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Published October 6, 2016
o r i g i n a l r es e a r c h
Genome-Wide Association Mapping Reveals
Novel QTL for Seedling Leaf Rust Resistance
in a Worldwide Collection of Winter Wheat
Genqiao Li, Xiangyang Xu,* Guihua Bai, Brett F. Carver, Robert Hunger,
J. Michael Bonman, James Kolmer, and Hongxu Dong
Abstract
Core Ideas
Leaf rust of wheat (Triticum aestivum L.) is a major disease that
causes significant yield losses worldwide. The short-lived nature
of leaf rust resistance (Lr) genes necessitates a continuous search
for novel sources of resistance. We performed a genome-wide
association study (GWAS) on a panel of 1596 wheat accessions. The panel was evaluated for leaf rust reaction by testing
with a bulk of Puccinia triticina Eriks. (Pt) isolates collected from
multiple fields of Oklahoma in 2013 and two predominant races
in the fields of Oklahoma in 2015. The panel was genotyped
with a set of 5011 single-nucleotide polymorphism (SNP) markers. A total of 14 quantitative trait loci (QTL) for leaf rust resistance were identified at a false discovery rate (FDR) of 0.01 using the mixed linear model (MLM). Of these, eight QTL reside in
the vicinity of known genes or QTL, and more studies are needed to determine their relationship with known loci. QLr.stars-7AL1
is a new QTL to bread wheat but is close to a locus previously
identified in durum wheat [Triticum turgidum L. subsp. durum
(Desf.) Husn.]. The other five QTL, including QLr.stars-1BL3, QLr.
stars-1DC1, QLr.stars-2BL1, QLr.stars-5BL1, and QLr.stars-7AS1, are
likely novel loci for leaf rust resistance. The uneven distribution of
the 14 QTL in the six subpopulations of the panel suggests that
wheat breeders can enhance leaf rust resistance by selectively
introgressing some of these QTL into their breeding materials. In
addition, another 31 QTL were significantly associated with leaf
rust resistance at a FDR of 0.05.
Published in Plant Genome
Volume 9. doi: 10.3835/plantgenome2016.06.0051
© Crop Science Society of America
5585 Guilford Rd., Madison, WI 53711 USA
This is an open access article distributed under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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The association panel used in this study may
represent the largest in wheat GWAS
Three pathotypes, including a bulked races collected
in the field, were used
A total of 14 QTL for leaf rust resistance were
identified at a FDR of 0.01.
Six QTL identified in this study are novel loci in
bread wheat
Thirty-one additional QTL were significant at a
FDR of 0.05.
•
•
•
•
L
eaf rust,
caused by Puccinia, is a major foliar disease
that has afflicted wheat for thousands of years (Bolton
et al., 2008). Leaf rust occurs nearly everywhere wheat is
grown and typically reduces yield by 5 to 15% or more
depending on crop developmental stage when the initial
rust infection occurs (Samborski, 1995). Yield loss is
G. Li, and X. Xu, Wheat, Peanut and Other Field Crops Research
Unit, USDA–ARS, Stillwater, OK 74075; G. Bai, Hard Winter Wheat
Genetics Research Unit, USDA–ARS, Manhattan, KS 66506; G. Li
and B.F. Carver, Plant and Soil Science Dep., Oklahoma State Univ.,
Stillwater, OK 74078; R. Hunger, Entomology and Pathology Dep.,
Oklahoma State Univ., Stillwater, OK 74078; J. M. Bonman, Small
Grains and Potato Germplasm Research Unit, USDA–ARS, Aberdeen, ID
83210; J. Kolmer, Cereal Disease Laboratory, USDA–ARS, St. Paul, MN
55106; H. Dong, Dep. of Crop Sciences & Energy Biosciences Institute,
Univ. of Illinois, Urbana, IL 61801. Received 2 June 2016. Accepted 24
July 2016. *Corresponding author ([email protected]).
Abbreviations: FDR, false discovery rate; GWAS, genome-wide
association study; HR, highly resistant; HS, highly susceptible; iCore,
informative core; IT, infection type; LD, linkage disequilibrium; LnP(D),
log probability of data; MLM, mixed linear model; MR, moderately
resistant; MS, moderately susceptible; NSGC, National Small Grains
Collection; PCR, polymerase chain reaction; Pt, Puccinia triticina Eriks.;
QTL, quantitative trait loci; SNP, single-nucleotide polymorphism.
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mainly through reduction of kernel weight and kernel
number per spike (Hershman, 1985).
Genetic resistance is vital to wheat leaf rust control.
Much effort has been directed to identifying Lr genes.
To date, 71 Lr genes have been officially designated (Lr1–
Lr74; Lr21 = Lr40; Lr39 = Lr41; Lr43 deleted) and 20 more
temporarily named. These Lr genes are distributed on
almost every chromosome. Of these genes, 52 originated
from bread wheat and the others were derived from alien
species, including Aegilops speltoides Tausch, Ae. tauschii Coss., rye (Secale cereale L.), T. timopheevii (Zhuk.)
