ArticleThe plant genome2020
Genome-based prediction of multiple wheat quality traits in multiple years.
Article in The plant genome, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Evaluating reaction norm and fixed effect models for genomic selection of end-use quality in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Evaluation and association analysis of quality traits in Chinese winter wheat landraces on the Qinghai-Tibet Plateau.BMC plant biology · 2026Article
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Multi-trait multi-environment genomic prediction strategies forFrontiers in plant science · 2026Article
- A novel matrix multiplication framework for modeling genotype-by-environment interaction in genomic prediction.Frontiers in genetics · 2026Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Genotyping analysis of over 130,000 CIMMYT bread wheat breeding lines: A decade-long effort in optimizing wheat genotyping.The plant genome · 2025Article
- Optimizing genomic prediction with transfer learning under a ridge regression framework.The plant genome · 2025Article
- Article
- Association mapping and genomic prediction for processing and end-use quality traits in wheat (Triticum aestivum L.).The plant genome · 2025Article
- DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences.Current genomics · 2024Article
- Genetic dissection of value-added quality traits and agronomic parameters through genome-wide association mapping in bread wheat (Frontiers in plant science · 2024Article
- Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection.The plant genome · 2023Article
- Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat.The plant genome · 2023Article
- Efficacy of plant breeding using genomic information.The plant genome · 2023Article
- Article
- Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize.BMC plant biology · 2023Article
- New flavors from old wheats: exploring the aroma profiles and sensory attributes of local Mediterranean wheat landraces.Frontiers in nutrition · 2023Article
- Article
- Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective.Current genomics · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi-trait models (Bayesian multi-trait multi-environment [BMTME] and multi-trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait ALVPL. In contrast, under Pearson's correlation, the best predictions depended on the data set. The results obtained suggest that the BMTME model should be preferred for multi-trait prediction analyses. This model allows to obtain not only the correlation among traits, but also the correlation among environments, helping to increase the prediction accuracy.
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