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ORIGINAL RESEARCH
published: 16 November 2020

doi: 10.3389/fpls.2020.583738

Edited by:
Jacqueline Batley,

The University of Western Australia,
Australia

Reviewed by:
Parveen Chhuneja,

Punjab Agricultural University, India
Jason Fiedler,

United States Department
of Agriculture (USDA), United States

*Correspondence:
Sylvie Cloutier

SylvieJ.Cloutier@Canada.ca

Specialty section:
This article was submitted to

Plant Breeding,
a section of the journal

Frontiers in Plant Science

Received: 19 July 2020
Accepted: 15 October 2020

Published: 16 November 2020

Citation:
Fatima F, McCallum BD,
Pozniak CJ, Hiebert CW,

McCartney CA, Fedak G, You FM and
Cloutier S (2020) Identification of New

Leaf Rust Resistance Loci in Wheat
and Wild Relatives by Array-Based
SNP Genotyping and Association

Genetics.
Front. Plant Sci. 11:583738.

doi: 10.3389/fpls.2020.583738

Identification of New Leaf Rust
Resistance Loci in Wheat and Wild
Relatives by Array-Based SNP
Genotyping and Association
Genetics
Fizza Fatima1,2, Brent D. McCallum3, Curtis J. Pozniak4, Colin W. Hiebert3,
Curt A. McCartney3, George Fedak1, Frank M. You1 and Sylvie Cloutier1,2*

1 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada, 2 Department
of Biology, University of Ottawa, Ottawa, ON, Canada, 3 Morden Research and Development Centre, Agriculture
and Agri-Food Canada, Ottawa, ON, Canada, 4 Crop Development Centre, University of Saskatchewan, Saskatoon, SK,
Canada

Leaf rust caused by Puccinia triticina is the most widespread rust disease of wheat.
As pathogen populations are constantly evolving, identification of novel sources of
resistance is necessary to maintain disease resistance and stay ahead of this plant-
pathogen evolutionary arms race. The wild genepool of wheat is a rich source of
genetic diversity, accounting for 44% of the Lr genes identified. Here we performed
a genome-wide association study (GWAS) on a diverse germplasm of 385 accessions,
including 27 different Triticum and Aegilops species. Genetic characterization using the
wheat 90 K array and subsequent filtering identified a set of 20,501 single nucleotide
polymorphic (SNP) markers. Of those, 9,570 were validated using exome capture and
mapped onto the Chinese Spring reference sequence v1.0. Phylogenetic analyses
illustrated four major clades, clearly separating the wild species from the T. aestivum
and T. turgidum species. GWAS was conducted using eight statistical models for
infection types against six leaf rust isolates and leaf rust severity rated in field trials for 3–
4 years at 2–3 locations in Canada. Functional annotation of genes containing significant
quantitative trait nucleotides (QTNs) identified 96 disease-related loci associated with
leaf rust resistance. A total of 21 QTNs were in haplotype blocks or within flanking
markers of at least 16 known Lr genes. The remaining significant QTNs were considered
loci that putatively harbor new Lr resistance genes. Isolation of these candidate genes
will contribute to the elucidation of their role in leaf rust resistance and promote their
usefulness in marker-assisted selection and introgression.

Keywords: wheat, leaf rust, GWAS, QTN, genotyping array, single nucleotide polymorphism, Aegilops, Triticum

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Fatima et al. Association Genetics for Leaf Rust Disease

INTRODUCTION

Triticum aestivum, commonly known as bread wheat, is an
allohexaploid (AABBDD) species, created through the sequential
hybridization of three grass species: T. urartu (AA), a species
(BB) closely related to Aegilops speltoides (SS) and Aegilops
tauschii (DD) (McFadden and Sears, 1946). Genetic diversity
bottlenecks such as polyploidization, domestication, and natural
and artificial selections have reduced diversity in modern wheat,
and consequently increased its vulnerability to diseases, pests and
environmental stresses (Tanksley and McCouch, 1997).

Leaf rust caused by Puccinia triticina is the most prevalent
wheat rust disease, causing tremendous annual yield losses.
Puccinia triticina attacks the foliage, covering its surface and thus
causing loss of photosynthates, dehydration and early defoliation.
Genetic resistance combatting yield losses can be categorized into
seedling resistance and adult plant resistance (APR). Typically,
seedling resistance is controlled by single major effect genes
that confer hypersensitive and other responses, causing necrosis
and preventing the pathogen from spreading (Dyck and Kerber,
1985). APR occurs at a post-seedling stage and confers either a
race-specific or a quantitative race non-specific response (Dyck
and Kerber, 1985; Samborski, 1985).

To date, 66 leaf rust resistance (Lr) genes have been
characterized, six of which, namely Lr1 (Cloutier et al., 2007),
Lr10 (Feuillet et al., 2003), Lr21 (Huang et al., 2003), Lr22a (Thind
et al., 2017), Lr34 (Krattinger et al., 2009) and Lr67 (Moore et al.,
2015), have been isolated. The majority of the Lr genes described
to date confer seedling-type resistance. Well-known APR genes
include the race-specific Lr12 (Dyck et al., 1966) and Lr13 (Dyck
et al., 1966) and the race non-specific Lr34 (Dyck, 1977) and
Lr67 (Hiebert et al., 2010). Of the 66 Lr genes designated to
date, 37 were identified in T. aestivum and T. turgidum and 29
originated from progenitors and other wild relative species such
as A. tauschii, A. speltoides, A. neglecta, and A. peregrina, among
others (McCallum et al., 2012; USDA, 2017).

The traditional approach for introgressing Lr genes into
adapted germplasm is by interspecific crossing a donor line
to an adapted line followed by backcrossing. Although many
Lr genes have been described, few are utilized by present-
day breeders because they have either been overcome by
virulence changes in the pathogen populations, are not
in an adapted background or suffer from linkage drag.
Modern approaches, such as gene cassettes and genome editing
may overcome some of the disadvantages of the crossing
method and have been proposed to provide long-lasting
broad spectrum resistance (Keller et al., 2016; Arora et al.,
2019). However, commercialization of transgenic wheat has
not received broad acceptance and introgression via crossing
remains commonly used.

Identification of novel sources of resistance in the cultivated
and the wild genepools of wheat is expected to contribute
to broadening and maintaining the genetic base of leaf rust
resistance. Array-based SNP genotyping platforms provide fast
and cost-effective access to genetic variation in a diverse
germplasm. In wheat, the Illumina’s Infinium iSelect and
Affimetrix’s Axiom array technologies enable simultaneous

genotyping of 9,000 to 819,571 SNP markers (Cavanagh
et al., 2013; Winfield et al., 2016). Genome-wide association
studies (GWAS) associate such genotypic data to phenotypic
data to identify significant marker-trait associations. To date,
numerous quantitative trait loci (QTL) associated with leaf
rust resistance in elite cultivars and landraces of bread and
durum wheat have been discovered (Aoun et al., 2016; Gao
et al., 2016; Riaz et al., 2018). These QTL were identified
based on traditional single-locus GWAS models whose inherent
limitations, such as failure to capture complex traits controlled
by multiple loci, are now widely recognized (Segura et al.,
2012). Multi-locus GWAS models overcome these drawbacks by
performing a multi-dimensional genome scan, and measuring
the effects of multiple SNPs simultaneously to identify small-
effect loci for complex traits (Wen et al., 2018). As multi-locus
association methodologies are recent, few have been reported
in wheat and the potential for multi-locus GWAS covering a
diverse range of cultivated wheat and wild relatives remains
largely untested.

Here, we performed a GWAS for leaf rust severity and reaction
types against six P. triticina isolates in a highly diverse germplasm
of 385 accessions. The wheat 90 K array was used to capture
the genetic variation in cultivated wheats, progenitor species,
synthetic hexaploid wheats (SHWs) and wild relatives (Wang
et al., 2014). We used one single-locus and seven multi-locus
models to identify quantitative trait nucleotides (QTNs) which
were mapped on the reference genome, thus validating known
loci and identifying new loci to be mined for novel candidate leaf
rust resistance genes.

MATERIALS AND METHODS

Plant Materials
A diverse collection of 385 accessions, encompassing 27 different
species of cultivated wheats, SHWs, progenitor species and wild
relatives were used in this study (Supplementary Table S1).
The AB and ABD genomes are represented by 170 accessions
representing T. vavilovii and several subspecies of T. turgidum
and T. aestivum as well as 65 primary SHWs. The A, B and
D genome progenitors (or their closely related species) and
the non-domesticated forms of tetraploid wheat comprised 93
accessions of T. urartu (A), T. monococcum (Am), Ae. tauschii
(D), Ae. speltoides (S), as well as T. turgidum ssp. dicoccon and
dicoccoides (AB). Another 47 accessions belonged to the following
Aegilops species: Ae. bicornis (Sb), Ae. longissima (Sl), Ae. searsii
(Ss), Ae. sharonensis (Ssh), Ae. markgrafii (C), Ae. comosa (M),
Ae. umbellulata (U), Ae. geniculata (MU), Ae. peregrina (SU),
Ae. triuncialis (UC/CU), Ae. columnaris (UM), Ae. cylindrica
(DC), Ae. crassa (DM/DDM), Ae. juvenalis (DMU), Ae. biuncialis
and Ae. neglecta (UM/UMN). The collection also contained
six accessions of T. timopheevii (AtG), five of T. zhukovskyi
(GAAm), and one of Haynaldia villosa (V), a related grass
species. Overall, the germplasm consisted of 75 diploid, 136
tetraploid, 165 hexaploid and nine accessions that could be either
tetraploid or hexaploid. The species names and genome symbols
are according to Kimber and Tsunewaki (1988).

