ArticleGenetics2007
A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize.
Article in Genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 1 of them a synthesis that pooled it.
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Who cites it
95 citing papers in PubMed, 1 synthesis or guideline pooled it.
- metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.PLoS genetics · 2025Pooled it
- A haplotype-based approach to explore genotype-by-environment interactions for barley yield.The plant genome · 2026Article
- Multienvironment multilocus association mapping reveals genomic regions for drought-related traits in tropical maize.G3 (Bethesda, Md.) · 2026Article
- Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.The plant genome · 2026Article
- Prediction of Australian wheat genotype by environment interactions and mega-environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Merging traditional practices and modern technology through computational plant breeding.Plant physiology · 2025Review
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Multi-environment GWAS uncovers markers associated to biotic stress response and genotype-by-environment interactions in stone fruit trees.Horticulture research · 2025Article
- Toward a general framework for AI-enabled prediction in crop improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- A naturally occurring promoter variation ofFrontiers in plant science · 2025Article
- Genetic loci associated with sorghum drought tolerance in multiple environments and their sensitivity to environmental covariables.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Article
- Optimizing multi-environment trials in the Southern US Rice belt via smart-climate-soil prediction-based models and economic importance.Frontiers in plant science · 2024Article
- Modeling QTL-by-environment interactions for multi-parent populations.Frontiers in plant science · 2024Article
- Plasticity QTLs specifically contribute to the genotype × water availability interaction in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels.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
- Understanding the genomic selection for crop improvement: current progress and future prospects.Molecular genetics and genomics : MGG · 2023Review
- Breeding crops for drought-affected environments and improved climate resilience.The Plant cell · 2023Review
35 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Complex quantitative traits of plants as measured on collections of genotypes across multiple environments are the outcome of processes that depend in intricate ways on genotype and environment simultaneously. For a better understanding of the genetic architecture of such traits as observed across environments, genotype-by-environment interaction should be modeled with statistical models that use explicit information on genotypes and environments. The modeling approach we propose explains genotype-by-environment interaction by differential quantitative trait locus (QTL) expression in relation to environmental variables. We analyzed grain yield and grain moisture for an experimental data set composed of 976 F(5) maize testcross progenies evaluated across 12 environments in the U.S. corn belt during 1994 and 1995. The strategy we used was based on mixed models and started with a phenotypic analysis of multi-environment data, modeling genotype-by-environment interactions and associated genetic correlations between environments, while taking into account intraenvironmental error structures. The phenotypic mixed models were then extended to QTL models via the incorporation of marker information as genotypic covariables. A majority of the detected QTL showed significant QTL-by-environment interactions (QEI). The QEI were further analyzed by including environmental covariates into the mixed model. Most QEI could be understood as differential QTL expression conditional on longitude or year, both consequences of temperature differences during critical stages of the growth.
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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.