RESEARCH ARTICLE
Genome-Wide Association Mapping for
Resistance to Leaf and Stripe Rust in WinterHabit Hexaploid Wheat Landraces
Albert Kertho1, Sujan Mamidi2, J. Michael Bonman3, Phillip E. McClean2,
Maricelis Acevedo1*
1 Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, United States of
America, 2 Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, United
States of America, 3 USDA-ARS, Small Grains and Potato Germplasm Research Unit, Aberdeen, Idaho,
United States of America
a11111
* maricelis.acevedo@ndsu.edu
Abstract
OPEN ACCESS
Citation: Kertho A, Mamidi S, Bonman JM, McClean
PE, Acevedo M (2015) Genome-Wide Association
Mapping for Resistance to Leaf and Stripe Rust in
Winter-Habit Hexaploid Wheat Landraces. PLoS
ONE 10(6): e0129580. doi:10.1371/journal.
pone.0129580
Academic Editor: Guihua Bai, USDA, UNITED
STATES
Received: February 9, 2015
Accepted: May 11, 2015
Leaf rust, caused by Puccinia triticina (Pt), and stripe rust, caused by P. striiformis f. sp. tritici (Pst), are destructive foliar diseases of wheat worldwide. Breeding for disease resistance is the preferred strategy of managing both diseases. The continued emergence of
new races of Pt and Pst requires a constant search for new sources of resistance. Here we
report a genome-wide association analysis of 567 winter wheat (Triticum aestivum) landrace accessions using the Infinium iSelect 9K wheat SNP array to identify loci associated with
seedling resistance to five races of Pt (MDCL, MFPS, THBL, TDBG, and TBDJ) and one
race of Pst (PSTv-37) frequently found in the Northern Great Plains of the United States.
Mixed linear models identified 65 and eight significant markers associated with leaf rust and
stripe rust, respectively. Further, we identified 31 and three QTL associated with resistance
to Pt and Pst, respectively. Eleven QTL, identified on chromosomes 3A, 4A, 5A, and 6D,
are previously unknown for leaf rust resistance in T. aestivum.
Published: June 15, 2015
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced, distributed,
transmitted, modified, built upon, or otherwise used
by anyone for any lawful purpose. The work is made
available under the Creative Commons CC0 public
domain dedication.
Data Availability Statement: All relevant data are
within the paper, its Supporting Information files and
the Triticae cap website: http://triticeaetoolbox.org/
wheat/display_genotype.php?trial_code=
NSGCwheat9K_winter_fac.
Funding: This work was supported by Ducks
Unlimited, Inc. (http://www.ducks.org/); and Triticeae
CAP (http://www.triticeaecap.org/). The funders had
no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Introduction
Wheat leaf rust, caused by Puccinia triticina (Pt), and wheat stripe rust, caused by P. striiformis
f. sp. tritici (Pst), are important foliar diseases of wheat (Triticum aestivum) worldwide [1,2].
Genetic resistance is the preferred method of protecting against yield losses due to these diseases [3,4]. Resistance has been broadly categorized into all-stage resistance (also called seedling resistance) and adult-plant resistance (APR) [3]. Seedling resistance is expressed at all
stages of plant growth, is mostly race-specific, and offers a high level of resistance; however it is
easily overcome by changes in virulence of rust pathogens [2,5]. Conversely, APR is effective at
later stages of plant growth and is mostly race-nonspecific and more durable [6]. The constant
evolution of races of leaf rust and stripe rust pathogens with new virulences has rendered many
wheat varieties susceptible [3,7–9]. Therefore there is a need to find new sources of resistance
to manage these two important wheat diseases.
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Competing Interests: The authors received funding
from Ducks Unlimited, Inc. and Triticeae CAP. This
funding does not alter the authors' adherence to
PLOS ONE policies on sharing data and materials.
Currently more than 70 leaf rust resistance (Lr) and more than 50 stripe rust resistance (Yr)
genes have been identified [10]. Most of these genes condition race-specific resistance in a
gene-for-gene fashion and many have been overcome by the emergence of new races [11]. The
most effective strategy of protecting wheat from rust is to deploy cultivars with both seedling
and adult plant resistance genes. The use of seedling resistance is necessary to protect plants
during early growth stages in production environments conducive to early-season disease development. Additionally, APR genes, such as high temperature adult plant resistance, is crucial
for protecting plants at the critical stage of development and at high temperatures [3,12].
Previously, molecular markers linked to genes for resistance to leaf rust and stripe rust have
been identified using bi-parental populations obtained by crossing resistant and susceptible
wheat genotypes [13,14]. Though it has been successful, bi-parental QTL mapping generally requires years to develop a mapping population and gene discovery is limited to the genetic background of the two parents. Association mapping (AM) is an alternative to bi-parental linkage
mapping that uses natural populations, thereby eliminating the need for developing mapping
populations. AM is credited for detecting quantitative trait loci (QTL) with great resolution
from populations of diverse origins [15]. AM uses linkage disequilibrium (LD) between alleles
within diverse populations to identify markers associated with particular traits [16]. Recently,
AM has been used to identify marker-trait associations in higher plants including disease resistance in potatoes [17] and wheat [18–23].
Wheat landraces are an important potential source of new resistance genes since relatively
few landraces have been used in modern plant breeding [24]. The co-existence of rust pathogens and wheat may have resulted in the accumulation of diverse resistance in wheat [25].
