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Predicting and Locating Sources of
Resistance to Stripe (Yellow) Rust in
Durum Wheat Genetic Resources
2nd International Wheat Stripe Rust Symposium
Izmir - Turkey, 28 April – 1st May 2014
Grain
Research &
Development
Corporation

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Outline
Challenges and opportunities
Sub setting PGR – FIGS approach
Stripe rust resistance case
Work ahead
Partnership (new)

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• More than 7 million accessions
• More than 1400 genebanks
• Data/concepts
• Search cost1 implications
• Time2 lags implications
-----------------
Challenges - opportunities
Gollin D, Smale M, Skovmand B (2000) Searching an ex situ
collection of wheat genetic resources.
Am J Agric Econ 82:812–827
Koo B, Wright BD (2000) The optimal timing of evaluation of
genebank accessions and the effects of biotechnology.
Am J Agric Econ 82:797–811
1
2

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The utilisation of genebanks has not kept pace
with their expansion! Gollin et al. (2000)

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New trait variation - FIGS
 Net blotch (barley)
 Powdery mildew
 Russian wheat aphid (RWA)
 Sunn pest
Braidotti, G. (2009)
Partners in Research for Development
A wheat landrace from Turkey
collected in 1948 was discovered
to carry genes of resistance to
fungal diseases in 1980s.
Atalan-Helicke N (2012) Conserving
diversity at the dinner table: plants, food
security and gene banks. Origins: Current
Events in Historical Perspective
Accessed 5 April 2014
Challenges - opportunities

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PGR
(Biodiversity)
Stratification/
Multl-stage
procedure
Sub-setting sub set
PGR
(Biodiversity)
Sub setting
Filtering/
Relationship
FIGS set
(Trait)
PGR sub-setting: FIGS approach
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By applying to plant genetic resources/agro-biodiversity
the same selection pressure exerted on plants by evolution.
Sub setting to overcome the problem of the large size
(search cost) of PGR collections

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Detect presence of patterns (environment x trait)
Presence of patterns -----> quantification and prediction
MacArthur (1972)
Assessing PGR/Agro-Biodiversity for rust resistance
Environment
(tmin, tmax, prec)
Trait (T)
(Resistance to stripe Rust)
Bayes – Laplace approach (inverse probability)
Learning based approach (risk minimization)
Cherkassky & Mulier (2007)
The Bayes-Laplace inverse theorem focuses on the
probability of causes in relation to their effects, in
contrast to the probability of effects in relation to their
causes. Fisher (1922, 1930)
(E)

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FIGS powdery mildew set
Results of screening
Accessions infected with 4 powdery mildewisolates
which were avirulent or virulent to the known Pm3
alleles
Of these 420 sites, 40% yielded accessions that were
resistant to the isolates used – 211 accessions
Starting with a total pool of 16,000 accessions collected
from 6,159 sites, the FIGS process chose 1,320
accessions collected from 420 sites
Kaur K; Street K; Mackay M; Yahiaoui N; Keller B (2008). Allele mining and sequence diversity at
the wheat powdery mildew resistance locus Pm3. 11th IWGS, 24-29 Aug., Brisbane

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Distribution of new Pm3 alleles
FIGS powdery mildew set

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Mining natural variation
By linking traits (phenotype), environments (and associated selection
pressures) with genebank accessions (e.g. landraces and crop relatives)
-> ‘focus’ in on those accessions most likely to possess trait specific
genetic variation.
0 50 100 150
0102030405060
Longitude
Latitude
Trait (disease score)Environnement FIGS subset
www.icarda.org/

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Focused Identification of
Germplasm Strategy
Geo-referencing of
collecting sites
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Evaluation
(phenotyping)
Environment (E)
Accession
(G)
Trait (T)
FIGS approach – summarized

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FIGS pathways – so far…
User defined trait
Evaluation (limited)
data
No evaluation data
Use filtering process
Identify environmental x trait
relationship (model)
Use relationship to predict candidate sites
Knowledge (Specialised)
Use a priori process

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Accuracy metrics
The ROC curve and the resulting pdf’s of trait distribution (trait states)
1
1
1-
ROC curve pdf’s of trait distribution
High AUC (area) values indication of potential trait-environment relationship
Patterns present in data
Predictions
Frequency
Truepositiverate
False positive rate
Environment

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Parameters which provide information on the accuracy of the predictions
(“trait x agro-climate”)
Observed
Tolerant Susceptible
Predicted Tolerant a b
Susceptible c d
Confusion matrix (2-by-2 contingency table)
Sensitivity = a/ (a + c)
Specificity = d/(b + d)
• Sensitivity refers to the proportion of accessions with resistance scored as
resistant, while
• Specificity refers to the proportion of accessions without resistance scored
as susceptible
Both are indicators of the models ability to correctly classify observations.
Accuracy metrics

