Presentation4 - ColNucleo & FIGS_R tools

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Presentation4 - ColNucleo & FIGS_R tools

  1. 1. Mauricio Parra Quijano FAO consultant International Treaty on Plant Genetic Resources for Nutrition and Agriculture CAPFITOGEN Program Coordinator Tools
  2. 2. ColNucleo Obtaining ecogeographical core collections based on ELC maps
  3. 3. Again about genetic representativeness A B C accggtccc accggtcgc accggtctc A B C A A A A B C A A A A B BB B C BA
  4. 4. When collections are very large (>1000)… ABB AAA CAB CAB ABB AAA AAA A B A A A A B BB B C BA A B C A A A AB B B B CBA C A A A A A A A A Random By genotype By phenotype ABB AAA AAA ABB AAA CAB CAB But not real
  5. 5. What information should we use to select? Characterization Morphological Biochemical/ Molecular Agronomic/ Physiological/ Phytopathology Entomology
  6. 6. Types of core collections according to data  Random  Political / Administrative  Phenotypic (morphological)  Phenotypic (quantitative traits of agronomic interest)  Genotypic (molecular markers - neutral)  Ecogeographical (adaptation to the abiotic environment)  Mixed / Cumulative
  7. 7. Ecogeographical core collections  The first ideas about using information on CC using adaptation data back to 1995  Only until 2000-2010 the use of GIS became popular in RFG  In 2005 the first ELC map was created  In 2009, two eco-geographical core collections were obtained and validated
  8. 8. Ecogeographical core collections
  9. 9. Determination of representativeness Mean Variance Matching Ranges Coefficient of variace
  10. 10. Ecogeographical CC vs Phenotypic CC
  11. 11. Determination of representativeness
  12. 12. What does ColNucleo offer? Starting with an ELC map (from ELC mapas tool) P C Sampling intensity 10% 15% 20% … 1000 100
  13. 13. What does ColNucleo offer? Seeds availability? Ecogeographical core collection In addition…  Phenotypic/Genotipic validation is advisable  Perform further stepwise strategy by selecting other types of variables (descriptors)  Selecting by pheno/genotypic representativeness, not randomly
  14. 14. One or more core collections?
  15. 15. FIGS_R Determination of subsets focused on traits of interest for breeders (Focused Identification of Germplasm Strategy)
  16. 16. Why is it so difficult to use germplasm? Poor visibility of the germplasm collections Lack of information on the preserved material The available information is not very useful in practice Limited accessibility to information Inaccessibility to germplasm Limited interest of breeders to use germplasm collections
  17. 17. Conflict of interests…  Curators Representativeness  Breeders Traits
  18. 18. The paradox of the use of PGR  Breeders frequently find collections of 1000 entries or more  They have limited availability to test  Breeders use 100 or 150 entries at the most to evaluate a trait of particular interest, as part of their routine activity  Breeders need information (characterization / evaluation data) on the preserved germplasm to make use of it.  PGR curators prioritize efforts to preserve and, only when enough funds are available, to characterize  There are very few evaluation data (or at least available)... which consequently leads to almost random selections by breeders…  There are always little or insufficient funds to characterize and evaluate the germplasm  Low level of use, reduced interest  Gradual reduction of funds for characterizing/evaluating
  19. 19. Focused Identification Germplasm Strategy  Original idea from Michael Mackay (1986,1990, 1995) Fenotype = Genotype + Environment + (GxE)  Identifies germplasm with high probability of containing genetic diversity for the trait of interest  Uses ecogeographical information for the prediction of traits occurrence as a preliminary step to field trials, where breeders ultimately confirm the existence of the trait No previous efforts on characterization/field evaluation are required and the number of entries that are delivered to the breeders to be evaluated is reduced Resistanc e/Tolerance = Genotype + Environment + (GxE)  Generating FIGS subcollections (≠ core collections) Enhancing the
  20. 20. First approach… Temperature Salinity score Elevation Rainfall Agro-climatic zone Disease distribution F I G SOCUSED DENTIFICATION OF ERMPLASM TRATEGY Datalayerssieveaccessions basedonlatitude&longitude Source: Figure from Mackay (1995) GISlayers/ Ecogeographicalvariables Germplasm FILTERED!!! We use expert knowledge  Species experts  Breeders  Entomologists, phytopathologists
  21. 21. Second approach… modeling Clasification method AUC Kappa Field validation Principal Component Regression (PCR) 0.69 0.40 ? Partial Least Squares (PLS) 0.69 0.41 ? Random Forest (RF) 0.70 0.42 ? Support Vector Machines (SVM) 0.71 0.44 ? Artificial Neural Networks (ANN) 0.71 0.44 ? Y = b + X1 + X2 + X3Resistance/ Tolerance Ecogeographical variables (Genebank: ICARDA wheat collection– Trait: Stem rust (Puccinia gramini) Source: Bari et al., 2012. Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables. Genet Resour Crop Evol 59(7):1465-1481 Predict on non-eval/characterized germplasmEval/characterized of germplasm Pattern
  22. 22. What does FIGS_R offer? It generates FIGS subsets via filtering Ecogeographical characterization Matrix Pasaport data table Elevation Average Annual Temperature Edaphic Organic Carbon Topsoil pH …. …. Y X ECOGEO  FIGS_R characterize ecogeographically the collection using the selected variables
  23. 23. What does FIGS_R offer?  FIGS_R characterize ecogeographically the collection using the selected variables  It uses up to three ecogeographical variables and perform a stepwise selection Annual Precipitation (primary variable) Edaphic clay (secondary variable) Slope (tertiary variable) 40 4 Intensidad de selección
  24. 24. What does FIGS_R offer?  FIGS_R characterize ecogeographically the collection using the selected variables  It uses up to three eco-geographical variables and perform a stepwise selection  It selects entries from a range of values ​​for each variable or a proportion of the distribution of values ​​(e.g. lower 30%), in separate processes for each variable. PROPORTION OF THE DISTRIBUTION 40% lower 35% higher Lower value Upper valueRANGE
  25. 25. What does FIGS_R offer?  FIGS_R characterize ecogeographically the collection using the selected variables  It uses up to three eco-geographical variables and perform a stepwise selection  It selects entries from a range of values ​​for each variable or a proportion of the distribution of values ​​(e.g. lower 30%), in separate processes for each variable.  It can use (depending on the user) an ELC map to try to balance the selection of accessions, taking the fraction of the distribution from each category
  26. 26. What does FIGS_R offer?  FIGS_R characterize ecogeographically the collection using the selected variables  It uses up to three eco-geographical variables and perform a stepwise selection  It selects entries from a range of values ​​for each variable or a proportion of the distribution of values ​​(e.g. lower 30%), in separate processes for each variable.  Like ColNucleo, it can take into account the availability of the germplasm indicated by the curator.
  27. 27. One or more FIGS subsets?

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