Studies have demonstrated that wheat landraces can be a good source of resistance to leaf rust,
stem rust, and stripe rust [21,26–29]. We therefore anticipate that new or underutilized genes
for resistance to these rust pathogens may exist in winter wheat landraces. The objective of this
research was to 1) identify potentially novel resistance QTLs to Pt and Pst in 575 winter wheat
landrace accessions from the USDA National Small Grain Collection (NSGC) using an AM approach and 2) identify accessions with broad-spectrum resistance to races of the pathogens
that are predominant in the U.S. northern Great Plains.
Materials and Methods
Wheat germplasm and pathogen races
A total of 567 winter wheat landrace accessions obtained through single plant selection from
the T. aestivum core subset were provided by the NSGC located in Aberdeen, ID, U.S.A. The
wheat accessions originated from 44 countries representing diverse geographic regions of the
world. Five races of Pt (MCDL, MFPS, TDBG, THBL, and TBDJ), and one race of Pst (PSTv37), representing prevalent races of the leaf rust and stripe rust pathogens in North Dakota
were used to screen these accessions at the seedling stage in a greenhouse [30,31]. The virulence/avirulence profile of the rust races are based on reactions on seedlings of standard differentials used in the United States (Table 1)
Phenotyping and data analysis
All the screening experiments were conducted at the North Dakota State University Agricultural Experiment Station Greenhouse Complex in Fargo, ND, U.S.A. The experiment was a randomized complete block design with three replicates and the entire experiment was repeated
for each race of rust pathogen. Five seeds of each genotype were planted in 50-cell trays containing sunshine mix #1 (Sungro Horticulture Distribution Inc., Quincy, MI, USA) and slowrelease commercial fertilizer (Osmocote 15-9-12, N-P-K, Everris NA Inc., Dublin, OH, USA)
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Table 1. Virulence/avirulence profile of leaf rust and stripe rust pathogen races based on US differential set seedlings.
Race
Virulent on genes
a
Avirulent on genes
PSTv-37
6,7,8,9,17,27,43,44,Tr1,Exp2
1,5,10,15,24,32,SP,Tye
MCDLb
1,3,17,26,B
2a,2c,3ka,9,10,11,14a,16,18,24,30
MFPSb
1,3,3ka,10,14,17,24,26,30,B
2a,2c,9,11,16,18
THBL
1,2a,2c,3,16,26,B
3ka,9,10,11,14a,17,18,24,30
TDBGb
1,2a,2c,3,10,24
3ka,9,11,14a,16,17,18,26,30,B
TBDJb
1,2a,2c,3,10,17,14a
3ka,9,11,16,18,24,26,30,B
b
a
Pst race nomenclature based on differentials lines in the United States (Wan & Chen, 2014)
Four letter for Pt race nomenclature used in North America (Long & Kolmer, 1989).
b
doi:10.1371/journal.pone.0129580.t001
in a rust-free greenhouse set at 22°C /18°C (day/night) with 16-hour photoperiod. Susceptible
checks ‘Little Club’ and ‘Avocet’ were included in each tray for leaf rust and stripe rust, respectively. Foliar fertilizer, Peat Lite 20-20-20, (Everris NA Inc., Dublin, OH, USA) was applied
after seedling emergence and once per week thereafter. At 10 days after planting, seedlings at
the two-leaf stage were spray inoculated with fresh rust urediniospores suspended in Soltrol170 oil (Phillips Petroleum, Bartlesville, OK, U.S.A) at a rate of 0.01g/mL and then left to
air dry.
Seedlings inoculated with Pt races were placed in a dark dew chamber for 16–24 hours at
20°C. The seedlings were then moved to a greenhouse until disease scoring. Infection types
(ITs) were scored 12–14 days post-inoculation using the 0–4 scale [32] where IT 0 = no visible
sign or symptom; 1 = small uredinia with necrosis; 2 = small to medium sized uredinia with
green islands and surrounded by necrosis or chlorosis; 3 = medium sized uredinia with or without chlorosis; 4 = large uredinia without chlorosis. Accessions with ITs of 0 to 2 were considered resistant, whereas those with scores of 3 and 4 were considered susceptible.
Seedlings inoculated with PSTv-37 were placed in a clean dark growth chamber for 16–24
hours at 13°C and 98% humidity and then incubated in a growth chamber at 17°C/ 12°C (day/
night) and 16-hour photoperiod. Disease reaction was assessed 16–18 days post-inoculation on
a scale of 0-to-9 [7,12,32] where IT 0 = no visible signs or symptoms; 1 = necrotic or chlorotic
flecks with no sporulation; 2 = necrotic and/or chlorotic blotches or stripes with no sporulation; 3 = necrotic and/or chlorotic blotches or stripes with only a trace of sporulation; 4, 5 and
6 = necrotic and/or chlorotic blotches or stripes with light, intermediate and moderate sporulation, respectively; and 7, 8 and 9 = abundant sporulation with necrotic and/or chlorotic stripes
or blotches, chlorosis behind the sporulation area, and no chlorosis or necrosis, respectively.
Plants with ITs 0–3 were considered resistant, 4–6 were considered intermediate and 7–9 were
considered susceptible.
To account for multiple infection types in a single plant, the 0–4 Stakman disease rating
scale [32] for leaf rust was converted to a linearized 0–9 disease scale [19] where rating 0–6
were considered resistant IT and 7–9 were considered as susceptible. Analysis of variance
(ANOVA) was performed in SAS software 9.3 (SAS Institute, Cary, NC) before pooling IT data
from two experiments. The median linear scale value for each accession, obtained from two experiments each with three replications, was used for association analysis.