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pdf’s of trait distribution
Accuracy metrics
Randomness (no pattern)
1
1
1- ROC curve
Predictions
Frequency
Truepositiverate
False positive rate

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Stripe rust – search for resistance
Aim
Predict accessions/areas
likely to be resistant
/conducive to stripe rust
appearance/presence
Hypothesis
Relationship exists between
the geographic distribution of
stripe rust resistance and
collection site climate
descriptors

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Sub-Setting procedure – a priori
ICARDA genebank ~ 20 000 accessions of durum wheat
2915 accs Entire
collection
Training set
Test set
~ 725
accs
(before
2011)
~ 2915
accs
(2011/12)
Training set
Validation
(actual
evaluation)
Prediction/
Location
(in silico
evaluation)

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Layers used in the studies:
• Precipitation (rainfall)
• Maximum temperatures
• Minimum temperatures
+ Derived GIS layers such as:
• Potential evapotranspiration (water-loss)
• Moisture/Aridity index
(mean values for month and year)
Eco-climate data (X)
ICARDA
Geo-Informatics
Current ICARDA eco-climatic database, average: annual temperature
(front), annual precipitation (middle), and winter precipitation (at the back)
(De Pauw 2008)
Site code prec01 prec02 prec03 prec04 prec05
….
. ari01 ari02 ari03 ari04 ari05
ETH-S893 25 36 72 154.22 148.88 0.167 0.246 0.439 1.098 1.169
NS_339 44 67 130.43 177.96 185.74 0.351 0.552 0.949 1.457 1.751
NS_559 23 40 61.89 129.04 102 0.226 0.397 0.511 1.206 0.998
Climate data (X)

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Trait data set (Y)
.
.
.
.
.
Trait data
(Y as dependent variable)
http://www.icarda.org/striperust2014/2nd-international-wheat-stripe-rust-symposium-2014/
Genetic Resources - ICARDA

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Modeling framework
20
Yi ~
Trait data (Y)
Y ~ f(X)
Environmental data (X)
X is the set of variables that contains
explanatory variables or predictors
(climate data) where X ∈ Rm,
Y ∈ Y that is either a categorical (label) or
a numerical response (trait descriptor
states).
Bari A. et al. (2011) Genetic Resources and Crop Evolution
http://www.springerlink.com/content/m7140x68v2065113/fulltext.pdf
Conceptual framework at:
Bernoulli distribution

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Sub setting - variables
Stripe rust Resistance/trait states
(Y) – Response variable
(X) – climate variables

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Geographical
Information System
(GIS)
Arc Gis
Environmental data/layers
(surfaces)
R language
(Development of algorithms)
> Data transformation ( )
> Model <- model(trait ~ climate)
> Measuring accuracy metrics
> ….
Platform - analysis
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Modeling purpose Generation of
environmental data
Algorithms :
to search for dependency, if it exists!
Climate data
to generate surfaces

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Machine learning classification (models)
algorithms
Support Vector Machines (SVM)
Random Forest (RF)
Neural Network (NN)
x1
x2
xp
F(x
)
Bari A, Street K, Mackey M, Endresen DTF, De Pauw
E, Amri A (2012) Focused identification of germplasm
strategy (FIGS) detects wheat stem rust resistance
linked to environmental variables.
Genet Resour Crop Evol 59:1465–1481

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Models used – non linear
• Y normally distributed at each value of X
• Variance of Y should be constant for each value of xi
(homogeneity of variance)
• No serial correlation – values of Y independent of one another
• A linear or curvi-linear response
Time consuming transformations – if
assumptions violated
Assumptions of the linear model
Limits to detecting relationships that have higher
dimensions or are more complex

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Model AUC Sensitivity Specificity
Proportion
correct Kappa
SVM mean 0.72 0.65 0.78 0.74 0.40
lower 0.69 0.61 0.74 0.72 0.35
upper 0.74 0.69 0.82 0.77 0.45
RF mean 0.70 0.64 0.76 0.73 0.37
lower 0.67 0.61 0.71 0.69 0.30
upper 0.73 0.67 0.81 0.76 0.44
NN mean 0.73 0.69 0.77 0.74 0.41
lower 0.70 0.58 0.69 0.70 0.35
upper 0.76 0.79 0.85 0.78 0.48
Results – accuracy metrics values
Training/validation set –
define dependency “approximation” function

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Model AUC Sensitivity Specificity
Proportion
correct Kappa
SVM mean 0.72 0.67 0.78 0.75 0.41
lower 0.71 0.64 0.74 0.73 0.36
upper 0.74 0.70 0.81 0.76 0.45
RF mean 0.71 0.63 0.80 0.75 0.40
lower 0.70 0.58 0.77 0.73 0.36
upper 0.73 0.67 0.84 0.77 0.45
NN mean 0.74 0.74 0.74 0.73 0.41
lower 0.72 0.65 0.67 0.69 0.37
upper 0.76 0.83 0.81 0.76 0.46
Test/unknown set –
in silico evaluation vs actual evaluation
Results – accuracy metrics values (Yr)