Zhuk., Agropyron elongatum (Host) P. Beauv., einkorn (T.
monococcum L.), wild emmer wheat [Triticum dicoccoides
(Korn. ex Asch. & Graebn.) Schweinf.], spelt [T. aestivum
L. subsp. spelta (L.) Thell.], Ae. ventricosa Tausch, Ae.
geniculate Roth, Ae. kotschyi Boiss., Ae. neglecta Req. ex
Bertol., Ae. peregrine (Hack.) Maire & Weiller, Ae. sharonensis Eig, Ae. umbellulata Zhuk., slender wheatgrass [Elymus trachycaulus (Link) Gould ex Shinners], T. armeniacum (Stolet.) Nevski, and Thinopyrum intermedium (Host)
Barkworth & D. R. Dewey (McIntosh et al., 2013, 2014;
http://wheat.pw.usda.gov/GG2/Triticum/wgc/2013/20132014_Supplement.pdf). The majority of these genes confer
race-specific resistance in a gene-for-gene manner, and
only a few of them, such as Lr34, Lr46, and Lr68, confer
race-nonspecific resistance. Race-specific Lr genes provide
high levels of resistance to specific races and have been
widely used in wheat breeding. However, 40 to 60 Pt races
are detected each year in North America, and growing
large acreages of susceptible varieties provides a reservoir
for new races in the southern Great Plains of the United
States (Kolmer and Hughes, 2015). The large genetic variation in pathogen populations typically makes race-specific
Lr genes ineffective in a few years after their deployment in
the region (Kolmer, 2005), which forces wheat breeders to
find new resistance sources.
Knowing what Lr genes are present in germplasm
allows for full use and effective deployment of these
genes in wheat breeding. Most known Lr genes have
been mapped using the biparental population mapping
approach. More recently, advances in genomics have
made it feasible to genotype a large collection of germplasm lines with thousands of SNP markers. As a result,
the GWAS approach, based on the principle of linkage
disequilibrium (LD), has been effectively used to exploit
existing allelic diversity for traits of agronomic importance. Compared with biparental population mapping,
GWAS may provide a higher mapping resolution because
all historical meiotic events that have occurred in ancestors of a diverse panel of germplasm can be used. Moreover, GWAS enables detection of genes or QTL present
in multiple germplasm lines rather than one or two
parental lines of a biparental population. Thus, GWAS is
an effective alternative to traditional linkage mapping.
In crops with reference genome sequences available, such
as rice (Oryza sativa L.) (Huang et al., 2010; 2012a,b;
Chen et al., 2014), maize (Zea mays L.) (Gore et al., 2009;
Buckler et al., 2009; Tian et al., 2011; Peiffer et al., 2013,
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2014; Poland et al., 2011; Chia et al., 2012), and foxtail
millet [Setaria italica (L.) P. Beauv. subsp. italica] (Jia et
al., 2013), high-density HapMaps have been constructed.
These HapMaps greatly facilitate GWAS and have the
potential to rapidly resolve complex traits to gene-level
resolution (Xu and Bai, 2015).
Genome-wide association study has been widely
used in wheat (Breseghello and Sorrells, 2006; Juliana et
al., 2015; Maccaferri et al., 2010, 2015; Zhang et al., 2011).
The self-pollination nature of wheat results in a high level
of LD, which reduces the minimum number of markers
needed to detect marker-trait association. Several studies
have examined the degree of LD in the wheat genome and
found that LD, represented by squared allele frequency
correlation coefficient (r2) value, decayed to <0.1 at about
10 to 20 cM (Chao et al., 2007; Zhang et al., 2010a; Juliana et al., 2015), <0.2 at 2 to 3 cM (Somers et al., 2007),
and <0.3 at 1.6 cM (Maccaferri et al., 2015). These results
suggested that GWAS can be performed with a limited
number of molecular markers in wheat. Recently, GWAS
has been successfully used to map gene and QTL for yield
(Sukumaran et al., 2015), quality traits (Breseghello and
Sorrells, 2006), leaf rust resistance (Maccaferri et al., 2010;
Kertho et al., 2015; Gao et al., 2016), stripe rust resistance
(Maccaferri et al., 2015; Kertho et al., 2015), stem rust
resistance (Yu et al., 2012; Zhang et al., 2014), and soilborne mosaic virus resistance in wheat (Zhang et al., 2011).
The successful application of GWAS in wheat has
prompted the development of genomic resources. A
wheat HapMap has been constructed to facilitate GWAS
in wheat (Jordan et al., 2015). Moreover, the USDA–ARS
National Small Grains Collection (NSGC) has assembled
a core subset of wheat accessions to maintain a high
degree of genetic diversity with a manageable number
of samples (Bonman et al., 2015). The NSGC wheat core
subset consists of 4007 accessions, accounting for ~10%
of total common wheat germplasm in the NSGC. The
core was assembled using the stratified random sampling
approach based on the country of origin and may reserve
rare genetic variations associated with traits of agronomic
importance. Thus, the core subset of wheat accessions is
a valuable resource for wheat improvement. The objective of this study was to identify novel leaf rust resistance
genes in the core subset of wheat germplasm using GWAS
and provide resistance sources for wheat breeding.
Materials and Methods
Plant Materials
The NSGC core set germplasm consists of 4007 accessions, and 3836 of them were genotyped with Illumina
iSelect 9K SNP array and Diversity Arrays Technology
(DArT) markers. Based on marker data, duplicate accessions were excluded from the core to form an informative
core (iCore) with 3230 accessions including 1674 winter–
facultative accessions and 1556 spring wheat accessions
(Bonman et al., 2015). Of the 1674 winter–facultative
accessions, 1596 were available at NSGC and used in this
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study. The details of these accessions, including name,
origin, and improvement status, etc., are available at the
TriticeaeCAP project website (http://www.triticeaecap.
org). This panel consists of 217 breeding lines, 589 cultivars, 773 landraces, and 17 genetic stocks and was collected from 85 countries with 594 from Asia, 794 from
Europe, 15 from Africa, 114 from North America, 57
from South America, four from Central America, six
from Australia, 10 from New Zealand, and two from the
Caribbean islands. Experimental seeds were provided by
the USDA–ARS NSGC. In addition, a set of 45 differential lines carrying known leaf rust resistance genes was
also used in this study (Supplemental Table S1).
