Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat
Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expande...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/126431 |
| _version_ | 1855542324837220352 |
|---|---|
| author | Atanda, Sikiru Adeniyi Velu, Govindan Singh, Ravi P. Robbins, Kelly R. Crossa, José Bentley, Alison R. |
| author_browse | Atanda, Sikiru Adeniyi Bentley, Alison R. Crossa, José Robbins, Kelly R. Singh, Ravi P. Velu, Govindan |
| author_facet | Atanda, Sikiru Adeniyi Velu, Govindan Singh, Ravi P. Robbins, Kelly R. Crossa, José Bentley, Alison R. |
| author_sort | Atanda, Sikiru Adeniyi |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs. |
| format | Journal Article |
| id | CGSpace126431 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1264312025-11-06T13:10:11Z Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat Atanda, Sikiru Adeniyi Velu, Govindan Singh, Ravi P. Robbins, Kelly R. Crossa, José Bentley, Alison R. genes breeding programmes genomics accuracy environment spring wheat forecasting Key message: Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Abstract: Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1–9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder’s advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs. 2022-06 2023-01-01T16:03:55Z 2023-01-01T16:03:55Z Journal Article https://hdl.handle.net/10568/126431 en Open Access application/pdf Springer Atanda, S. A., Govindan, V., Singh, R., Robbins, K. R., Crossa, J., & Bentley, A. R. (2022). Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat. Theoretical and Applied Genetics, 135(6), 1939–1950. https://doi.org/10.1007/s00122-022-04085-0 |
| spellingShingle | genes breeding programmes genomics accuracy environment spring wheat forecasting Atanda, Sikiru Adeniyi Velu, Govindan Singh, Ravi P. Robbins, Kelly R. Crossa, José Bentley, Alison R. Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat |
| title | Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat |
| title_full | Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat |
| title_fullStr | Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat |
| title_full_unstemmed | Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat |
| title_short | Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat |
| title_sort | sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat |
| topic | genes breeding programmes genomics accuracy environment spring wheat forecasting |
| url | https://hdl.handle.net/10568/126431 |
| work_keys_str_mv | AT atandasikiruadeniyi sparsetestingusinggenomicpredictionimprovesselectionforbreedingtargetsinelitespringwheat AT velugovindan sparsetestingusinggenomicpredictionimprovesselectionforbreedingtargetsinelitespringwheat AT singhravip sparsetestingusinggenomicpredictionimprovesselectionforbreedingtargetsinelitespringwheat AT robbinskellyr sparsetestingusinggenomicpredictionimprovesselectionforbreedingtargetsinelitespringwheat AT crossajose sparsetestingusinggenomicpredictionimprovesselectionforbreedingtargetsinelitespringwheat AT bentleyalisonr sparsetestingusinggenomicpredictionimprovesselectionforbreedingtargetsinelitespringwheat |