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Optimizing genomic-enabled prediction in small-scale maize hybrid breeding programs: a roadmap review

Creator: Fritsche-Neto, R.
Creator: Galli, G.
Creator: Borges, K.L.R.
Creator: Costa-Neto, G.
Creator: Alves, F.C.
Creator: Sabadin, F.
Creator: Lyra, D.H.
Creator: Morais, P.P.P.
Creator: Braatz de Andrade, L.R.
Creator: Granato, I.
Creator: Crossa, J.
Year: 2021
URI: https://hdl.handle.net/10883/21594
Language: English
Publisher: Frontiers
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Switzerland
Volume: 12
DOI: 10.3389/fpls.2021.658267
Keywords: Accuracy
Keywords: Quantitative Genomics
Keywords: R Packages
Keywords: Genomic Selection
Keywords: Breeding Schemes
Description: 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.
Agrovoc: QUANTITATIVE GENETICS
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: BREEDING PROGRAMMES
Agrovoc: COMPUTER APPLICATIONS
Related Datasets: https://data.mendeley.com/research-data/?page=0&search=%22Roberto%20Fritsche%20Neto%22
ISSN: 1664-462X
Journal: Frontiers in Plant Science
Article number: 658267


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    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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