Ozair Fatma, Adak Alper, Murray Seth C, Alpers Ryan T, Aviles Alejandro C, Lima Dayane C, Edwards Jode, Ertl David, Gore Michael A, Hirsch Candice N, Knoll Joseph E, Schnable James C, Singh Maninder P, Sparks Erin E, Thompson Addie, Weldekidan Teclemariam, Xu Wenwei
Interdisciplinary Graduate Program in Genetics and Genomics (Department of Biochemistry and Biophysics), Texas A&M University, College Station, Texas, USA.
Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA.
Plant Genome. 2025 Sep;18(3):e70078. doi: 10.1002/tpg2.70078.
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.
在多环境试验中观察到复杂性状的表型变异背后的基因型与环境（G×E）相互作用，对于生物学发现和作物改良具有重要意义。均值回归模型是一种用于观察复杂性状在不同环境输入梯度下G×E趋势的方法。通过在特定时间窗口内关联环境均值和环境参数，可以利用生物学相关的环境指标值来量化个体的表型可塑性。通过考虑性状稳定性，可以改进涉及大量环境、基因型及其相互作用效应数据的全基因组关联研究和基因组预测模型。在此，我们分析了通过国家杂交玉米（Zea mays L.）基因组到田间项目收集的田间数据。使用三个不同的测试群体，在29个不同环境中，从杂交籽粒产量（GY）的光热比（PTR）指标中获得反应规范参数。发现与平均GY最相关的PTR时间窗口因测试者而异，但受区域混淆。利用10万个单核苷酸多态性（SNP），我们发现了96个与GY显著相关的数量性状位点（QTL）和6个与GY稳定性显著相关的QTL。使用每个杂交种的PTR估计反应规范参数的改进均值回归基因组预测模型，其效果几乎与使用G×E相互作用项的传统加性基因组预测模型相同，但所需时间减少了192倍。PTR基因组预测模型对未测试环境性能（0.57 - 0.71）的预测优于未测试杂交种性能（0.26 - 0.37）。这项研究表明，通过纳入环境测量来剖析基因型在不同环境中表现差异的复杂性，多环境基因组预测具有更大潜力。
