Cooper Mark, Messina Carlos D
Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia.
Corteva Agriscience, Johnston, IA, United States.
Front Plant Sci. 2021 Sep 10;12:735143. doi: 10.3389/fpls.2021.735143. eCollection 2021.
The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.
基因型与环境互作（GxE）的多样后果决定了作物在生物组织各层面的性状表型，这对我们仅依据基因组信息预测性状表型的目标构成了挑战。GxE互作对于通过植物育种优化遗传增益以及通过田间农艺管理提高作物生产力具有诸多影响。基因组技术的进步为GxE互作的基因型维度提供了许多合适的预测指标。高通量近程和远程传感器技术的新进展推动了“环境组学”作为一种实践领域的发展，其有潜力为GxE互作的环境维度提供合适的预测指标。最近，出现了几个定制案例，展示了通过在预测模型中纳入GxE互作效应来增强作物产量及其他复杂性状表型预测的初步潜力。这些令人鼓舞的结果促使开发新的预测方法以加速作物改良。如果我们能够自动化识别和利用合适的协调基因型和环境预测指标集的方法，这将为扩大GxE互作后果预测的规模并使其可操作化带来新机遇。这将为通过整合育种和农艺学的贡献来加速作物改良奠定基础。在此，我们借鉴在美国玉米带不同水分驱动环境下提高玉米生产力的经验。我们从玉米案例研究中提供观点，以优先考虑有前景的机会，进一步开发并自动化“环境组学”方法，通过综合育种和农艺方法，针对更广泛的作物和环境目标加速作物改良。