SNP marker genotyping and analysis
Five hundred and sixty seven winter wheat accessions were genotyped through the Triticeae Coordinated Agricultural Project using the Illumina iSelect 9K wheat array [33] at the USDA-ARS
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
genotyping laboratory in Fargo, ND, U.S.A. A total of 5633 high quality polymorphic SNPs were
selected and used for association analysis. Marker data are available at http://triticeaetoolbox.
org/wheat/display_genotype.php?trial_code=NSGCwheat9K_winter_fac. Missing SNP data was
imputed using fastPhase 1.3 [34] software with default settings. Markers with minor allele frequency (MAF) of less than 5% were removed, since the power of association with the phenotype
are low for these alleles [35]. The genetic position of the SNP markers was estimated based on
the wheat consensus map developed from Illumina iSelect 9K wheat array [33].
Population structure and kinship
Population structure (Q-matrix) [36] was evaluated via principal component (PC) analysis
using the PRINCOMP procedure in SAS 9.3 (SAS Institute, Cary, NC). PCs that explain 25%
(PC25) and 50% (PC50) cumulative variation were used in mixed models for association analysis. An identity-by-state matrix (K-matrix) [37] estimated as a centered relatedness matrix in
Gemma 0.92 [38] was used to estimate population relatedness.
Association analysis
Four linear regression models were used to test for marker-trait associations. A Wilcoxon rank
sum test was performed in SAS 9.3 using the npar1way procedure, for a Naïve model that did
not account for population structure and kinship. Three other models that accounted for kinship (kinship, PC25 + kinship, PC50 + kinship) were analyzed in Gemma 0.92 [38]. The regression equation for mixed linear models used for association analysis takes the general form, y =
Xß + Qv + Iu + e, where y is a vector of recorded phenotype, X is a vector representing SNP genotype effects, ß is a vector of fixed effects due to the genotype, Q is a matrix estimating population structure, v is a vector of fixed effects arising from population effects, I is an identity
matrix, u is a vector of random effects relating to co-ancestry and e is a vector of residual effects. The variances of random, u and residual, e effects are derived from the following assumptions; Var(u) = 2KVg and Var(e) = VR, where K is a relative kinship matrix that compares the
proportion of shared alleles between two individuals, Vg is the genetic variance and VR is the
residual variance [20,39]. The best model for each pathogen race was selected based on mean
Squared Difference (MSD) between observed and expected p-values [40] since p-values of random markers follow a uniform distribution [39].
Marker-trait associations were considered significant at threshold of a positive false discovery rate (pFDR) of less than 0.1, a multiple comparison correction [41] calculated using the
multtest procedure in SAS 9.3. Furthermore, stepwise regression was performed on all significant markers of each race using the REG procedure in SAS 9.3 in order to determine the minimum number of SNPs independently associated with disease resistance [21,42]. The selected
markers from the stepwise regression explains the most phenotypic variation similar to variation explained by all markers considered together for each trait [42].
In silico annotation of SNPs
Due to the incomplete genome sequence of T. aestivum, sequences of significant markers were
searched for syntenic regions in related cereals whose genome sequence information are
available. The putative biological functions of significant SNPs were determined by searching
the sequences of the SNPs against protein sequences of sorghum (Sorghum bicolor), rice
(Oryza sativa), and Brachypodium distachyon available in phytozome data base (http://www.
phytozome.net). The homology search was performed using blastx.
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Results
Seedling disease evaluations
Phenotypic data was homogeneous based on the ANOVA of residuals of ITs for each pathogen
race. Therefore, phenotypic data were pooled for each race and overall medians were used for
association analysis (S1 Table). Most accessions were susceptible to the Pt races tested, with
only 2.6%, 1.9%, 4.8%, 3.2%, and 1.9% accessions resistant to Pt races THBL, MCDL, TDBG,
TBDJ and MFPS, respectively (Fig 1). For each of these races, the largest number of resistant
accessions had median IT scores of 2. Disease reaction for Pst race PSTv-37 ranged from immunity (IT = 0) to complete susceptibility (IT = 9). Sixty-nine (12%), 73 (12.7%), and 433
(75.3) accessions were highly resistant (IT = 0–3) (Fig 1), moderately resistant (IT = 4–6) and
susceptible (IT = 7–9), respectively. Among the accessions that were highly resistant, three accessions originating from Georgia, Egypt, and Chile showed immune infection type during
both Pst experiments but were susceptible to the five races of Pt. Moreover, six accessions originating from Iran were highly resistant to all five races of Pt and the one race of Pst tested in this
experiment (Table 2).
Imputation, population structure, and model selection
A total of 5633 high quality SNPs were obtained from the 9K iSelect wheat SNP array (S2
Table). The 1.4% missing SNP data were imputed and 4234 SNPs with MAF of greater than 5%
were selected for further analysis. Of the 4234 SNP markers, 3992 (94.3%) were previously
mapped to the A (45.4%), B (43.7%), and D (5.2%) genomes of wheat [31]. Principal component (PC) analysis show that two and 20 PCs explain a cumulative 24.63% and 50.43% of the
genotype variation, respectively. The first two PCs grouped the entries into two major clusters
based on geographic location. One cluster contained accessions mainly from Asia and the
other cluster had accessions mainly from Europe. The few accessions from Africa and South
America grouped with accessions from Europe (Fig 2). Based on MSD values of the four linear
Fig 1. Number of accessions resistant to each race of P. triticina and P. striiformis f. sp. tritici tested.
