GENADAPT - Genotypic and Environmental Adaptation through Data Driven Prediction Techniques

英国农业的可持续集约化是一个重大挑战，需要许多创新来应对。一个关键方面集中在需要改进我们的谷类作物，以便在相同的土地面积和在多变和不可预测的气候下获得更高的产量。英国的主要谷类作物是小麦(Triticum Aestivum)，其产量随环境条件的不同而变化很大。然而，改良和区域适应性小麦的发展是一个缓慢的过程，仍然依赖于多次杂交和田间表型选择的反复试验。另一个限制是，目前的方法产生的新品种只针对它被选择的地区进行改良，因此这个过程必须在多个不同的地区重复，这使得它非常劳动强度。理想的是，我们需要一种方法来加速适应过程，以便需要更少的杂交，从而减少浪费在植物生长和表型鉴定上的时间。作为这一点的一部分，数学模型的使用一直很重要，并导致了未来产量预测的加速方法。然而，到目前为止，数学建模还没有充分利用从基因组选择和大规模现场表型鉴定中发展出来的新一波数据。在这个项目中，我们将结合遗传、环境和现场表型数据，以实现对目标环境的基于遗传的预测。我们将通过结合我们对面包小麦机器学习模型中与开花时间适应有关的基因的理解来实现这一点，该模型可以检验遗传假设。通过控制机器学习模型使用的遗传组合，我们将能够对新的遗传组合以及定义的遗传组合在特定环境条件下的表现得出新的理解。然后，我们将通过测量受控柜子条件下的开花时间反应来挑战这种新的理解，该条件模拟了模型中使用的环境条件。该项目的结果将是开发出遗传驱动的机器学习模型，该模型可以对我们的主要可耕作作物小麦的开花时间做出准确的预测。这些预测将在现实条件下在受控生长柜中进行实验验证。其次，该项目将提供一个实用的框架，可以适用于新的环境条件，从而适用于不同的目标国家。

The sustainable intensification of UK agriculture is a major challenge that will require many innovations to address. One key aspect centres on the need to improve our cereal crops to enable higher yields in the same land area, and under variable and unpredictable climates. The primary cereal crop in the UK is wheat (Triticum aestivum), the yield of which is highly variable depending on environmental conditions. However, the development of improved and regionally adapted wheat is a slow process which remains reliant on trial and error of multiple crosses and in-field phenotypic selection. A further limitation is that the current approach produces a new cultivar which is improved only for the region it has been selected in, therefore the process has to be repeated in multiple different regions making it extremely labour intensive.Ideally, we need a method which will accelerate the adaptation process so that fewer crosses are required and therefore less time is wasted growing and phenotyping plants. As part of this, the use of mathematical models has been important and has resulted in accelerated methods for future yield predictions. However, mathematical modelling has so far not fully utilised the new wave of data developed from genomic selection and large scale field-based phenotyping. In this project we will combine the genetic, environmental and field phenotyping data to enable genetic-based predictions for target environments. We will achieve this by combining our understanding of the genes involved in flowering time adaptation in bread wheat machine learning models that can test genetic hypothesise. Through controlling the genetic combinations which are used by the machine learning models we will be able to derive new understanding regarding novel genetic combinations and how the defined genetic combinations perform under specified environmental conditions. We will then challenge this new understanding by measuring flowering time responses under controlled cabinet conditions which mimic the environmental conditions used in the model.The outcomes of this project will be the development of genetically driven machine learning models which can make precise predictions regarding flowering time of our primary arable crop, wheat. These predictions will be experimentally tested under realistic conditions in controlled growth cabinets. Secondly, the project will provide a practical framework which can be applied to new environmental conditions and therefore for different target countries.