ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022
Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Antagonistic pleiotropy at the stem solidness 1 locus balances wheat stem architecture and yield components under terminal drought.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Longitudinal genomic prediction of cacao disease resilience identifies robust witches' broom disease targets.Frontiers in plant science · 2026Article
- PCA-Driven Multivariate Trait Integration in Alfalfa Breeding: A Selection Model for High-Yield and Stable Progenies.Plants (Basel, Switzerland) · 2025Article
- Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions.PloS one · 2024Article
- Accuracy of Selection in Early Generations of Field Pea Breeding Increases by Exploiting the Information Contained in Correlated Traits.Plants (Basel, Switzerland) · 2023Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices.Plants (Basel, Switzerland) · 2022Article
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14 authors.
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Abstract
key messageThis study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.
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