A total of 567 accessions were screened at the seedling stage with five races of P. triticina and one race of P.
striiformis f. sp. tritici.
doi:10.1371/journal.pone.0129580.g001
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Table 2. Infection type of six accessions from Iran that show resistance to all five races of Puccinia triticina (Pt) and a race of Puccinia striiformis f.
sp. tritici (Pst).
Accession
Pt race
MCDL
Pt race
MFPS
Pt race
THBL
Pt race
TDBG
Pt race
TBDJ
Pst race
PSTv-37
PI 621539
2 (5)
2 (5)
2- (4)
;2 (2)
2/3 (6)
4
PI 621674
1 (2)
2- (4)
2+ (6)
2- (4)
1/2 (3)
6
PI 622111
2 (5)
1 (2)
2- (4)
1 (2)
12- (3)
1
PI 622129
1 (2)
1 (2)
1 (2)
1 (2)
1 (2)
1
PI 622243
1 (2)
2 (5)
2- (4)
2 (5)
12- (3)
1
PI 622246
1 (2)
1 (2)
1 (2)
1 (2)
1 (2)
1
(-) indicates slightly smaller uredinia than the standard, (+) indicates slighter larger uredinia, two infection types (IT) (such as 12-) indicates a mixed
reaction on the same leaf, two IT separated by slash (such as 2/3) indicates varying reaction among seedling plants of the same accession (some
seedlings are 2, other seedlings are 3). The linearized disease rating for leaf rust shown in parentheses was used in association analysis.
doi:10.1371/journal.pone.0129580.t002
Fig 2. A graph showing two principal components obtained from 4234 polymorphic SNPs. PC1 and PC2 explain 19.41% and 5.22%
variation, respectively.
doi:10.1371/journal.pone.0129580.g002
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
regression models tested, no single model was best for all traits. The best models were as follows; Kinship for TDBG and THBL, PC25+Kinship for MCDL and MFPS and PC50+Kinship
for TBDJ and PSTv-37 (Table 3).
Marker-trait associations and in silico annotation of SNPs
Statistically significant disease resistance QTL were determined by applying a FDR-adjusted
P < 0.1 threshold (S3 Table). Thirty-four markers were significantly associated with resistance
to race MCDL and were located on chromosomes 1A, 2B, 3A, 3B, 4A, 4B, 4D, 5B, 6A, and 6B
(Table 4, Fig 3). Twenty-one of the 34 significant markers corresponded to a gene model based
on a homology search against the protein sequences (Table 4). Eleven out of 34 significant
markers fit into a regression model and together accounted for 38.2% of the phenotypic variation (Table 5). These markers are located at 11 QTL regions on chromosomes 1A, 3A, 3B, 4A,
4B, 5B, and 6A.
Two SNP markers located on chromosome 1B were significantly associated with resistance
to race MFPS (Table 4, Fig 3). One of the two markers fit into a stepwise regression. Seventeen
SNP markers were associated with resistance to race TBDJ and were identified across the following chromosomes; 1A, 1B, 2A, 2B, 3A, 4A, 4B, 5B, and 6D (Table 4, Fig 3). Eight of the
markers corresponded to protein sequences searched in other cereals (Table 4). Furthermore,
eight of the 17 markers fit into a stepwise regression and accounted for 32.6% of the phenotypic
variation (Table 5). The eight SNP markers are found at eight QTL regions and were identified
on seven chromosomes 1A, 1B, 2A, 3A, 4B, 5B, and 6D.
Eighteen SNP markers detected on eleven chromosomes (1B, 2A, 2B, 3A, 4A, 5A, 5B, 6B,
6D, 7A, and 7B) were significantly associated with resistance to race TDBG (Table 4, Fig 3).
Sixteen of the markers matched with protein sequences searched in three related cereals
(Table 4). Ten out of the 18 significant markers fit into a stepwise regression and accounted for
43.5% of the phenotypic variation (Table 5). These 10 markers are spread among 10 QTL regions on chromosomes 1B, 3A, 4A, 5A, 6B, 6D, and 7A. One SNP marker identified on chromosome 1B was associated with resistance to race THBL (Table 4, Fig 3).
A total of seven SNP markers located on chromosomes 2B, 3A, 4B, and 5B were associated
with resistance to both races MCDL and TBDJ (Table 4). Three out of the seven significant
markers corresponded to the protein sequences searched in three cereals related to T. aestivum
(Table 4). Three (IWA5977, IWA2126 and IWA8375) of the seven markers fit into a stepwise
regression model.
A total of eight markers associated with resistance to PSTv-37 were located on chromosomes 1A, 1B, and 6A (Table 4, Fig 3). Four of the eight markers corresponded to protein sequences searched in other cereals (Table 4). Three out of eight significant markers fit into a
Table 3. Mean square difference (MSD) for each disease race and model.
Model
MCDL
MFPS
TBDJ
TDBG
THBL
PSTv37
Naïve
6.23E-02
5.45E-02
9.39E-02
1.00E-01
8.11E-02
1.57E-01
Kinship
1.03E-04
2.80E-04
2.99E-04
1.54E-04
1.48E-04
5.32E-04
PC2+Kinship
9.82E-05
2.69E-04
2.69E-04
1.75E-04
1.63E-04
5.03E-04
PC20+Kinship
1.24E-04
2.84E-04
8.78E-05
2.24E-04
1.51E-04
3.82E-04
The best model was used to investigate SNP-rust resistance associations.
