Smart control of crop diseases: how can we best combine fungicides and plant resistance genes?

小麦纹枯病是欧洲小麦上危害最大的病害，也是全球小麦生产的最大制约因素之一。由于有利的气候条件，这种疾病在英国尤为严重。由于ZT能够迅速形成对杀菌剂的抗性，并能适应抗病小麦品种和环境条件，因此防治STB变得越来越困难。面对病原菌臭名昭著的适应能力，没有一种单一的防治措施是持久的，因此，两种关键的防治方法--杀菌剂和小麦抗病基因--需要结合在一起，不仅在短期内优化防治效果，而且在长期内优化其可持续性。这个跨学科的项目将通过大规模田间试验与新的高通量表型鉴定技术、生物信息学分析、最新的机器学习和数学模型的强大结合来为这一目标做出重大贡献。目标1：揭示小麦对STB的数量抗性的新的遗传基础。由于当前表型方法的局限性，对STB的数量抗性的遗传基础仍未得到很大程度的探索：它们使用不够准确的疾病严重程度的视觉评估，人工接种有限数量的病原菌，以及在小麦生长季节进行单一测量。这一目标将通过使用我们最近开发的精确、高通量的数字表型方法来表征现场自然的STB疫情发展来实现。选择的小麦群体是多亲高级世代间杂交(MAGIC)群体，它结合了高度的遗传多样性和丰富的重组，是鉴定新的抗病数量性状基因座(QTL)的强大资源。新的抗病数字表型以前从未与小麦魔术种群一起使用过。目标2：实现对STB流行发展的准确和稳健的预测。该项目中设计的预测模型将使用先进的机器学习方法来结合三种类型的大数据集：精确的疾病测量、小麦基因组数据和气象数据。基因组数据包括魔术种群中分离的13,000个单核苷酸多态(SNPs)。我们将首先使用常规的QTL定位技术来确定与流行发展/抗病相关的性状相关的SNPs。接下来，我们将使用基于惩罚线性回归的线性机器学习方法，该方法能够组合这三种类型的数据。此外，我们还将采用基于决策树捕获非线性依赖关系的计算密集型算法。目标3：优化杀菌剂和抗病基因在小麦中的组合使用。这将通过将目标1和目标2的成果纳入最先进的数学建模框架来实现。我们将把目标1中获得的关于定量抗病能力的知识与目标2中设计的STB预测模型以及杀菌剂剂量-反应数据集整合到一个流行病学/进化建模框架中。一种多目标优化算法将用于在单一生长季的短期内优化杀菌剂处理方案和抗病小麦品种的选择。我们将把它们与在较长时间内进行的多个连续生长季节的优化结果进行比较。

The fungal pathogen Zymoseptoria tritici (Zt) causes septoria tritici blotch (STB), the most damaging disease of wheat in Europe and one of the largest constraints on wheat production globally. The disease is especially serious in the UK because of conducive climatic conditions. It is becoming increasingly difficult to control STB, because Zt is capable of rapidly evolving resistance to fungicides and adapting to disease-resistant wheat varieties and environmental conditions. No single control measure is durable in the face of the pathogen's notorious adaptive capacity, hence the two key control methods - fungicides and disease resistance genes in wheat - need to be combined in a manner that optimizes not only control efficacy in the short term, but also their sustainability in the longer term. This interdisciplinary project will make a major contribution to this goal using a powerful combination of large-scale field experimentation with novel high-throughput phenotyping techniques, bioinformatic analyses, state-of-the-art machine learning and mathematical modelling.Objective 1: Reveal the novel genetic bases of quantitative STB resistance in wheat. Genetic basis of quantitative resistance to STB remains largely unexplored because of the limitations in the current phenotyping methods: they use insufficiently accurate visual assessments of disease severity, artificial inoculation with limited numbers of pathogen strains, and single measurements during the wheat growing season. The objective will be achieved by characterizing natural STB epidemic development in the field using precision, high-throughput digital phenotyping approaches that we recently developed. The wheat population of choice is the Multiparent Advanced Generation Inter-Cross (MAGIC) population, which combines a high genetic diversity with abundant recombination, representing a powerful resource for identifying new quantitative trait loci (QTL) responsible for disease resistance. New digital phenotyping of disease resistance has not been previously used in conjunction with wheat MAGIC populations.Objective 2: Achieve accurate and robust predictions of STB epidemic development. The predictive models devised in the project will use advanced machine learning approaches to combine large datasets of three types: precision disease measurements, wheat genome data and meteorological data. Genomic data consists of >13,000 single nucleotide polymorphisms (SNPs) segregating in MAGIC population. We will first use conventional QTL mapping techniques to identify the SNPs associated with the traits related to epidemic development/disease resistance. Next, we will use linear machine learning approaches based on penalized linear regression that are capable of combining the three types of data. Furthermore, we will employ more computationally intensive algorithms based on decision-trees capturing nonlinear dependencies. Most powerful predictors will be identified to construct models that provide sufficient accuracy, while minimizing the costs of data acquisition.Objective 3: Optimize the combined use of fungicides and disease resistance genes in wheat. This will be achieved by incorporating the outcomes of Objectives 1 and 2 into the state-of-the-art mathematical modelling framework. We will integrate the knowledge on quantitative disease resistance acquired in Objective 1 with predictive models of STB devised in Objective 2 together with fungicide dose-response datasets into an epidemiological/evolutionary modelling framework. A multi-objective optimization algorithm will be used to optimize choices of fungicide treatment programmes and disease-resistant wheat cultivars over a short term of a single growing season. We will compare them with the outcomes of optimization conducted over a longer term of a number of consecutive growing seasons.