Genomic prediction applied to multiple traits and environments in second season maize hybrids

Oliveira, Amanda Avelar de; Resende, M. D. V. de; Ferrão, Luís Felipe Ventorim; Amadeu, Rodrigo R.; Guimarães, L. J. M.; Guimarães, C. T.; Pastina, Maria Marta; Margarido, Gabriel Rodrigues Alves

doi:10.1038/s41437-020-0321-0

Cited by 20 publications

(24 citation statements)

References 60 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%

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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%

Mega-environment analysis of maize breeding data from Brazil

Pereira

Ramalho

Resende

et al. 2022

Sci. agric. (Piracicaba, Braz.)

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“…(2019) Maize Multi-trait models were always better than their univariate counterparts in a single testing environment. They also improved predicting the performance of hybrids not yet evaluated in any environment de Oliveira et al. (2020) Soybean If grain yield weighs the selection for superior genotypes, then both single-trait and multi-trait genomic predictions led to significant improvements when some genotypes were fully or partially tested, though single-trait model got the best results Persa et al.…”

Section: Biotechnology-led Approaches To Minimize Tradeoff In Plant Breedingmentioning

confidence: 99%

Mitigating tradeoffs in plant breeding

Dwivedi¹,

Reynolds

Ortíz

2021

iScience

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Summary Tradeoffs among plant traits help maintain relative fitness under unpredictable conditions and maximize reproductive success. However, modifying tradeoffs is a breeding challenge since many genes of minor effect are involved. The intensive crosstalk and fine-tuning between growth and defense responsive phytohormones via transcription factors optimizes growth, reproduction, and stress tolerance. There are regulating genes in grain crops that deploy diverse functions to overcome tradeoffs, e.g., miR-156- IPA1 regulates crosstalk between growth and defense to achieve high disease resistance and yield, while OsALDH2B1 loss of function causes imbalance among defense, growth, and reproduction in rice. GNI-A1 regulates seed number and weight in wheat by suppressing distal florets and altering assimilate distribution of proximal seeds in spikelets. Knocking out ABA-induced transcription repressors (AITRs) enhances abiotic stress adaptation without fitness cost in Arabidopsis . Deploying AITRs homologs in grain crops may facilitate breeding. This knowledge suggests overcoming tradeoffs through breeding may expose new ones.

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“…The presence of both genotype-envirotype and envirotype-phenotype covariances might explain the gains in the predictive ability due to the use of multi-environment GP models in contrast to singleenvironment GP models (Bandeira e Souza et de Oliveira et al, 2020) and why deep learning approaches have successfully captured intrinsic G×E patterns and translated them into gains in accuracy (Montesinos-López et al, 2018;Crossa et al, 2019;Cuevas et al, 2019) . Conversely, this also might explain the need to incorporate secondary sources of information in the prediction of grain yields across multiple environments (Westhues et al, 2017;Ly et al, 2018;Millet et al, 2019;Costa-Neto et al, 2021a;2021b;Jarquín et al, 2020) , as well as the possible limitations of CGM approaches contrasting scenarios differing from those targeted near-iso conditions of CGM calibration (e.g., Cooper et al, 2016;Messina et al, 2018).…”

Section: Why Are Enviromics Important For Multi-environment Genomic Prediction?mentioning

confidence: 99%

Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity

Costa‐Neto

Crossa

Fritsche‐Neto

2021

Preprint

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Quantitative genetics states that phenotypic variation is a consequence of genetic and environmental factors and their subsequent interaction. Here, we present an enviromic assembly approach, which includes the use of ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as E-GP). We propose that the quality of an environment is defined by the core of environmental typologies (envirotype) and their frequencies, which describe different zones of plant adaptation. From that, we derive markers of environmental similarity cost-effectively. Combined with the traditional genomic sources (e.g., additive and dominance effects), this approach may better represent the putative phenotypic variation across diverse growing conditions (i.e., phenotypic plasticity). Additionally, we couple a genetic algorithm scheme to design optimized multi-environment field trials (MET), combining enviromic assembly and genomic kinships to provide in-silico realizations of the future genotype-environment combinations that must be phenotyped in the field. As a proof-of-concept, we highlight E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity using optimized phenotyping efforts. Our approach was tested using two non-conventional cross-validation schemes to better visualize the benefits of enviromic assembly in sparse experimental networks. Results on tropical maize show that E-GP outperforms benchmark GP in all scenarios and cases tested. We show that for training accurate GP models, the genotype-environment combinations' representativeness is more critical than the MET size. Furthermore, we discuss theoretical backgrounds underlying how the intrinsic envirotype-phenotype covariances within the phenotypic records of (MET) can impact the accuracy of GP and limits the potentialities of predictive breeding approaches. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

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Genomic prediction applied to multiple traits and environments in second season maize hybrids

Cited by 20 publications

References 60 publications

Mega-environment analysis of maize breeding data from Brazil

Mega-environment analysis of maize breeding data from Brazil

Mitigating tradeoffs in plant breeding

Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity

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