Kevin P. Smith, Vikas Vikram, Ahmad Sallam, Aaron Lorenz, Jean-­‐Luc Jannink, Jeffrey Endleman, Richard Horsley, Shiaoman Chao, and Brian Steffenson

1. Using
Genomic
Selec.on
in
Barley
to
Improve
Disease
Resistance
Kevin
P.
Smith,
Vikas
Vikram,
Ahmad
Sallam,
Aaron
Lorenz,
Jean-­‐Luc
Jannink,
Jeffrey
Endleman,
Richard
Horsley,
Shiaoman
Chao,
and
Brian
Steffenson

2. Genomic
Selec.on
Training
populaGon
Line
1
76
1
1
1
Line
2
56
1
1
1
Line
3
45
1
1
1
Line
4
67
0
1
0
Line
n
22
1
1
1
Line
Yield
Mrk
1
Mrk
2
…
Mrk
p
…
Model
training
SelecGon
candidates
Line
A
1
1
1
Line
B
1
1
1
Line
C
1
1
1
Line
D
0
1
0
Line
n
1
1
1
Line
Yield
Mrk
1
Mrk
2
…
Mrk
p
…
Parent
selecGon
Line
A
80
1
1
1
Line
B
67
1
1
1
Line
C
56
1
1
1
Line
D
89
0
1
0
Line
n
23
1
1
1
Line
GEBV
Mrk
1
Mrk
2
…
Mrk
p
…
Basic
framework
GEBV
=
genomic
es.mated
breeding
value
PredicGon
1
ˆ
p
i j i j
j
GEBV b x
=
= ∑1
p
i j i j
j
y b x
=
= ∑

3. R/T
=
i
r
∂A
Gain per
Year
Selection
Intensity
Accuracy
Genetic
Variance
# Breeding
Cycles
Year
Crossing
1
F1
F2
F3
2
F4
F5
Head
Rows
3
1st
Year
Yield
4
2nd
Year
Yield
5
3rd
Year
Yield
6
Regional/Industry
7
Regional
Industry
8
Variety
Release
Genomic
Selec,on
Improves
Gain
per
Time

4. Barley
Predic.on
Data
Sets
USDA Regional Genotyping Centers
Fargo
Triticeae Toolbox http://
triticeaetoolbox.org/
SNP Map Ten U.S. Barley Breeding Programs
Fargo,
ND
Raleigh,
NC
Manha[an,
KS
Pullman,
WA

5. Assessing
Predic.on
Accuracy
Training
PredicGon
Sub-­‐sample
Single
Data
Set
Dis.nct
Training
and
Predic.on
Data
Sets
Training
=
Parents
Predic.on
=
Progeny
CROSS
VALIDATION
INTER-­‐SET
VALIDATION
PROGENY
VALIDATION
RelaGve
Accuracy
=
CorrelaGon
(GEBV,
Observed)/Sqrt(Heritability)

6. Fusarium
Head
Blight
(FHB)
Another
challenging
disease
in
Barley
Major
outbreak
in
Midwest
U.S.
in
1993
Mycotoxin
deoxynivalenol
(DON)
Sources
of
resistance
are
unadapted
Quan.ta.vely
inherited
resistance
Many
QTL
with
small
effects
Challenging
to
phenotype

7. Barley
CAP
FHB
Six-­‐row
Midwest
Data
Set
896
six-­‐row
lines
3,072
SNPs
Mean
of
4
trials
Evaluated
over
4
years
Busch
Agriculture
BA
U.
Minnesota
UM
North
Dakota
State
ND
CAP
I
CAP
II
CAP
III
CAP
IV
96
96
96
96
32
32
32
32
96
96
96
96

8. Training
Panel
and
Marker
Set
Size
Lorenz
et
al.,
2012
Training
Pop
=
200;
384
Markers

9. Cross
and
Inter-­‐Set
ValidaGon
Training
PopulaGon
POP1
POP2
POP1
POP2
POP1
+
POP2
POP1
+
POP2
ValidaGon
PopulaGon
POP1
POP2
POP2
POP1
POP1
POP2
RelaGve
Accuracy
0.78
0.56
0.38
0.24
0.65
0.68
UM
BA
ND
Lorenz
et
al.,
2012

10. UM
–
ND
CollaboraGve
Breeding
UM
ND
480
480
480
21
Parents
Random
Progeny
100
100
100
UM
x
UM
UM
x
ND
ND
x
ND

11. Progeny
ValidaGon
Progeny
Panel
UM
x
UM
ND
x
ND
UM
x
UM
ND
x
ND
UM
x
UM
ND
x
ND
UM
x
ND
Training
anel
POP1
POP1
POP2
POP2
POP1
+
POP2
POP1
+
POP2
POP1
+
POP2
RelaGve
Accuracy
0.58
0.07
0.26
0.48
0.56
0.40
0.35
Cross
ValidaGon
Accuracy
0.78
0.38
0.24
0.56
0.65
0.68
Vikram
et
al.,
in
prep.

