As the world is moving forward into an uncertain future, our agro-ecosystems are under increasing pressure, threatening our food security. In fact, climate change, dwindling water supplies, rising energy costs, the emergence of new pests and diseases, loss of arable land and population growth demands higher yield on less land, with fewer inputs under increasingly harsh conditions. For this reason, plant breeders are forced to mine global plant genetic resources collections for variation that can be used to future-proof the crop plants. However, the genetic resource collections are large and we cannot afford to evaluate every accession in a collection to hunt for desirable traits. To address such issues the concept of Focused Identification of Germplasm Strategy (FIGS) was developed to unlock the variation in gene banks and make it more accessible to the crop improvement.

            The first step in the FIGS process is to identify type of trait that would be most ideal to confer a desired outcome. Then information about adaptive and nonadaptive evolutionary processes shaping this trait and its geographic distribution should be obtained and applied in the form of eco-climatic and bio-geographic layers in an eco-geographic profile that highlights the most intense evolutionary hotspots in the plant’s range. Focusing trait mining efforts on germplasm from these hotspots should be considerably more fruitful than focusing on other areas.

            The research effort on identifying the resistance sources for sunn pest using FIGS set resulted into significant accomplishments showing how science does not have to be complicated to solve a problem; it can be quite simple. The Sunn Pest devastates small holders’ plots right across the northern hemisphere. So, the process was started to select a set of over 16,000 accessions. Among 16,000 accessions through simple filtering method, a FIGS set of 534 accessions was developed and screened based on the eco-geographic variation for adaptive traits, among them, 10 resistant accessions were found.

        The International Center for Agricultural Research in the Dry Areas (ICARDA) has a unique germplasm collection of barley, and those contains landraces, wild relatives and most of them are geo-referenced. About the FIGS approach which links adaptive traits to environments (and associated selection pressures) through filtering and machine learning which focuses on accessions that are most likely to possess trait specific genetic variation and predictive characterization on ICARDA barley collection using the FIGS approach and its algorithms, combining several machine learning methods, and using several characterization traits were presented. Most of the studied traits have shown high predictability and the analysis results were used to make a predictive characterization of the entire ICARDA barley collection by assigning probabilities of each trait to the non-evaluated accessions.

        The study was conducted to identify the sources of resistance to scald in Generation Challenge Program (GCP) and FIGS subsets, and to assess different predictive models for efficient mining of genetic resources for resistance to scald. Two subsets of gene bank accessions were used: one extracted from the Reference set developed within the GCP with 191 accessions, and the other with 101 accessions selected using the filtering approach of the FIGS. The results showed that both GCP and FIGS subsets were able to identify sources of resistance to leaf scald at both seedling and adult plant growth stages.

References:

 1. AZOUGH, Z., KEHEL, Z., BENOMAR, A., BELLAFKIH, M. AND AMRI, A., 2019, Predictive Characterization of ICARDA Gene bank Barley Accessions Using FIGS and Machine Learning. Intelligent environments., 28:121-129.

2. HIDDAR, H., REHMAN, S., LAKEW, B., VERMA, R. P. S., AL-JABOOBI, M. AND AMRI, A., 2021, Assessment and modeling using machine learning of resistance to scald (Rhynchosporium commune) in two specific barley genetic resources subsets. Scientific Reports., 11: 15967. 

3. STENBERG, J. A. AND ORTIZ, R., 2021, Focused identification of germplasm strategy (FIGS): polishing a rough diamond. Current Opinion in Insect Science., 45: 1-6. 

4. STREET, K., 2017, Case study: Genebank mining with FIGS, the Focused Identification of Germplasm Strategy.