1. Utilization of
Genetic Resources
in Breeding for
Stripe Rust
Resistance
Mike Pumphrey
Peter Bulli
Xianming Chen
2nd International Wheat Stripe Rust
Symposium
April 29, 2014
m.pumphrey@wsu.edu

2. Thoughts about improving stripe rust
resistance,
from a breeding program with very good
resources (germplasm access, greenhouses,
equipment, land, genotyping, human),
in an area with recurrent and severe
epidemics
Context

3. Several effective seedling and ~3 APR
genes, in each variety, is best when
considering farmers, the environment,
and epidemiology of rusts
“Complex resistance”
My Opinion

4. The primary and secondary genepools
are sufficient for stripe rust resistance
gene discovery
My Next Opinion

5. Using complex resistance
1. Identify defined resistance loci, then develop/use
diagnostic markers for all segregating genes (APR and
seedling)
2. Genomic predictions for APR, if elite germplasm has
enough, and breed for seedling resistance gene
combinations
3. Trans/cis-genics (plus or minus strategy 1 and 2)
Develop germplasm, then develop SUPERIOR varieties
with a combination of seedling and adult plant resistance

6. Utilization of Genetic Resources in
Breeding for Stripe Rust Resistance
1. USDA-ARS National Small Grains Germplasm
Collection. Elite advanced lines and varieties from
North America and other regions.
-$25M USD project
-USDA-ARS and WSU
-association/bi-parental mapping
2. Global tetraploid germplasm
-two Monsanto Beachell-Borlaug Projects and other
collaborations

7. Data and Germplasm
http://triticeaetoolbox.org/wheat/
http://www.ars-grin.gov/npgs/acc/acc_queries.html

8. Phenotyping
• 3 routine locations – Pullman,
Central Ferry, and Mt. Vernon,
plus UC Davis
• 2-3 years data per study
• Infection type (0-9) and
severity (%) are recorded at
seedling and adult stage
• Greenhouse seedling tests
with defined races

9. Panel Entries Phenotyping Genotyping
NSGC
Core
5000 Seedling: (WA)
PSTv14, 18, 37, 40, 51
9K SNP
NSGC
Core
5000 Field: Spring-CA, WA(2)
Winter-KS, WA (2)
9K SNP
T-CAP
Triticeae CAP Grant
730 Breeding line
1393 Cultivar
1623 Landrace
1372 No classification
5118 Total
North America
South America
North Africa
Southern Africa
West Africa
Central America
East Africa
Australia
Northern Europe
West Asia
South Asia Southeast Asia
East Asia
Central Asia
Northern Eurasia
Southern Europe
Western Europe Eastern Europe
Central Europe
Central Africa

10. Panel Entrie
s
Phenotyping Genotyping
Winter Yr
diversity
384 Seedling and field-WA (2) 90K SNP
Spring Hard
elite
256 Seedling and field-MN, WA (3) , E. Africa
(4)
90K SNP
Eastern SRW
elite
384 Field: NC, WA (3) 9K SNP
Spring PNW
Elite
427 Seedling and field-WA (2) 9K SNP
Small NAM 384 Field: CA 9K SNP
Spring Lr
diversity
384 Seedling and field- WA (2) 90K SNP
T-CAP
Triticeae CAP Grant

11. Panel Entries Phenotyping Genotyping
Emmer
(tetraploid)
wheat
196 Seedling and field-WA (2) 9K SNP
Ethiopian
landraces/
cultivars
300 Seedling and field-WA (3), Ethiopia (3) 90K SNP
Wild tetraploids 200 Seedling and field-WA (3) 90K SNP
Ethiopian
durum
200 Seedling and field-WA(2) 90K SNP
Elite Durum 300 Seedling and field-WA(2) 9K SNP
Other Projects

12. Origin
Cluster, kinship, and structure analyses of 1000 spring wheat core accessions
T-CAPWardcluster(W)
Kinship matrix (K)
Q 4 Q 5 Q 6 Q 7
America
Asia
Africa
Europe
Structure (Q)