Numbers in bold indicate lowest mean square difference (MSD) and best model for each rust race.
doi:10.1371/journal.pone.0129580.t003
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Table 4. Significant markers associated with resistance to each rust pathogen race.
Trait
Marker
Chroma
cMb
-log10(pvalue)
pFDRc
SNP
MAFd
IWA5702
1A
57.95
5.11
4.61E03
[T/C]
9.17
IWA2768
1AS
72.53
3.19
8.60E02
[T/C]
6.35
IWA2887
2B
76.02
3.25
8.38E02
[T/C]
5.11
IWA295
2B
76.02
3.25
8.38E02
[A/
C]
5.11
IWA762
2B
76.02
3.25
8.38E02
[A/
G]
5.11
IWA5977
3AL
47.75
3.47
6.14E02
[T/C]
39.15
x
IWA6244
3BL
71.14
5.8
1.65E03
[T/C]
40.74
x
Arginyl-tRNA synthetase
IWA4030
4A
4.06
17.18
9.20E15
[A/
G]
38.45
IWA2816
4A
74.81
65.56
3.79E63
[A/
G]
15.87
IWA3756
4AL
93.49
58.36
6.11E56
[T/C]
15.7
x
Conserved oligomeric golgi complex
component, COG2
IWA7859
4AL
151.32
5.7
1.65E03
[A/
G]
31.22
x
Cation transporter/ATPase
IWA2126
4B
16.37
4.22
2.78E02
[T/C]
12.7
x
Peptidase family M1
IWA3815
4D
52.44
5.23
4.05E03
[T/C]
9.52
IWA286
4D
52.81
3.23
8.38E02
[T/C]
9.17
IWA8375
5B
82.62
3.67
5.92E02
[T/C]
39.51
x
IWA6694
5BL
168.74
3.22
8.38E02
[A/
G]
34.22
x
DNA binding
IWA6737
6A
89.87
3.31
8.38E02
[A/
G]
40.74
x
DNA photolyase
IWA185
6B
73.70
3.5
5.92E02
[A/
C]
11.82
leucine-rich repeat receptor-like protein
kinase
IWA3131
6B
73.70
3.5
5.92E02
[T/C]
11.82
Coatomer WD associated region
IWA3133
6B
73.70
3.5
5.92E02
[A/
G]
11.82
Coatomer WD associated region
IWA5785
6B
73.70
3.8
5.92E02
[A/
C]
11.99
ATPase activity
IWA6142
6B
73.70
3.5
5.92E02
[A/
G]
11.82
PRP38 family
IWA6825
6B
73.70
3.5
5.92E02
[A/
G]
11.82
Coatomer WD associated region
IWA6826
6B
73.70
3.5
5.92E02
[T/C]
11.82
Coatomer WD associated region
IWA7873
6B
73.70
3.5
5.92E02
[A/
C]
11.82
RNA recognition motif
Included in Stepwise
Regression
Gene Annotation
MCDL
Heat shock 70kDa protein
x
Protein of unknown function, DUF288
Ankyrin repeat domain
(Continued)
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Table 4. (Continued)
Trait
Marker
Chroma
cMb
-log10(pvalue)
pFDRc
SNP
MAFd
IWA8192
6B
73.70
3.5
5.92E02
[T/C]
11.82
IWA596
6B
83.04
3.15
9.23E02
[T/C]
41.8
IWA2121
Unke
Unk
3.23
8.38E02
[A/
G]
9.17
Ribulose-phosphate 3 epimerase
IWA2122
Unk
Unk
3.57
5.92E02
[A/
G]
10.05
Ribulose-phosphate 3 epimerase
IWA287
Unk
Unk
3.57
5.92E02
[A/
G]
10.05
IWA397
Unk
Unk
3.09
9.92E02
[A/
C]
5.64
IWA55
Unk
Unk
3.57
5.92E02
[A/
G]
10.05
IWA6340
Unk
Unk
3.12
9.48E02
[A/
G]
6.88
IWA8186
Unk
Unk
4.32
2.51E02
[T/C]
40.04
IWA7466
1BS
47.53
4.34
9.78E02
[T/C]
5.82
IWA5418
1BS
47.53
4.34
9.78E02
[T/C]
5.82
x
IWA3160
1AS
51.12
6
1.54E03
[T/C]
16.23
x
IWA435
1BL
30.47
5.17
3.53E03
[T/C]
8.47
x
Protein kinase domain
IWA574
2AS
103.39
5.18
3.53E03
[T/
G]
45.68
x
Protein phosphatase 2C
IWA2887
2B
76.02
5.65
1.54E03
[T/C]
5.11
IWA295
2B
76.02
5.65
1.54E03
[A/
C]
5.11
IWA762
2B
76.02
5.65
1.54E03
[A/
G]
5.11
IWA2557
2B
76.37
4.86
5.77E03
[A/
G]
5.29
RhoGAP domain
IWA3824
2B
77.53
5.07
3.91E03
[A/
G]
5.47
Phosphatidylethanolamine-binding protein
IWA5977
3AL
47.75
3.43
9.16E02
[T/C]
39.15
x
IWA3546
3AS
118.07
5.74
1.54E03
[T/C]
7.76
x
IBR (In between ring finger) domain
IWA285
4A
192.37
3.75
5.28E02
[T/
G]
11.29
IWA54
4A
192.37
3.75
5.28E02
[T/
G]
11.29
IWA8389
4A
192.37
3.59
7.06E02
[A/
G]
11.46
IWA2126
4B
