Harnessing the predictive power of epidemiological modelling for wheat yellow rust disease

Vanessa Bueno-Sancho


John Innes Centre

Christopher,Judge, Francesca, Minter, Nik, Cunniffe, Richard, Morris, Diane, Saunders, , , , , , , , , , , , , , , , , , , ,

    



Wheat yellow rust is a disease caused by the fungus Puccinia striiformis f. sp tritici (PST) that is a significant threat to wheat production worldwide. Recently, a novel approach called "Field Pathogenomics" was developed that allows acquisition of genotypic data from field samples of PST-infected wheat. This has enabled us to study the re-emergence of this pathogen in the UK and understand the different races that form the current PST population. However, the dynamics of pathogen transmission and dispersal still remain unknown and understanding this is essential for designing effective surveillance. The objective of this project is to develop a spatially-explicit model for the spread of PST that can contribute to better management of the disease and be used as a warning system for wheat yellow rust infection in the UK. The first aim is to study how PST spreads at the field level and determine whether there are differences between PST races in terms of disease dynamics. To this end, a set of markers have been designed that can be used to genotype field-collected isolates and determine which race they belong to. Field trials were also undertaken across the UK using wheat varieties that are known to be susceptible to the disease, with PST-infected wheat samples collected during the 2015-2016 and 2016-2017 seasons. These samples will be genotyped to study the prevalence of different PST races and determine whether PST genotypes identified early in the season are predictive of dominant genotypes found later in the season. Understanding PST dynamics within a field is key to build an epidemiological model that can predict how this disease behaves. This would improve disease management, targeting of chemical sprays and optimize pathogen surveillance.