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Seed Increase
Seeds were planted and grown under controlled conditions at the
Ottawa Research and Development Centre (RDC), Agriculture
and Agri-Food Canada (AAFC) (Ottawa, Canada). Depending
on their growth habit, the seeds were divided into spring and
winter panels. For the spring panel, the growth conditions were
20◦C/16 h light, and 16◦C/8 h dark. The winter panel was grown
under the same conditions for approximately three weeks, i.e.,
the 4–5 leaf stage, at which time they were transferred to a
vernalization cabinet (constant 2◦C/12 h photoperiod) for ten
weeks to trigger meristem differentiation prior to being returned
to the original growing conditions. Seeds harvested from all
accessions were used for the greenhouse and field experiments
described below.

Leaf Rust Race-Specific Response
Consecutive inoculations with six P. triticina isolates
were performed for 360 accessions of the panels
(Supplementary Table S2). All tests were performed under
controlled greenhouse conditions at the Morden RDC, AAFC
(Morden, Canada). Briefly, test lines and the Thatcher and
Emerson check lines were sown into fiber trays at a rate of
approximately 5 seeds per clump and 3 cm between clumps,
which were inoculated with individual P. triticina isolates at the
two-leaf stage as described by McCallum and Seto-Goh (2006).
The isolates tested were 12-3 MBDS, 128-1 MBRJ, 74-2 MGBJ,
11-180-1 TDBG, 06-1-1 TDBG, and 77-2 TJBJ, which represent
the prevalent leaf rust race groups across Canada (McCallum
et al., 2016). Virulence and avirulence formulas for these isolates
are given (Supplementary Table S3). For simplicity, these
will be referred to as MBDS, MBRJ, MGBJ, TDBG1, TDBG2,
and TJBJ, respectively. Infection type (IT) was rated 12 days
post-inoculation using a 0–4 scale (Stakman et al., 1962), where
“;” = hypersensitive flecks, “0” = no uredinia or macroscopic sign
of infection, “1” = small uredinia with necrosis, “2” = small to
medium uredinia with chlorosis, “3” = medium uredinia without
chlorosis or necrosis, “4” = large uredinia without chlorosis or
necrosis. IT “;” and “0” to “2” were considered resistant, while “3”
and “4” were considered susceptible (Long and Kolmer, 1989).
The “+” or “−” IT qualifiers indicate larger or smaller than
average uredinia, respectively. The “ = ” IT qualifier represents
the lower size limit of the uredinia for the IT (Long and Kolmer,
1989). Plants with randomly distributed uredinia of variable
sizes, or mesothetic response, were considered resistant and were
rated with an “X” IT (Roelfs and Martens, 1988).

For downstream analysis, the IT scores were converted into
a 1–9 linear scale, where “0/0;/;” = 1, “;1 = /;1-” = 2, “1-
/1/1+” = 3, “;12/1-2-” = 4, “2-/2/2+” = 5, “X” = 6, “3-/3/3+” = 7,
“3+4/34” = 8, and “4” = 9 (Supplementary Table S2B). Scores
1–6 were considered resistant and 7–9 susceptible.

Field Leaf Rust Severity
Phenotyping of rust severity was performed in separate field
trials for the spring and winter panels. The spring panel
included 213 accessions, of which 20 were SHWs, while the
remaining were subspecies of T. aestivum and T. turgidum.

Trials for the spring panel were carried out in Morden,
Manitoba, Canada (2016–2019), Ottawa, Ontario, Canada
(2017–2019) and Saskatoon, Saskatchewan, Canada (2019). The
winter panel comprised 164 diverse Aegilops and Triticum
species, including 115 progenitors and wild relatives, 45 SHWs,
and four winter wheat cultivars. Screening for the winter
panel was performed in Morden (2017–2019) and in Ottawa
(2017 and 2019).

For each panel, year and location, a completely randomized
design with two replicates was used, except for the 2016
Morden field trial where a single replicate was used due
to the limited seed availability in the first year. For the
spring panel, 65 seeds/accession were planted in 1 m-long
rows with 20 cm between rows. A mixture of P. triticina
isolates was inoculated onto spreader rows of susceptible lines
Thatcher, Morocco, and Little Club, planted every six test
rows in Morden and every ten in Saskatoon. The mixture of
isolates comprised more than 50 different virulence phenotypes
representing the P. triticina population in western Canada
identified during the annual virulence survey. In Ottawa,
infection relied on natural inoculum but used the same
interspersed spreader-row design as Morden with the Morocco
spreader. Cultivars Thatcher, Roblin and Eurostar were used
as checks and five plots of each were randomly distributed
across each replicate.

For the winter panel, ten seeds/accession were planted indoors
in early March at both Morden and Ottawa RDCs. At the 3–5
leaf stage, the plants were transferred into vernalization chambers
as described above. Approximately ten days after planting the
spreader-rows, the vernalized plantlets were transplanted as
hills in the field. The cultivar Emerson served as a check.
At peak infection and prior to senescence, the flag leaves
were rated for leaf rust severity using a modified Cobb’s scale
(Peterson et al., 1948).

Leaf rust severity ratings across locations and years were
modeled using the R package Lme4 (Bates et al., 2014)
with the following mixed linear model (MLM) equation:
y = lmer(Severity ∼ Location + (1| Genotype) + (1| Year)
+ (1| Genotype:Location) + (1| Genotype:Year:Location)). In
this model, location was considered a fixed effect, while year,
genotype and interaction were considered random. The lmerTest
package was used to generate an ANOVA-like table for the
random effects and calculate P-values from the Satterthwaite’s
t-tests for the fixed effect (Kuznetsova et al., 2017). Best linear
unbiased predictors (BLUP) estimates, also known as conditional
means, were extracted for the random effects to account for
environmental deviations and provide more precise estimates of
phenotypic values (Supplementary Table S4) (Mi et al., 2011;
Wang et al., 2017).

Genotyping and SNP Filtering
Young leaf tissue (75–100 mg) from the germplasm grown
in growth chambers was sampled at the 4–5 leaf stage. The
DNA was extracted using the DNeasy Plant kit (Qiagen,
Valencia, CA, United States) and quantified using the Quant-it
PicoGreen kit (Thermo Fisher, Waltham, MA, United States).
Genotyping was performed using the wheat 90 K array (Illumina,

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San Diego, CA, United States) on the iScan instrument
(Wang et al., 2014).

Genotype calling was performed for the entire collection
using the default genotyping module, and separately for
the different ploidy levels using the polyploidy module in
GenomeStudio software v2.0.4 (Illumina). The tetraploid
and hexaploid sets also included the nine accessions
of unknown ploidy. The SNP markers with <80%
missing data, <5% minor allele frequency (MAF), and
>5% heterozygosity were removed. For the polyploid
module, markers with >3 clusters were also removed
(Hourcade et al., 2019).

SNP Validation
To validate the filtered SNP dataset, we used an exome dataset
obtained from 136 accessions of the panels. First, the position
of the SNP markers in the protein coding regions of the
Chinese Spring (CS) reference genome v1.0 was obtained by
mapping the SNP probe sequences of the wheat Infinium array to
“161010_Chinese_Spring_v1.0_gene_sequences_for_exome.fasta”
which corresponds to the exome sequence of all the high-
confidence annotated genes of the CS reference sequence
and their 5 Kb upstream and downstream sequences (Wang
et al., 2014; IWGSC, 2018). The SNP probes were aligned to
the indexed exome reference sequence using the MEM-BWA
algorithm (v0.7.12, http://bio-bwa.sourceforge.net/). As BWA
does not accept IUPAC letter codes, two sequences were used
for each SNP probe, where one had allele A and the other allele
B. Samtools (v1.3, http://samtools.sourceforge.net/) was used
to generate and sort the BAM file alignment. The positions
of the mapped SNPs were extracted using BBMap (v.38.43
https://sourceforge.net/projects/bbmap/). The mapped SNP
probes were filtered using R to remove the misaligned probes,
i.e., those for which the A and B allele sequences aligned to
different chromosomal positions. The coordinates of the mapped
markers were converted to their actual positions on the CS
reference genome v1.0 (IWGSC, 2018).