16.37
5.7
1.54E03
[T/C]
12.7
x
Peptidase family M1
Included in Stepwise
Regression
Gene Annotation
x
Leucine rich repeat
x
Peptidase M16 inactive domain
MFPS
TBDJ
Ankyrin repeat domain
(Continued)
PLOS ONE | DOI:10.1371/journal.pone.0129580 June 15, 2015
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Table 4. (Continued)
Trait
Marker
Chroma
cMb
-log10(pvalue)
pFDRc
SNP
MAFd
Included in Stepwise
Regression
IWA8375
5B
82.62
3.43
9.16E02
[T/C]
39.51
x
IWA619
6D2S
45.42
3.84
5.05E02
[T/C]
5.11
x
IWA6340
Unk
Unk
4.75
6.68E03
[A/
G]
6.88
IWA6290
1BL
30.47
6.43
1.37E03
[A/
G]
7.58
IWA7429
2A
91.42
3.36
8.92E02
[A/
G]
18.69
Protein phosphatase 2C
IWA2195
2A
97.14
3.36
8.92E02
[A/
G]
18.69
OTU (ovarian tumor)-like cysteine
protease
IWA3924
2B
110.85
3.68
7.68E02
[A/
G]
9.88
Biological process
IWA5006
3A
68.77
3.46
8.92E02
[T/C]
6
IWA5005
3A
69.47
3.46
8.92E02
[T/C]
6
IWA5786
3AS
72.50
3.37
8.92E02
[A/
G]
7.05
x
ABC transporter
IWA1900
4AL
198.84
3.69
7.68E02
[A/
C]
5.82
x
F-box domain
IWA7014
5A
53.71
5.43
6.82E03
[A/
G]
23.46
x
IWA3996
5A
87.89
3.36
8.92E02
[T/C]
23.28
x
UDP-glucose pyrophosphorylase
IWA2445
5A
122.72
4.3
3.70E02
[A/
C]
6.7
x
F-box domain
IWA7361
5A
184.89
3.63
7.75E02
[A/
G]
8.29
Domain of unknown function DUF221
IWA8395
5B
71.11
3.44
8.92E02
[A/
G]
5.47
Zinc finger, ZZ type
IWA3699
6BS
95.67
4.17
4.10E02
[A/
G]
24.87
x
Zinc finger
IWA7506
6BS
106.47
3.99
4.69E02
[T/C]
8.29
x
myosin
IWA7616
6D2S
69.20
4.52
3.70E02
[A/
G]
11.99
x
Ribonuclease II domain
IWA5526
7AS
102.85
4.11
4.11E02
[A/
G]
8.47
x
GTP cyclohydrolase II
IWA5000
7B
129.51
4.33
3.70E02
[A/
C]
6.17
IWA6512
1BS
140.72
8.51
1.29E05
[T/C]
14.29
x
RecF/RecN/SMC N terminal domain
IWA4240
1AL
0.00
4.27
5.98E02
[A/
G]
28.75
x
NB-ARC domain
IWA7331
1BL
10.98
5.64
4.89E03
[T/
G]
20.28
x
WD domain, G-beta repeat
Gene Annotation
Leucine rich repeat
TDBG
x
Glycerophosphoryl diester
phosphodiesterase
ABC transporter
TRAF-type zinc finger
THBL
PSTv37
(Continued)
PLOS ONE | DOI:10.1371/journal.pone.0129580 June 15, 2015
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Table 4. (Continued)
Trait
Marker
Chroma
cMb
-log10(pvalue)
pFDRc
SNP
MAFd
IWA3526
6A
98.55
3.95
5.98E02
[T/C]
8.11
IWA3527
6A
98.55
3.95
5.98E02
[T/C]
8.11
IWA2416
6A
98.98
3.95
5.98E02
[T/
G]
8.11
IWA8110
6A
99.63
3.95
5.98E02
[T/
G]
8.11
IWA6853
6A
193.68
4.02
5.98E02
[A/
G]
5.82
IWA62
Unk
Unk
7.01
4.17E04
[A/
G]
13.23
Included in Stepwise
Regression
Gene Annotation
Methyltransferase domain
C-5 cytosine-specific DNA methylase
x
Markers labelled with ‘x’ were maintained after stepwise regression.
Chrom = Chromosome;
a
b
cM = Marker position on consensus map;
c
pFDR = Positive false discovery rate;
d
MAF = Minor allele frequency;
e
Unk = Chromosomal location is unknown.
doi:10.1371/journal.pone.0129580.t004
stepwise regression model and accounted for 29.43% of the phenotypic variation (Table 5).
These three markers were detected at three QTL regions on chromosomes 1A and 1B.
Discussion
The evolution of new races of the leaf rust and stripe rust pathogens is a continuous threat to
winter wheat production in the northern Great Plains of the United States. The available host
genetic resistances is mostly race specific and easily overcome by pathogen evolution [3,10].