Evaluation of Leaf Rust Resistance
Three Pt pathotypes were used as inocula in this
study including Pt2013, Pt52-2 (MMPSD), and Pt54-1
(TNBGJ). Pt2013 was a bulked Pt race collected in
multiple fields of Oklahoma in 2013. Previous studies
suggested that a bulk field collection of Pt pathotypes
provided the broadest spectrum of virulence (Dr. Robert
Hunger, personal communication, 2016), and leaf rust
reaction to a mixture of races in the seedling test best
resembled field reaction (Gao et al., 2016). Pt52-2 and
Pt54-1 were two predominant races in Oklahoma fields
and were collected and isolated by USDA–ARS Cereal
Disease Laboratory at St. Paul, MN, in 2015.
All 1596 accessions were evaluated for Pt infection
type (IT) in a greenhouse at the USDA–ARS Wheat, Peanut, and Other Field Crops Research Unit in Stillwater,
OK. A randomized complete block design was used with
two replicates. Eight seeds per entry were planted in each
cell of a 73-cell growing tray (Growing Systems, Inc.)
containing Sunshine Redi-earth Growing Mix (SunGro
Horticulture Cabada Ltd.). Each tray had 70 cells for the
test entries and three for the susceptible check, TAM110.
Seedlings at the two-leaf stage (~12 d) were inoculated
with urediniospores of Pt2013 using a method described
previously (Xing et al., 2014) in 2015. Inoculated seedlings were placed in a dew chamber with no lighting at
~20C with 100% relative humidity for 24 h and then
transferred to a greenhouse at 22  2C with supplemental light and a photoperiod of 16:8 (light/dark) h. A
0-to-9 scale (McNeal, 1971) was used to score leaf rust IT
of each plant when leaf rust was fully developed on susceptible checks at ~14 d after inoculation. The mean IT
of eight plants in each replicate was calculated for each
accession and used for further analysis.
Based on the leaf rust IT data from 1596 accessions,
a subset of accessions that met one of the following three
criteria were chosen for a confirmation test in 2015: (i) an
accession with a mean IT  3, (ii) plants with heterogeneous reactions to Pt2013 within a replicate, or (iii) the
difference in IT  2 between two replicates. The purpose
of the confirmation test was to get accurate data and
identify reliable resistance sources, and the experimental design and disease evaluation protocols described
above were employed in the confirmation test. The
li et al .: novel qtl for seedling leaf rust resistance confirmation test identified 59 heterogeneous accessions,
which were excluded from further analysis, leaving 1537
accessions available for association analysis. The leaf rust
IT data of two replicates were highly consistent with a
correlation coefficient of 0.947, and the mean IT over two
replicates was calculated for each accession and used for
GWAS analysis. In early 2016, the 1537 accessions were
evaluated with the races Pt52-2 and Pt54-1 using the
same experimental design.
Identification of the Lr47 Gene
using a Diagnostic Marker
A total of 61 accessions showing high resistance to
Pt2013 were tested for the presence or absence of the Lr47
gene using a T. speltoides (Tausch) Gren. ex K. Richt.-specific marker (Helguera et al., 2000). CItr 17884, a genetic
stock carrying the Lr47 gene, was used as a positive control. Genomic DNA was extracted from seeds of each
accession using a protocol reported by Dubcovsky et al.
(1994). Primer sequences and polymerase chain reaction
(PCR) conditions described previously were employed in
this study (Helguera et al., 2000). The PCR products were
separated by electrophoresis in 2% agarose gels and visualized using ethidium bromide and ultraviolet light.
Data Analysis
The method for collecting genotypic data of 1537 accessions was described previously (Bonman et al., 2015),
and 5011 polymorphic SNP data from the TCAP project
(http://www.triticeaecap.org) were used for the GWAS.
Among the 5011 SNPs that were analyzed with
HAPLOVIEW 4.2 (Barrett et al., 2005), 4091 were identified as nonredundant SNPs using the tagger function r2
= 1.0. A subset of 2110 informative SNPs were selected
for structure (Q matrix) analysis using the tagger function r2 = 0.25. The Q matrix was estimated using the
program STRUCTURE 2.3.4 (Pritchard et al., 2000)
and was integrated as a covariate to correct the effects
of population structure. An admixture model was used
to test the hypotheses for two to 15 subpopulations (K =
2–15). For each K, five independent runs were performed
with a burn-in length of 100,000, and the number of
Markov Chain Monte Carlo iterations of 50,000. The log
probability of data [LnP(D)] was estimated for each run,
and an ad hoc statistic K that was based on the rate of
change in LnP(D) between successive K values was used
to determine the true number of subpopulations (Evanno
et al., 2005). Based on the inferred subpopulation number, a Q matrix representing population membership
coefficients of each accession was obtained.
Of the 5011 SNPs, 4716 were mapped on a consensus
reference map (Cavanagh et al., 2013; Wang et al., 2014)
and thus used for genome-wide LD analysis. An R package (R Development Core Team, 2010) was used to calculate the LD for all pairwise comparisons between intrachromosomal SNPs and estimate the genome-wide LD
decay by plotting LD r2 from all 21 chromosomes against
the corresponding genetic distances. A trend line was
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Fig. 1. Scatter plot showing the genome-wide linkage disequilibrium (LD) decay over genetic distances. Pair-wise LD r2 value is plotted
against the intermarker map distance, which was based on a reference consensus map (Wang et al., 2014). The blue curve represents
the fitted model, and the red dashed line shows LD declines to 0.1 at 11 cM.
fitted by second-degree loess (Cleveland 1979) using the
statistical program R (R Development Core Team, 2010).
The mixed linear model (Yu et al., 2006) was
employed to identify loci governing Pt response in the
1537 accessions using TASSEL 5.0 (Bradbury et al., 2007).
The SNP markers and the Q matrix were used as fixed
effects. To correct the family relatedness, a kinship (K)
matrix was calculated with the scaled identity-by-state
method that gives a reasonable estimate of additive
genetic variance (Endelman and Jannink, 2012) and
used as a random effect component in the MLM model.