Upon mapping of the SNP markers, the genotyping
dataset was re-filtered with the following updated criteria:
markers with <80% of χi missing data, <5% MAF, and >5%
heterozygosity were removed, where χi is the proportion
of each sub-genome represented in the germplasm. This
less stringent criterion ensures retention of SNPs from
underrepresented sub-genomes. The positions of the filtered
SNPs from the wheat 90 K array was compared to the variant
call results of the exome-sequence data obtained from 136
of the 385 accessions. The exome sequencing data were
obtained using the Nimblegen SeqCap EZ wheat exome
design (120426_Wheat_WEC_D02, https://sequencing.roche.
com/en/products-solutions/by-category/target-enrichment/shar
eddesigns.html). Raw reads were mapped to the same exome
reference genome using the BWA-Samtools pipeline, and
variant calling was performed using Bcftools (v1.3, https://
samtools.github.io/bcftools/bcftools.html). SNPs common
between the filtered wheat 90 K array and the exome capture
datasets were identified using Bcftools.

Phylogenetic Relationships, Population
Structure and Kinship
To illustrate evolutionary relationships between the species
in the collection, the filtered set of SNPs was used to
perform a phylogenetic analysis. A maximum likelihood
(ML) tree was generated with 1,000 bootstrap iterations
using the default parameters of MEGA-CC (Nearest-Neighbor-
Interchange heuristic and Tamura-Nei models) (Kumar et al.,
2012). The tree was graphically displayed using iTol v3 (Letunic
and Bork, 2016). Principal component analyses (PCAs) were
performed using the filtered set of SNPs for each ploidy level
and the results were displayed using the R package ggbiplot
(https://github.com/vqv/ggbiplot).

Population structure analyses were carried out using the R
packages LEA (Frichot and François, 2015) and PCAdapt (Luu
et al., 2017), as well as the software Admixture v1.3 (Alexander
et al., 2009). Both LEA and PCAdapt estimate structure using
PCA-based methods. The proportion of variance explained by
each PC was graphically illustrated in the form of scree plots. The
“knee” in the scree plot (Cattell’s rule) was used to determine the
number of sub-populations. Admixture is an ML-based approach
which uses cross-validation to approximate the K number of
sub-populations (Alexander and Lange, 2011). Cross-validation
errors for K = 2–30 were graphically illustrated using R and
the value of K was selected using the rule described above. The
approximate number of sub-populations was selected based on
the congruity between the plots. The SNMF approach in LEA was
used to visualize ancestry proportions in the Q matrix through
structure plots. The kinship coefficient matrix was generated
using Tassel v5.0 (Bradbury et al., 2007).

Genome-Wide Association Analysis
GWAS was conducted for race-specific response and leaf rust
severity rated in the field. For the race-specific response, the
converted IT scores for each isolate were considered as individual
traits. For leaf rust severity, genotypic and location-specific
BLUP estimates were used as phenotypic inputs, where the
former summarizes the severity ratings across all locations and
years, and the latter represents the severity ratings separately
for each location.

GWAS was performed using one single-locus and seven multi-
locus models. The MLM in Tassel v5.0 (Bradbury et al., 2007)
was used for single-locus association analysis. Here, population
structure and kinship were accounted for using Tassel-generated
Q matrix for K principle components and the Tassel-generated
kinship matrix. The P-values were adjusted using the false
discovery rate (FDR) (Benjamini and Hochberg, 1995). QTNs
with FDR (False discovery rate) -adjusted P-values < 0.05 were
considered significant.

Of the seven multi-locus models, the six from the R package
mrMLM (Wen et al., 2018) were mrMLM (Wang et al., 2016),
FASTmrMLM (Tamba and Zhang, 2018), FASTmrEMMA (Wen
et al., 2018), pLARmEB (Wang et al., 2016), pKWmEB (Ren et al.,
2018) and ISIS EM-BLASSO (Tamba et al., 2017). As the mrMLM
package does not have built-in support for calculating covariates,
the Q matrix generated by Admixture and the Tassel-generated

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https://sequencing.roche.com/en/products-solutions/by-category/target-enrichment/shareddesigns.html
https://sequencing.roche.com/en/products-solutions/by-category/target-enrichment/shareddesigns.html
https://samtools.github.io/bcftools/bcftools.html
https://samtools.github.io/bcftools/bcftools.html
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kinship matrix were used to account for population structure
and kinship, respectively. The seventh multi-locus model, RTM-
GWAS, first grouped SNPs into linkage disequilibrium blocks
(SNPLDBs) and then utilized a restricted two-stage multi-locus
analysis for QTL identification (He et al., 2017). Here, population
structure was accounted for by the RTM-generated covariate
matrix and kinship and by the Tassel-generated kinship matrix.
As with the single-locus MLM, the P-values of QTNs from all
the multi-locus models used the same FDR-adjusted threshold.
Allelic effect of QTNs was determined using the Kruskal–Wallis
statistics to test the phenotypic variation of the associated traits
between homozygous alleles.

In silico Annotation of Significant
Markers
Because only markers that aligned to the exome sequence of the
CS reference genome v1.0 were used for the association analyses,
all significant QTNs were within or close to high-confidence
annotated genes. Transcript IDs of the genes containing the
significant QTNs were used to extract the protein products
using EnsemblPlants1 (Kersey et al., 2016; IWGSC, 2018). For
significant SNPLDBs detected by RTM-GWAS that contained
multiple SNPs, annotation was carried out for the first and last
SNP marker of each SNPLDB.

Positioning Lr Genes and QTNs Onto the
Wheat Reference Sequence
Sequences coding for the six previously cloned Lr genes
were retrieved from GenBank and mapped against the CS
reference sequence v1.0 (IWGSC, 2018) using default BLASTn
parameters on the GrainGenes website2. Through the same
exercise, sequences of flanking or co-segregating markers were
also mapped onto the reference genome so that a total of 55
of the 66 Lr genes were positioned (Supplementary Table S5).
A physical map of previously cloned or mapped Lr genes was
constructed using the R package KaryoploteR (Gel and Serra,
2017). Linkage between the QTNs detected and known Lr
genes, or their markers, was determined using haplotype block
analysis. The SNP dataset was split into haplotype blocks using
the R package gpart (Kim et al., 2019) and pairwise linkage
disequilibrium between the SNPs was calculated using Tassel
v5.0 (Bradbury et al., 2007). Known Lr genes and QTNs within
the same haplotype block were considered linked, while the
relationship between those in neighboring blocks was determined
by comparing D’ statistics between the blocks.

RESULTS

Race-Specific Resistance
IT response against six P. triticina isolates (MBDS, MBRJ,
MGBJ, TDBG1, TDBG2, and TJBJ) was evaluated in the
greenhouse for 360 accessions. Of these, 156, 171, 173,

1https://plants.ensembl.org/Triticum_aestivum/Info/Index
2https://wheat.pw.usda.gov/cgi-bin/seqserve/blast_wheat.cgi

177, 209 and 206 accessions were resistant (IT rating < 3,
linear score < 7) to isolates MBDS, MBRJ, MGBJ, TJBJ,
TDBG1 and TDBG2, respectively (Supplementary Table S2 and
Supplementary Figure S1). The resistant accessions included
85–131 SHW and cultivated species, 44–56 progenitors and
25–32 wild relatives. Overall, a total of 102 accessions were
resistant to all six isolates, and another 153 to at least five isolates.

Field Resistance
Phenotypic variation across the different environments was
modeled. For both spring and winter panels, the year effect
explained the smallest proportion of the variance with 2.1%
and 0.31% for each panel, respectively, while the largest
proportion was accounted for by the genotype effect with
43.0% and 60.2%, respectively (Supplementary Table S6).
The P-values from Satterthwaite’s t-tests were <0.005 for all
location effects and the genotype-location interaction explained
16.7% and 21.1% of the variation in the spring and winter
panels, respectively. The genotypic and location-specific BLUP
estimates were extracted from the models and compared to raw
aggregate genotypic and location-specific mean values. A linear
relationship was observed between the raw mean values and
BLUP estimates (Supplementary Figures S2A,B). However, due
to the inherent nature of BLUP estimation to shrink outliers
to the mean, the interquartile ranges (Q3-Q1) of location-
specific BLUP estimates were smaller than the raw mean values
(Supplementary Figures S2C–F).

In the spring panel, 73 accessions were rated resistant (average
severity <10%) and 70 were moderately resistant (11–30%
average severity) (Supplementary Figure S3A). The majority
of the moderately resistant to resistant accessions belonged to
the subspecies of T. turgidum (Supplementary Table S7A). In
the winter panel, respectively, 90 and 38 accessions were rated
resistant and moderately resistant; these included 52 progenitors,
36 wild relatives and 12 SHWs (Supplementary Table S7B and
Figure S3B). These distributions, however, standardized, were
also reflected in the genotypic BLUP estimates calculated for each
panel (Supplementary Figures S3C,D).

SNP Filtering, Mapping and Validation
A total of 27,418 SNPs from the 385 accessions had a call
rate > 80%, of these, 20,501 had a MAF >5% and a maximum
heterozygosity <5%. Genotype calling and filtering performed
separately for the three ploidy levels yielded 34,614 SNPs in the
hexaploid, 24,142 in the tetraploid and 15,364 in the diploid
datasets. Shared and private SNPs between the three ploidy levels
are illustrated (Figure 1A).

