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Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)
Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations am...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206060/ https://www.ncbi.nlm.nih.gov/pubmed/37235007 http://dx.doi.org/10.3389/fpls.2023.1171135 |
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author | Miller, Mark J. Song, Qijian Fallen, Benjamin Li, Zenglu |
author_facet | Miller, Mark J. Song, Qijian Fallen, Benjamin Li, Zenglu |
author_sort | Miller, Mark J. |
collection | PubMed |
description | Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding. |
format | Online Article Text |
id | pubmed-10206060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102060602023-05-25 Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) Miller, Mark J. Song, Qijian Fallen, Benjamin Li, Zenglu Front Plant Sci Plant Science Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding. Frontiers Media S.A. 2023-05-10 /pmc/articles/PMC10206060/ /pubmed/37235007 http://dx.doi.org/10.3389/fpls.2023.1171135 Text en Copyright © 2023 Miller, Song, Fallen and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Miller, Mark J. Song, Qijian Fallen, Benjamin Li, Zenglu Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) |
title | Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) |
title_full | Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) |
title_fullStr | Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) |
title_full_unstemmed | Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) |
title_short | Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) |
title_sort | genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (glycine max) |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10206060/ https://www.ncbi.nlm.nih.gov/pubmed/37235007 http://dx.doi.org/10.3389/fpls.2023.1171135 |
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