| 000 | 03585nab|a22004097a|4500 | ||
|---|---|---|---|
| 999 |
_c64026 _d64018 |
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| 001 | 64026 | ||
| 003 | MX-TxCIM | ||
| 005 | 20240919020953.0 | ||
| 008 | 20217s2021||||sz |||p|op||||00||0|eng|d | ||
| 022 | _a1664-462X (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.3389/fpls.2021.658267 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aFritsche-Neto, R. _96507 |
|
| 245 | 1 | 0 |
_aOptimizing genomic-enabled prediction in small-scale maize hybrid breeding programs : _ba roadmap review |
| 260 |
_aSwitzerland : _bFrontiers, _c2021. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aThe 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. | ||
| 546 | _aText in English | ||
| 650 | 7 |
_aQuantitative genetics _2AGROVOC _91233 |
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| 650 | 7 |
_aMarker-assisted selection _2AGROVOC _910737 |
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| 650 | 7 |
_aBreeding programmes _2AGROVOC _921704 |
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| 700 | 1 |
_aGalli, G. _98650 |
|
| 700 | 1 |
_aBorges, K.L.R. _921705 |
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| 700 | 1 |
_915939 _aCosta-Neto, G. _8001712813 _gGenetic Resources Program |
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| 700 | 1 |
_aAlves, F.C. _98651 |
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| 700 | 1 |
_aSabadin, F. _921706 |
|
| 700 | 1 |
_aLyra, D.H. _919851 |
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| 700 | 1 |
_aMorais, P.P.P. _921707 |
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| 700 | 1 |
_aBraatz de Andrade, L.R. _921708 |
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| 700 | 1 |
_aGranato, I. _97519 |
|
| 700 | 1 |
_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959 |
|
| 773 | 0 |
_tFrontiers in Plant Science _gv. 12, art. 658267 _dSwitzerland : Frontiers, 2021. _w56875 _x1664-462X |
|
| 856 | 4 |
_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21594 |
|
| 942 |
_cJA _n0 _2ddc |
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