Therefore, there is need to find new sources of resistance and incorporate into adapted local
cultivars. One major goal of this research was to identify winter wheat accessions possessing a
wide spectrum of seedling resistance to leaf rust and to a predominant race of the stripe rust
pathogen. Six landrace accessions (PI 621539, PI 621674, PI 622111, PI 622129, PI 622243, and
PI 622246) that were resistant to all five races of Pt and one race of Pst tested at the seedling
stage were identified. Geographic information available from NSGC established that all six accessions originated from Iran and were collected in the same year (NSGC 2010; http://www.
ars.usda.gov/main/docs.htm?docid=2884). Four accessions were collected from Mazandaran
province in northern Iran while the other two accessions were each collected from Tehran and
Hamadan provinces located in northern and western Iran, respectively. Two of the accessions
from Mazandaran (PI 622243 and PI 622246) were collected from the same exact location, but
they exhibit differential reactions to races of Pt tested in this study. Moreover, the SNP genotype for both accessions was only 91% similar which suggests that these two accessions are not
duplicates. Field evaluations at two locations in Washington, USA, where stripe rust is a major
constraint to wheat production, showed these accessions to be highly resistant to the local
stripe rust pathogen population (X. M. Chen, http://www.ars-grin.gov/npgs/acc/acc_queries.
html). The identification of highly resistant accessions from Iran is not surprising as Iran is located in the Fertile Crescent, which is known as the center of origin and diversity of wheat [43].
PLOS ONE | DOI:10.1371/journal.pone.0129580 June 15, 2015
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Fig 3. Manhattan plots showing P values across 21 wheat chromosomes for SNP markers associated with resistance to races of P. triticina (A-E)
and P. striiformis f. sp. tritici (F). The horizontal black line indicates significant threshold at p-value = 0.001. SNPs included in stepwise regression are
shown in red.
doi:10.1371/journal.pone.0129580.g003
PLOS ONE | DOI:10.1371/journal.pone.0129580 June 15, 2015
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
Table 5. Stepwise regression for each rust race.
Rust race
No. of significant markers
Markers included in stepwise regression
% phenotypic variation explained
MCDL
34
11
38.16
MFPS
2
1
TBDJ
17
8
32.62
TDBG
18
10
43.50
THBL
1
1
PSTv-37
8
3
0.015
0.002
29.43
doi:10.1371/journal.pone.0129580.t005
Additionally, rust epidemics are common in Iran which could provide an opportunity for natural selection and maintenance of resistant genotypes by farmers [44,45]. This result also suggests that we might expect to obtain many accessions from Iran with resistance to leaf rust and
stripe rust since the co-existence of rust pathogens and wheat is believed to result in accumulation of diverse resistance in wheat [25]. Though phenotypic and genotypic data show these accessions as different, allelism tests will be needed to determine if these accessions carry the
same or different resistance genes.
Association mapping can produce spurious marker-trait associations if not corrected for
population structure and relatedness among individuals [39,46]. Population structure analysis
grouped the winter wheat accessions in this study into two major subpopulations. Therefore,
we tested multiple models taking into consideration relatedness (K) and population structure
(Q). Model analysis revealed that the best models are those that accounted for familial relatedness (K) and/or population structure (Q). Also, multiple testing corrections were used to further eliminate false positive associations. Initially, Manhattan plots of p-values showed many
significant markers associated with resistance to each race of rust pathogen tested at a significant cutoff of p-value = 0.001. After multiple testing corrections, only a few markers were significantly associated. We further applied the power of stepwise regression to identify the
minimum number of markers for each rust pathogen race that explains nearly the same
amount of variation as explained by all the markers considered together. Stepwise regression
allows selection of markers from major QTL and makes it easy to choose a subset of markers to
use in marker assisted selection [42].
Association analysis identified a total of 31 QTL markers in winter wheat landrace accessions associated with seedling resistance to leaf rust. These markers were located on chromosomes 1A, 1B, 2A, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 6D and 7A. Of the 13 chromosomes that
contained markers associated with resistance to one or more races of Pt, four chromosomes
3A, 4A, 5A, and 6D have not been previously shown to contain any leaf rust resistance genes
originally from T. aestivum [44, Cereal Disease Lab, http://www.ars.usda.gov/main/docs.htm?
docid=10342]. Therefore the eleven markers identified in genomic regions in 3A (47.75 cM,
72.50 cM, 118.07 cM), 4A (93.49 cM, 151.32 cM, 198.84 cM), 5A (53.71 cM, 87.89 cM, 122.72
cM) and 6D (45.42 cM, 69.20 cM) appear to be associated with novel sources of resistance and
could be useful in breeding programs for seedling resistance to leaf rust (Table 4). Based on the
general chromosome locations of previously identified leaf rust resistance genes in T. aestivum
and their effectiveness on Pt races used in this study, markers identified in chromosomes 1A,
2A, and 4B could possibly be for Lr10, Lr11 and Lr30, respectively. The markers identified in
the other chromosomes could possibly be for seedling resistance genes Lr31 (4BS), Lr33 (1BL)
and Lr52 (5BS) that are not included in the differential set but have been previously identified
in T. aestivum [47]. Comparison to a W7984/OpataM85 double haploid map integrating SSR,
DArT, iSelect 9K SNP, and GBS markers [48] indicated that the markers IWA3160 and
PLOS ONE | DOI:10.1371/journal.pone.0129580 June 15, 2015
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
IWA435 associated with resistance to race TBDJ are located in the general chromosome region
close to genes Lr10 and Lr33, respectively. Similarly, the marker IWA6290 association for race
TDBG is near the region where Lr33 has been previously mapped [48]. To our knowledge,
mapping information for Lr11, Lr30, Lr31, and Lr52 is not available to allow for comparison
with markers found in chromosomes where these resistance genes are located.
