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Genebank mining with the Focused Identification of Germplasm Strategy (FIGS) International Center for Agricultural Research in the Dry Areas (ICARDA) ICARDA Genetic resource section (ICARDA-GRS) Mission: collect conserve characterize evaluate document distribute genetic diversity of wheat, barely, food legumes, pasture and forage species from diverse origins for use in research and plant breeding The Gene pools Wild relatives and progenitors Biosphere Landraces Evaluate for desired trait Introgress into adapted backgrounds Breeding material ICARDA conserves over 35,000 unique wheat accessions Over 560,000 wheat accessions worldwide How do you choose accessions to evaluate ? How do I make a rational decision How do we maximize the chance of finding what we want, while minimizing the sub-set size ? What’s this Focused Identification of Germplasm Strategy FIGS is an approach The aim of FIGS is to catch useful traits in small sub-subsets of genebank accessions FIGS premise Emergence of adaptive traits are influenced by environmental factors FIGS in a nutshell If there is a dependency between trait and environmental then we can use the relationship to select best-bet germplasm for specific traits FIGS: in other words Look in environments that are likely to impose a selection pressure for the trait of interest. FIGS filter pathway Salinity score Elevation Rainfall Agro-climatic zone Disease Data layers sieve accessions based on latitude & longitude Temperature distribution FOCUSED IDENTIFICATION OF GERMPLASM STRATEGY FIGS requirements Geo-referenced landraces or wild relatives Accurate data about the collection site • Climatic • Soil • Land-use • Ecological • Anecdotal Digital soil maps (surfaces) High resolution climatic surfaces – Long term average daily data at 1 km2 grid size Surfaces 1. 2. 3. 4. 5. 6. 7. Precipitation Max temperature Min temperature Photosynthetically active radiation Relative humidity Wind speed Vapour pressure deficit Applying FIGS within the crop cycle Nov Filter at specific phases of crop development June Oct OK, but does FIGS work? Net-blotch in barley Screening of FIGS subset of Net blotch Response of Net Blotch subset to Net Blotch 30 24 # of barley accessions 25 Immune 20 17 15 Resistant 12 Medium Resistant 9 10 Medium susceptible Susceptible Inoculation 63 accessions Mixture of 20 isolates 5 1 0 §Infection Number of accessions 40 responses Response of Net-blotch subset to Spot Blotch 38 35 28 30 Immune Resistant 25 20 15 15 Medium Resistant Medium susceptible 83 accessions Mixture of 19 isolates 10 5 0 0 Susceptible 2 ¥Infection responses § Tekauz, A. 1985. Can. J. Plant Pathol. 7:181-183. Steffenson. 1999. Plant Dis. 83:213-217. ¥ Fetchand Novel trait variation – using FIGS Sunn Pest - wheat Russian wheat aphid (Syrian biotype) – wheat Barley yellow dwarf virus - barley Powdery mildew – wheat, barley (new genes) Leaf miner resistance - chickpea Virus resistance (lentil, chickpea- AMV, BWYV, CpCSV, CMV) Modelling pathway Non-linear classification models Random forest Support vector machines Neural networks svm Likelihood of finding yellow rust resistance (dark red – 60 highly likey) 50 1.0 0 0 0.6 0.6 0.6 0.4 0.2 1 0.6 30 Latitude 0.2 0 0 0.8 0 0.2 0.6 0.6 0.2 0.4 0.6 0.4 0.4 20 0.2 0 0.4 0.2 10 Y 0.8 0.8 0.4 40 0.6 0.8 0 0 0 20 0.4 40 0.0 0.2 60 X Longitude 80 100 120 Web-based software application Acknowledgments Funded by • GRDC • VIR wheat department • AWCC • Jan Konopka • Eddy De-Pauw • Michael Mackay