Fradgley Nick S, Gerard Guillermo S, Govindan Velu, Nicol Julie M, Singh Amit, Tadesse Wuletaw, Zwart Alexander B, Trethowan Richard, Trevaskis Ben, Whan Alex, Hyles Jessica
CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia.
International Maize and Wheat Improvement Center (CIMMYT), KM 45 Carretera Mexico-Veracruz, 56237, Texcoco, Mexico.
Theor Appl Genet. 2025 Sep 4;138(9):241. doi: 10.1007/s00122-025-05023-6.
Latent environmental effects of genotype by environment interactions could be predicted from observed environmental covariates. Predictions into the wider target population of environments revealed greater insights. Wheat is grown across a diverse range of environments in Australia with contrasting environmental constraints. Targeted breeding to optimise genotypes in target environments is hindered by large and ubiquitous genotype by environment interactions (GEI). Common GEI in multi-environment trial experiments, which sample the target population of environments, can be efficiently modelled using latent environmental effects from factor analytic mixed models. However, generalised prediction into the full target population of environments is difficult without a clear link to observed environmental covariates (ECs) that are defined from high-resolution weather and soil data. Here, we used a large wheat multi-environment trial dataset and demonstrated that latent environmental effects can be associated with and predicted from observed ECs. We found GEI-based environment classes could be defined by combinations of key ECs. Prediction of main and latent effects in a wider set of environments covering the full TPE across the Australian grain belt over 13 years revealed the complex trends of environmental effects and GEI over regional scales demonstrating high year-to-year variability. Regional environment types often shifted year-to-year. Cross-validation of forward genomic prediction into untested year environments demonstrated that increased accuracy is possible if estimated genetic effects are also accurate and ECs of new environments are known. These findings may guide Australian wheat breeders to better target specifically adapted material to mega-environments defined by static GEI while also considering broad adaptability and non-static GEI resulting from year-to-year variability.
基因型与环境互作的潜在环境效应可通过观测到的环境协变量进行预测。对更广泛目标环境群体的预测能带来更多见解。澳大利亚种植小麦的环境多样，环境限制差异较大。由于广泛存在且显著的基因型与环境互作（GEI），在目标环境中优化基因型的定向育种受到阻碍。在多环境试验中对目标环境群体进行抽样时，常见的GEI可通过因子分析混合模型中的潜在环境效应进行有效建模。然而，如果无法与基于高分辨率气象和土壤数据定义的观测环境协变量（EC）建立明确联系，就难以对整个目标环境群体进行广义预测。在此，我们使用了一个大型小麦多环境试验数据集，证明潜在环境效应可与观测到的EC相关联并由其预测。我们发现基于GEI的环境类别可由关键EC的组合来定义。对涵盖澳大利亚谷物带13年的整个目标环境群体的更广泛环境集的主效应和潜在效应进行预测，揭示了区域尺度上环境效应和GEI的复杂趋势，显示出较高的年际变异性。区域环境类型常常逐年变化。对未测试年份环境进行正向基因组预测的交叉验证表明，如果估计的遗传效应准确且新环境的EC已知，则有可能提高预测准确性。这些发现可能会指导澳大利亚小麦育种者更好地将特定适应材料靶向由静态GEI定义的大环境，同时也考虑到年际变异性导致的广泛适应性和非静态GEI。
