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Response to early generation genomic selection for yield in wheat

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Title Response to early generation genomic selection for yield in wheat
 
Creator Bonnett, David G.
Yongle Li
Crossa, Jose
Dreisigacker, Susanne
Basnet, Bhoja R.
Perez-Rodriguez, Paulino
Alvarado Beltrán, Gregorio
Jannink, Jean-Luc
Poland, Jesse A.
Sorrells, Mark E.
 
Subject marker-assisted selection
genomics
wheat
plant breeding
breeding methods
 
Description We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.
 
Date 2022-01-11
2022-12-27T08:51:32Z
2022-12-27T08:51:32Z
 
Type Journal Article
 
Identifier Bonnett, D., Li, Y., Crossa, J., Dreisigacker, S., Basnet, B., Pérez-Rodríguez, P., Alvarado, G., Jan-nink, J. L., Poland, J. and Sorrells, M. 2022. Response to early generation genomic selection for yield in wheat. Frontiers in Plant Science 12:718611. https://hdl.handle.net/10883/21936
1664-462X
https://hdl.handle.net/10568/126319
https://hdl.handle.net/10883/21936
https://doi.org/10.3389/fpls.2021.718611
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Source Frontiers in Plant Science