ArticleThe plant genome2021
Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Genomic prediction of single cross families of perennial ryegrass under two nitrogen managements.The plant genome · 2026Article
- Improving hybrid breeding efficiency in winter oilseed rape under sparse multi-environment testing.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Near-Infrared Spectroscopy-Based Phenomics Data Can Improve Genomic Prediction of Agronomic and Grain Quality Traits Across Multi-Environment Sorghum Hybrid Trials.Plants (Basel, Switzerland) · 2025Article
- Classification of Heterotic Groups and Prediction of Heterosis in Sorghum Based on Whole-Genome Resequencing.International journal of molecular sciences · 2025Article
- Combining ability of banana triploid hybrid progenitors and genomic prediction of cross performance for agro-morphological traits.Genetics · 2025Article
- EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models.Briefings in bioinformatics · 2025Article
- Prediction of biomass sorghum hybrids using environmental feature-enriched genomic combining ability models in tropical environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Optimizing the single-step model for predicting fumonisins resistance in maize hybrids accounting for the genotype-by-environment interaction.Frontiers in genetics · 2025Article
- Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.The plant genome · 2024Article
- Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population.Plants (Basel, Switzerland) · 2024Article
- Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses.The plant genome · 2023Article
- Genomic prediction of hybrid performance for agronomic traits in sorghum.G3 (Bethesda, Md.) · 2023Article
- Evaluating Introgression Sorghum Germplasm Selected at the Population Level While Exploring Genomic Resources as a Screening Method.Plants (Basel, Switzerland) · 2023Article
- Assessing combining abilities, genomic data, and genotype × environment interactions to predict hybrid grain sorghum performance.The plant genome · 2021Article
- Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize.Frontiers in plant science · 2021Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genomic selection in maize (Zea mays L.) has been one factor that has increased the rate of genetic gain when compared with other cereals. However, the technological foundations in maize also exist in other cereal crops that would allow prediction of hybrid performance based on general (GCA) and specific (SCA) combining abilities applied through genomic-enabled prediction models. Further, the incorporation of genotype × environment (G × E) interaction 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 classical GCA-SCA-based and genomic (GB) models under a hierarchical Bayesian framework. To incorporate population structure, GB was fitted using the GRM of both parents and hybrids. For GB models, G × E interaction 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. Classical and genomic models effectively predicted hybrid performance and prediction accuracy increased by including genomic data. Genomic models effectively partitioned the variation due to GCA, SCA, and their interaction with the environment. A strategy to implement genomic selection for hybrid sorghum [Sorghum bicolor (L.) Moench] breeding is presented herein.
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