The MLM was run with the optimum level of compression and the P3D approach (Zhang et al., 2010b) using
TASSEL 5.0. The FDR of 0.01 was used as a threshold to
declare significant marker–trait association with the procedure developed by Benjamini and Hochberg (1995).
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Results
Linkage Disequilibrium Analysis
Among 4716 SNPs that were mapped in the 90K SNP consensus map (Cavanagh et al., 2013; Wang et al., 2014), 2220
were from the A genome, 2200 from the B genome, and 296
from the D genome. They were used to estimate genomewide LD (Fig. 1). Based on the fitted model, genome-wide
r2 values declined to 0.1 at 11 cM. Thus a genomic region
covering 11 cM from each side of the peak of significant
associations on the same chromosome arm was defined as
a QTL-harboring region, and all significant associations in
this region were considered as the same QTL.
Population Structure
The model-based structure analysis revealed six subpopulations (S1– S6), which include 339 (22.1%), 78 (5.1%), 437
(28.6%), 379 (24.7%), 133 (8.7%), and 171 (11.1%) accessions
with the Fst values of 0.6502, 0.7943, 0.3240, 0.4588, 0.4323,
and 0.4562 from S1 to S6, respectively (Fig. 2).
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Fig. 2. STRUCTURE analysis revealed six subpopulations (S1–S6). S1, S2, S4, S5, and S6 represent Iranian landrace group 1, eastern
European cultivar group, western European cultivar group, east-Asian accession group, and Iranian landrace group 2, respectively.
Origins of wheat accessions in S3 were very diverse. Vertical lines represent membership coefficients of accessions corresponding to
the six subpopulations.
Fig. 3. Distribution of mean leaf rust infection types of 1537 accessions inoculated with Pt2013, Pt52-2, and Pt54-1.
Iranian Landraces were predominant in S1, accounting for 87.6% in the subpopulation, and the remaining
12.4% originated from central Asia and Europe; therefore, this subpopulation was designated as Iranian landrace group 1. Iranian landraces were well represented
in the iCore subset. Among 425 Iranian accessions used
in this study, 297 belong to S1. Wheat cultivars from
eastern Europe (Romania, Bulgaria, Russia, Croatia, and
Ukraine) were predominant in S2, accounting for 87.2%
(eastern European cultivar group). The others in this
group were from Asia (9%), United States (2.6%), and
South America (1.3%). The origins of accessions in S3
were diverse. The 437 accessions in this subpopulation,
including 79 breeding lines, 207 registered cultivars, 139
landraces, and 12 genetic stocks, were collected from
60 countries in Europe, North America, Asia, Africa,
South America, Australia, New Zealand, and the Caribbean islands (Cuba). European cultivars, especially
western European cultivars, were predominant in S4,
accounting for 77% of all accessions. The majority of
cultivars released from western Europe, including Germany, England, the Netherlands, Sweden, Switzerland,
Portugal, Spain, Italy, France, Finland, Demark, and
li et al .: novel qtl for seedling leaf rust resistance Belgium, belonged to S4 (western European cultivar
group). Some cultivars and landraces from other regions,
including Asia, United States, and eastern Europe,
were also included in this group. Subpopulation S5 was
characterized by cultivars and landraces collected from
eastern Asia, including China, Japan, and South Korea,
and southern Asia including Bhutan, Nepal, and India.
Accessions from these countries accounted for 68% of
the total accessions in S5. A few landraces from western
Asia (Iran), and one or two each from other 29 countries,
were also included in this group (east-Asian accession
group). Most (91.4%) of group S6 were landraces with a
large portion (66.3%) from Iran. The remaining accessions in this group came from Turkey, Afghanistan,
Macedonia, Armenia, Azerbaijan, Georgia, United
States, Lebanon, and Jordan (Iranian landrace group 2).
Evaluation of Leaf Rust Reaction
Among the tested accessions, only a limited number
of them (4%) showed a high level of seedling resistance
to Pt2013 (highly resistant [HR]; IT = 0–3), and their
mean IT distribution was significantly skewed toward
susceptibility (Fig. 3). About 17.3, 36.6, and 43.1% were
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Fig. 4. Box plot showing distribution of wheat leaf rust infection types (ITs) in six subpopulations inoculated with Pt2013. The diamond
represents mean IT value of each subpopulation.
moderately resistant (MR; IT = 4–5), moderately susceptible (MS; IT = 6–7), and highly susceptible (HS; IT
= 8–9), respectively. The paucity of HR germplasm was
consistent with the fact that most known Lr genes have
lost their effectiveness to Pt2013 (Supplemental Table S1).
In the present study, we also tested a set of 45 differential
lines with known Lr genes and found that only a few of
them were highly resistant to Pt2013 (Supplemental Table
S1) including lines with Lr19 (IT = 0;), Lr25 (IT = 1), Lr29
(IT = 3), Lr45 (IT = 0;), Lr47 (IT = 1), and Lr51 (IT = 3).
Compared with Pt2013, Pt52-2 and Pt54-1 were less
virulent. About 8.0, 4.0, 18.6, and 69.4% accessions in the
association panel was rated as HR, MR, MS, and HS to
race Pt52-2, respectively, and 7.4, 12.2, 36.1, and 44.3% as
HR, MR, MS, and HS to Pt54-1, respectively. These results
were in agreement with the leaf rust reactions of differential lines carrying known resistance genes. Pt52-2 was
virulent to Lr3, Lr3bg, Lr3ka, Lr10, Lr12, Lr14a, Lr14b, Lr15,
Lr17, Lr20, Lr22a, Lr35, Lr37, Lr13, and Lr23 and avirulent
to Lr2a, Lr2b, Lr2c, Lr18, Lr19, Lr21, Lr24, Lr25, Lr28, Lr29,
Lr36, Lr38, Lr45, Lr47, Lr51, Lr52, Lr60, Lr63, and Lr64.
