Download FIGS purpose

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