ArticleFrontiers in plant science2019
Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis.
Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- Multi-trait multi-environment genomic prediction strategies forFrontiers in plant science · 2026Article
- Performance of multi-trait and single-trait genomic selection for grain Fe and Zn concentrations in sorghum under different breeding constraints.Scientific reports · 2025Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- Article
- Genomic selection shows improved expected genetic gain over phenotypic selection of agronomic traits in allotetraploid white clover.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genome-wide association study and genomic prediction of root system architecture traits in Sorghum (Sorghum bicolor (L.) Moench) at the seedling stage.BMC plant biology · 2025Article
- Genetic progress in rice yield: preliminary insights from historically released varieties in Sub-Saharan Africa.Frontiers in plant science · 2025Article
- From tradition to trait: stay-green alleles in SudaneseFrontiers in plant science · 2025Article
- Genomic prediction of the performance of tropical doubled haploid maize lines under artificial Striga hermonthica (Del.) Benth. infestation.G3 (Bethesda, Md.) · 2024Article
- Decoding the genetic blueprint: regulation of key agricultural traits in sorghum.Advanced biotechnology · 2024Review
- Evaluating and Predicting the Performance of Sorghum Lines in an Elite by Exotic Backcross-Nested Association Mapping Population.Plants (Basel, Switzerland) · 2024Article
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
- The potentialities of omics resources for millet improvement.Functional & integrative genomics · 2023Review
- Genomic prediction of hybrid performance for agronomic traits in sorghum.G3 (Bethesda, Md.) · 2023Article
- Breeding crops for drought-affected environments and improved climate resilience.The Plant cell · 2023Review
- Recent advancements in the breeding of sorghum crop: current status and future strategies for marker-assisted breeding.Frontiers in genetics · 2023Review
- Genomic prediction and selection response for grain yield in safflower.Frontiers in genetics · 2023Article
- Identification of environment types and adaptation zones with self-organizing maps; applications to sunflower multi-environment data in Europe.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea.Frontiers in plant science · 2022Article
- QTL mapping and genomic prediction of resistance to wheat head blight caused byFrontiers in genetics · 2022Article
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6 authors.
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Abstract
Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines
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