Isolate Pt54-1 was highly virulent to Lr10 and avirulent
to Lr18, Lr19, Lr21, Lr25, Lr29, Lr36, Lr38, Lr45, Lr47, Lr51,
Lr52, Lr60, Lr63, and Lr64 (Supplemental Table S1). Other
genes confer MR or MS reactions to Pt54-1.
Significant association was observed between population structure and leaf rust resistance, suggesting that
population structure should be considered in GWAS. For
example, ANOVA among subpopulations revealed significant variation in IT scores for Pt2013 (p < 0.01) (Fig. 4).
The subpopulation S2 showed the highest level of resistance as a group with a mean IT score of 4.04, which was
significantly lower than all other five groups (p < 0.01).
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Of the 78 accessions in this group, the majority were
rated HR (34.6%) or MR (35.9%), and only a few of them
were rated MS (16.7%) or HS (12.8%). In contrast, none
or only a few HR accessions were identified in the subpopulations S1 and S6, the two Iranian landrace groups.
In the S1 group, 0, 2.1, 30.1, and 67.8% of the accessions
were rated HR, MR, MS, and HS, respectively, and in the
S6 group, these numbers were 0.6, 9.4, 32.7, and 57.3%,
respectively. The mean IT scores of S3 (6.53) and S5 (6.83)
were significantly lower than those of S1 (7.61) and S6
(7.34) (p < 0.01) but significantly higher than that of S4
(5.91) (p < 0.01). Similar results were observed when the
association panel was inoculated with Pt52-2 and Pt54-1.
Quantitative Trait Loci for Leaf Rust Resistance
The MLM analysis revealed a total of 14 QTL that were
significantly associated with leaf rust resistance at FDR
of 0.01 in three experiments (Table 1). Six, eight, and five
significant QTL were detected for resistance to the pathotypes Pt2013, Pt52-2, and Pt54-1, respectively. Each QTL
explained 1.36 to 8.35% of the phenotypic variance for
resistance to Pt2013, 1.35 to 7.23% for resistance to Pt52-2,
and 1.48 to 2.64% for resistance to Pt54-1. Of these QTL,
QLr.stars-1BC1 was significant for resistance to all three
pathotypes, while QLr.stars-1BS1 and QLr.stars-7AL1 were
significant for resistance to pathotypes Pt2013 and Pt541. QLr.stars-2DS1 was significant for resistance to Pt52-2
and Pt54-1. The remaining 10 QTL were significant for
resistance to only a single pathotype at FDR of 0.01 (Table
1). Among them, however, QLr.stars-1DC1, QLr.stars-1BL1,
QLr.stars-1BL2, QLr.stars-4AL1, and QLr.stars-4BL1 were
significant for resistance to two to three pathotypes if a
FDR was set at 0.05 (Supplemental Table S1). Moreover, 31
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Table 1. Designated name, representative single-nucleotide polymorphism (SNP; TagSNP), chromosome arm,
location on the Illumina SNP9K consensus map, p-value, and R2-value of each quantitative trait loci (QTL) that was
significant at a false discovery rate of 0.01 when the association panel was evaluated using Pt pathotypes Pt2013,
Pt52-2, and Pt54-1.
Pt pathotype
Pt2013
Pt52-2
Pt54-1
QTL
TagSNP
SNP
Chromosome arm†
Map location
p-value
R2
QLr.stars-1BC1
QLr.stars-1BL1
QLr.stars-1BS1
QLr.stars-1BS1
QLr.stars-1DC1
QLr.stars-2DL1
QLr.stars-7AL1
QLr.stars-1BC1
QLr.stars-1BL2
QLr.stars-1BL3
QLr.stars-2DS1
QLr.stars-4AL1
QLr.stars-4BL1
QLr.stars-5BL1
QLr.stars-7AS1
QLr.stars-7AL1
QLr.stars-1BC1
QLr.stars-2DS1
QLr.stars-2BL1
QLr.stars-1BS2
IWA435
IWA8153
IWA1566
IWA6110
IWA362
IWA1083
IWA6736
IWA435
IWA6758
IWA579
IWA609
IWA2460
IWA6480
IWA4571
IWA3760
IWA6736
IWA435
IWA4012
IWA1488
IWA6110
5
1
7
3
1
14
3
1
1
3
6
2
2
4
1
1
1
3
1
3
1BC
1BL
1BS
1BS
1DC
2DL
7AL
1BC
1BL
1BL
2DS
4AL
4BL
5BL
7AS
7AL
1BS
2DS
2BL
1BS
cM
60.13
148.23
48.08–57.72
–
48.81
176.19–177.99
188.95
60.13
81.58
115.79
79.50–92.80
73.40–83.38
72.30–82.13
106.17–106.94
45.42
188.95
42.93
92.80–93.40
193.90
–
2.09  10−13
3.31  10−5
2.31  10−11
9.06  10−15
1.73  10−5
1.67  10−6
5.71  10−24
2.25  10−5
5.96  10−6
4.08  10−5
2.19  10−23
2.65  10−5
2.11  10−6
1.85  10−6
6.38  10−6
6.29  10−8
3.43  10−7
7.87  10−7
1.31  10−5
1.87  10−10
%
4.76
1.36
3.64
4.83
1.84
1.82
8.35
1.43
1.60
1.35
7.23
1.40
1.75
1.75
1.59
1.95
1.97
2.64
1.48
1.79
† L, S, and C refer to long and short chromosome arms, and centromere, respectively.
additional significant loci were detected at a FDR of 0.05
(Supplemental Table S1).
