ArticleThe plant genome2019
Hybrid Wheat Prediction Using Genomic, Pedigree, and Environmental Covariables Interaction Models.
Article in The plant genome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.The plant genome · 2026Article
- Multi-trait and multi-environment genomic prediction enhances yield components improvement in durum wheat.Frontiers in plant science · 2026Article
- Optimizing the single-step model for predicting fumonisins resistance in maize hybrids accounting for the genotype-by-environment interaction.Frontiers in genetics · 2025Article
- Unraveling the genetic basis of general combining ability in CIMMYT elite bread wheat germplasm: implications for breeding strategies optimization.Frontiers in plant science · 2025Article
- mmGEBLUP: an advanced genomic prediction scheme for genetic improvement of complex traits in crops through integrative analysis of major genes, polygenes, and genotype-environment interactions.Briefings in bioinformatics · 2024Article
- Genomic prediction of synthetic hexaploid wheat upon tetraploid durum and diploid Aegilops parental pools.The plant genome · 2024Article
- Article
- Genomic prediction in hybrid breeding: II. Reciprocal recurrent genomic selection with full-sib and half-sib families.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Efficacy of plant breeding using genomic information.The plant genome · 2023Article
- GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat.Molecular breeding : new strategies in plant improvement · 2023Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- Integrating Parental Phenotypic Data Enhances Prediction Accuracy of Hybrids in Wheat Traits.Genes · 2023Article
- Green revolution to genome revolution: driving better resilient crops against environmental instability.Frontiers in genetics · 2023Review
- Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction.Frontiers in plant science · 2023Review
- Genomics combined with UAS data enhances prediction of grain yield in winter wheat.Frontiers in genetics · 2023Article
- On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops.Frontiers in plant science · 2023Article
- A novel method for genomic-enabled prediction of cultivars in new environments.Frontiers in plant science · 2023Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
- Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Capturing Wheat Phenotypes at the Genome Level.Frontiers in plant science · 2022Review
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Authors and funding
9 authors.
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Abstract
In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1-M5) under four cross-validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat ( L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments.
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