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GENADAPT - Genotypic and Environmental Adaptation through Data Driven Prediction Techniques

GENADAPT - 通过数据驱动的预测技术进行基因型和环境适应

基本信息

批准号：
BB/X005925/1
负责人：
Laura Dixon
金额：
$ 20.06万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FX005925%2F1
关键词：
GENADAPT Genotypic Environmental Adaptation through

项目摘要

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.

英国农业的可持续集约化是一个重大挑战，需要许多创新来解决。一个关键方面是需要改进我们的谷类作物，以便在相同的土地面积上，在多变和不可预测的气候下实现更高的产量。英国的主要谷类作物是小麦（Triticum aestivum），其产量因环境条件而变化很大。然而，改良和区域适应性小麦的开发是一个缓慢的过程，仍然依赖于多次杂交和田间表型选择的试错。另一个限制是，目前的方法产生的新品种只针对它所选择的地区进行改良，因此该过程必须在多个不同的地区重复进行，这使得劳动强度非常大。理想情况下，我们需要一种方法来加速适应过程，从而需要更少的杂交，因此浪费更少的时间种植和表型植物。作为其中的一部分，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，数学建模到目前为止还没有充分利用从基因组选择和大规模基于田间的表型分析中开发的新一波数据。在这个项目中，我们将结合联合收割机的遗传，环境和现场表型数据，使基于遗传的预测目标环境。我们将通过结合我们对面包小麦机器学习模型中开花时间适应基因的理解来实现这一目标，这些模型可以测试遗传假设。通过控制机器学习模型所使用的遗传组合，我们将能够获得关于新的遗传组合以及所定义的遗传组合在特定环境条件下如何表现的新理解。然后，我们将通过在模拟模型中使用的环境条件的受控橱柜条件下测量开花时间响应来挑战这一新的理解。该项目的成果将是开发基因驱动的机器学习模型，该模型可以精确预测我们的主要耕地作物小麦的开花时间。这些预测将在受控生长柜中的现实条件下进行实验测试。第二，该项目将提供一个切实可行的框架，可适用于新的环境条件，从而适用于不同的目标国家。