ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2017
Optimization of multi-environment trials for genomic selection based on crop models.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.Frontiers in plant science · 2021Pooled it
- Optimizing resource allocation inFrontiers in plant science · 2026Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Redefining the accumulated temperature index for accurate prediction of rice flowering time in diverse environments.Plant biotechnology journal · 2025Article
- EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.Frontiers in plant science · 2025Article
- Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.Frontiers in plant science · 2024Article
- Multi-environment Genomic Selection in Rice Elite Breeding Lines.Rice (New York, N.Y.) · 2023Article
- Sustaining productivity gains in the face of climate change: A research agenda for US wheat.Global change biology · 2023Article
- Genomic selection in tropical perennial crops and plantation trees: a review.Molecular breeding : new strategies in plant improvement · 2022Review
- Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection.Biology · 2022Review
- Integration of Crop Growth Models and Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Crop genomic selection with deep learning and environmental data: A survey.Frontiers in artificial intelligence · 2022Review
- Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat.Frontiers in genetics · 2022Article
- Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture.G3 (Bethesda, Md.) · 2021Article
- Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks.Frontiers in plant science · 2021Article
- The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data.Frontiers in plant science · 2021Article
- Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize.Frontiers in plant science · 2021Article
- Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageWe propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.
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