FIGS in use

The following case studies demonstrate how the FIGS approach can improve the effectiveness of breeding for national breeding programs and international research centers.  

 

FIGS as a strategy to detect wheat stem rust resistance, linked to environmental variables

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.

 

Getting the best from bread wheat landraces

Wheat is the world’s most important food crop. In the developing world it is the second most important crop after rice. Demand for wheat currently outstrips the world’s ability to produce it – an ability that is also constantly under threat from new diseases, rampant pests and a changing climate.

The Grains Research and Development Corporation (GRDC) of Australia funded a four-year project looking at the targeted exploitation of the N. I. Vavilov Institute of Plant Industry, ICARDA and Australian bread wheat landrace germplasm for the benefit of wheat breeding programs. The project involves a collaborative effort between the famous Vavilov Institute of St Petersburg, Russia, the Australian Winter Cereals Collection hosted by the Department of Primary Industries in NSW, Australia, and ICARDA.

While the idea behind FIGS encompasses all crop types held in all genebanks, this project focuses only on bread wheat landraces held in three genebanks to help develop prototype systems. Each of the above institutes house sizable collections of bread wheat landraces. They are special collections because they are from very diverse environments and feature accessions that were collected early in the 20th Century and, as such, are unique in that many of them could no longer be found in the field due to displacement by modern varieties.

In this project a database containing information about the wheat genotypes and where they came from was compiled. Using historic collection mission reports, the geographic coordinates were captured thus allowing a connection to be built between derived agro-climatic and other parameters at collection sites. The idea is to build up detailed environmental profiles of the habitats within which a given genotype evolved with subsets designed to capture variation for resistance/tolerance to the following traits - drought, salinity, powdery mildew and Russian wheat aphids.

A core set was developed, using methodologies based on geographic locations of collection sites, to use as a check when screening the FIGS sets. The hypothesis being that researchers are more likely to find trait specific variation in the FIGS sets than in the core set.

The suite of agro-geographic parameters used to describe the collections sites are currently being expanded and further FIGS sets are under development and will be screened when funding permits.

 

A new tool in the battle against Sunn Pest

FIGS was applied in the ICARDA genebank to find sources of resistance to Sunn pest (Eurygaster integriceps), the major pest of wheat in West and Central Asia and eastern Europe. Sunn Pest can cause 100 per cent losses, but most importantly affects the bread quality even at very low infestation rates . It is reckoned to affect up to 15 million hectares of wheat annually and in excess of US$150 million is spent each year on pesticide treatments in the pest-prone regions.

A FIGS search identified 534 likely accessions. Initial field screening reduced these to 57, and advanced screening resulted in nine candidates with resistance to Sunn pest at the vegetative stage. The resistant varieties developed using the resistance genes found through the application of FIGS form an important part of integrated pest management development, designed to reduce populations of adult insects.

These nine entries are the first wheat sources found with good levels of resistance to Sunn pest at the vegetative stage. They are being used in the ICARDA wheat breeding program to develop wheat varieties resistant to Sunn pest feeding at the vegetative stage.

The 534 accessions identified by the FIGS filtering process were field screened at the ICARDA research station at Tel Hadya during 2007. In the initial evaluation, 10 seeds per hill were planted, in an augmented design, with bread wheat cultivar ‹Cham 6› as a susceptible check every 10 test entries.

Plants were covered by mesh screen cages and infested with three adult Sunn pests per hill in mid-March, the time when they usually migrate to wheat fields. The nine key accessions identified through FIGS are being used in ICARDA wheat breeding programs to develop resistant varieties against overwintered Sunn pest adults, which damage wheat at the vegetative stage (shoot and leaves).

Resistance at this stage could be important in reducing overwintered Sunn pest adult populations, as well as nymphal and new generation adult populations, which reduce wheat quality by feeding on spikes (grains). The introduction of wheat varieties carrying resistance at the vegetative stage should be one component of a total integrated pest management program against Sunn pest.

Since all the accessions resistant to Sunn pest came from the same geographical area, either from Afghanistan or from neighboring Tajikistan, it would be advisable to concentrate on these countries when screening other accessions for Sunn pest resistance in the future, and to return for additional sampling.

 

Seeking natural genetic variation for climate change adaptive traits

Natural plant genetic variation displays patterns within boundaries set up by ecological and co-evolutionary processes. These patterns have been explored in this case study to seek natural variation of tolerance to drought in the context of climate change. This is based on the assumption that a trait, as a response variable, is driven by stochastic ecological and co-evolutionary processes where modelling could be used for its location.

The findings suggest further the possibility to carry out in silico evaluation and thus manage the lack of ex ante evaluation that would help tremendously in the use of plant genetic resources in crop improvement and, ultimately, of biodiversity to sustain agriculture production and adaptation to climate change.

This research, led by the University of Helsinki, uses faba bean as a model crop to better pinpoint these drought-related traits. This research validates the efficiency of FIGS to identify subsets of germplasm with a high probability of finding climate change related-traits in a plant genetic resources collection. This study supports the premise that such approaches can be effective at identifying plant resistance to changing climate conditions.