Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program
Genomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was per...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Oxford University Press
2019-07-01
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| Series: | G3: Genes, Genomes, Genetics |
| Subjects: | |
| Online Access: | http://g3journal.org/lookup/doi/10.1534/g3.118.200917 |
| _version_ | 1819082415517204480 |
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| author | Benjamin B. Stewart-Brown Qijian Song Justin N. Vaughn Zenglu Li |
| author_facet | Benjamin B. Stewart-Brown Qijian Song Justin N. Vaughn Zenglu Li |
| author_sort | Benjamin B. Stewart-Brown |
| collection | DOAJ |
| description | Genomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was performed using RR-BLUP and predictive abilities (rMP) of 0.81, 0.71, and 0.26 for protein, oil, and yield, were achieved at the largest tested training set size. Minimal differences were observed when comparing different marker densities and there appeared to be inflation in rMP due to population structure. For comparison purposes, two additional methods to predict breeding values for lines of four bi-parental populations within the GS dataset were tested. The first method predicted within each bi-parental population (WP method) and utilized a training set of full-sibs of the validation set. The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations (AP method). The AP method is more practical as the WP method would likely delay the breeding cycle and leverage smaller training sets. Averaging across populations for protein and oil content, rMP for the AP method (0.55, 0.30) approached rMP for the WP method (0.60, 0.52). Though comparable, rMP for yield was low for both AP and WP methods (0.12, 0.13). Based on increases in rMP as training sets increased and the effectiveness of WP vs. AP method, the AP method could potentially improve with larger training sets and increased relatedness between training and validation sets. |
| first_indexed | 2024-12-21T20:16:19Z |
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| id | doaj.art-5501738c3b3c4aca9fb34ffb5b8225c6 |
| institution | Directory Open Access Journal |
| issn | 2160-1836 |
| language | English |
| last_indexed | 2024-12-21T20:16:19Z |
| publishDate | 2019-07-01 |
| publisher | Oxford University Press |
| record_format | Article |
| series | G3: Genes, Genomes, Genetics |
| spelling | doaj.art-5501738c3b3c4aca9fb34ffb5b8225c62022-12-21T18:51:35ZengOxford University PressG3: Genes, Genomes, Genetics2160-18362019-07-01972253226510.1534/g3.118.20091719Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding ProgramBenjamin B. Stewart-BrownQijian SongJustin N. VaughnZenglu LiGenomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was performed using RR-BLUP and predictive abilities (rMP) of 0.81, 0.71, and 0.26 for protein, oil, and yield, were achieved at the largest tested training set size. Minimal differences were observed when comparing different marker densities and there appeared to be inflation in rMP due to population structure. For comparison purposes, two additional methods to predict breeding values for lines of four bi-parental populations within the GS dataset were tested. The first method predicted within each bi-parental population (WP method) and utilized a training set of full-sibs of the validation set. The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations (AP method). The AP method is more practical as the WP method would likely delay the breeding cycle and leverage smaller training sets. Averaging across populations for protein and oil content, rMP for the AP method (0.55, 0.30) approached rMP for the WP method (0.60, 0.52). Though comparable, rMP for yield was low for both AP and WP methods (0.12, 0.13). Based on increases in rMP as training sets increased and the effectiveness of WP vs. AP method, the AP method could potentially improve with larger training sets and increased relatedness between training and validation sets.http://g3journal.org/lookup/doi/10.1534/g3.118.200917Genomic selectionRR-BLUPSeed compositionSeed yieldSoybeanGenomic PredictionGenPredShared Data Resources |
| spellingShingle | Benjamin B. Stewart-Brown Qijian Song Justin N. Vaughn Zenglu Li Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program G3: Genes, Genomes, Genetics Genomic selection RR-BLUP Seed composition Seed yield Soybean Genomic Prediction GenPred Shared Data Resources |
| title | Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program |
| title_full | Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program |
| title_fullStr | Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program |
| title_full_unstemmed | Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program |
| title_short | Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program |
| title_sort | genomic selection for yield and seed composition traits within an applied soybean breeding program |
| topic | Genomic selection RR-BLUP Seed composition Seed yield Soybean Genomic Prediction GenPred Shared Data Resources |
| url | http://g3journal.org/lookup/doi/10.1534/g3.118.200917 |
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