Three QTL were associated with resistance to Pst race PSTv-37. Two of these markers,
IWA4240 and IWA7331 were located on the long arms of chromosomes 1A and 1B, respectively. Chromosome 1B contains several known stripe rust seedling resistance genes (Yr3a,
Yr3b, Yr3c, Yr10, and Yr21) originating from T. aestivum [49–51]. Yr10 is effective against
PSTv-37, but is located on the short arm of chromosome 1B; therefore it cannot be responsible
for the resistance response associated with the IWA7331 locus. Monosomic analysis by Chen
et al. [51] showed that Yr21 is located on chromosome 1BL. The Yr3 alleles (Yr3a, Yr3b, and
Yr3c) are not assigned to a specific chromosome arm [49]. Therefore, the association of marker
IWA7331 with stripe rust resistance could possibly represent resistance genes Yr3a, Yr3b, Yr3c
and Yr21 or a new resistance locus. On chromosome 1A, only the temporarily designated seedling resistance gene YrDa1 from T. aestivum has been previously identified, but not assigned to
a specific chromosome arm [52] (Cereal Disease Lab 2014, http://www.ars.usda.gov/main/
docs.htm?docid=10342). This suggests that IWA4240 could be a marker representing YrDa1
or a novel resistance locus for Pst resistance. Further investigation using bi-parental population
QTL mapping and additional comparative analysis as information becomes available, will provide more information about the relationship between YrDa1 and the locus identified in chromosome 1AL.
The genome sequence of wheat is available but incomplete even with the rapid advancement
in sequencing technology. This study searched for gene models in sorghum, rice, and Brachypodium that correspond to probe sequences for SNPs associated with leaf rust and stripe rust resistance (Table 4). Most of the sequences corresponded to putative proteins with enzyme
activity such as protease (IWA2195), phosphatase (IWA7429, IWA574), and peptidase
(IWA8186, IWA2126). However, several SNPs were found associated with genes that encode
for putative proteins involved in disease resistance (R-proteins). IWA4240 on chromosome 1A,
which is associated with PSTv-37 resistance, corresponded to a putative NB-ARC domain containing protein. The nucleotide binding (NB) domain is a domain found in the NB-LRR gene
family which is often associated in plant disease resistance. The NB-ARC domain refers to the
nucleotide binding domain of apoptotic protease activating factor 1 (APAF-1), R-proteins and
Caenorhabditis elegans Death-4 (CED-4) [53]. Ooijen et al. [54] conducted a structured-function analysis and found that the NB-ARC is involved in regulation of R-protein. Another important R-protein domain, leucine-rich repeat (LRR), was associated with two SNP markers
(IWA185 and IWA6340) and is part of the NBS-LRR superfamily that characterizes most Rproteins [55]. The LRR domain is mainly involved in recognition and interaction with other
proteins in disease resistance pathways. Other important protein families associated with associated SNP markers include a protein kinase (IWA435), ABC transporters (IWA5005 and
IWA5786), and zinc fingers (IWA8395, IWA3699 and IWA5000). Bruggeman et al. [56] found
the Rpg1 protein for barley stem rust resistance to contain two tandem protein kinase domains.
Similarly, the Lr34 gene associated with durable resistance to leaf rust, stripe rust, and powdery
mildew of wheat belongs to ABC transporter gene family [57]. Many proteins in the zinc finger
superfamily are involved in biotic and abiotic stress in plants. Guo et al. [58] demonstrated that
overexpression of a zinc finger protein, GhZFP1, enhanced tolerance to salt stress and resistance
to Rhizoctonia solani.
The results of our study demonstrate the use of AM for the identification of potentially new
genomic regions associated with leaf and stripe rust resistance that can help to broaden the
PLOS ONE | DOI:10.1371/journal.pone.0129580 June 15, 2015
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Association Mapping for Leaf and Stripe Rust Resistance in Wheat
genetic base of resistance in winter wheat breeding programs. The accessions identified in this
study carrying resistance to multiple races and even both rust pathogens used in evaluations,
could be excellent choices as parental lines in breeding programs interested in incorporating
leaf and stripe rust resistance. Future work will focus on developing bi-parental populations of
accessions to validate the resistance loci and develop user friendly, tightly linked markers that
can be used to accelerate the incorporation of the novel resistance into elite breeding wheat
lines.
Supporting Information
S1 Table. List of 567 winter-habit Triticum aestivum accessions along with their origin and
linearized infection type to five Puccinia triticina (Pt) and one Puccinia striiformis f. sp. tritici (Pst) races.
(XLSX)
S2 Table. Properties for the 5,633 high quality SNP markers obtained from the wheat Illumina iSelect beadchip assay.
(XLSX)
S3 Table. List of resistant accessions, linearized infection type (IT), allele and chromosomal
location for SNP markers associated with resistance.
(XLSX)
Acknowledgments
The authors would like to thank Shiaoman Chao for genotyping work. Special thanks to
Amanda Swank, Jade Glasgow, Jason D. Zurn and Matthew Breiland for their
technical support.
Author Contributions
Conceived and designed the experiments: MA JMB. Performed the experiments: AK MA. Analyzed the data: AK SM. Contributed reagents/materials/analysis tools: MA JMB PEM. Wrote
the paper: AK SM PEM JMB MA.
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