Leveraging genomic prediction to scan germplasm collection for crop improvement.
The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5466325?pdf=render |
| _version_ | 1811281482178625536 |
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| author | Leonardo de Azevedo Peixoto Tara C Moellers Jiaoping Zhang Aaron J Lorenz Leonardo L Bhering William D Beavis Asheesh K Singh |
| author_facet | Leonardo de Azevedo Peixoto Tara C Moellers Jiaoping Zhang Aaron J Lorenz Leonardo L Bhering William D Beavis Asheesh K Singh |
| author_sort | Leonardo de Azevedo Peixoto |
| collection | DOAJ |
| description | The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops. |
| first_indexed | 2024-04-13T01:34:31Z |
| format | Article |
| id | doaj.art-9c979526bb094c5a9ebbf8ba7a166ece |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T01:34:31Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-9c979526bb094c5a9ebbf8ba7a166ece2022-12-22T03:08:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01126e017919110.1371/journal.pone.0179191Leveraging genomic prediction to scan germplasm collection for crop improvement.Leonardo de Azevedo PeixotoTara C MoellersJiaoping ZhangAaron J LorenzLeonardo L BheringWilliam D BeavisAsheesh K SinghThe objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.http://europepmc.org/articles/PMC5466325?pdf=render |
| spellingShingle | Leonardo de Azevedo Peixoto Tara C Moellers Jiaoping Zhang Aaron J Lorenz Leonardo L Bhering William D Beavis Asheesh K Singh Leveraging genomic prediction to scan germplasm collection for crop improvement. PLoS ONE |
| title | Leveraging genomic prediction to scan germplasm collection for crop improvement. |
| title_full | Leveraging genomic prediction to scan germplasm collection for crop improvement. |
| title_fullStr | Leveraging genomic prediction to scan germplasm collection for crop improvement. |
| title_full_unstemmed | Leveraging genomic prediction to scan germplasm collection for crop improvement. |
| title_short | Leveraging genomic prediction to scan germplasm collection for crop improvement. |
| title_sort | leveraging genomic prediction to scan germplasm collection for crop improvement |
| url | http://europepmc.org/articles/PMC5466325?pdf=render |
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