Mapping was performed to locate the position of the 81,587
SNPs of the wheat Infinium assay on the exome sequence
of the CS reference genome v1.0 (IWGSC, 2018). A total of
52,550 SNP marker sequences were successfully mapped, of
which 43,013 were retained after filtering out the misaligned
probes (Supplementary Table S8). Exome-capture sequencing
and subsequent variant calling of 136 accessions identified a
subset of 27,852 SNPs which belonged to the 43,013 mapped
from the array. Re-filtering of the genotyping dataset from the
complete germplasm (call rate > 80% of χi, MAF > 5%, and

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FIGURE 1 | Filtered single nucleotide polymorphism (SNP) markers. (A) Shared SNPs between the hexaploid, tetraploid and diploid datasets. (B) Distribution of
9,570 filtered and validated SNPs across the chromosomes of the CS reference genome v1.0 (IWGSC, 2018).

heterozygosity <5%) positioned 12,627 SNPs on the exome
reference genome, including 9,570 that were also called using
exome capture. Chromosomal assignments of these 9,570 filtered
and mapped SNPs illustrate the uneven distribution across
genomes (Figure 1B).

Phylogenetic Relationships and Principal
Component Analysis
A ML phylogenetic tree was constructed to illustrate the
relationships between the species in the collection. Four
main clades were observed (Figure 2). The first consisted
of all the Aegilops and non-domesticated Triticum species,
where accessions clustered based on their shared sub-genomes.
The second and largest clade comprised accessions with
the ABD genome: SHWs, T. vavilovii and T. aestivum
subspecies. The other two clades were primarily a mixture of
T. turgidum subspecies and SHWs. Ancient tetraploid species
T. turgidum ssp. dicoccum (emmer wheat) and the non-
domesticated T. turgidum ssp. dicoccoides formed one clade,
while modern cultivated species, such as T. turgidum ssp.
durum and T. turgidum ssp. carthlicum, formed the other.
SHWs were distributed between these clades based on their
tetraploid parent species.

PCAs were performed to assess the genetic variation at
different ploidy levels. In the hexaploid dataset, the first three
PCs explained 29.0% of the variation (Figure 3A). Three to
four main clusters were observed: the ABD genome species,
T. aestivum and T. vavilovii, formed one cluster, Ae. crassa
(DM/DDM) and Ae. juvenalis (DMU) formed a second closely
related cluster, while Ae. neglecta (UM/UMN) and T. zhukovskyi
(GAAm) clustered into two distinct groups. Similarly, in the
tetraploid dataset, the first three PCs explained 28.3% of the
variation (Figure 3B). Here, T. turgidum subspecies clustered
into three groups, while T. timopheevii (AtG), Ae. crassa and
Ae. cylindrica (DC) clustered into individual groups. Accessions
belonging to species with the U or M sub-genome (Ae. geniculata,

Ae. peregrina, Ae. triuncialis, Ae. biuncialis, Ae. columnaris and
Ae. neglecta) clustered together. In the diploid dataset, the first
three PCs explained 39.9% of the variation. Ae. tauschii (D)
accessions clustered into two groups, Ae. speltoides (S) and
T. monococcum (Am) clustered separately, and nine other species,
each represented by few accessions, all clustered as individual
groups (Figure 3C). Eight accessions did not cluster with other
individuals of their respective species. They were assumed to
have been mis-labeled and were removed from the datasets
(Supplementary Table S9).

Genome-Wide Association Analysis
GWAS was performed using IT scores against six
P. triticina isolates and the leaf rust severity measured
in multiple field environments. For each dataset, the
population structure was estimated using three tools and
the optimal number of sub-populations was selected based
on agreement between methods. For IT scores, K = 8
was selected, and for leaf rust severity, K = 8 and K = 6
were selected for the spring and winter panels, respectively
(Supplementary Figures S4, S5).

GWAS was conducted using one single-locus and seven multi-
locus models, all of which accounted for kinship and population
structure. For IT response, the single-locus MLM identified
five QTNs for which the proportion of variance explained (r2)
ranged from 6–12% (Supplementary Table S10). Of these, four
QTNs were identified for response against the isolate MBDS.
The six multi-locus models from mrMLM identified a total
of 116 unique QTNs across the genome, of which 32 were
identified by more than one model and 23 were associated with
more than one isolate (Supplementary Table S10). Of note,
markers Tdurum_contig18471_456 and IAAV6025 associated
with MBDS and Kukri_c12869_154 associated with TDBG1 had
r2 values > 27%, while r2 values ranged from 1–23% for the
remaining QTNs. RTM, the seventh multi-locus model, grouped
the SNPs into 7,607 SNPLDBs and identified 15 QTL with r2

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FIGURE 2 | Rooted (left) and unrooted (right) phylogenetic trees illustrating the relationships between species. The tree was generated using the maximum likelihood
approach with 1,000 bootstrap iterations. The size of the internal node symbols reflects the bootstrap confidence level and the leaf node labels correspond to the
identification number and genome of each accession. The species are color-coded as indicated in the legend.

FIGURE 3 | Two-dimensional scatter plots of the first two principal components (PCs) estimated for the (A) hexaploid, (B) tetraploid, and (C) diploid single nucleotide
polymorphism datasets. The accessions are colored based on their species. The percentages of the variance explained by each PC are in brackets on the axes.

of 4–15%, including eight that had previously been detected by
other multi-locus models (Supplementary Table S10). Of the five
QTNs identified by single-locus GWAS, four were identified by at
least one of the seven multi-locus models.

GWAS for leaf rust severity was conducted separately for
the spring and winter panels. MLM identified five significant
QTNs (r2 = 18–24%), all of which were associated with leaf
rust severity in Morden and located in the D sub-genome

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TABLE 1 | Number of QTNs or SNP linkage disequilibrium blocks identified by
each statistical model for infection type (IT) and leaf rust (LR) severity.

Model IT LR severity
spring panel

LR severity
winter panel

Total

Single-locus

MLM 5 1 4 10

Multi-locus

FASTmrEMMA 14 14 1 29

FASTmrMLM 33 20 8 61

ISIS
EM-BLASSO

30 22 14 66

mrMLM 54 36 10 100

pKWmEB 38 30 10 78

pLARmEB 41 25 13 79

RTM 15 21 16 52

Total 230 (124) 169 (104) 76 (53)

Values in parentheses indicate the number of non-redundant loci, i.e., loci identified
by at least one statistical model.

(Supplementary Table S11). In the spring panel, mrMLM
identified 85 unique QTNs (r2 = 1–22%) associated with leaf rust
severity, of which 30 were identified by more than one model
and 57 were location-specific (Supplementary Table S11). In
the winter panel, 38 QTNs were identified including 10 by more
than one model and one at both Morden and Ottawa locations
(Supplementary Table S11). Marker wsnp_Ex_c6548_11355524
on 5B explained the highest proportion of the variance
(40%), while r2 of the remaining QTNs ranged from 2–24%.
RTM identified 37 QTL in the two panels, including seven
that were also identified by other multi-locus models
(Supplementary Table S11). Overall, five QTNs associated
with leaf rust severity were also associated with race-specific
IT response against at least one isolate. The number of QTNs
identified by each model, for both, IT response and leaf rust
severity, are shown in Table 1. For each phenotypic dataset,
the multi-locus model mrMLM identified the highest number
of QTNs, while the single-locus model MLM identified the
fewest. The eight GWAS models were further compared based
on r2, FDR-adjusted P-values and the number of common QTNs
(Supplementary Figures S6, S7).

Functional Annotation
The transcript IDs of the genes within 5 Kb of one
or more QTN were extracted along with their functional
annotation. Between 79–85% of the QTNs associated with race-
specific IT response and leaf rust severity were successfully
annotated for gene function (Supplementary Table S12).
A total of 46 loci associated with race-specific response and
50 associated with LR severity (37 in the spring panel and
13 in the winter panel) were within 5 Kb of genes coding
for known plant disease resistance proteins such as CC-NBS-
LRR, F-box-like domain-containing proteins, proteins with
kinase domains, zinc finger-types and ABC transporter proteins,
among others (Supplementary Table S12). A combined total
of 53 QTNs with r2 > 5% were located within plant disease
resistance genes (Table 2). For each of these, Kruskal–Wallis

tests were performed to test the statistical significance in
phenotypic values of the alternate alleles. Significant allele-
phenotype differences (P-value < 0.05) were obtained for
35 of the 53 QTNs or SNPLDBs (Table 2), where the
favorable alleles came from the domesticated T. aestivum
and T. turgidum species as well as the wild relative species
(Supplementary Figure S8). Phenotypic variation for 11 of these
significant QTNs present within CC-NBS-LRR, ABC-transporter
and protein kinase domains are illustrated (Figure 4). Such QTNs
were considered strong candidate genes as their function and
allelic variation are congruent.

