ArticleG3 (Bethesda, Md.)2019
A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Improved bivariate analysis of canola survivability against blackleg disease.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops.Plant methods · 2025Article
- Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.Scientific reports · 2023Article
- Article
- Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.The plant genome · 2022Article
- Article
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction.G3 (Bethesda, Md.) · 2022Article
- Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Multi-trait genome prediction of new environments with partial least squares.Frontiers in genetics · 2022Article
- Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.Frontiers in genetics · 2022Article
- Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat.Frontiers in plant science · 2022Article
- Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials.G3 (Bethesda, Md.) · 2021Article
- Application of multi-trait Bayesian decision theory for parental genomic selection.G3 (Bethesda, Md.) · 2021Article
- Genome-based prediction of multiple wheat quality traits in multiple years.The plant genome · 2020Article
- Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.Genes · 2020Article
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9 authors.
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Abstract
In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is a generalization of the multi-trait regressor stacking method. The proposed BMORS model consists of two stages: in the first stage, a univariate genomic best linear unbiased prediction (GBLUP including genotype × environment interaction GE) model is implemented for each of the L traits under study; then the predictions of all traits are included as covariates in the second stage, by implementing a Ridge regression model. The main objectives of this research were to study alternative models to the existing multi-trait multi-environment (BMTME) model with respect to (1) genomic-enabled prediction accuracy, and (2) potential advantages in terms of computing resources and implementation. We compared the predictions of the BMORS model to those of the univariate GBLUP model using 7 maize and wheat datasets. We found that the proposed BMORS produced similar predictions to the univariate GBLUP model and to the BMTME model in terms of prediction accuracy; however, the best predictions were obtained under the BMTME model. In terms of computing resources, we found that the BMORS is at least 9 times faster than the BMTME method. Based on our empirical findings, the proposed BMORS model is an alternative for predicting multi-trait and multi-environment data, which are very common in genomic-enabled prediction in plant and animal breeding programs.
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