using genomic selection in barley to improve disease resistance
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Kevin P. Smith, Vikas Vikram, Ahmad Sallam, Aaron Lorenz, Jean-‐Luc Jannink, Jeffrey Endleman, Richard Horsley, Shiaoman Chao, and Brian SteffensonTRANSCRIPT
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
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
ˆp
i j i jj
GEBV b x=
=∑1
p
i j i jj
y b x=
=∑
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
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
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)
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
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
Training Panel and Marker Set Size
Lorenz et al., 2012
Training Pop = 200; 384 Markers
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
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
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.
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
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
DON rela.ve to variety Quest
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
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
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
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
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
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
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
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.
Minnesota Agricultural Experiment Sta.on
SMALL GRAINS INITIATIVE U.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
Ques.ons
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