Comparing Associated Loci With
Previously Reported Lr Genes
To identify novel putative disease resistance loci, the physical
positions of the QTNs identified were compared to the positions
of the 66 previously reported Lr genes (Supplementary Table S5).
All QTNs and Lr genes, except for Lr10, Lr14 (a,b), Lr25, Lr26,
Lr29, Lr30, Lr36, Lr44, Lr56, Lr59, and Lr66, were physically
mapped on the CS reference genome v1.0 (IWGSC, 2018). The
positions of these mapped Lr genes and the IT and leaf rust
severity QTNs identified herein by at least two models are
illustrated (Figure 5).

Of the Lr genes mapped using both proximal and distal
flanking markers, markers for Lr12, Lr13, Lr15, Lr19, Lr27,
Lr28, Lr49, Lr64, and Lr75 co-located with 13 of the QTNs
identified (Supplementary Table S13). These include seven
QTNs associated with leaf rust severity and six with IT response.
Haplotype black analysis was used to evaluate the relationships
between the QTNs detected and the Lr genes mapped using
gene sequences or single genetic markers. A total of 2113
haplotype blocks (D’ ≥ 0.5) were obtained, with an average
block size of 4.9 MB. Two QTNs, BS00094333_51, associated
with leaf rust severity, and D_GDS7LZN02F1Q5F_180, with IT
caused by isolates TDBG1, MGBJ and TJBJ, were in the same
haplotype blocks as the cloned genes Lr1 and Lr34, respectively,
while three co-located in the same blocks as genetic markers
of Lr16, Lr32 and Lr73 (Supplementary Table S13). Apart
from this, another three QTNs were in neighboring blocks of
the markers linked to Lr18 and Lr54. Pairwise linkage analysis
between these blocks resulted in mean D’ statistics ranging from
0.44 to 0.66. Overall, Kruskal–Wallis tests identified significant
allele-phenotype differences (P-value < 0.05) for 14 of the 21
QTNs mapping near positions of known Lr genes (Table 2,
Supplementary Table S13).

DISCUSSION

Puccinia triticina populations are constantly evolving, as
exemplified by the presence of more than 70 races detected
in North America each year (Ellis et al., 2014). This can
quickly render the deployed Lr genes ineffective. Identification
of novel sources of disease resistance is necessary to stay
ahead in this plant-pathogen evolutionary arms race and to
maintain disease resistance in crops. The ability to detect novel
Lr genes through marker-based association studies depends

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TABLE 2 | Chromosomal location and functional annotation of quantitative trait nucleotides (QTNs) or linkage disequilibrium blocks (LDBs) associated with race-specific
infection type and leaf rust severity.

QTN/LDB Chr Position Trait Model R2 KW R allele Co-located
Lr gene†

Gene annotation

Infection type

IAAV6025 1B 211312483 MBDS FASTmrEMMA 28.0 **** G PC-Esterase, PMR5
N-terminal domain

MLM 5.9

Kukri_rep_c115699_270 2D 39829875 TDBG2 FASTmrEMMA 5.8 ** G Lr15 Serpin superfamily

tplb0052b23_2493 2D 621964724 TDBG1 FASTmrEMMA 6.2 Ns − LRR domain, NBS, CC
domain

pKWmEB 5.3

RFL_Contig3121_1979 2D 648470008 MBDS pKWmEB 5.8 Ns − Lr54 LRR domain, S/T-protein
kinase

Tdurum_contig18471_456 3A 75030422 MBDS mrMLM 34.6 **** G F-box-like domain

D_GCE8AKX02HMJXL_374 3B 15138756 MBDS MLM 12.6 **** A Lr27 Jacalin-like lectin domain

pKWmEB 5.4

IAAV3924 3B 20450624 TDBG2 pKWmEB 6.8 * G LRR domain, NBS, CC
domain

Kukri_c12869_154 3B 130647769 TDBG1 mrMLM 27.9 *** C F-box-like domain

Excalibur_c25515_95 3D 28331100 TJBJ ISIS EM-BLASSO 7.7 **** G Lr32 S/T-protein kinase

pKWmEB 6.6

pLARmEB 5.2

FASTmrMLM 5.2

D_contig10567_587 3D 141408492 MBDS MLM 6.2 **** C Glycoside hydrolase

D_contig29825_215 4D 82020798 TDBG1 FASTmrMLM 11.2 ns − LRR domain, NBS, CC
domain

pLARmEB 8.2

TDBG2 pKWmEB 11.0

pLARmEB 5.1

RAC875_c9984_1003 5A 585458451 TDBG1 TDBG1 8.7 **** A P-loop NTPase, Kinesin
motor domain

wsnp_BJ224975A_Ta_2_2 5A 588737306 TDBG2 RTM 5.4 Ns − Protein kinase, ATP binding
site

Kukri_c17055_189 5A 588742167 TDBG2 RTM 5.4 **** T P-loop NTPase, ABC
transporter

RAC875_s116069_221 5B 506951332 TDBG2 pKWmEB 7.2 **** G Serine/threonine-protein
kinase

D_contig18780_204 5D 486259068 TDBG2 FASTmrEMMA 9.3 * A LRR domain, NBS, CC
domain

wsnp_Ra_c3766_6947263 6B 151130562 MBDS RTM 6.6 **** A ZTL, PAS domain,
beta-propeller, F-box-like

Kukri_c39321_112 6B 151131531 TDBG2 pKWmEB 11.3 **** C ZTL, PAS domain,
beta-propeller, F-box-like

TJBJ pKWmEB 6.7

MBDS RTM 6.6

Kukri_c3664_1071 6D 10910854 MGBJ mrMLM 6.8 * G P-loop NTPase, AAA+
ATPase domain

Ex_c54863_29 7B 561748617 TDBG2 RTM 14.7 **** C Zinc finger, TAZ/
FYVE/PHD-type,

BS00063208_51 7B 637618402 MGBJ mrMLM 5.4 ** T LRR domain

Kukri_c19466_627 7D 59936619 MGBJ pKWmEB 18.8 * C LRR domain, NBS, CC
domain

Leaf rust severity in the spring panel

wsnp_Ex_c3372_6195001 1A 257573729 SK ISIS EM-BLASSO 6.4 *** T LRR domain

RTM 5.8

Excalibur_c33567_363 1A 427819541 SK mrMLM 17.6 ns − Zinc finger, FYVE/PHD-type

(Continued)

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TABLE 2 | Continued

QTN/LDB Chr Position Trait Model R2 KW R allele Co-located Lr
gene†

Gene annotation

wsnp_Ex_rep_c67474_66076379 1A 591093391 OTT pLARmEB 9.2 ns − P-loop NTPase,
Armadillo-type fold

FASTmrMLM 8.8

Kukri_c55909_1109 2B 770681399 OTT FASTmrMLM 7.9 **** C WD40-repeat-containing
domain

CAP8_c3568_256 3A 724200935 SK mrMLM 10.0 ns − Alpha/Beta hydrolase fold

pLARmEB 6.8

Excalibur_c21395_291 4A 734000589 SK FASTmrMLM 5.0 * G Lr28 LRR domain, NBS, CC
domain

RAC875_c2099_2066 4B 670439103 Overall pKWmEB 5.1 **** T Zinc finger, FYVE/PHD-type

Tdurum_contig10128_593 5A 48464957 OTT RTM 9.7 * C Papain-like cysteine
peptidase

Excalibur_rep_c67473_320 5B 506789600 MDN pKWmEB 5.1 ns − P-loop NTPase, AAA+
ATPase domain

BS00094333_51 5D 559922461 Overall FASTmrMLM 9.6 *** A Lr1 LRR domain, NBS, CC
domain

FASTmrEMMA 8.4

ISIS EM-BLASSO 8.4

pKWmEB 7.6

pLARmEB 6.5

BS00037002_51 6A 2972683 MDN FASTmrEMMA 5.8 Ns − F-box-like domain

RAC875_c68525_284 6B 657946526 SK mrMLM 13.3 * G Zinc finger, FYVE/PHD-type

BobWhite_rep_c66074_232 6B 706118869 Overall FASTmrEMMA 5.1 Ns − LRR domain

Kukri_c58096_480 6B 712390096 MDN RTM 14.7 *** T Papain-like cysteine
peptidase

CAP7_c2923_366 6D 129784133 MDN ISIS EM-BLASSO 11.8 *** T Plant lipoxygenase,
PLAT/LH2 domain

FASTmrEMMA 11.6

CAP11_c5372_271 6D 465742757 MDN RTM 10.4 Ns − LRR domain, F-box-like
domain

Excalibur_rep_c67475_1759 7B 498523612 MDN mrMLM 5.9 **** T P-loop NTPase, PDR ABC
transporter

ISIS EM-BLASSO 5.7

tplb0021f14_1700 7B 653898244 Overall mrMLM 9.5 Ns − Serine/threonine-protein
kinase

Excalibur_rep_c74234_183 7B 655037014 Overall ISIS EM-BLASSO 5.1 ns − Serine/threonine-protein
kinase

