This document discusses tools for selecting core collections and subsets of plant genetic resources collections based on ecogeographical and characterization data. It introduces ColNucleo, which can generate ecogeographical core collections from Ecogeographical Land Characterization (ELC) maps. It also introduces FIGS_R, which implements the Focused Identification of Germplasm Strategy (FIGS) to filter collections and select subsets of accessions based on ecogeographical variables associated with traits of interest to breeders. Both tools aim to select reduced, informative samples from large collections in ways that consider representativeness and availability of accessions.

1. Mauricio Parra Quijano
FAO consultant
International Treaty on Plant Genetic Resources
for Nutrition and Agriculture
CAPFITOGEN Program Coordinator
Tools

2. ColNucleo
Obtaining ecogeographical core collections based
on ELC maps

3. Again about genetic representativeness
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B
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C BA

4. When collections are very large (>1000)…
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C BA
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Random
By genotype
By phenotype
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CAB
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But not real

5. What information should we use to select?
Characterization
Morphological
Biochemical/
Molecular
Agronomic/
Physiological/
Phytopathology
Entomology

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. 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. Ecogeographical core collections

9. Determination of representativeness
Mean Variance Matching Ranges Coefficient of variace

10. Ecogeographical CC vs Phenotypic CC

11. Determination of representativeness

12. What does ColNucleo offer?
Starting with an ELC map
(from ELC mapas tool)
P
C
Sampling
intensity
10%
15%
20%
…
1000
100

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. One or more
core
collections?

15. FIGS_R
Determination of subsets focused on traits of interest
for breeders (Focused Identification of Germplasm
Strategy)

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. Conflict of interests…
 Curators Representativeness  Breeders Traits

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. 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. 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. 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. 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. 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. 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. 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. 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. One or more
FIGS
subsets?