The 14 QTL significant at FDR of 0.01 were located
on eight chromosomes including five loci on chromosome 1B, two each on chromosomes 2D and 7A, and one
each on chromosomes 1D, 2B, 4A, 4B, and 5B (Table 1).
For two of the QTL on 1B, QLr.stars-1BS1 is on the short
arm, while QLr.stars-1BC1 is in the centromere region
where recombination is usually suppressed. The genetic
distance between them is <10 cM. However, the physical
distance between them may be greater than that indicated by the genetic distance because of recombination
suppression. We further calculated the LD between the
two tag SNPs underlying these two QTL, IWA1566 and
IWA435, and found that the LD was only 0.089, suggesting that these two QTL were likely different loci.
Three SNPs cosegregating in the association panel,
IWA6110, IWA6541 and IWA6129, were significantly
associated with leaf rust resistance to both pathotypes
Pt2013 and Pt54-1. These SNPs have not been mapped
to the 90K SNP consensus map yet. To determine the
genomic locations of IWA6110, IWA6129, and IWA6541,
we compared their sequences (~200 bp) with other SNP
sequences from 90K SNP arrays and found that IWA6110
and IWA6541 were identical to IWB3972 and IWB63777,
respectively. IWB3972 was previously mapped to the
centromere region of chromosome 1B and IWB63777
on the proximal region of 1BS. IWB3792 and IWB6377
li et al .: novel qtl for seedling leaf rust resistance Fig. 5. Inferred locations of single-nucleotide polymorphism
(SNP) marker IWA6541, IWA6110, and IWA6129 on the SNP9K
consensus map (right) and homeologous sequence corresponding to each marker on the Brachypodium chromosome 2 physical
map (left). IWA6129 is tentatively mapped in between IWA6541
and IWA6110.
were 1.54 cM apart on the 90K consensus map (Fig. 5).
We further searched these three SNP sequences against
Chinese Spring reference genome sequence (urgi.versailles.inra.fr/blast/blast.php). Three contigs mapped to
chromosome 1B, including 1BS_3441348, 1BS_3445781,
7
of
12
Fig. 6. Chromosome locations of 14 quantitative trait loci (QTL) for leaf rust resistance identified in this study and previously mapped
Lr genes and QTL (left side of bars). The vertical lines on the left show genomic regions harboring known genes or QTL. Chromosome
lengths were standardized to the same relative length. Marker positions were based on the 90K SNP consensus linkage map. The tag
SNP of each QTL was shown on the linkage map, and suggested QTL name was given in parentheses.
and 1BS_3456976, contained IWA6541, IWA6129, and
IWA6110 sequence, respectively. We searched these contigs against Brachypodium physical map using BLASTn
(http://www.plantgdb.org/BdGDB/cgi-bin/blastGDB.pl)
and located them to 23.8, 34.8, and 59.8 Mb of chromosome 2, respectively (Fig. 5). Considering the collinearity
between wheat and the Brachypodium genome (Brenchley et al., 2012), we located IWA6129 in between IWA6110
and IWA6541 at the 58.37 to 59.91 cM region on the
consensus map. Given that LDs between these SNPs and
two tag SNPs, IWA1566 and IWA435, were estimated to
be 0.2 and 0.06, respectively, we conclude that these SNPs
represent the same locus as IWA1566, QLr.stars-1BS1.
Three QTL were detected on 1BL. QLr.stars-1BL1 was
significant for resistance to Pt2013, while QLr.stars-1BL2
and QLr.stars-1BL3 were significant for resistance to Pt522. Their locations on the 90K consensus map are 148.23,
81.58, and 115.79 cM, respectively, thus they are likely different QTL. The other nine QTL were identified in different chromosome arms of seven chromosomes (Fig. 6).
8
of
12
Allele Effects of the Quantitative Trait Loci
Identified in This Study
In the Pt2013 experiment, the allelic effects of the six
QTL identified in this study varied from 0.26 to 2.27 for
IT score. For two of them, allele substitution resulted in
IT change >1.0 (Table 2). The favorable allele number in
each accession ranged from 0 to 5 with an average of 1.57
at six loci. The mean IT values decreased from 7.42 for
germplasm without any favorable allele to 3.82 for those
possessing five favorable alleles (Supplemental Fig. S1).
All 23 accessions having five favorable alleles were rated
as either HR or MR to Pt2013. It is of great interest to
know whether the leaf rust resistance of these accessions
would prove to be durable.
A significant correlation between favorable allele
numbers and IT values was observed (r = −0.401, p <
10−7). The observed phenotypic values and the predicated
phenotypic values (calculated as the sum of all QTL
effects for each accession) were also significantly correlated (r = −0.53, p < 10−7), suggesting that pyramiding
these QTL may enhance leaf rust resistance.
