Population Genomics of Cercospora beticola

Fungal plant pathogens pose a serious threat to global food safety and security, and can result in significant yield loss. Fungal plant pathogens have evolved with their hosts during the history of crop domestication. While some fungal phytopathogens of modern crops have maintained the ability to infect the wild relatives of these crops, many have evolved host-specificity due to the evolutionary arms race. Co-evolution between plants and their pathogens spans many generations. Therefore, we have employed the pathosystem of the fungal pathogen Cercospora beticola and its hosts Beta vulgaris ssp. (domesticated beet) and B. maritima (sea beet). This pathosystem is exceptional as sugar beet has a relatively short domestication history of ~300 years, compared to several thousand year history of other modern crops. Investigating the effect crop domestication has on fungal evolution in such a short time frame may provide insight into the early processes underlying the evolution of host-specificity. The availability of whole genome sequencing data for entire populations of fungal plant pathogens has enabled detailed analyses of genomic variation within and among field populations. Using population genomic data, we are able to detect population structure of a phytopathogenic fungus, identify regions that are highly differentiated between isolates, and predict the evolutionary trajectory of disease epidemics. The primary focus of this thesis was to describe the population genomics of the fungus Cercospora beticola, and determine the influence of host domestication on recent evolution and population structure of the fungus. Chapter 1 addressed the challenge of assembling and analysing population genomic data of species with structural variation, as is the case for many pathogenic fungi. We compared and contrasted two variant calling methods used in population genomics. We show that the commonly used method of variant calling, reference mapping-based approaches, as well as more recently adapted multiple genome ...