Inclusion of genotype by environment interactions into genomic prediction models for sorghum seed parents in hybrid testcrosses

Authors: KENT, MITCHELL - Texas A&M University; FONSECA, JALES - Texas A&M University; KLEIN, PATRICIA - Texas A&M University; Klein, Robert; Hayes, Chad; ROONEY, WILLIAMS - Texas A&M University

Submitted to: Journal of Crop Improvement
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/25/2025
Publication Date: 2/6/2025
Citation: Kent, M.A., Fonseca, J.M., Klein, P.E., Klein, R.R., Hayes, C.M., Rooney, W.L. 2025. Inclusion of genotype by environment interactions into genomic prediction models for sorghum seed parents in hybrid testcrosses. Journal of Crop Improvement. 39(2):146-166. https://doi.org/10.1080/15427528.2025.2460571.
DOI: https://doi.org/10.1080/15427528.2025.2460571

Interpretive Summary: The yield potential in grain sorghum hybrids has increased at a slower rate than other cereal crops including its close relative maize. While there are many reasons for this lag, increasing hybrid performance through genomic selection has the potential to accelerate the rate of genetic gain in sorghum to levels that parallel gains in hybrid maize. To address this issue, we implemented a program to determine what are the important parameters to include in the computer model to increase the accuracy of the prediction model. This work provides the necessary knowledge to breeders who work to exploit genomic technologies in improving grain yield of hybrid cereal crops including sorghum.

Technical Abstract: Multi-environment trials are routinely used to screen sorghum hybrids for quantitative traits, such as grain yield and days to mid anthesis. By nature, quantitative traits are greatly affected by environmental effects, and it is common that hybrids have unequal relative performance across environments. This unequal relative performance, referred to as genotype by environment (GxE) interaction effects, hinders plant breeder's ability to select superior hybrids. This study aims at addressing GxE by assessing the efficacy of including GxE effects into genomic prediction models of sorghum hybrid performance for grain yield and days to anthesis across environments. Testcross hybrids from two B-line populations were evaluated across multiple environments to generate prediction in four different cross validation (CV) schemes. These were: CV1, individual environment predictions; CV2, multi-environment predictions where the predicted hybrids were not observed in any environment; CV3, multi-environment predictions where the predicted hybrids were sparsely tested across environments; and CV4, where a grand mean of hybrid performance across environments was predicted. The inclusion of GxE effects into genomic prediction models were no more predictive under a CV2 scheme than individual environment genomic prediction models (CV1). However, genomic prediction models that included GxE effects in a CV3 scheme, improved prediction accuracies for both GY and DY. The overall predictive ability of CV4 was higher than that of CV1 and CV2, but there was no clear difference between CV4 and CV3. The results herein provide a framework on how genomic prediction can be incorporated in sorghum breeding programs to predict quantitative traits and increase genetic gain.