Abstract
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.
Highlights
Genomic selection (GS) (Meuwissen et al, 2001; Bernardo and Yu, 2007) has become possible through the rapid development of next-generation sequencing technologies that allow the use of abundant and low-cost molecular markers
The proof-of-concept Experiment 2 reported here demonstrates the potential of early generation genomic prediction to increase genetic gain over conventional selection methods by allowing the ability to increase the number of crossing cycles per year
In Experiment 2 of our study, F2 Genomic Estimated Breeding Values (GEBVs) generated by Gaussian Kernel (GK) and Genomic Best Linear Unbiased Predictor (GBLUP) methods showed significant positive correlations with the yield of derived lines
Summary
Genomic selection (GS) (Meuwissen et al, 2001; Bernardo and Yu, 2007) has become possible through the rapid development of next-generation sequencing technologies that allow the use of abundant and low-cost molecular markers. Models that incorporated genomic × environment and marker × environment and genomic × environmental covariables were earlier developed to improve the accuracy for predicting unobserved cultivars in new environments (Burgueño et al, 2012; Heslot et al, 2014; Jarquín et al, 2014; Lopez-Cruz et al, 2015; Crossa et al, 2016) After these initial studies, an increasing number of research articles have been published effectively testing the integration of GS into conventional plant breeding pipelines for different traits measured in different environments (Crossa et al, 2017; Dreisigacker et al, 2021). The application of GS offers attractive benefits but comes with challenges when implemented into current conventional breeding systems
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