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

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.