13. Spring panel Winter panel
Pullman Mt. Vernon Across location Pullman Mt. Vernon Across location
0-9 0-100% 0-9 0-100% 0-9 0-100% 0-9 0-100% 0-9 0-100% 0-9 0-100%
Pop mean 4.08 45.27 4.48 49.57 4.28 47.42 5.07 52.45 4.88 54.92 4.97 53.66
Range low 0 0 0 0 0 0 2 2 1 0 1 0
Range high 9 100 9 100 9 100 9 100 9 100 9 100
2.87**** 379**** 3.94**** 677**** 3.37**** 510**** 3.64**** 640**** 4.40**** 620**** 4.09**** 622****
0.03NS 306**** 0.96**** 312**** -- -- 0.11* 398**** 0.14* 161**** -- --
-- -- -- -- 0.00 336**** -- -- -- -- 0.18**** 297****
2.03 1.00 1.10 1.00 2.20* 0.00 1.01 1.00 1.53 1.03 1.21 1.00
0.76 0.70 0.85 0.85 0.88 0.85 0.86 0.75 0.77 0.88 0.88 0.89
Model describing the data: Yijk = µ + gi + yj + gyij + lk(j) + eijk
Summary of reactions from field study, and covariance estimates from random model
T-CAP

14. MajorSNP
Associatedmarkers
Positionrange
Chrom.
chrom
Pos
KeptwhenremovingIT0,1,2
IT2_2011_UCD_ZEW_transf
IT2_2012_UCD_ZEW_transf
IT_fill_MTV2012_transf
IT_flower_MTV2013_transf
IT_flower_PLM2012_transf
IT_head_PLM2011_transf
SEV2_2011_UCD_Zew_transf
SEV2_2012_UCD_Zew_transf
SEV_fill_MTV2012_transf
SEV_flower_MTV2013_transf
SEV_flower_PLM2012_transf
SEV_head_PLM2011_transf
PST37
PST40
PSTV14
PSTV4
IT.ALL.BLUE
IT.MTV.BLUE
IT.PLM.BLUE
IT.UCD.BLUE
SEV.ALL.BLUE
SEV.MTV.BLUE
SEV.PLM.BLUE
SEV.UCD.BLUE
DH_2011_UCD
DH_2012_UCD
hd.PLM.2011
hd.PLM.2012
ht.MTV.2011
ht.MTV.2013
ht.PLM.2011
ht.PLM.2013
PH_2011_UCD
PH_2012_UCD
Pseudo_Black_chaff_UCD_2012
Pubescence_Glume_UCD_2012
Waxiness
fl.PLM.2012
glume.MTV.2011
glume.PLM.2011
glume.PLM.2013
ped.PLM.2011
Leaf_errectness_UCD_2012
IWA6441 39 11A 39 0 0.2 0 0 0.3 0.6 0 0 0 0 0.1 0.4 0.2 0.2 0.6 0.4 0 0 0.5 0 0 0 0.2 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 1
IWA5194 58 11A 58 0 0 0.1 0.8 0 0.2 0.1 0.1 0.2 0.3 0 0.1 0.9 0.1 0.4 0.4 0.1 0.2 0.1 0 0 0.2 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0
IWA5174 88.1 11A 88.1 0 0 0.1 0.2 0 0.6 0 0 0 0.1 0.1 0.3 0.9 0.8 0.9 0.6 0 0.1 0.2 0 0 0 0.1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 1 1 1 0
IWA1225 120.3 11A 120.3 0.4 0 0.1 0 0 0 0.1 0 0 0 0 0 0.3 0.6 0 0.6 0 0.1 0 0.2 0 0 0 0.1 1 0 1 0 0 0 1 0 0 0 1 1 1 1 0 1 0 1 0
IWA672 148.1 11A 148.1YES 0 0 0 0 0.1 0.2 0 0.1 0 0.1 0.1 0.3 0.4 0.1 0.2 0.1 0 0 0.2 0 0 0 0.1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 1
IWA2035 173.7 11A 173.7 0.5 0.4 0.1 0 0 0 0.3 0.3 0 0 0 0 0.7 0.7 1.0 0.3 0 0 0 0.2 0 0 0 0.3 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0
IWA962 35.5 21B 35.5 0.2 0.3 0 0 0.2 0 0 0.1 0 0 0.3 0 0.9 0.1 1.0 0.2 0 0 0.1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0
IWA6758 51.9 21B 51.9 0.4 0 0 0 0.1 0.1 0.1 0 0 0 0.1 0 0.1 0.2 0.6 0 0 0 0.1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 1 1 1 0 0
IWA3307 57.6 21B 57.6 0 0 0 0 0.2 0 0 0.1 0.1 0 0.4 0 0.3 0.6 0.6 0.6 0 0 0.1 0 0 0 0 0 1 0 1 1 1 1 1 0 1 1 1 0 0 1 1 0 0 0 1
IWA1825 IWA5847, IWA3043 107.4-109.5 21B 109.3 0.1 0 0.2 0 0 0 0 0 0.1 0 0.1 0 0.8 0.5 0.2 0.1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0
IWA3892 IWA846 123.4-123.6 21B 123.4YES 0 0 0 0 0 0 0 0 0 0 0 0 0.7 0 0.8 0.2 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 1
