ArticleThe plant genome2025
Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients.
Article in The plant genome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Phenomics-derived temporal maize health and environmental index enhance physiology-informed genomic prediction of yield across environments.Plant physiology · 2026Article
- Achieving High-Density and Stress-Resilient Maize Breeding Via Germplasm Innovation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Genetic Dissection of Resistance to Northern Corn Leaf Blight in a Large Commercial Maize Hybrid Population.International journal of molecular sciences · 2026Article
- Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients.The plant genome · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Understanding genotype-by-environment (G × E) interactions that underlie phenotypic variation, when observed for complex traits in multi-environment trials, is important for biological discovery and for crop improvement. The regression-on-the-mean model is an approach to observe G × E trends for complex traits across a gradient of environmental inputs. Biologically relevant environmental index values can be utilized to quantify phenotypic plasticity of individuals by correlating environmental means and environmental parameters within specific time windows. By accounting for trait stability, improvements can be made in genome-wide association studies and genomic prediction models involving data with high volumes of environments, genotypes, and their interaction effects. Here, field data collected through the national hybrid maize (Zea mays L.) Genomes-to-Fields project was analyzed. Reaction norm parameters were obtained from photothermal ratio (PTR) indices for hybrid grain yield (GY) using three separate tester populations across 29 diverse environments. The PTR time windows most correlated with the average GY were discovered to differ by tester but were confounded by region. Using 100,000 single-nucleotide polymorphisms (SNPs), we discovered 96 quantitative trait loci (QTLs) significantly associated with GY and six QTLs significantly associated with GY stability. The modified, regression-on-the-mean genomic prediction model using PTR-estimated reaction norm parameters of each hybrid worked nearly as well as a traditional, additive genomic prediction model using the G × E interaction terms but took 192× less time. The PTR genomic prediction model predicted untested environment performance (0.57-0.71) better than untested hybrid performance (0.26-0.37). This study suggests improved potential for multi-environment genomic predictions by incorporating environmental measures to dissect the complexities of differential performance of genotypes across environments.
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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.