ReviewMethods in molecular biology (Clifton, N.J.)2022
Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.
Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.NAR genomics and bioinformatics · 2026Article
- Multimodal genomic prediction is not a buzzword: why modern plant breeding must integrate genomics, enviromics, and phenomics.G3 (Bethesda, Md.) · 2026Article
- The Unified AMMI-GGE (UAG) Model: A Continuous Framework for Integrating Yield Stability and Adaptability in Multi-Environment Trials.Plants (Basel, Switzerland) · 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
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- Genotype-Specific Photosynthetic Plasticity and Leaf Yield ofPlants (Basel, Switzerland) · 2026Article
- Multi-trait and multi-environment genomic prediction enhances yield components improvement in durum wheat.Frontiers in plant science · 2026Article
- EasyKASP: a simple and fast tool for KASP primer design.BMC bioinformatics · 2025Article
- Genomic prediction models for morpho-phenological traits in durum wheat based on Vrn, Ppd, and Rht alleles.The plant genome · 2025Article
- Leveraging the multi-trait donor Pusa Srijan for biofortification and yield improvement in bread wheat.Scientific reports · 2025Article
- Enhancing genomic predictive ability of yield and yield-related traits in spring wheat by integrating major plant adaptation genes as a fixed effect.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.bioRxiv : the preprint server for biology · 2025Article
- Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.Molecular breeding : new strategies in plant improvement · 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
- Article
- Improving wheat grain yield genomic prediction accuracy using historical data.G3 (Bethesda, Md.) · 2025Article
- Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan.Rice (New York, N.Y.) · 2025Article
- Using genome-wide associations and host-by-pathogen predictions to identify allelic interactions that control disease resistance.The plant genome · 2025Article
- Genomic prediction of the performance of tropical doubled haploid maize lines under artificial Striga hermonthica (Del.) Benth. infestation.G3 (Bethesda, Md.) · 2024Article
- Prediction of resistance, virulence, and host-by-pathogen interactions using dual-genome prediction models.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
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