School of Agronomy, University of Goiás (UFG), Goiânia, GO, 74690-900, Brazil.
Biostatistics Unit, University of Hohenheim, 70593, Stuttgart, Germany.
Theor Appl Genet. 2021 Jan;134(1):95-112. doi: 10.1007/s00122-020-03684-z. Epub 2020 Sep 22.
We propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an "omics" scale of environmental attributes drives the prediction of unobserved genotype performances. Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of "envirotypes" at multiple "enviromic" markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the "GIS-GEI") which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.
我们提出将环境组学应用于育种实践，通过在环境属性的“组学”尺度上评估站点之间的相似性，从而预测未观察到的基因型表现。植物育种中的基因型与环境互作（GEI）研究主要集中在估计遗传参数上，这些遗传参数是在有限数量的实验试验中得出的。然而，最近的地理信息系统（GIS）技术为更好地理解和处理 GEI 开辟了新的前沿。这些进展允许在所有感兴趣的地点提高选择准确性，包括那些尚未进行实验试验的地点。在这里，我们在环境型辅助育种框架内引入了环境组学一词。总之，就像 DNA 标记上的基因型一样，任何特定的地点都由一组在多个“环境组学”标记上的“环境型”来表示，这些标记对应于可能与遗传背景相互作用的环境变量，从而为在不同环境下进行优化决策提供了有价值的重新排序。基于模拟数据，我们展示了一种基于索引的环境组学方法（“GIS-GEI”），由于其比标准方法更高的粒度分辨率，因此允许：（1）准确匹配地点及其最合适的基因型；（2）更好地定义具有高遗传相关性的育种区，以确保在不同环境中进行选择增益；（3）高效确定最佳的实验地点，以进行进一步分析。环境情景也可以进行优化，以提高生产力和遗传资源管理，特别是在当前动态气候变化的展望下。环境型为遗传研究提供了一类新的标记，这些标记相对便宜、越来越容易获得并且可以在物种间转移。当与下一代基因型/表型和遗传多样性的强大统计建模相结合时，我们预计环境组学方法将在植物育种中得到很好的应用。
