The appearance of virulent new crop diseases are a typical recurring scenario in agricultural production that can lead to severe yield losses, across countries and entire regions. Stem rust (also known as Black Rust) caused by the fungi Puccinia graminis f. sp. tritici, has re-emerged as a major threat to world wheat following the appearance of new virulent races. Ug99, a particularly aggressive strain of stem rust, was first found in Uganda in 1999 and has since increased the vulnerability of global wheat yields.
Ug99 has taken its toll on wheat production in Sub-Saharan East Africa, Yemen and Iran and now threatens crops in Central Asia and the Caucasus and South Asia; an area that accounts for nearly 40% of global wheat production. Because of its virulence and potential to spread, Ug99 has attracted both public and research community attention worldwide and global efforts to track and monitor its expansion are underway to counter its potential impact.
Plants usually react to virulent strains through the so-called R (resistance) genes group. In the case of wheat, there are about 45 to 50 genes, known as Sr genes, which are thought to confer resistance to different races of stem rust.
Using novel disease resistance genes found in plant genetic resources collections in agricultural genebanks, can help protect wheat crops from attacks by UG99. The genetic resources in these genebanks contain crop landraces and wild relatives that have potential sources for pest and disease resistance, critical for the stability and sustainability of global wheat production.
These traits however are rare and may not be captured in representative or fixed genetic resources collections such as core collections. The need to rationalize and streamline the search for rare and adaptive traits has led genebank mangers to develop alternative approaches including the creation of specific or thematic genetic resource collections.
By using the FIGS ‘eco-geographical’ data of a dataset of plant genetic material with resistance to a specific trait, such as resistance to diseases or pests, researchers were able to successfully identify a number of novel genes in the dataset from environmentally similar sites.
For stem rust resistance, modeling used geographical information system (GIS) approaches detected a relationship between geographical areas and incidence of resistance to stem rust. Some of the traits that have been found to carry strong climatic signals in wild species are being used to model the impact of climate changes.
The FIGS trait-based approach allows breeders to have a targeted strategy to select potentially useful genetic materials for crop improvement. It was conceived to provide indirect evaluation of plant genetic materials in genebanks – targeting specific traits using environmental parameters. The approach is based on the hypothesis that the genetic material is likely to reflect the selection pressures of the environment from which it was originally sampled.
The FIGS approach addresses the lack of available evaluation data as well as the temporal (the moment when the accession is evaluated) issue of evaluation.
The objective of this FIGS-Ug99 research was to detect whether there is a link between stem rust resistance and climate. The results of which will be used to develop a subset of plant genetic material with an increased probability of finding new resistance to stem rust; and to develop algorithms to use in future applications of FIGS for ‘trait mining’ of large genebank collections.
Overall, the results of this work give indications that the trait distribution of resistance to stem rust is confined to certain environments or areas, whereas the susceptible types appear to be limited to other areas with some degree of overlapping of the two classes. The FIGS team also says that the results point to a number of issues to consider for improving the predictive performance of their models.