12. UM
–
ND
Breeding
Lines
UM
ND
480
480
480
21
Parents
Random
Progeny
100
100
100
89
UM
Phenotypic
SelecGon
89
CAP
Training
Panel
384
SNP
markers
DON
and
Yield
2
Loca.on
/
2
Rep
FHB
and
DON

13. Gain
from
SelecGon
for
DON
(Cycle
1)
0
50
100
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
0
20
40
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
Genomic
Selec.on
Random
Selec.on
0
20
40
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
Phenotypic
Selec.on

14. Vulnerability
of
Barley
to
Race
TTKSK
• Over
2,800
Hordeum
accessions
evaluated
as
seedlings
&
adults
• More
than
97%
were
suscep.ble
including
those
carrying
Rpg1

15. Genetics of Resistance to Race TTKSK
• Six diverse resistant Hordeum
accessions were subject to
genetic analysis
• All were found to carry rpg4/
Rpg5 complex, the only major
genes known to confer
resistance to TTKSK
• Further highlights the extreme
vulnerability of barley

16. Univ.
Minnesota
North
Dakota
State
(2-­‐row)
North
Dakota
State
(6-­‐row)
Washington
State
Montana
State
USDA
–
Idaho
Utah
State
Busch
Agriculture
8
Spring
Barley
Breeding
Programs
Screened
in
Kenya
for
Ug99
CAP
I
CAP
II
CAP
III
CAP
IV
Screened
in
2010
Screened
in
2011
Barley
CAP
Spring
Barley
Adult
Stem
Rust
(TTKSK)
Data
Set

17. Barley
CAP
Mapping
and
Breeding
Infrastructure
University
of
Minnesota
Breeding
Program
U.
Minnesota
UM
CAP
I
CAP
II
CAP
III
CAP
IV
192
lines
192
lines

18. Kenya
Adult
Plant
Screening
for
UM
Breeding
Lines
0
20
40
60
80
100
0
10
20
30
40
50
60
70
0
50
100
150
200
S
MS
MR
R
InfecGon
Type
Disease
Severity

19. Kenya
Adult
Plant
Screening
for
UM
Breeding
Lines
Inter-­‐Set
Valida.on
Rela.ve
Training
Predic.on
Accuracy
I
&
II
III
&
IV
0.28
III
&
IV
I
&
II
0.29

20. Expand
Training
PopulaGon
and
Parents
0
20
40
60
80
100
0
10
20
30
40
50
60
70
CAP
III
and
IV
All
Programs
CAP
MN
Only
0
200
400
600
0
10
20
30
40
50
60
70
80

21. Summary
Reasonable
rela.ve
accuracies
(>0.50)
possible
with:
Training
panels
of
200
individuals
384
SNP
markers
“Relevant”
training
popula.ons
Good
predic.on
accuracy
seems
to
translate
into
gain
from
selec.on
GS
takes
into
account
mul.ple
traits
in
addi.on
to
disease
resistance.
GS
for
adult
plant
stem
rust
resistance
in
elite
germplasm
could
complement
deployment
of
major
genes.

22. Minnesota
Agricultural
Experiment
Sta.on
SMALL GRAINS INITIATIVEU.S.
Wheat
&
Barley
Scab
IniGaGve
Project
Members
/
Collaborators
/
Support
American
Mal.ng
Barley
Associa.on
University of Minnesota
Brian Steffenson,
Ruth Dill-Macky
Yanhong Dong,
Smith Lab
Ed Schiefelbein
Guillermo
Velasquez
Karen Beaubien
Ahmad Sallam
Stephanie
Navarra
Vikas Vikram
Danelle Dykema
Chris Kucek
Mathilde Chapuis
Other Institutions
Kay Simmons, USDA
Dave Marshall, USDA
Shiaoman Chao, USDA;
Richard Horsley, NDSU;
Jean-Luc Jannink, USDA
Jeff Endelman, Univeristy of Wisconsin
Aaron Lorenz, University of Nebraska

23. Ques.ons

24. Genomic
SelecGon
2006 2007 2008 2009 Training
Popula.on
2009
2010
2011
2012
Fall
Crossing
21
parents
Winter
F1
Summer
F2
Fall
F3
Winter
F4
Summer
C1
Ran
C1
Sel
F1
Fall
F2
Winter
F3
Crossing
Parents
Summer
C2
Ran
C2
Sel
F1
Fall
F2
Winter
F3
Summer
C2
Ran
C2
Sel
2013
Crossing
Parents