ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022
Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Sparse phenotyping for wheat grain yield enabled by multiomics prediction.The plant genome · 2026Article
- 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
- Variability in crop responses as a function of environment affects the NDVI relationship with grain yield in wheat.Plant phenomics (Washington, D.C.) · 2026Article
- Evolution of crop phenotypic spaces through domestication.The New phytologist · 2026Article
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- Exploring the efficacy of phenomic and genomic selection for yield and fruit quality traits in strawberry.The plant genome · 2025Article
- Genomic and hyperspectral imaging-based prediction blending enables selection for reduced deoxynivalenol content in wheat grains.G3 (Bethesda, Md.) · 2025Article
- Near-Infrared Spectroscopy-Based Phenomics Data Can Improve Genomic Prediction of Agronomic and Grain Quality Traits Across Multi-Environment Sorghum Hybrid Trials.Plants (Basel, Switzerland) · 2025Article
- Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields.Genes · 2025Review
- EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models.Briefings in bioinformatics · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Using phenomic selection to predict hybrid values with NIR spectra measured on the parental lines: proof of concept on maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- 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
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- 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
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Authors and funding
11 authors.
Funding
Abstract
key messagePhenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.
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