Spatiotemporal Changes in Varietal Resistance to Wheat Yellow Rust in France Reveal an Increase in Field Resistance Level During the Period 1985-2018.

Monitoring spatiotemporal changes in varietal resistance and understanding its drivers seem essential to managing plant diseases but require having access to the genetic basis of disease resistance and to its deployment. In this study, we focused on yellow rust (Puccinia striiformis f. sp. tritici) for three decades in France, by using field adult plant resistance levels, Yr race-specific resistance genes of varieties, presence of Puccinia striiformis f. sp. tritici pathotypes and their virulence profiles, and systematic surveys of the acreages of bread wheat varieties available at a yearly survey time and at a district level. Based on these data, we studied spatiotemporal changes in varietal resistance over the period from 1985 to 2018 in 54 French administrative districts (hereafter "departments") by using a set of relevant indicators weighted by the relative acreage proportion of the varieties sown at the department level. Our analyses revealed an increase in varietal resistance over decades that would be due to the accumulation of both quantitative resistance and different race-specific resistance genes. We suggest that, beyond breeders, several actors, including examination offices, agricultural advisory services, and farmers, may have had a substantial influence on these spatiotemporal changes, promoting more resistant varieties and the rapid replacement of newly susceptible varieties by still resistant ones at the beginning of each epidemic.

A reaction norm model for genomic selection using high-dimensional genomic and environmental data.

New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17-34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.

Triticale powdery mildew: population characterization and wheat gene efficiency.

Powdery mildew has emerged on triticale in the early 2000s in many locations, probably due to a host range expansion of the wheat formae speciales, Blumeria graminis f.sp. tritici. Many triticale cultivars are highly susceptible to powdery mildew, mainly in seedling stage, revealing a probably narrow genetic basis for powdery mildew resistance genes (Pm). Moreover, as Blumeria graminis is an obligate biotrophic fungus, it is very time consuming and difficult to maintain powdery mildew isolates for a non-specialized laboratory and populations can evolve. In order to identify wheat Pm genes efficient against natural populations of powdery mildew, wheat differential hosts and triticale seedlings were inoculated below susceptible triticale crop naturally contaminated by mildew, in several locations and several years. Symptoms on seedlings were measured after approximately two weeks of incubation in favorable fungus growth conditions. According to these data, we classified the Pm genes presents in our wheat differential hosts set in 3 classes: Pm already overcame by triticale powdery mildew, Pm having variable effects and Pm still efficient against triticale mildew. Data on triticale seedlings allowed us to identify some few triticale cultivars resistant to Blumeria graminis in seedling stage. We will try to identify Pm genes present in those cultivars next year by testing them with the characterized isolates of powdery mildew from Gent University. Nevertheless, interspecific crossing of wheat, resistant to powdery mildew in seedling stage, and rye have been initiated to introduce potentially interesting genes for resistance in triticale.

Influence of cultivated landscape composition on variety resistance: an assessment based on wheat leaf rust epidemics.

In plant pathology, the idea of designing variety management strategies at the scale of cultivated landscapes is gaining more and more attention. This requires the identification of effects that take place at large scales on host and pathogen populations. Here, we show how the landscape varietal composition influences the resistance level (as measured in the field) of the most grown wheat varieties by altering the structure of the pathogen populations. For this purpose, we jointly analysed three large datasets describing the wheat leaf rust pathosystem (Puccinia triticina/Triticum aestivum) at the country scale of France with a Bayesian hierarchical model. We showed that among all compatible pathotypes, some were preferentially associated with a variety, that the pathotype frequencies on a variety were affected by the landscape varietal composition, and that the observed resistance level of a variety was linked to the frequency of the most aggressive pathotypes among all compatible pathotypes. This data exploration establishes a link between the observed resistance level of a variety and landscape composition at the national scale. It illustrates that the quantitative aspects of the host-pathogen relationship have to be considered in addition to the major resistance/virulence factors in landscape epidemiology approaches.