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Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat

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Title Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat
 
Creator Atanda, Sikiru Adeniyi
Velu, Govindan
Singh, Ravi P.
Robbins, Kelly
Crossa, Jose
Bentley, Alison R.
 
Subject genes
breeding programmes
genomics
accuracy
environment
spring wheat
forecasting
 
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.
 
Date 2022-03-28
2023-01-01T16:03:55Z
2023-01-01T16:03:55Z
 
Type Journal Article
 
Identifier Atanda, S.A., Govindan, V., Singh, R., Robbins, K.R., Crossa, J. and 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://hdl.handle.net/10883/22045
1432-2242
https://hdl.handle.net/10568/126431
https://hdl.handle.net/10883/22045
https://doi.org/10.1007/s00122-022-04085-0
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Source Theoretical and Applied Genetics