IWA2077 141.2 21B 141.2 0.3 0.1 0 0 0 0 0.2 0 0 0 0.1 0.1 0.2 0.2 0.2 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 1 0 1 1 0 0
IWA422 IWA423 9.9 42A 9.9YES 0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.3 0.9 0 0.1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
IWA5272 IWA5273 96.2 42A 96.2 0 0 0 0.2 0 0 0.1 0 0 0.1 0.1 0 0.6 0.5 0.4 0.6 0 0.1 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 0 1 1 1 0 1
IWA200 160.2 42A 160.2 0 0 0 0.1 0 0.1 0 0 0 0 0 0.2 0.8 0.3 0.2 0.2 0 0.1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0
IWA905 112.3 52B 112.3 0.2 0.2 0 0 0.1 0 0.3 0 0 0 0.4 0.1 0.5 0.3 0.9 0.8 0 0 0 0.1 0 0 0.1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1
IWA586 IWA587 147.3 52B 147.3 0 0.1 0 0.3 0 0.2 0.1 0.1 0 0.1 0.1 0.1 0.3 0.1 0.3 0.7 0 0 0 0 0.1 0.1 0.1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0
IWA226 163.4 52B 163.4 0 0 0 0.1 0.1 0.7 0 0 0 0 0.2 0.3 0.2 0 0 0 0 0 0.5 0 0 0 0.2 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 0 1
IWA692 260.6 52B 260.6 0 0.2 0.3 0.5 0 0 0.1 0.1 0.5 0.4 0.4 0 0.5 0.1 0.6 0.7 0 0.3 0 0 0.1 0.4 0 0.1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0
IWA5969 13.2 73A 13.2 0.1 0 0 0 0.2 0.4 0.2 0.1 0 0.1 0 0 0 0.1 0.6 0.2 0 0 0.3 0 0 0 0 0.2 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 1
IWA2049 27.5 73A 27.5 0.2 0 0 0 0 0.1 0.1 0.1 0 0 0 0 0.8 0.3 0.4 1.0 0 0 0 0.1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 1
IWA1996 35 73A 35 0 0 0 0.7 0 0.1 0 0 0 0.3 0 0.1 0.7 0.1 0 0.1 0 0.2 0 0 0 0.1 0 0 1 0 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 1 0
IWA6877 IWA5039 58.4-59.4 73A 59.4YES 0.2 0 0 0 0.2 0 0.4 0.1 0.1 0.2 0.3 0.1 0.1 0 0.1 0.3 0 0 0.1 0.1 0.2 0.2 0.1 0.3 1 0 1 0 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0
IWA8215 70.4 73A 70.4 0 0.2 0.1 0.4 0 0 0 0.1 0.1 0.5 0.1 0.2 0.7 0.9 0.4 0 0 0.1 0 0 0.1 0.2 0.1 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0
IWA7011 75.2 73A 75.2 0.3 0.1 0 0.2 0 0 0.5 0.2 0.1 0.4 0 0.1 0.7 0.7 0 0.2 0 0.1 0 0.3 0 0.2 0 0.5 1 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 1 1 1
IWA2332 102.9 73A 102.9 0 0.3 0 0.2 0 0 0 0.2 0 0 0 0 0.5 0.5 0.6 0.9 0 0.1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0
IWA4796 1.9 83B 1.9 0.1 0 0.1 0.1 0 0 0.7 0.1 0.1 0.1 0 0 0.2 0.8 0 0.1 0 0 0 0 0 0.1 0 0.5 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 1 1 0 1
IWA5202 3.9 83B 3.9YES 0 0.1 0 0.1 0 0 0.4 0.5 0.1 0.1 0 0 0.9 0.1 0.1 0.5 0 0 0 0 0.1 0.1 0 0.6 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0
IWA6632 57.4 83B 57.4YES 0.2 0 0 0.2 0 0 0.5 0.4 0.2 0.2 0 0 0.7 0.3 0.8 1.0 0 0 0 0.1 0.1 0.2 0 0.4 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 1 0 1
IWA377 IWA2622 73.8 83B 73.8 0 0 0 0.2 0.2 0.3 0 0 0 0.7 0.2 0.1 0.5 0.8 0.2 0.2 0.1 0.1 0.3 0 0.1 0.2 0.2 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0
IWA8480 77.5 83B 77.5 0 0 0 0.1 0.3 0.1 0 0 0 0.1 0 0.1 0.8 0.5 0.7 0.8 0.1 0.1 0.2 0 0 0 0.1 0 0 0 1 1 0 0 0 1 0 1 1 0 1 1 0 0 0 0 1
IWA5890 84.5 83B 84.5 0 0.4 0 0 0 0 0 0.1 0 0 0.1 0 0.5 0.8 0.3 0.2 0 0 0 0.1 0 0 0 0.1 0 0 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 0 1
