CAPFITOGEN Programme for the Strengthening of Capabilities in National Plant Genetic Resources Programmes, International Treaty on Plant Genetic Resources for Food and Agriculture - FAO
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Presentation 6 col nucleo_figs_r
1. Mauricio Parra Quijano
FAO consultant
International Treaty on Plant Genetic Resources
for Nutrition and Agriculture
CAPFITOGEN Program Coordinator
http://www.capfitogen.net
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. 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. 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
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
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
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.