ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2019
Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
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- Identifying genetic loci associated with drought tolerance in foxtail millet (Setaria italica).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genotype-by-Environment Interaction Stability Analysis of New Quinoa (Plants (Basel, Switzerland) · 2025Article
- Prediction of Australian wheat genotype by environment interactions and mega-environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- 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
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- MegaLMM improves genomic predictions in new environments using environmental covariates.Genetics · 2025Article
- From leaf to multiscale models of photosynthesis: applications and challenges for crop improvement.Photosynthesis research · 2024Review
- High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize.Journal of experimental botany · 2023Review
- Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics.The plant genome · 2023Article
- Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.Frontiers in genetics · 2023Article
- learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.G3 (Bethesda, Md.) · 2022Article
- Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Identification of environment types and adaptation zones with self-organizing maps; applications to sunflower multi-environment data in Europe.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment.The plant genome · 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
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
key messageWe propose new methods to predict genotype × environment interaction by selecting relevant environmental covariates and using an AMMI decomposition of the interaction. Farmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios. One solution to this challenge is to develop varieties adapted to specific environmental stress scenarios. For this, plant breeders can use genomic predictions coupled with environmental characterization to identify promising combinations of genes in relation to stress covariates. One way to do it is to take into account the genetic similarity between varieties and the similarity between environments within a mixed model framework. Molecular markers and environmental covariates (EC) can be used to estimate relevant covariance matrices. In the present study, based on a multi-environment trial of 220 European elite winter bread wheat (Triticum aestivum L.) varieties phenotyped in 42 environments, we compared reference regression models potentially including ECs, and proposed alternative models to increase prediction accuracy. We showed that selecting a subset of ECs, and estimating covariance matrices using an AMMI decomposition to benefit from the information brought by the phenotypic records of the training set are promising approaches to better predict genotype-by-environment interactions (G × E). We found that using a different kinship for the main genetic effect and the G × E effect increased prediction accuracy. Our study also demonstrates that integrative stress indexes simulated by crop growth models are more efficient to capture G × E than climatic covariates.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.