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Classifier method AUC Cohen’s Kappa
Principal Component
Regression (PCR)
0.69 (0.68-0.70) 0.40 (0.37-0.42)
Partial Least Squares
(PLS)
0.69 (0.68-0.70) 0.41 (0.39-0.43)
Random Forest (RF) 0.70 (0.69-0.71) 0.42 (0.40-0.44)
Support Vector
Machines (SVM)
0.71 (0.70-0.72) 0.44 (0.42-0.45)
Artificial Neural
Networks (ANN)
0.71 (0.70-0.72) 0.44 (0.42-0.46)
0.0 0.4 0.8
0.00.20.40.60.81.0
0.0 0.5 1.0
0.00.51.01.52.02.53.0
Results – accuracy metrics values
Stem rust – previous research

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False positive rate
Truepositiverate
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
-0.2900.290.580.871.16
-0.5 0.0 0.5 1.0 1.5
01234
Distribution bytrait state
Truepositiverate
Frequency
Bari et al. (2014). Predicting resistance to stripe (yellow) rust in wheat genetic resources using
Focused Identification of Germplasm Strategy (FIGS). Journal of Agricultural Science
ROC plots (left) and density plots class prediction (right)
False positive rate Predicted probability
Results – Graphs -Stripe rust

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0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.0 0.2 0.4 0.6 0.8 1.0
01234
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.0 0.2 0.4 0.6 0.8 1.0
0.00.51.01.52.02.53.0
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.0 0.2 0.4 0.6 0.8 1.0
012345
Results – Model predictions
SVM
RF
NN
Accuracy metrics (ROC)
plots for the SMV, RF and
NN models applied to the
evaluation data not made
known to the model.
The histograms are about
predictions of resistance
and susceptibility, where
= susceptibility
= resistance.

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Results – spatial patterns
Likelihood of an area yielding traits of resistance to stripe rust
(yellow colour)
Longitude
Latitude

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Sub-Setting procedure – adjustment
based on phenology
Alignment of data based
on phenology
To reduce:
• The “out phase”
differences due to
different growing
seasons/periods
The daily data were derived from models involving the proposed model
by Epstein (1991) as a sum of harmonic components.

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Modelling/predictions
Capturing the shift induced by climate
Based on the estimation of the duration of the period during the year
in which neither moisture nor temperature are limiting to plants.
Target specific
phase of crop
development
Bari et al. (in press). Searching for climate change related traits in plant genetic resources collections
using Focused Identification of Germplasm Strategy (FIGS). Options Méditerranéennes.
Alignment of data based on phenology

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Accuracy and agreement parameters of
aligned data
Sub-Setting procedure – adjustment
based on phenology - results
Data type AUC
Omission
rate Sensitivity Specificity
Correct
classification Kappa
monthly 0.81 0.28 0.72 0.90 0.86 0.61
daily data 0.82 0.30 0.70 0.93 0.88 0.64
aligned
daily data 0.83 0.28 0.72 0.95 0.90 0.70
210
days
False positive rate
Truepositiverate
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
-0.2900.290.580.871.16

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Modelling/predictions
Capturing the shift induced by climate - verification
0 100 200 300
020406080
x$x
x$ysmth
Data
alignment to
growing season
Algorithms
Separate phase
variation from
amplitude variation
0 100 200 300
50100150200
x$x
x$ysmth
Site (i) : Si(xi, yi) Site (j): Sj(xj, yj)
day
rainfall
day
http://mpe2013.org/
We are not there yet …

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Future directions (in summary)
Trait data (Y)Environmental data (X)
x
u
y
u for yet unknown variables
FIGS aims to deal with unobserved inputs, uncertainty,
and un-ambiguity (v)
CC induced shift (bias)
Z to eventually capture the dynamics (complexity)
v
Climate change
FIGS
Z(t)

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“Applied Mathematics and
Omics Technologies for
Discovering Biodiversity and
Genetic Resources for Climate
Change Mitigation and
Adaptation to Sustain
Agriculture in Drylands”
http://mpe2013.org/
Future directions
Explore the use of a variety of applied
mathematics approaches in relation to
phenology aspects of both the pathogen
and the host.
Expect to appear also at MPE
host pathogen
Summary proceedings

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Teşekkür Ederim
Thank you
Abdallah Bari
Kumarse Nazari
Miloudi Nachit
Ahmed Amri
Ken Street
Chandra Biradar
Amor Yahyaoui
Dag Endresen