Response to early generation genomic selection for yield in wheat

Authors: BONNETT, DAVID - International Maize & Wheat Improvement Center (CIMMYT); LI, YONGLE - University Of Adelaide; CROSSA, JOSE - International Maize & Wheat Improvement Center (CIMMYT); DREISIGACKER, SUSANNE - International Maize & Wheat Improvement Center (CIMMYT); BASNET, BHOJA - International Maize & Wheat Improvement Center (CIMMYT); PEREZ-RODRIGUEZ, PAULINO - Colegio De Postgraduados; ALVARADO, G. - International Maize & Wheat Improvement Center (CIMMYT); Jannink, Jean-Luc; POLAND, JESSE - Kansas State University; SORRELLS, MARK - Cornell University

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/22/2021
Publication Date: 1/11/2022
Citation: Bonnett, D., Li, Y., Crossa, J., Dreisigacker, S., Basnet, B., Perez-Rodriguez, P., Alvarado, G., Jannink, J., Poland, J., Sorrells, M. 2022. Response to early generation genomic selection for yield in wheat. Frontiers in Plant Science. 12.718611. https://doi.org/10.3389/fpls.2021.718611.
DOI: https://doi.org/10.3389/fpls.2021.718611

Interpretive Summary: In this study, scientists wanted to find ways to improve wheat yield using genetics. They started by studying 1,334 specially chosen wheat varieties for three growing seasons. They used the plants' genetic information and three different methods to predict which ones would give the most grain. They created new generations of wheat by crossing the best-performing plants from their predictions. They hoped to combine the genes that make wheat produce more grain. The results showed that the different prediction methods didn't always agree with each other. In one experiment, they found that picking plants based on their genetic predictions didn't increase grain yields compared to regular methods. In another experiment, they compared how well these genetic predictions matched the actual grain yields of individual plants. They discovered that some prediction methods could predict grain yields, while one method wasn't reliable. In summary, this study suggests that using genetics to improve wheat yields in the early stages of breeding might work, but it's crucial to choose the right method for making predictions because not all methods work equally well.

Technical 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.