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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1171135/full |
| _version_ | 1797830029305970688 |
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| author | Mark J. Miller Qijian Song Benjamin Fallen Zenglu Li |
| author_facet | Mark J. Miller Qijian Song Benjamin Fallen Zenglu Li |
| author_sort | Mark J. Miller |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T13:29:32Z |
| format | Article |
| id | doaj.art-5998c9601062413182e4e3b818719446 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-09T13:29:32Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-5998c9601062413182e4e3b8187194462023-05-10T05:00:20ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-05-011410.3389/fpls.2023.11711351171135Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)Mark J. Miller0Qijian Song1Benjamin Fallen2Zenglu Li3Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United StatesSoybean Genomics and Improvement Laboratory, United States Department of Agriculture - Agricultural Research Service, Beltsville, MD, United StatesSoybean and Nitrogen Fixation Research Unit, United States Department of Agriculture - Agricultural Research Service, Raleigh, NC, United StatesInstitute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United StatesImproving 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.https://www.frontiersin.org/articles/10.3389/fpls.2023.1171135/fullcross combinationmolecular markersgenomic selectioncross predictionsoybean breeding |
| spellingShingle | Mark J. Miller Qijian Song Benjamin Fallen Zenglu Li Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max) Frontiers in Plant Science cross combination molecular markers genomic selection cross prediction soybean breeding |
| 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 | cross combination molecular markers genomic selection cross prediction soybean breeding |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1171135/full |
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