BS00065623_51 7D 4005296 MDN pKWmEB 18.4 **** A LRR domain, NBS, CC
domain

FASTmrEMMA 11.2

D_GDS7LZN02FSYZC_227 7D 58491641 OTT pLARmEB 5.1 ns − LRR domain, NBS, CC
domain

wsnp_JD_c69_109951 Un 24402452 MDN FASTmrMLM 13.5 **** C LRR domain, NBS, CC
domain

pLARmEB 11.0

ISIS EM-BLASSO 6.4

BS00110940_51 Un 81996418 OTT pKWmEB 7.9 **** C Serine/threonine-protein
kinase

Leaf rust severity in the winter panel

BS00067436_51 1A 578204373 MDN FASTmrMLM 12.7 ** G Glycoside hydrolase

Ex_c6145_2193 1D 12534520 MDN RTM 6.5 ** T LRR domain, NBS, CC
domain

mrMLM 7.8

Kukri_c59403_339 2D 75001895 MDN MLM 19.7 Ns − WD40-repeat-containing
domain

(Continued)

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TABLE 2 | Continued

QTN/LDB Chr Position Trait Model R2 KW R allele Co-located Lr
gene†

Gene annotation

Excalibur_c3862_837 3B 245969143 MDN FASTmrMLM 8.5 **** A Peroxidase

BS00022555_51 5B 435769407 OTT pKWmEB 14.0 ns − F-box-like domain,
beta-propeller

wsnp_Ex_c6548_11355524 5B 439725143 MDN ISIS EM-BLASSO 21.7 ns − WRKY domain

pKWmEB 22.8

mrMLM 40.1

Kukri_c855_2107 7A 708138675 MDN ISIS EM-BLASSO 7.3 ns − Zinc finger, CCCH-type

D_contig28902_391 7D 456495802 Overall ISIS EM-BLASSO 5.0 * A F-box-like domain

Only QTNs/LDBs located within disease resistance-related proteins explaining greater than 5% of the phenotypic variation are shown. †Lr genes present within the same
or neighboring haplotype block, or Lr genes mapped using flanking markers. Chr, Chromosome; MDN, Morden; OTT, Ottawa; SK, Saskatoon; R allele, allele for resistance;
KW, Kruskal–Wallis test significance level where “ns,” “*,” “**,” “***,” and “****” correspond to not-significant and P-value ≤ 0.05, 0.01, 0.001, and 0.0001, respectively.

FIGURE 4 | Boxplots showing significant allelic effects for quantitative trait nucleotides (QTNs) present within genes encoding known disease resistance protein
types. (A) QTNs associated with race-specific IT response. (B) QTNs associated with leaf rust severity in the spring panel. (C) QTNs associated with leaf rust severity
in the winter panel. Labels at the top of each boxplot show the associated trait, i.e., the isolate for (A) or the overall mean or location-specific leaf rust severity (B, C).
QTNs, chromosomal locations and annotation are indicated below each plot. Locations are Morden (MDN), Ottawa (OTT) and Saskatoon (SK). Kruskal–Wallis
significance levels “∗,” “∗∗,” “∗∗∗,” and “∗∗∗∗” correspond to P-value ≤ 0.05, 0.01, 0.001, and 0.0001, respectively.

greatly on the phenotypic and genetic variation present in the
germplasm. The majority of the GWAS in wheat are based on
elite cultivars, breeding lines or landraces sourced from breeding
programs, genebanks or private seed collections, mainly because
introgression into adapted germplasm is easier and faster from
the primary genepool as compared to more distant germplasm
(Gao et al., 2016; Riaz et al., 2018). These collections, although
geographically adapted, often provide limited genetic diversity
due to the domestication and selective breeding bottlenecks.
Conversely, ancestors and wild relatives of wheat lack adaptation
traits for agriculture, but are a rich source of genetic variation,
accounting for 44% of the Lr genes identified to date (McCallum
et al., 2012; USDA, 2017). In the past century, research to

identify and transfer resistance genes from wild relatives was
laborious, lengthy and focused on one gene at the time. Recent
developments in genotyping technologies and the release of
the wheat reference genome are enabling high throughput
identification of new resistance genes regardless of the genepool,
and thus accelerating their gene cloning (IWGSC, 2018; Arora
et al., 2019). Here, we described an efficient method to identify
new Lr gene loci and candidate genes from many Triticum and
Aegilops species using an array-based SNP genotyping platform
and eight GWAS models. Through this approach, we identified
a total of 50 and 46 disease-related QTNs associated with field
leaf rust severity and IT response against six P. triticina isolates,
respectively, several of which were located near known Lr genes

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FIGURE 5 | Physical map illustrating the position of 55 known leaf rust resistance genes (Lr) and quantitative trait nucleotides (QTNs) associated with leaf rust
severity (LRS) and infection type (IT) against six leaf rust races. The positions of the previously cloned Lr genes are indicated by a single vertical line on the
chromosome. Regions shaded in gray indicate linkage disequilibrium blocks, while those shaded in blue indicate the location of the proximal and distal markers of
mapped Lr genes. Lr genes previously mapped with a single marker are indicated with an asterisk (*). For simplicity, QTNs associated with race-specific IT are
shown in orange and those associated with LRS in the spring and winter panels are color-coded in yellow and blue, respectively. Solid dot (•) QTNs indicate
association through a multi-locus model while star (?) QTNs were identified with the single-locus model MLM. Centromeres are denoted with a “c” symbol. Only
QTNs identified by more than one model are shown.

and others were linked to putative new ones. The QTNs identified
in this study provide the framework for investigating novel and
effective Lr genes from this diverse germplasm and for cloning
known Lr genes.

Genetic Diversity
Bread wheat, an allohexaploid species, comprises an estimated
17 billion nucleotides, more than 85% of which is repetitive
DNA (IWGSC, 2018). Array-based SNP genotyping platforms
provide a quick and cost-effective opportunity to survey whole
genomes of a large number of samples. We used the wheat
90 K array to genotype a diverse collection of 385 accessions.
A total of 34.1% of the SNPs were shared between the hexaploid

and tetraploid datasets, similar to a previous report of 33.9%
(Wang et al., 2014). The high percentage of shared SNPs is
indicative of the extensive gene flow from the tetraploid ancestors
to hexaploid wheat (Dvorak et al., 2006). Because nearly half
of the diploid accessions were Ae. tauschii, the D genome
donor of hexaploid wheat, the total of 7,243 (47.2%) of shared
SNPs between the diploid and hexaploid datasets also agrees
with the gene flow between these species. Mapping against the
CS exome sequence and subsequent comparison with exome
capture data identified 9,570 SNPs, from which the B (45.1%),
A (36.6%) and D (17.3%) sub-genome distribution compared to
several previous reports (Wang et al., 2014; Daba et al., 2018;
Pont et al., 2019).

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Structure Analysis
Relationships between the 27 species in the collection were
explored using phylogenetic tree analysis. Four major clades were
observed, clearly separating the wild species from the T. aestivum
and T. turgidum subspecies. T. aestivum ssp. spelta, hypothesized
to have emerged from hybridization between T. aestivum and
T. turgidum ssp. dicoccum (Blatter et al., 2004; Pont et al.,
2019), was observed to cluster among the T. aestivum subspecies,
separately from all T. turgidum ssp. dicoccum and dicoccoides.
SHWs created by crossing tetraploid T. turgidum with diploid
Ae. tauschii, were distributed between the two tetraploid clades
based on the genetic characterization of their tetraploid parent.
For example, 12 SHW accessions created by crossing the durum
wheat cultivar ‘Langdon’ with different Ae. tauschii accessions,
clustered with ‘Langdon’ in the modern tetraploids clade.
Similarly, multiple SHWs created by crossing wild emmer wheat
accessions PI113961 and PI355465 with Ae. tauschii, clustered
with their tetraploid parents in the ancient tetraploid clade.
Similar studies using SNP, SSR and AFLP markers have reported
the genetic diversity of SHWs to clearly reflect the sub-species,
geographical origin and morphological traits of their tetraploid
parent, possibly due to the fact that it contributed two-third
of their genome (Lage et al., 2003; Dreisigacker et al., 2008;
Bhatta et al., 2018).

