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

Krause, Matheus Dalsente; Dias, Kaio Olímpio das Graças; Santos, José Fortunato Ferreira; Oliveira, Amanda Avelar de; Guimarães, L. J. M.; Pastina, Maria Marta; Margarido, Gabriel Rodrigues Alves; Garcia, Antônio Augusto Franco

doi:10.1002/csc2.20253

Cited by 20 publications

(15 citation statements)

References 67 publications

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“…Agric. v.79, n.2, e20200314, 2022 genetic variance to each site and different covariances between pairs of environments evaluated (Smith et al, 2002;Burgueño et al, 2012;Krause et al, 2020;Oliveira et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, provide subsidies for the genomic prediction study, within will be carried out in a subsequent step, based on the genotyping of the parental lines of the hybrids evaluated in this study. Studies published recently in the literature, involving the prediction of hybrids under different environmental conditions, suggest that the inclusion of the component genotype × environment interaction in genomic prediction models, may improve hybrids predictions if the environmental component is reliable (Krause et al, 2020;Oliveira et al, 2020). The question remains, given that the experiments are very unbalanced, if the component of the interaction to be used in the model will be able to improve its predictive capability, since, as found in this study, the component of the hybrid × environment interaction is very expressive.…”

Section: Discussionmentioning

confidence: 99%

“…Because of this interaction, the responses of the hybrids do not coincide in the diverse environments evaluated. An alternative is to include this effect in the predictive models to obtain more accurate information (Lado et al, 2016;Ferrão et al, 2018;Dias et al, 2018a;Montesinos-López et al, 2019;Krause et al, 2020). The question is whether the hybrid × environment interaction component obtained from highly unbalanced experiments can contribute to the predictive models.…”

Section: Introductionmentioning

confidence: 99%

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Mega-environment analysis of maize breeding data from Brazil

Pereira

Ramalho

Resende

et al. 2022

Sci. agric. (Piracicaba, Braz.)

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The development and recommendation of single cross maize hybrids (SH) to be used in extensive land areas (mega-environments), and in different crop seasons requires many experiments under numerous environmental conditions. The question we asked is if the data from these multi-environment experiments are sufficient to identify the best hybrid combinations. The aim of this study was to critically analyze the phenotype data of experiments of yield, established by a large seed producing company, under a high level of imbalance. Data from evaluation of 2770 SH were used from experiments conducted over four years, involving the first and second crop seasons, in 50 locations of different years and regions of Brazil. Different types of analysis were carried out and genetic and non-genetic components were estimated, with emphasis on the different interactions of the SH with the environments. Results showed that the coincidence of common hybrids in these experiments is normally small. The estimates of the correlations between of the hybrids coinciding in the environments two by two is of low magnitude. The hybrid × crop season interaction was always expressive; however, the interactions of hybrids and other environmental variables were also important. Under these conditions, alternatives were discussed for making with the information obtained from the experiments, can be more efficient on the process to obtain new hybrids by companies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mega-environment analysis of maize breeding data from Brazil

Pereira

Ramalho

Resende

et al. 2022

Sci. agric. (Piracicaba, Braz.)

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“…(2018) and Krause et al. (2020) employed FAMM for a thorough study of the GEI, whereas Mengesha et al. (2019) selected the best genotypes using the FAMM through an AMMI approach.…”

Section: Discussionmentioning

confidence: 99%

Employing factor analytic tools for selecting high‐performance and stable tropical maize hybrids

et al. 2023

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Genotype‐by‐environment interaction (GEI) is a major concern in tropical maize breeding. Here, we used factor analytic mixed models (FAMM) to study GEI in a tropical maize dataset. Our goal was to select high‐performance and stable hybrids using factor analytic selection tools (FAST) derived from FAMM outputs. Because the dataset comprised two different planting seasons, we also investigated differences in the genetic gains between seasonal and general selection (i.e., for both seasons simultaneously). The trials were installed in a lattice design with 36 hybrids containing two replicates and blocks with six plants each. We evaluated 53 hybrids and seven checks in 48 environments, represented by a combination of locations, years and seasons. We fitted the FAMMs with distinct sets of factors. The best‐fitted FAMM was selected considering a parsimony‐explained variance balance. Using the best FAMM for both seasonal and general analyses, we estimated the following selection tools: overall performance (OP$\textit{OP}$); root mean square deviation (RMSD$\textit{RMSD}$), which represents general stability; and responsiveness , which represents specific stability. We selected hybrids based on an index built with OP$\textit{OP}$ and RMSD$\textit{RMSD}$. Five and three hybrids were selected considering both general and seasonal analyses for the first and second season, respectively. Two hybrids were amongst the top eight for both seasonal analyses (i.e., only first season and only second season simultaenously). Higher genetic gains may be obtained by employing FAMM and using FAST in each season rather than analysing both seasons in a joint fashion.

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“…Since then, several efforts have been made to extend those modeling approaches when considering different kernel methods and structures. For example, different G × E approaches to include genomics and large-scale environmental data (enviromics) (Bandeira e Sousa et al, 2017;Costa-Neto et al, 2020;Rogers et al, 2021) using explicit covariates for modeling reaction-norms (Millet et al, 2019) or implicit covariates derived from multivariate structures (e.g., Dias et al, 2018;Krause et al, 2020).…”

Section: Modeling Genotype × Environment Interaction (G × E) In Gp Finding Novel Kernel Methods and Modeling Structures For G × Ementioning

confidence: 99%

Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

et al. 2021

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The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

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

Cited by 20 publications

References 67 publications

Mega-environment analysis of maize breeding data from Brazil

Mega-environment analysis of maize breeding data from Brazil

Employing factor analytic tools for selecting high‐performance and stable tropical maize hybrids

Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

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