Assessing combining abilities, genomic data, and genotype x environment interactions to predict hybrid grain sorghum performance

Authors: FONSECA, JALES - Texas A&M University; KLEIN, PATRICIA - Texas A&M University; CROSSA, JOSE - International Maize & Wheat Improvement Center (CIMMYT); PACHECO, ANGELA - International Maize & Wheat Improvement Center (CIMMYT); PEREZ-RODRIGUEZ, PAULINO - Colegio De Postgraduados; PERUMAL, RAMASAMY - Kansas State University; Klein, Robert - Bob; ROONEY, WILLIAM - Texas A&M University

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/8/2021
Publication Date: 8/9/2021
Citation: Fonseca, J.M., Klein, P.E., Crossa, J., Pacheco, A., Perez-Rodriguez, P., Perumal, R., Klein, R.R., Rooney, W.L. 2021. Assessing combining abilities, genomic data, and genotype x environment interactions to predict hybrid grain sorghum performance. The Plant Genome. https://doi.org/10.1002/tpg2.20127.
DOI: https://doi.org/10.1002/tpg2.20127

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 pilot program to predict hybrid grain yield performance in sorghum using genome prediction models. This study will provide the necessary knowledge to breeders who work to exploit genomic technologies in improving grain yield of hybrid cereal crops including sorghum.

Technical Abstract: Genomic selection in maize has a significantly increased rate of genetic gain when compared to other cereals. However, the technological foundations in corn exists in many cereal crops to allow implementation of methods that predict hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic-enabled prediction models as a strategy to improve grain productivity. Further, the incorporation of genotype-by-environment (GxE) effects present an opportunity to deploy hybrids to targeted environments. To test these concepts, a factorial mating design of elite yet divergent grain sorghum lines generated hybrids for evaluation. Inbred parents were genotyped, and markers were used to assess population structure and develop the genomic relationship matrix (GRM). Grain yield, height, and days to anthesis were collected for hybrids in replicated trials, and best linear unbiased estimates were used to train non-genomic and genomic (GB) hierarchical Bayesian models. GB was fitted using the GRM of both parents and hybrids to incorporate population structure. For GB models, GxE effects were included by the Hadamard product between GRM and environments. A leave-one-out cross-validation scheme was used to study the prediction capacity of models. Non-genomic and genomic models effectively predicted hybrid performance using GCA and SCA, and prediction accuracy was improved by including genomic models. GB models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum breeding is detailed in this study.