IWA6221 95.5 83B 95.5 0 0 0 0.3 0 0 0.1 0 0 0.1 0.1 0 0.7 0.6 0.5 0.1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 0 0 0 1
IWA6100 IWA4251 35.2-36.6 104A 35.2 0 0 0 0.1 0 0.1 0.1 0.1 0 0.1 0.1 0 0.4 0.1 0.5 0.6 0 0 0 0 0 0 0 0.1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 1
IWA1570
IWA5687, IWA3489, IWA3490,
IWA5036, IWA8, IWA7203 66.6-69.3 104A 68.1 0 0.1 0 0 0 0 0 0 0 0 0 0 0.2 0.1 0.5 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 0 0 1
IWA2170 IWA7765, IWA1066, IWA6690 164.3-167.3 104A 167.3YES 0 0 0 0 0.2 0 0 0 0 0 0.2 0 0.5 0.8 0.1 0.3 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 0 1 1 0 0 0 1 1
IWA1034 181.7 104A 181.7YES 0 0 0 0.1 0 0 0 0 0 0.1 0 0 0.4 0.2 0.3 0.2 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1
IWA2745
IWA1923, IWA1910, IWA3846,
IWA4330, IWA6898, IWA7437 64.5-65.8 114B 65.8 0 0 0.1 0 0 0 0 0 0 0 0.1 0.1 0.2 0 0 0 0 0 0 0 0 0 0.1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0
IWA4347
IWA2398, IWA1007, IWA3736,
IWA4347, IWA4348, IWA1006 67.9-68.7 114B 68.3YES 0 0 0 0.1 0 0 0 0 0 0 0 0 0.6 0.3 0.3 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0
IWA6461 85.2 114B 85.2 0 0.1 0.3 0 0.3 0 0.1 0 0.2 0 0.3 0.2 0.2 0.8 0.2 0.1 0 0 0.1 0 0 0 0.2 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
IWA6277 20.6 124D 20.6 0 0 0 0 0 0.2 0 0 0 0 0 0.2 0.9 0.6 0.6 0 0 0 0.1 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1
IWA5375 IWA5766 26.9 124D 26.9 0 0 0 0 0 0.1 0 0 0 0 0 0 0.8 0.1 0.6 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1
IWA2144 IWA2143, IWA2146 4.9 135A 4.9 0.1 0 0 0 0 0 0 0 0.1 0 0.1 0 1.0 0.7 0.1 0.6 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0 1 0 1 0 1 1 1
IWA1486 IWA4648 119.3 135A 119.3 0 0.1 0.1 0 0 0 0.2 0.1 0.1 0.1 0 0 0 0.1 0.1 0.1 0 0 0 0 0 0.1 0 0.1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1
IWA6988 189.2 135A 189.2 0 0 0 0.3 0 0.1 0 0 0.1 0.1 0 0.2 0.2 0.9 0 0.7 0 0.1 0 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 0
IWA2646 194.9 135A 194.9 0 0 0 0.2 0 0 0 0 0 0.1 0 0.1 0 0.3 0.1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
IWA868 0 145B 0YES 0 0.2 0 0.1 0.1 0 0.1 0.1 0.2 0.2 0 0 0.7 0.8 0.2 0.6 0 0 0 0 0 0.2 0 0.1 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 0 0
IWA7227 68.3 145B 68.3 0.2 0 0.1 0.1 0 0 0.5 0 0 0 0 0 0 0.6 0 0.1 0 0.1 0 0.1 0 0 0 0.1 1 1 0 0 0 1 0 0 0 0 1 0 1 0 1 1 1 1 1
IWA3633 85.9 145B 85.9YES 0 0 0 0 0 0.3 0 0 0 0 0 0 0.5 0.1 0.7 0.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 0
IWA4280 IWA8069 115.3-119.9 145B 119.9YES 0.1 0.1 0 0.1 0 0 0 0.2 0.1 0.1 0 0 0 0 0 0.9 0 0 0 0.2 0 0.1 0 0.1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
IWA4824 75.5 186A 75.5 0 0.1 0.5 0 0 0 0 0 0.4 0 0 0 0 0 0.3 0 0 0.1 0 0 0 0.1 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 1
IWA3066 217.7 186A 217.7 0 0 0.1 0 0 0.1 0 0 0 0 0 0 0.9 0.1 0.2 0 0 0.1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1
IWA8134 37.9 196B 37.9 0 0.1 0 0.1 0 0.5 0 0.2 0 0 0 0 0 0.2 0.1 1.0 0 0 0.2 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0
IWA2888 38.5 196B 38.5 0.1 0 0 0.8 0 0.2 0.1 0 0 0.1 0 0 0.1 0.4 0 0.5 0 0.1 0 0.1 0 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1
