Disentangling the origin of chemical differences using GHOST
Antagonistic Interactions Among Stripe and Stem Rust Resistance QTLs in Wheat
1. Antagonistic Interactions Among Stripe and
Stem Rust Resistance QTLs in Wheat
Abdulqader Jighly
The International Center for Agricultural Research in the Dry Areas (ICARDA)
Borlaug Global Rust Initiative Technical Workshop
Obregon - Mexico, 22-28 March, 2014
2. Acknowledgments
Funding Agencies Colleagues and collaborators
ICARDA
K. Nazari, W. Tadesse
O. Abdalla
GRDC
F.C. Ogbonnaya
Bonn University
B.C. Oyiga
University of Aleppo
F. Makdis
Yokohama City University
M. Alagu
EIAR
A. Badebo
GCSAR
O. Youssef
Organizers of the BGRI
workshop
3. Stem and Stripe Rusts on Wheat
10
–
80%
yield
loss
in
CWANA
2010
Puccinia
graminis
f.
sp.
tri0ci
Up
to
100%
loss
Puccinia
striiformis
f.sp.
tri0ci
7. Gene-Gene Interaction
in Plant Breeding
Epistasis
Antagonis2c
(nega2ve)
Synergis2c
(posi2ve)
Searching
for
neutral
alleles
(don’t
interact)
Avoiding
them
in
the
following
crosses
The
investment
in
these
requires
con?nuous
tracking
for
both
genes
8. Gene-Gene Interaction Based on
Multiple Disease Resistance Data
• The
aim
is
to
avoid
the
pyramiding
of
nega?vely
interac?ng
resistance
loci
that
are
associated
with
different
diseases.
• For
example,
the
materials
that
have
Sr2
gene
on
3BS
with
a
Leaf
rust
QTL
on
3AL
in
a
synthe?c
hexaploid
wheat
germplasm
exhibited
a
suscep?ble
stem
rust
response
(Jighly
et
al.
submi:ed).
• The
strategy:
1. Phenotyping
for
different
diseases
2. Genome
wide
associa?on
mapping
analysis
3. Gene-‐Gene
interac?on
analysis
among
all
detected
QTLs
for
the
described
phenotype
and
the
detected
QTLs
that
are
associated
with
the
other
phenotypes.
9. Objectives of this Research
• To
characterize
stripe
and
stem
rust
resistances
in
a
collec?on
of
ICARDA
elite
germplasm
• To
detect
stripe
and
stem
rust
QTLs
through
genome
wide
associa?on
mapping
• To
define
gene-‐gene
interac?ons
among
the
detected
QTLs
10. Materials and Methods
• Plant
material:
200
elite
germplasm
mostly
of
ICARDA
origin,
synthe?c
deriva?ves
and
some
Australian
cul?vars
• Phenotyping:
Data
from
stripe
rust
screening
in
2010
and
2011
in
two
loca?ons;
and
data
from
stem
rust
screening
in
2010
in
one
loca?on
• Genotyping:
1. A
set
of
4235
polymorphic
SNP
markers
2. A
set
of
2504
polymorphic
DArT
markers
11. Materials and Methods
• Sta?s?cal
analyses:
1. STRUCTURE
(Pritchard
et
al.
2000)
for
popula?on
structure
2. Tassel
3
(Bradbury
et
al.
2007)
for
marker/trait
associa?on:
Mixed
Linear
Model
(MLM)
3. Gene-‐gene
Interac?on:
Linear
regression
model
was
used
to
calculate
P
values
for
pair-‐
wise
marker
interac?ons.
The
significance
threshold
for
the
interac?ons
analysis
was
P
≤
10-‐5
4. The
interac?on
graph
was
drawn
using
the
soeware
Circos
0.63-‐4
(Krzywinski
et
al.
2009)
12. Results 1- Response to the disease
29
22
12
24
20
53
37
0
10
20
30
40
50
60
2
3
4
5
6
7
8
Number
of
Plants
Field
Score
Stripe
Rust
Response
10
36
73
81
0
10
20
30
40
50
60
70
80
90
R
MR
MS
S
Number
of
Plants
Infec2on
Type
Stem
Rust
Response
13. 2- Population Structure
0%
20%
40%
60%
80%
100%
0%
20%
40%
60%
80%
100%
Using
DArT
markers
Using
SNP
markers
21. 6- Gene-Gene Interaction Yr Phenotype
Yr/Sr
Yr/Yr
QTL
First
(R)
Allele
Mean
Pheno
Second
(R)
Allele
Mean
Pheno
Both
Yr
(R)
Alleles
Mean
Pheno
6AL/6AL
3.2
3.9
4.7
No
sig
LD
22. 6- Gene-Gene Interaction Sr Phenotype
Sr/Yr
QTLs
R
Allele
Mean
Pheno
S
Allele
Mean
Pheno
Sr
(R)
Allele
&
Yr
(S)
Allele
Mean
Pheno
Sr
(R)
Allele
&
Yr
(R)
Allele
Mean
Pheno
2BS/2BS
23.3
28.2
16.4
29.1
3DL/2BS
23.5
27.8
10.3
30.6
3DL/6AL
22.5
27.8
7.3
30.1
Sr/Sr
No
sig
LD
