ArticleG3 (Bethesda, Md.)2019
Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (
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 46 papers.
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Who cites it
46 citing papers in PubMed.
- Haplotype Analysis of the Wx and SSIIa Genes and Their Association with Starch Physicochemical Properties in Diverse Rice Germplasm.Molecular biotechnology · 2026Review
- Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.The plant genome · 2026Article
- Genotype × environment interactions underlying geographic divergence in carotenoid accumulation and kernel pigmentation of foxtail millet.BMC plant biology · 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
- A novel matrix multiplication framework for modeling genotype-by-environment interaction in genomic prediction.Frontiers in genetics · 2026Article
- Modeling production curves in a strawberry breeding program to optimize early season productivity.Frontiers in plant science · 2026Article
- A selection index with minimal genetic relatedness for multi-trait data via binary quadratic programming.Plant methods · 2025Article
- Ensemble of Bayesian alphabets via constraint weight optimization strategy improves genomic prediction accuracy.G3 (Bethesda, Md.) · 2025Article
- Optimizing genomic prediction with transfer learning under a ridge regression framework.The plant genome · 2025Article
- Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction.The plant genome · 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
- Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
- Feature engineering of environmental covariates improves plant genomic-enabled prediction.Frontiers in plant science · 2024Article
- MSXFGP: combining improved sparrow search algorithm with XGBoost for enhanced genomic prediction.BMC bioinformatics · 2023Article
- Genetic Diversity and Relationship of Shanlan Upland Rice Were Revealed Based on 214 Upland Rice SSR Markers.Plants (Basel, Switzerland) · 2023Article
- Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice.Rice (New York, N.Y.) · 2023Article
- Efficacy of plant breeding using genomic information.The plant genome · 2023Article
- Sparse multi-trait genomic prediction under balanced incomplete block design.The plant genome · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the genetic and environmental basis of genotype × environment interaction (G×E) is of fundamental importance in plant breeding. If we consider G×E in the context of genotype × year interactions (G×Y), predicting which lines will have stable and superior performance across years is an important challenge for breeders. A better understanding of the factors that contribute to the overall grain yield and quality of rice (
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Registered trials
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.