ArticleThe plant genome2023
Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat.
Article in The plant genome, 2023. 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.
- 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
- Improving multi-trait genomic prediction using synthetic traits from hyperspectral data based on co-heritability.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic prediction in dolichos bean (Lablab purpureus L. Sweet) breeding: choice of the type of training population and selection generation.BMC plant biology · 2026Article
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).The plant genome · 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
- The Role ofInternational journal of molecular sciences · 2025Review
- Implementing marker covariates and multi-trait genomic selection models to improve grain milling, appearance, cooking, and edible quality in rice (Oryza sativa L.).The plant genome · 2025Article
- Low density marker-based effectiveness and efficiency of early-generation genomic selection relative to phenotype-based selection in dolichos bean (Lablab purpureus L. Sweet).The plant genome · 2025Article
- Association mapping and genomic prediction for processing and end-use quality traits in wheat (Triticum aestivum L.).The plant genome · 2025Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.The plant genome · 2024Article
- Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.Frontiers in plant science · 2024Article
- The Plant Genome special section: Grain quality and nutritional genomics for breeding next-generation crops.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
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12 authors.
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Abstract
Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains.
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