Figuring out FIGS: The Next-Gen of Genotyping Improving Food Security

As the Moroccan proverb goes it’s better a handful of dried figs and content with that, than to own the gate of peacocks and be kicked in the eye by a broody camel. For this week’s Development Agriculture Wednesday we look into the fruity world of FIGS and how it’s helping the industry adapt to ever increasing challenges.

Figuring out FIGS

We all know that feeling when you just can’t find the right sized screw out of the assortment in the drawer, but imagine the difficulties of finding a particular gene amongst possibly millions.

Well, thankfully, a new system has been developed to help just that. The Focused Identification of Germplasm Strategy – otherwise known as FIGS – allows researchers to pinpoint exact genes more accurately and quickly than traditional methods. The system allows researchers to screen collections of plant genetic resources to identify targeted genes to improve crops.

The improved genetic ‘agility’ enabled by this technique means better and more rapid control of new diseases. Virulent crop diseases can have huge impacts on yields across entire regions, potentially jeopardising regional food security. Using this system researchers can identify specific genes that may have resistance to particular diseases/pests much more rapidly than with traditional techniques, aiding the development of improved varieties.

How FIGS works?

Across the world there are millions of plant seed samples which are conserved in agricultural gene banks. Many of you may have heard of the doomsday vault in Svalbard, Norway, which exists to protect genetic material in case of global catastrophe. In a similar theme, slightly more widespread agricultural gene banks are developed to ensure agricultural biodiversity is maintained, and potentially vital genes are not lost. Some genes may be more tolerant to drought, extreme hot or cold weather, or particular pests and diseases yet are only present in the wild type of the crop. For example there are varieties of wheat that are incredibly tall and short but may have a particular drought or disease resistant trait contained within. Being able to develop crops which contain these traits and are resistant to negative environmental stressors can help overcome low-yields in many low-income countries (and high-income countries too).

The CSIRO and many private plant breeding companies (e.g. AGT) in Australia are always on the lookout for disease, heat and drought tolerant varieties.

But, the problem is, that searching through these millions of samples is timely, costly and incredibly inefficient. The FIGS system however, applies Bayesian mathematics, with key information (geographical and agroclimatic) to find the genes. By using environmental and agroclimatic parameters, the system predicts the places where plant traits (such as drought resistance) are likely to have evolved.  The system can find genes with a higher probability of having the sought after trait, by finding genebank accessions from selected locations which have the required environmental locations to produce that gene trait. This means that researchers have smaller amounts of material to search through.

The FIGS System

An overview of the FIGS process (source)

How FIGS was developed

In the 1980s, Michael Mackay was in search for a boron-tolerant wheat variety for farmers in Australia. He examined accessions from marine-origin soils from Mediterranean sites,  with toxic levels of boron. This material had the genetic variation required to develop boron-tolerant cultivars. Since then, ICARDA and partners have continued to develop the program.

FIGS in use

FIGS has already identified resistant varieties of wheat to sunn pest, powdery mildew (with a 16% success rate compared to a previous 5—6%), Russian wheat aphid (the first ever sources of resistance) and stem rust; of barley to net blotch; and of faba bean to drought.  Previous searches for resistant genes had proven unsuccessful by traditional means, but using FIGS, 12 resistant accessions to sunn pest were found in wheat.

Case Study: Strengthening resistance against stem rust

The Ug99 strain of stem rust first appeared in Uganda in 1999, and has had significant impacts on wheat production in Sub-Saharan East Africa, Yemen and Iran. It now threatens crops in Central and South Asia – an area accounting for 40% of global wheat production. Developing a resistant variety is crucial to stabilising global wheat supplies.

Plants react to virulent strains through ‘R (resistance) genes’.  These traits, however, are rare. There are about 45-50 of these genes known in wheat to have resistance to types of stem rust. Using genetic resources from genebanks, researches can use these resistant genes from wild relatives to develop resistant varieties – and ensure stability for global wheat production.

Researchers used geographical information system (GIS) approaches and found a relationship between incidence of stem rust resistance and geographic location. The hypothesis in this case, was that environmental factors from the site of collection influence the genetic materials traits. The results found that stem rust resistance was confined to particular areas, and susceptibility was also confined to different areas. The researchers could then develop a series of algorithms for future ‘trait mining’, which allowed them to create models to predict the performance of particular genes from given areas.

Figured it out?

The FIGS system has a lot to offer both researchers and development agencies in the search for more resistant crops to stabilise global food production, reduce vulnerability to shocks in the system, and overall ensure improved food security.

The FIGS system is one of many topics which will be covered at the Crawford Fund 2017 Conference to be held in Canberra. The former genebank curator at the International Center for Agricultural Research in the Dry Areas (ICARDA) and project leader of FIGS – Dr Ken Street – will be presenting. For more information, check out the full conference program here.

Read more on FIGS here:

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