IWA7625
IWA2419, IWA1655, IWA2417,
IWA2420, IWA4823, IWA4825,
IWA4827 50.7-50.8 196B 50.8 0 0 0.3 0 0 0 0 0 0.2 0 0 0 0 0 0.3 0 0 0.1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 1
IWA3796 81.3 196B 81.3 0.1 0 0 0 0 0.1 0.1 0 0 0.1 0 0 0 0 0.1 0.7 0 0 0.1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0
IWA6770 84.5 196B 84.5 0 0.1 0 0.1 0 0 0 0 0 0 0 0 0.2 0.1 0.1 0.1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 0 1 1 0 0 0 1 0
IWA3289 87.8 196B 87.8 0 0.1 0 0.1 0 0 0 0 0 0 0 0 0.2 0.1 0.1 0.1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 0 1 1 0 0 0 1 0
IWA6660 91 196B 91 0 0 0.1 0 0.2 0.2 0 0 0.1 0 0.1 0.1 0.7 0 0.6 0.6 0 0 0.2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1
IWA7257 112.3 196B 112.3YES 0 0 0 0 0 0 0 0 0 0 0 0 0.4 0.4 0.8 0.2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 1 0 1
IWA7816 68.6 216D2 68.6 0 0 0 0 0 0.4 0 0 0 0.1 0 0.1 1.0 0.5 1.0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 0 1 1 0 0 1 0 0
IWA167 73.2 216D2 73.2YES 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.1 0.1 0.2 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 1
IWA7306 6.2 237A 6.2 0.1 0 0 0.4 0 0.5 0 0 0 0 0 0 0.5 0.7 0.5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1
IWA7121 49.9 237A 49.9 0 0 0 0.1 0.2 0.8 0.1 0 0 0 0.4 0.4 0.9 0.3 0.7 0.8 0 0 0.3 0 0 0 0.3 0 0 1 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0 0
IWA7549 105.5 237A 105.5 0 0 0.1 0.2 0 0 0 0 0 0.1 0 0.1 0.2 0.1 0.1 0.2 0 0.1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 1 1 1 1 0
IWA1108 IWA6143, IWA8233 40.6-42.8 247B 40.6 0.9 0 0 0 0 0 0.5 0 0.1 0 0 0 0.1 0.5 0 0 0 0 0 0.4 0 0 0 0.4 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 0 0 1 0
IWA615 107.4 247B 107.4 0.2 0 0 0.1 0 0 0.5 0 0 0 0.1 0.3 0.1 0 0 0.4 0 0 0 0.1 0 0 0.1 0.1 0 0 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 0 1
IWA917 Unknown 284BS Unknown 0 0 0 0.1 0 0 0.1 0 0 0.1 0.1 0.1 0.6 0.8 0.2 0.1 0 0 0 0 0 0 0.1 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 1 1 0 0 1
IWA1135 Unknown 284BS Unknown 0 0 0 0.1 0 0 0 0 0 0.1 0.1 0.1 0.7 0.6 0.2 0.3 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 1
IWA3090 Unknown 287AL Unknown 0.1 0.4 0 0 0.1 0 0.9 0.2 0 0 0.2 0.1 1.0 0.3 0.8 0.7 0 0 0 0.1 0.1 0 0.2 0.5 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1
IWA6629 Unknown 287AL Unknown 0.1 0.4 0 0 0 0 0.6 0.1 0 0 0.1 0 0.7 0.4 0.8 0.5 0 0 0 0.1 0 0 0.1 0.3 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1
IWA481 Unknown 285AS Unknown 0.2 0 0 0.1 0 0 0 0 0.1 0.1 0.1 0 0.8 1.0 0 0.7 0 0 0 0 0 0.1 0 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 0 1 1 1
IWA2142 Unknown 285AS Unknown 0.2 0 0 0.1 0 0.1 0 0 0.1 0.1 0.2 0 0.8 1.0 0 0.7 0 0 0 0 0 0.1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 1 0 1 0 1 1 1
IWA2791 Unknown 285BL Unknown 0 0.1 0 0 0 0.5 0 0 0 0 0 0.2 0.7 0.1 0.7 0.2 0 0 0.1 0 0 0 0.1 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 1 1 1 0
GWAS “hits” from 1000 spring wheat core accessions phenotyped in 6 env + 4 races as seedling
T-CAP
IT-ENV SEV-ENV IT-RACE IT-LOC SEV-LOC Phenological and Morphological Traits

