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Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients

Fatma Ozair,Alper Adak,Seth C. Murray,R. T. Alpers,A. C. Aviles,D. C. Lima,J. Edwards,David Ertl,M. A. Gore,C. Hirsch,J. Knoll,James c. Schnable,M. Singh,Erin E. Sparks,A. Thompson,T. Weldekidan,Wenwei Xu

Published 2025 in The Plant Genome

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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