Inbred phenotypic data and non-additive effects can enhance genomic prediction models for hybrid grain sorghum

Authors: CROZIER, DANIEL - Texas A&M University; LEON, FABIAN - Texas A&M University; FONSECA, JALES - Texas A&M 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: 2/2/2023
Publication Date: 2/3/2023
Citation: Crozier, D., Leon, F., Fonseca, J.M., Klein, P.E., Klein, R.R., Rooney, W.L. 2023. Inbred phenotypic data and non-additive effects can enhance genomic prediction models for hybrid grain sorghum. Crop Science. Article e20927. https://doi.org/10.1002/csc2.20927.
DOI: https://doi.org/10.1002/csc2.20927

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 provides the necessary knowledge to breeders who work to exploit genomic technologies in improving grain yield of hybrid cereal crops including sorghum.

Technical Abstract: Implementation of genomic prediction can bolster rates of genetic gain in sorghum improvement and permit more efficient allocation of resources within hybrid breeding programs. In the present study, alternative genomic prediction models were compared to access the potential benefits of including inbred phenotypic records, dominance effects, and genotype-by-environment interactions in predicting hybrid grain sorghum performance. Comparisons were made in a set of 395 hybrid combinations derived from 92 parental inbred lines tested in a sparse multi-environment trial. Phenotypic data was collected on hybrids and inbreds for days to mid-anthesis, grain yield, and plant height, and genomic data on parental inbreds was collected by Genotyping-By-Sequencing. A significant increase in prediction accuracy was observed when modeling genotype-by-environment effects; however, dominance effects did not contribute to the overall predictive ability of models in this data set. Including phenotypic data from parental lines significantly improved the prediction of hybrid merit by as much as 18 percent for days to mid-anthesis, 19 percent for grain yield, and 37 percent for plant height when there were no testcross records for a given parental line. Alternatively, similar improvements were not observed when the training set included lines already tested in hybrid combinations. Thus, hybrid crop breeders can further optimize genomic prediction models by including non-additive effects and inbred data.