ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2019
Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Large scale wheat data integration improves genomic prediction accuracy with the potential to facilitate international breeding collaborations.Communications biology · 2026Article
- Impact of environmental covariates summarization on predictive ability in genomic selection.The plant genome · 2026Article
- Improving genomic prediction for plant disease using environmental covariates.Plant methods · 2025Article
- Stable pleotropic loci controlling the accumulation of multiple nutritional elements in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Optimising desired gain indices to maximise selection response.Frontiers in plant science · 2024Article
- Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice.Rice (New York, N.Y.) · 2023Article
- Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding.Global change biology · 2023Article
- Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.G3 (Bethesda, Md.) · 2023Article
- Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Molecular breeding : new strategies in plant improvement · 2022Article
- Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.Frontiers in genetics · 2022Article
- Genomic selection can accelerate the biofortification of spring wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing.International journal of molecular sciences · 2021Review
- Meta-analysis of genome-wide association studies reveal common loci controlling agronomic and quality traits in a wide range of normal and heat stressed environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping.Frontiers in plant science · 2021Article
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7 authors.
Funding
Abstract
key messageA multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance-covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.
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