Envirotyping can increase genomic prediction accuracy of new environments in grain sorghum trials depending on mega-environment

Authors: WINANS, NOAH - Texas A&M University; FONSECA, JALES - Texas A&M University; PERUMAL, RAMASAMY - Kansas State University; KLEIN, PATRICIA - Texas A&M University; Klein, Robert - Bob; ROONEY, WILLIAM - Texas A&M University

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/3/2024
Publication Date: N/A
Citation: N/A

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 mathematical modeling of genetic and environmental factors that control grain yield is commonly hypothesized as a way to boost the rate of gain. To address this issue, we developed equations that utilize genetic and environmental data to predict the performance of offspring from specific parental lines grown in a series of field locations. This study will provide the necessary knowledge to breeders who work to exploit genetic diversity and environmental data in improving grain yield of hybrid cereal crops including sorghum.

Technical Abstract: Grain sorghum is an important crop native to Africa and grown in many subtropical and temperate regions worldwide. The variability in production environments underscores the plasticity of sorghum genotypes and opens an opportunity to predict sorghum hybrid performance across multiple environments. Reaction norms informed by detailed envirotyping may aid in modeling the differential responses of genotypes across multi-environmental trials, and ultimately increase model prediction accuracies. In this study, a combination of genomic and enviromic information was applied to predict grain sorghum hybrid performance across standard U.S. production environments. Five models based on additive, dominance, environment, envirotype effects and their interactions were tested under three cross validation schemes that simulate genomic prediction scenarios encountered by hybrid breeding programs. Relationship matrices for hybrids and environments were created from molecular and envirotypic data, respectively, to predict hybrid performance under a hierarchal Bayesian framework. Of these models, the regular GxE model produced the highest prediction accuracies. The envirotype-informed reaction norm produced prediction accuracies comparable to the regular GxE model, despite the envirotype data explaining only 5.3% of the GxE variation. Based on these observations, the utilization of envirotype data can expand the information available to a sorghum breeding program to create more robust genomic prediction models and for characterizing the target population of environments to aid in the effective selection of environments for hybrids evaluation.