Similarly, favorable alleles were negatively correlated with the IT scores when plants were inoculated
the pl ant genome

november 2016

vol . 9, no . 3
Table 2 Allelic effects of the tag single-nucleotide polymorphisms (SNPs) underlying quantitative trait loci (QTL) for
leaf rust resistance and the distribution of favorable alleles in six subpopulations
Allele effects
QTL name
QLr.stars-1BC1
QLr.stars-1BL1
QLr.stars-1BS1
QLr.stars-1DC1
QLr.stars-2DL1
QLr.stars-7AL1
QLr.stars-1BL2
QLr.stars-1BL3
QLr.stars-2BL1
QLr.stars-2DS1
QLr.stars-2DS1
QLr.stars-4AL1
QLr.stars-4BL1
QLr.stars-5BL1
QLr.stars-7AS1
TagSNP
IWA435
IWA8153
IWA6110
IWA362
IWA1083
IWA6736
IWA6758
IWA579
IWA1488
IWA609
IWA4012
IWA2460
IWA6480
IWA4571
IWA3760
SNP allele†
T/C
T/C
A/G
T/C
T/C
T/C
A/G
T/C
A/G
A/G
A/G
T/C
A/C
T/C
A/C
Pt2013
0.72
0.52
2.27
0.26
0.60
1.26
–
–
–
–
–
–
–
–
–
Pt52-2
0.80
–
–
–
–
–
0.40
0.31
–
2.50
–
0.42
0.56
0.30
0.48
Favorable allele frequency‡
Pt54-1
0.37
–
1.70
–
–
0.69
–
–
1.00
–
1.02
–
–
–
–
S1
S2
S3
S4
S5
S6
All
————­­­————————————— % —————————————————
0.9
6.4
17.6
3.4
29.3
8.2
9.8
2.1
30.8
29.3
66.0
15.0
11.7
29.2
0.0
25.6
0.5
5.5
0.0
0.0
2.8
78.8
16.7
35.2
29.3
77.4
56.1
48.4
2.1
30.8
29.7
65.4
14.3
11.7
29.1
5.0
74.4
33.4
67.8
6.8
19.9
33.9
2.1
6.4
14.9
10.3
44.4
15.2
11.6
40.6
6.4
9.8
9.2
84.2
68.4
29.3
3.8
0.0
1.6
4.2
14.3
7.0
4.4
3.2
5.1
2.3
2.1
23.8
9.0
5.1
5.3
22.2
4.5
7.7
29.2
14.9
10.2
1.5
3.8
13.7
8.4
70.7
14.6
14.2
96.5
16.7
28.4
20.3
78.9
97.7
52.0
81.4
88.5
71.6
92.3
70.7
93.0
80.9
1.8
74.4
33.4
71.0
14.3
36.3
39.2
† The favorable alleles are in bold type.
‡ S1, Iranian landrace group 1; S2, eastern European cultivar group; S4, western European cultivar group; S5, east-Asian accession group; S6, Iranian landrace group 2.
with Pt52-2 (r = −0.35, p < 10−7) and Pt54-1 (r = −0.45,
p < 10−7). The favorable alleles in each accession varied
from 0 to 7 with a mean allele number of 2.4 at eight QTL
resistant to Pt52-2, and 0 to 4 with an average of 0.6 at five
loci resistant to Pt54-1. The allele substitution effects for
IT score ranged from 0.3 to 2.5 and 0.37 to 1.7 in the two
experiments, respectively (Table 2). Notably, the favorable
allele of QLr.stars-1BS1 was not detected in east-Asian
cultivars or landraces (S5), while the favorable allele of
QLr.stars-2BL1 was not present in cultivars from eastern
Europe (S2)(Table 2), suggesting that the two QTL may
diversify leaf rust resistance sources for breeding programs in eastern Asia and eastern Europe, respectively.
Discussion
The availability of high-density consensus linkage maps
makes it feasible to determine the relationship of QTL
identified in this study with known Lr genes or QTL.
Based on 90K SNP consensus map (Wang et al., 2014),
the relationship between the known loci for leaf rust
resistance and the genomic regions harboring QTL identified in this study was illustrated in Fig. 6.
Two named genes, Lr71 and LrZh84, and three
QTL, QLr.pser-1BL, QLr.sfr-1BS, and QLr.sfr-1B, for leaf
rust resistance were previously mapped to the genomic
regions harboring QLr.stars-1BC1 and QLr.stars-1BS1
(Zhou et al., 2013; Li and Bai, 2009; Schnurbusch et al.,
2004; Messmer et al., 2000; Singh et al., 2013). Lr71 is
a gene identified in spelt wheat cultivar Altgold Rotkorn (Singh et al., 2013). Given that no spelt wheat was
included in the association panel, QLr.stars-1BC1 and
QLr.stars-1BS1 are unlikely Lr71. LrZh84, likely originated from wheat cultivar Predgomaia, has been effective
li et al .: novel qtl for seedling leaf rust resistance in the field for >30 yr in the Huang-Huai Valley winter
wheat zone of China (Zhao et al., 2008; Zhou et al., 2013),
while both QLr.sfr-1BS and QLr.sfr-1B express slow leaf
rusting resistance in wheat cultivar Forno (Messmer
et al., 2000; Schnurbusch et al., 2004). Another QTL
mapped in this region, QLr.pser-1BL, showed adult plant
resistance (Li and Bai, 2009). Thus, the relationship
between these known genes or QTL and the QTL identified in this study remain to be investigated.
Similarly, QLr.stars-1BL1, QLr.stars-1BL2, QLr.
stars-2DS1, QLr.stars-2DL1, QLr.stars-4AL1, and QLr.
stars-4BL1 are in the proximity of known genes or QTL
(Fig. 6). QLr.stars-1BL1 and QLr.stars-1BL2 reside in
the genomic regions where Lr46 and Lr33 were located,
respectively (William et al., 2003; Dyck and Sykes, 1994).
Lr46 is an important durable leaf rust resistance gene that
exhibits adult plant resistance. Lr46 was first identified
in the wheat cultivar Pavon. The effect of Lr46 resembles
that of Lr34 at the adult plant stage but is weaker than
that of Lr34 at the seedling stage (Martínez et al., 2001).
The QLr.stars-1BL1 was significant at FDR0.01 in the
Pt52-1 experiment and FDR0.05 in the Pt2013 experiment.
The allele effect of this QTL was relatively small (Table
2), suggesting that, with accurate phenotypic data, genes
with minor effects can be identified using GWAS. QLr.
stars-1BL1 and QLr.stars-1BL2 may represent Lr46 and
Lr33, respectively. QLr.stars-2DL1 was in the vicinity of
Lr54 and a QTL identified in the wheat cultivar Arina
(Schnurbusch et al., 2004) (Fig. 6). Lr54 originated from
chromosome 2L of Ae. kotschyi (Marais et al., 2005;
Heyns et al., 2011). Given that genetic stocks known to
carry Lr54 were not included in this association panel,
QLr.stars-2D may be associated with the QTL identified
9
of
12
in wheat cultivar Arina, rather than Lr54. In addition,
QLr.stars-2DS1, QLr.stars-4AL1, and QLr.stars-4BL1 coincided with Lr2 (Lr15), Lr30, and Lr12 (Lr31) (Singh and
Bowden, 2011), respectively. Further studies may facilitate
determination of the relationship between known resistance genes and QTL identified in this study.