15. SNP Chrom
IWA137 Unknown
IWA148 Unknown
IWA627 Unknown
IWA3084 Unknown
IWA6460 Unknown
Genomic regions associated with field resistance to stripe rust in Spring wheat core (1000)
T-CAP
IWA6441
IWA5194
1A
36.15
68.15
IWA422
(9.58)
IWA4213
(51.52)
2A
Yr17
Yr32
Yr1
IWA3413
IWA4719
5A
Yr34/Yr48
77.19
120.88
IWA4637
7A
82.34
3B
IWA5202 3.87
Yr4
Yr30
4B
IWA3874
Yr50
IWA4347 68.33
63.97
5B
IWA7815 121.78
Yr40
Yr47
6B
IWA7098
IWA7257 112.30
147.90
Yr35
Yr36
7B
IWA3416
IWA1108
164.88
Yr52
Yr39
40.62
1D
IWA7154 67.29
2D
IWA5750 139.65
6D
IWA4592 126.26
4D
Lr67/Yr46IWA5375

16. T-CAP
Locus Chrom Pos (cM) MAF Pullman, WA Mount Vernon, WA
P-value FDR P-value FDR
IWA5505 1AL 134.80 0.49 **** ** **** ****
IWA108 4AS 19.91 0.10 **** * -- --
IWA5452 4AS 69.77 0.07 **** ** -- --
IWA3774 4AS 131.65 0.20 **** ** **** **
IWA1067 4AL 166.59 0.42 *** NS **** *
IWA1835 4AL 197.23 0.31 **** NS **** **
IWA5002 5AL 187.00 0.19 *** NS **** *
IWA5915 1BL 97.13 0.34 **** ** **** ****
IWA1810 5BS (Yr47?) 39.37 0.05 **** *** -- --
IWA7372 5BS? 63.66 0.09 **** * NS NS
IWA7815 5BL 121.78 0.16 -- -- **** ****
IWA4711 2DS 11.20 0.18 **** * -- --
Ppd Group 2 -- 0.43 **** *** **** ***
IWA62 -- -- 0.07 **** ** **** ****
IWA2265 -- -- 0.11 **** NS **** **
IWA3401 -- -- 0.10 **** * **** ***
IWA8279 -- -- 0.05 **** * -- --
Winter wheat panel GWAS summary for 2012 and 2013 field stripe rust data