The major clade of wild relatives was separately
analyzed to highlight the relationships between the species
(Supplementary Figure S9). With the exception of Ae.
sharonensis, clustering of the Aegilops species of the Sitopsis
section was consistent with previous studies, where Ae.
speltoides ssp. speltoides and Ae. speltoides ssp. ligustica, formed
one clade and Ae. longissima, Ae. bicornis and Ae. searsii
formed the other (Bahrman et al., 1988; Sasanuma et al.,
1996; Miki et al., 2019). The majority of the Triticum species
with an A genome also grouped together, where accessions of
T. zhukovskyi were clustered closer to their tetraploid ancestor
T. timopheevii (Dvorak et al., 1993). The unique amphiploid
EKC22_RL5347 resulting from a cross between Ae. speltoides
(S) and T. monococcum (Am) also clustered with the A-genome
species. The close relationship between Ae. crassa (DM or DDM)
and Ae. juvenalis (DMU) species was also expected because they
both share a D and an M genomes (Baum et al., 2012; Edet et al.,
2018). Both genomes of Ae. triuncialis (UC or CU) are nearly
identical to the diploid genomes of Ae. umbellulata (U) and Ae.
markgrafii (C) (Badaeva et al., 2004) and, unsurprisingly, the
Ae. triuncialis cluster located between the diploid accessions
with the U and C genomes. With the exception of Ae. juvenalis
(DMU), which clustered with its D genome progenitor, polyploid
species carrying a U genome were closely related to one another
and to Ae. umbellulata despite having different non-U genomes
(Badaeva et al., 2004; Kilian et al., 2011). Some of the wild
relative species were sparsely represented in our collection,
somewhat limiting our ability to establish clear relationships
between the various genomes. Increasing their sample size
is expected to refine this evolutionary relationship picture.
Regardless, the collection had ample diversity to detect many
putative new Lr genes.

The relationships observed in the phylogenetic tree were
also observed by PCA. Overall, clustering patterns hinted at
possible ascertainment biases; species of the A, B or D sub-
genomes segregated more clearly, with few to no outliers,
compared to other species. As the 90 K array consisted of
SNPs previously discovered in cultivars of polyploid wheat,
and its D genome progenitor Ae. tauschii (Wang et al.,
2014), genotype calling may be limited to common alleles
identified in the initial SNP discovery process (Albrechtsen et al.,
2010). Although the genotyping data may not be sufficient
to uncover novel ancestral relationships, it was nonetheless
effective in revealing genetic variations at the species level and
corroborating previously observed relationships (Bahrman et al.,
1988; Badaeva et al., 2004).

Detection of Previously Reported Lr
Genes
A total of 13 QTNs identified were present within the mapped
flanking markers of nine cataloged Lr genes. The QTN
Excalibur_c21395_291 mapped between psr119 and mag3092,
two markers tightly linked to Lr28 (McIntosh et al., 1982;
Sohail et al., 2014). Similarly, the QTN Excalibur_rep_c68362_62,
mapped 1.6 Mb upstream, in a neighboring haplotype block of
IWB41960, a marker tightly linked to the resistant gene Lr18
(Dyck and Samborski, 1968; Carpenter et al., 2018). Both Lr18
and Lr28 loci QTNs were present within CC-NBS-LRR genes and
showed significant allele-specific phenotypic differences, making
them candidate genes worthy of further investigation.

Five QTNs were found to be in the same haplotype blocks as
the cloned genes Lr1 and Lr34 and the genetic markers for Lr16
(wmc764), Lr32 (wmc43), and Lr73 (wPt-4453) (McCartney et al.,
2005; Cloutier et al., 2007; Krattinger et al., 2009; Thomas, 2010;
Park et al., 2014). The QTNs close to Lr1 and Lr32 were present
within CC-NBS-LRR and serine/threonine kinase domains, while
the others were located within a 3-ketoacyl-CoA synthase domain
or within genes of unknown function. These QTNs identified
had within-block D’ statistics ranging from 0.54 to 0.85, where
higher values suggest high linkage disequilibrium and similar
association with phenotypic traits between pairs of SNPs in the
same block (Cuyabano et al., 2014). Moreover, four of these five
QTNs showed significant allele-specific phenotypic variation.

Overall, the lack of cloned genes or tightly linked markers
restrict the ability to pinpoint the precise physical position of
some Lr genes. QTNs linked to or within flanking markers
of known Lr genes may serve as novel markers for gene
cloning, however, fine-mapping, allelism tests, transformation
genome editing (e.g., CRISPR) experiments must be performed
to ascertain their identities.

Identification of Novel Sources of Leaf
Rust Resistance
The most prevalent class of known resistance genes encode
intracellular immune receptors with NBS-LRR domains, many
of which also possess a coiled-coil (CC) N-terminal motif.
These genes play an important role in pathogen recognition

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and initiation of downstream signaling cascades. In the wheat
genome, as many as 661 to 1,560 full-length NBS-LRR genes have
been reported, higher than any other plant species (Gu et al.,
2015; Steuernagel et al., 2020). Four of the six Lr genes cloned to
date encode CC-NBS-LRR proteins (Feuillet et al., 2003; Huang
et al., 2003; Cloutier et al., 2007; Thind et al., 2017).

GWAS for race-specific IT response and leaf rust severity
identified a total of 16 QTNs within genes encoding complete
CC-NBS-LRR domains. Of these, 11 explained greater than 5%
of the phenotypic variation, while the remaining were small-
effect loci. As discussed above, QTNs close to Lr1, Lr18, Lr28,
and Lr54 were in CC-NBS-LRR genes but the remaining were
located where no known Lr genes have been mapped to date.
For IT response, the most prominent QTNs within CC-NBS-
LRR genes included D_contig18780_204 and Kukri_c19466_627,
where for the former, all species in the U-genome group, SHWs
and Ae. tauschii var. strangulata expressed the favorable allele,
while for the latter, resistant accessions included wild relatives in
the D-genome group and some SHWs, among others.

The highest number of QTNs within genes encoding CC-NBS-
LRR proteins was identified in the spring panel rated for leaf rust
severity. Of note is BS00065623_51 on the distal end of 7DS,
where different subspecies of T. aestivum, such as T. aestivum
ssp. spelta, was associated with the low-severity A allele, while
most T. aestivum ssp. aestivum and all SHWs associated with
the high-severity G allele. While the spring panel was made
up of subspecies of T. aestivum, T. turgidum and SHWs, the
winter panel was predominantly a collection of SHWs and wild
relative species. The most notable leaf rust severity associated
QTN identified in the winter panel may be Ex_c6145_2193. This
QTN, present within a CC-NBS-LRR gene, was located on the
distal end of the short arm of chromosome 1DS. Here, all the
wild relatives including Ae. crassa, Ae. juvenalis and Ae. cylindrica
among others, associated with the low-severity T allele, but the
high-severity C allele was only detected in some Ae. tauschii and
SHW accessions. Candidate CC-NBS-LRR genes identified here,
in the primary as well as the wild gene pool, are valuable sources
of genetic resistance.

Plant disease resistance is driven by complex mechanisms
involving several layers of defense. Not surprisingly, the classes
of known disease resistance genes have expanded greatly in the
past few years. For leaf rust, in addition to Lr34 encoding an ABC
transporter (Krattinger et al., 2009), the cloned Lr67 gene codes
for a hexose transporter (Moore et al., 2015). Other pathogen
resistance genes cloned in wheat encode serine/threonine protein
kinases and wall-associated kinases (Cao et al., 2011; Shi et al.,
2016). Identification of these diverse resistance proteins supports
the possibility of uncovering novel classes of disease resistance
genes. Consequently, in addition to those in CC-NBS-LRR genes,
we identified a number of QTNs present in genes coding for
other known resistance proteins in wheat and other plant species.
A key QTN identified herein was Excalibur_rep_c67475_1759
located within a pleiotropic drug resistance-type ABC transporter
protein, known to be involved in the secretion of fungal defense-
related metabolites, including resistance to DON accumulation
in wheat Fusarium head blight infection (Jasiński et al., 2001;
Shang, 2009). Similarly, QTN Kukri_c39321_112 on 6B was

associated with IT responses against three isolates (TDBG2,
MBDS, and TJBJ). It was found within a gene encoding a ZTL-
type beta-propeller/F-box domain protein known to regulate
plant flowering time and provide resistance against yellow rust in
wheat and powdery mildew in barley (Kim et al., 2005; Bozkurt
et al., 2007; Dagdas et al., 2009). Here, species with the favorable
allele included modern T. turgidum cultivars, Ae. speltoides,
Ae. sharonensis and T. timopheevii, among others. These loci,
located in novel genomic regions, are also recognized as putative
candidate leaf rust resistance genes, and some may potentially
confer resistance against multiple leaf rust isolates.

Overall, twice as many QTNs were identified in the spring
panel as compared to the winter panel. This imbalance may be
due to the difference in the number of accessions in each panel
or the nature of the germplasm within each one where the spring
panel comprised mostly the species used to design the wheat 90 K
array, thereby providing higher quality genotyping. In addition,
the potential for identifying novel disease resistance genes is
also dependent on the mapping of the QTNs to the T. aestivum
reference genome. As the reference only represents the A, B and
D genomes of a single genotype, it may limit, but not prevent, our
ability to identify rare resistance genes unique to the contrasting
genomes of the wild relatives.

