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 Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: November 01, 2016
• End date: June 25, 2020
• Field of knowledge: Agronomical Sciences - Agronomy
• Principal Investigator: Antonio Augusto Franco Garcia
• Grantee: Kaio Olimpio das Graças Dias
• Host 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

(7)

(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)

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; et al. 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. (16/12977-7)

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; et al. 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. (16/12977-7)

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. (16/12977-7)

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.; et al. Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. THEORETICAL AND APPLIED GENETICS, v. 133, n. 2, NOV 2019. (18/00634-3, 16/12977-7)

KRAUSE, MATHEUS DALSENTE; DAS GRACAS DIAS, KAIO OLIMPIO; RIGAL DOS SANTOS, JHONATHAN PEDROSO; DE OLIVEIRA, AMANDA AVELAR; MOREIRA GUIMARAES, LAURO JOSE; PASTINA, MARIA MARTA; ALVES MARGARIDO, GABRIEL RODRIGUES; FRANCO GARCIA, ANTONIO AUGUSTO. Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models. CROP SCIENCE, v. 60, n. 6, p. 3049-3065, NOV-DEC 2020. (16/12977-7)

CHAVARRIA-PEREZ, LOURDES MARIA; GIORDANI, WILLIAN; GRACAS DIAS, KAIO OLIMPIO; COSTA, ZIRLANE PORTUGAL; MASSENA RIBEIRO, CAROLINA ALBUQUERQUE; BENEDETTI, ANDERSON ROBERTO; CAUZ-SANTOS, LUIZ AUGUSTO; PEREIRA, GUILHERME SILVA; BACHEGA FEIJO ROSA, JOAO RICARDO; FRANCO GARCIA, ANTONIO AUGUSTO; et al. Improving yield and fruit quality traits in sweet passion fruit: Evidence for genotype by environment interaction and selection of promising genotypes. PLoS One, v. 15, n. 5, MAY 14 2020. (12/09100-5, 16/12977-7, 10/06702-9, 18/09069-7, 17/04216-9)

DIAS, KAIO O. G.; DOS SANTOS, JHONATHAN P. R.; KRAUSE, MATHEUS D.; PIEPHO, HANS-PETER; GUIMARAES, LAURO J. M.; PASTINA, MARIA M.; GARCIA, ANTONIO A. F.. Leveraging probability concepts for cultivar recommendation in multi-environment trials. THEORETICAL AND APPLIED GENETICS, v. 135, n. 4, p. 15-pg., 2022-02-22. (16/12977-7, 17/03625-2)