GWAS reveals a rapidly evolving candidate avirulence effector in the Cercospora leaf spot pathogen.

The major resistance gene BvCR4 recently bred into sugar beet hybrids provides a high level of resistance to Cercospora leaf spot caused by the fungal pathogen Cercospora beticola. The occurrence of pathogen strains that overcome BvCR4 was studied using field trials in Switzerland conducted under natural disease pressure. Virulence of a subset of these strains was evaluated in a field trial conducted under elevated artificial disease pressure. We created a new C. beticola reference genome and mapped whole genome sequences of 256 isolates collected in Switzerland and Germany. These were combined with virulence phenotypes to conduct three separate genome-wide association studies (GWAS) to identify candidate avirulence genes. We identified a locus associated with avirulence containing a putative avirulence effector gene named AvrCR4. All virulent isolates either lacked AvrCR4 or had nonsynonymous mutations within the gene. AvrCR4 was present in all 74 isolates from non-BvCR4 hybrids, whereas 33 of 89 isolates from BvCR4 hybrids carried a deletion. We also mapped genomic data from 190 publicly available US isolates to our new reference genome. The AvrCR4 deletion was found in only one of 95 unique isolates from non-BvCR4 hybrids in the United States. AvrCR4 presents a unique example of an avirulence effector in which virulent alleles have only recently emerged. Most likely these were selected out of standing genetic variation after deployment of BvCR4. Identification of AvrCR4 will enable real-time screening of C. beticola populations for the emergence and spread of virulent isolates.

Non-linear transformation of enzyme-linked immunosorbent assay (ELISA) measurements allows usage of linear models for data analysis.

BACKGROUND: In research questions such as in resistance breeding against the Beet necrotic yellow vein virus it is of interest to compare the virus concentrations of samples from different groups. The enzyme-linked immunosorbent assay (ELISA) counts as the standard tool to measure virus concentrations. Simple methods for data analysis such as analysis of variance (ANOVA), however, are impaired due to non-normality of the resulting optical density (OD) values as well as unequal variances in different groups. METHODS: To understand the relationship between the OD values from an ELISA test and the virus concentration per sample, we used a large serial dilution and modelled its non-linear form using a five parameter logistic regression model. Furthermore, we examined if the quality of the model can be increased if one or several of the model parameters are defined beforehand. Subsequently, we used the inverse of the best model to estimate the virus concentration for every measured OD value. RESULTS: We show that the transformed data are essentially normally distributed but provide unequal variances per group. Thus, we propose a generalised least squares model which allows for unequal variances of the groups to analyse the transformed data. CONCLUSIONS: ANOVA requires normally distributed data as well as equal variances. Both requirements are not met with raw OD values from an ELISA test. A transformation with an inverse logistic function, however, gives the possibility to use linear models for data analysis of virus concentrations. We conclude that this method can be applied in every trial where virus concentrations of samples from different groups are to be compared via OD values from an ELISA test. To encourage researchers to use this method in their studies, we provide an R script for data transformation as well as the data from our trial.