17. 0 1 2 3
Expected –log10(p)
Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7 Group X
-log10(p)
12345
2B
7D
7B
X
Observed–log10(p)
012345
PSTV-14 Race
Virulence formula: Yr1, Yr6, Yr7, Yr8, Yr9, Yr17, Yr27, Yr43, Yr44,
YrTri, YrExp2, YrTye
Locus Chrom Pos(cM) 0.1 FDR Mapped Yr gene
IWA7815 5BL 121.78 1.86E-26 --
PSTV-40 Race
Virulence formula: Yr3, Yr6, Yr7, Yr8, Yr9, Yr10, Yr24, Yr27, Yr32,
Yr43, Yr44, YrTri, YrExp2
Locus Chrom Pos(cM) 0.1 FDR Mapped Yr gene
IWA8601 2BL 149.36 2.00E-2 Yr5 and Yr53
IWA6 7BL 58.27 2.51E-2
IWA118 7DS 0 2.00E-2
IWA7 -- -- 2.00E-2 --
IWA2356 -- -- 1.14E-2 --
IWA3389 -- -- 2.00E-2 --
Spring panel GWAS for seedling reaction to stripe rust races
T-CAP

18. Yr52 (YrPI183527)
1.0
3.9
1.1
0.7
1.2
Xgpw1144
Xgwm577
5.9
Xwmc276
4.4
Xbarc32
5.7
Xwmc273
0.8
Xwgp5668
Xcfa2040
1.5
Xwgp5271
Xwgp5175
Xwgp5258
1.1
Xbarc182
7BL
7BL-14 (0.14)
7BL-1 (0.40)
7BL-9 (0.45)
7BL-5 (0.69)
7BL-10 (0.78)
7BL-6 (0.84)
7BL-3 (0.86)
7BL-7 (0.63)
Yr52
2.4
9.9
0.4
4.0
1.3
5.6
2.7
1.4
3.3
1.8
7.7
5.5
Yr5STS7/8
Yr5
Xbarc349
Yr44
Xgwm501
Xwmc441
Yr53 (YrPI480148)
XLRRrev/NLRRrev350
Ptokin2/Xa1NBS-F234 (STS2F/1R219)
Ptokin1/NLRR-INV1800
Xwmc149
Yr43
Xwgp109
2BL
Yr53
APR resistance from PI 183527 and
seedling resistance from PI 480148
Ren et al. 2012 TAG 125:847-857 Xu et al. 2013 TAG 126:523-533

19. Yr59 in PI 178759
Zhou et al. 2014 TAG 127:935-945

20. Yr62 & a QTL in PI 192252
Lu et al. 2014 TAG
DOI: 10.1007/s00122-014-2312-0

21. Concerns
•Many opportunities… not enough well developed tools
•How do we prioritize resistance loci?
Fitness costs, interactions, durability, linkage relationships, etc.?
•What genes are we missing (or false positives)?
Masking, power, MAF, marker density and bias?
•How do we identify lines that have each resistance
locus, for sure?- Haplotypes, not single markers
Developing Nested Association Mapping population with ~200 accessions
in Avocet S background, and other populations for seedling R genes

22. Acknowledgements
Stripe rust collaborators
Arron Carter
Steven Xu
Jorge Dubcovsky
Roberto Tuberosa
Bekele Abeyo
Bedada Girma
Kim Campbell
Shiaoman Chao
Deven See
Gina Brown-Guedira
Mike Bonman