QLr.stars-7AS1 is <4 cM away from Lr47 (Fig. 6). Lr47,
transferred from chromosome 7S of T. speltoides to chromosome 7A of common wheat (Dubcovsky et al., 1998),
was highly resistant to all three pathotypes used in this
study (Supplemental Table S1). To determine the relationship between QLr.stars-7AS1 and Lr47, we used a diagnostic
marker (Helguera et al., 2000) to detect wheat accessions
carrying the Lr47 gene from those showing high resistance
to Pt2013. Our results suggested that no accession in this
panel carries Lr47. Thus QLr.stars-7AS1 is a novel locus.
QLr.stars-7AL1 was significantly associated with resistance to Pt2013 and Pt54-1 and is 15.4 cM distal to Lr20
on the linkage map (Neu et al., 2002; Wang et al., 2014),
indicating that QLr.stars-7AL1 may be independent from
Lr20 and likely a novel locus in bread wheat. A recent
GWAS study revealed that Xcfa2257, a simple-sequence
repeat marker that is 2 cM distal to QLr.stars-7AL1, was
associated with leaf rust resistance in durum wheat (Maccaferri et al., 2010), lending credence to QLr.stars-7AL1.
The SNP allele associated with the resistant form of this
QTL was present at high frequencies in the eastern European cultivar group (74.4%) and the western European
cultivar group (67.8%) but at a low frequency in the eastAsian accession group (6.8%). Therefore, it is worthy to
evaluate the effectiveness of this QTL against Pt races
in eastern Asia, and it may diversify leaf rust resistance
sources in this region if it turns out to be resistant.
Another four QTL, QLr.stars-1BL3, QLr.stars-1DC1,
QLr.stars-2BL1, and QLr.stars-5BL1, are likely novel loci
for leaf rust resistance. Based on locations of molecular
markers flanking known Lr genes on the 90K consensus
map, QLr.stars-1BL3 is 30 cM from QLr.stars-1BL2/Lr33
and at least 17.5 cM from Lr46 (William et al., 2003). The
genetic distance between QLr.stars-2BL1 and the two
closest leaf rust resistance genes, Lr35 and Lr13 (Seyfarth
et al., 1999, 2000), is at least 27.2 cM (Fig. 6). Moreover,
no gene has been identified in the genomic regions harboring QLr.stars-1DC1 and QLr.stars-5BL1. These novel
loci represent valuable additions to the leaf rust resistance gene pool available for wheat breeding.
QTL identified in this study showed obvious regional
distribution. For example, 10 SNPs at the QLr.stars1BS1 locus were significantly associated with leaf rust
resistance, and three of them, IWA6110, IWA6129, and
IWA6541, showed the largest effect. The favorable alleles
of these SNPs were mainly found in eastern and western
European cultivars (S2 and S4), and were completely
absent in east-Asian accessions and Iranian landraces
(S1, S4, and S5). Thus, if QLr.stars-1BS1 is effective to Pt
races in eastern Asia, it should be a valuable resistance
source for wheat breeding programs in this region. On
the contrary, the favorable allele of IWA1488, the tagSNP
10
of
12
of QLr.stars-2BL1, is present at a relatively higher frequency in east-Asian accessions (S3), and no cultivars
in eastern Europe (S2) carry this allele. Use of this QTL
may diversify resistance sources if it confers resistance to
Pt races in this region. Given that SNP markers associated with these QTL are available and high-throughput
assays, such as Kompetitive Allele-Specific PCR, can be
easily developed for these markers (Cabral et al., 2014),
QTL identified in this study can be reliably introgressed
into elite cultivars using marker-assisted selection.
Conclusions
Genome-wide association study is an effective gene discovery approach complementary to the traditional linkage mapping. In this study, 14 QTL were significantly
associated with leaf rust resistance at a FDR of 0.01. Of
these, eight QTL were mapped in the vicinity of known
genes or QTL in bread wheat. Allelism tests may confirm their relationship with known genes or QTL. QLr.
stars-7AL1 was the most significant QTL identified in
this study, explaining 8.4% of the total phenotypic variance. This QTL is new to bread wheat but is close to a
locus previously identified in durum wheat. The other
five QTL, including QLr.stars-1BL3, QLr.stars-1DC1, QLr.
stars-2BL1, QLr.stars-5BL1, and QLr.stars-7AS1, are likely
novel loci for leaf rust resistance. The distribution of
these 14 QTL was uneven in the six subpopulations, suggesting that wheat breeding programs can enhance leaf
rust resistance by introgressing specific QTL identified in
this study into new cultivars. In addition, another 31 loci
were significantly associated with leaf rust resistance at a
less stringent FDR of 0.05 and will be valuable additions
to the leaf rust resistance gene pool.
Supplemental Information Available
Supplemental information is available with the online
version of this manuscript.
Acknowledgments
We thank Dr. Harold Bockelman of USDA–ARS National Small Grain
Collection for providing germplasm used in this study, the National
Research Initiative Competitive Grant 2011-68002-30029 (Triticeae-CAP)
from the USDA National Institute of Food and Agriculture for providing
SNP data, and Todd Lenger for his excellent technical support. Mention
of trade names or commercial products in this article is solely for the
purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is equal-opportunity
provider and employer.
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the pl ant genome
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vol . 9, no . 3