evaluated in multiple environments trials. For this, a statistical-genetics model that accounts for genotype-by-environment interaction, additive and non-additive genetic effects will be proposed; and the accuracy of the proposed model for the prediction of the genotypic values of single hybrids will be evaluated in multiple environments trials. The models proposed in this project and also the results achieved will be of interest to plant breeding programs of other species, that uses multiple environments trials for phenotyping, and also use hybrids as a way to explore heterosis. (AU)

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DIAS, K. O. G.; PIEPHO, H. P.; GUIMARAES, L. J. M.; GUIMARAES, P. E. O.; PARENTONI, S. N.; PINTO, M. O.; NODA, R. W.; MAGALHAES, V, J.; GUIMARAES, C. T.; GARCIA, A. A. F.; PASTINA, M. M. Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. THEORETICAL AND APPLIED GENETICS, NOV 2019. Web of Science Citations: 0.
DAS GRACAS DIAS, KAIO OLIMPIO; GEZAN, SALVADOR ALEJANDRO; GUIMARAES, CLAUDIA TEIXEIRA; NAZARIAN, ALIREZA; DA COSTA E SILVA, LUCIANO; PARENTONI, SIDNEY NETTO; DE OLIVEIRA GUIMARAES, PAULO EVARISTO; ANONI, CARINA DE OLIVEIRA; VILLELA PADUA, JOSE MARIA; PINTO, MARCOS DE OLIVEIRA; NODA, ROBERTO WILLIANS; GOMES RIBEIRO, CARLOS ALEXANDRE; DE MAGALHAES, JURANDIR VIEIRA; FRANCO GARCIA, ANTONIO AUGUSTO; DE SOUZA, JOAO CANDIDO; MOREIRA GUIMARAES, LAURO JOSE; PASTINA, MARIA MARTA. Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. HEREDITY, v. 121, n. 1, p. 24-37, JUL 2018. Web of Science Citations: 5.
FERNANDES, SAMUEL B.; DIAS, KAIO O. G.; FERREIRA, DANIEL F.; BROWN, PATRICK J. Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum. THEORETICAL AND APPLIED GENETICS, v. 131, n. 3, p. 747-755, MAR 2018. Web of Science Citations: 19.
DAS GRACAS DIAS, KAIO OLIMPIO; GEZAN, SALVADOR ALEJANDRO; GUIMARAES, CLAUDIA TEIXEIRA; PARENTONI, SIDNEY NETTO; DE OLIVEIRA GUIMARAES, PAULO EVARISTO; CARNEIRO, NEWTON PORTILHO; PORTUGAL, ARLEY FIGUEIREDO; BASTOS, EDSON ALVES; CARDOSO, MILTON JOSE; ANONI, CARINA DE OLIVEIRA; DE MAGALHAES, JURANDIR VIEIRA; DE SOUZA, JOAO CANDIDO; MOREIRA GUIMARAES, LAURO JOSE; PASTINA, MARIA MARTA. Estimating Genotype X Environment Interaction for and Genetic Correlations among Drought Tolerance Traits in Maize via Factor Analytic Multiplicative Mixed Models. CROP SCIENCE, v. 58, n. 1, p. 72-83, JAN-FEB 2018. Web of Science Citations: 4.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.