CONCLUSION

The GWAS described herein highlights the multi-genic and
complex nature of pathogen disease resistance where multiple
markers were associated with different field environments and
pathogen races. We identified several QTNs located near known
Lr resistance genes providing, at the very least, novel markers
for the cloning of these genes. Some of them were located
within known resistance gene classes such as CC-NBS-LRR. As
such, these become prime candidates for direct investigations.
This study also identified novel leaf rust resistance loci from
the domesticated T. aestivum and T. turgidum species that can
be capitalized upon quickly, but also others from wild relative
species that may be harnessed to add to the leaf rust resistance
repertoire of wheat. Once cloned, the novel Lr genes can be
transferred into adapted germplasm using modern genome-
assisted breeding strategies, such as gene cassettes and genome
editing (Wulff and Moscou, 2014; Wang et al., 2018). Gene
cassettes allow multiple cloned disease resistance genes to be
transformed simultaneously into a single genome to provide
durable and broad-spectrum resistance, because the closely
linked genes will not segregate, will be easy to select for, and
will essentially have the advantages of gene pyramiding (Kolmer
et al., 2009; Arora et al., 2019). Gene-specific markers can also
be developed to facilitate the transfer of these genes through
conventional breeding. The recently introduced CRISPR-Cas9
system in wheat (Zhang et al., 2016; Liang et al., 2017) offers
many advantages. It can facilitate the investigation of candidate
genes in any germplasm, bypassing the laborious fine-mapping
experiments and enabling their functional analyses. We believe
that gene editing could also be capitalized upon to “transfer”
resistance genes from wild relatives through the allelic conversion

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of the orthologous domesticated alleles, providing that sufficient
sequence similarity exists between the wheat and the wild relative
alleles. This “long-shot” strategy would eliminate the need for
the long, laborious and difficult introgression via crossing, and
eradicate its associated linkage drag drawbacks. In conclusion,
we described a powerful approach to identify QTN markers and
candidate genes for leaf rust resistance through combining a
broad germplasm including cultivated species and wild relatives,
array-based genotyping, field severity and IT phenotyping and,
through the use of several GWAS models.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online
repositories. The names of the repository/repositories
and accession number(s) can be found in the
manuscript/Supplementary Material.

AUTHOR CONTRIBUTIONS

FF performed the data analysis and wrote the manuscript; FY
provided bioinformatics and statistics guidance; BM, SC and
CP performed leaf rust severity phenotyping; BM performed
race-specific phenotyping; CM and CH produced the wheat
90 K array data; GF co-developed the original experiment
and provided some of the key germplasm; SC designed the
experiments and co-wrote the manuscript. All authors read and
edited the manuscript.

FUNDING

This project was funded through an Agriculture and Agri-
Food Canada A-base grant (project J-001372) and a Canadian
Agricultural Partnership Wheat Cluster grant (project J-001961).

ACKNOWLEDGMENTS

The authors would like to thank Elsa Reimer and Winnie McNabb
for leaf rust phenotyping in Morden, Dr. Jemanesh Haile for leaf
rust phenotyping in Saskatoon, Tara Edwards and Leslie Bezte for
the genotyping data, Dr. Eric Kerber for providing some of the
unique germplasm, Nick Manseau for help with editing and, Drs.
Sridhar Ravichandran and Demissew Sertse Desta for help with
some of the bioinformatics analyses.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.
583738/full#supplementary-material

Supplementary Figure 1 | Stacked bar plot summarizing genotype distribution of
infection type response against six leaf rust isolates. Scores from the Stakman

scale were converted to a linear 1–9 scale. Each bar represents the distribution of
scores for a specific leaf rust isolate.

Supplementary Figure 2 | Comparison between raw mean values and BLUP
estimates for leaf rust severity. Scatter plots show mean values (y-axis) vs. BLUP
estimates (x-axis) for overall severity in the spring (A) and winter (B) diversity
panels. Boxplots show the interquartile range and median severity calculated
using aggregate means (C–E) and BLUP estimates (D–F) for Morden (MDN),
Ottawa (OTT) and Saskatoon (SK) in the spring diversity panel, and MDN and OTT
in the winter diversity panel.

Supplementary Figure 3 | Histograms describing the distribution of overall
severity scores in the spring and winter panels. Raw mean values of the spring (A)
and winter (B) diversity panels and genotypic best linear unbiased predictor
(BLUP) estimates of the spring (C) and winter (D) diversity panels are illustrated.
Genotypic BLUP estimates are conditional means which summarize phenotypic
variation across all locations and years.

Supplementary Figure 4 | Scree plots for estimating the K number of
sub-populations in the SNP datasets for the race-specific infection type (A) and
field leaf rust severity of the spring (B) and winter (C) diversity panels. PCAdapt
and LEA are both PCA-based methods, where PCAdapt plots the number of PCs
vs. proportion of variance explained, and LEA plots the Tracy–Widom statistic vs.
K. Admixture detects population structure through trends in
cross-validation error vs. K.

Supplementary Figure 5 | LEA structure plots illustrating the ancestry mix of the
subpopulations using K = 8 for the race-specific infection type (A), K = 8 for leaf
rust severity of the spring (B) and K = 6 for the winter (C) diversity panels.

Supplementary Figure 6 | Box plots showing (A) percentage variance explained
(r2) and (B) false discovery rate (FDR) adjusted P-values for quantitative trait
nucleotides (QTNs) identified by each of the eight genome-wide association
study (GWAS) models.

Supplementary Figure 7 | Plot showing intersecting sets of quantitative trait
nucleotides (QTNs) identified by each of the eight genome-wide association study
(GWAS) models for all leaf rust traits. Names of the GWAS models are listed on
the left side below the x-axis. Empty light-gray dots indicate GWAS models that
are not part of the intersection, while filled black dots indicate GWAS models that
are participating in the intersection. The size of each intersecting set is illustrated in
the bar plot. Note that the intersection sets are disjointed, i.e., they are
non-overlapping. For simplicity, only intersecting sets of size ≥ 5 are shown.

Supplementary Figure 8 | Heatmap showing genotype calls for quantitative trait
nucleotides (QTNs) that displayed significant allele-specific phenotype differences
for (A) infection type response against six leaf rust isolates and leaf rust severity in
the (B) spring and (C) winter diversity panel. Rows represent the entry number
and genome symbol of the accessions in the germplasm and columns represent
the QTNs. Genotypes AA represent homozygous alleles associated with low leaf
rust infection type (A) or severity scores (B,C) and genotype BB represent
homozygous alleles associated with high scores. Genotype AB and NA represent
heterozygous alleles and no genotype calls, respectively. Different colors in the left
panel represent the different groups of species in the germplasm.

Supplementary Figure 9 | Rooted maximum likelihood phylogenetic tree
illustrating the relationships between the wild Aegilops, Triticum and Haynaldia
species in the germplasm. The leaf node labels are the entry number and genome
symbol of each accession.

Supplementary Table 1 | List of accessions included in the study and their
respective genome, ploidy, growth habit, origin, and seed source.

Supplementary Table 2 | Infection type scores of accessions rated for response
against six leaf rust isolates. Scores are based on the Stakman scale (A) and their
linear conversion using a 1–9 scale (B).

Supplementary Table 3 | Virulence and avirulence formulas for the six leaf rust
pathogen isolates used to measure infection type response.

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Supplementary Table 4 | Genotypic and location-specific best linear unbiased
predictor (BLUP) estimates for leaf rust severity of the spring (A) and winter (B)
diversity panels.

Supplementary Table 5 | List of known wheat leaf rust resistance genes, their
source, gene and marker references, and position on the Chinese Spring
reference genome v1.0 (IWGSC, 2018) mapped using gene sequences or flanking
and linked markers.

Supplementary Table 6 | Linear mixed model for leaf rust severity of the spring
(A) and winter (B) panels across years and locations.

Supplementary Table 7 | Average leaf rust severity of accessions of the spring
(A) and winter (B) diversity panels calculated over all locations,
years and replicates.

Supplementary Table 8 | Chromosomal position of the wheat 90 K array SNP
markers on the Chinese Spring exome sequence (ES) and reference genome
sequence (CS) v1.0 (IWGSC, 2018).

Supplementary Table 9 | List of eight accessions which did not cluster with other
individuals of their respective species.

Supplementary Table 10 | Significant quantitative trait nucleotides (QTNs) and
linkage disequilibrium blocks (LDBs) for infection type (IT) response against six leaf
rust isolates identified by association mapping using one single-locus and seven
multi-locus models.

Supplementary Table 11 | Significant quantitative trait nucleotides (QTNs) and
linkage disequilibrium blocks (LDBs) associated with leaf rust severity in the spring
and winter diversity panels as identified by association mapping using one
single-locus and seven multi-locus models.

Supplementary Table 12 | Functional annotation of genes located within 5 KB of
quantitative trait nucleotides (QTNs) or peak single nucleotide polymorphism (SNP)
markers of linkage disequilibrium blocks (LDBs) associated with infection type
response against six leaf rust isolates and leaf rust (LR) severity of the spring and
winter diversity panels.

Supplementary Table 13 | Quantitative trait nucleotides (QTNs) located within
flanking markers of leaf rust (Lr) resistance genes positioned onto the Chinese
Spring reference genome sequence v1.0 (IWGSC, 2018) (A), within the same
linkage disequilibrium block (LDB) (B), or a neighboring
LDB (C).

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