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Molecular Breeding
New Strategies in Plant Improvement
ISSN 1380-3743
Mol Breeding
DOI 10.1007/s11032-012-9790-z
Association mapping for soilborne
pathogen resistance in synthetic hexaploid
wheat
Muhammad A.Mulki, Abdulqader
Jighly, Gouyou Ye, Livinus C.Emebiri,
David Moody, Omid Ansari & Francis
C.Ogbonnaya
1 23
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Association mapping for soilborne pathogen resistance
in synthetic hexaploid wheat
Muhammad A. Mulki •Abdulqader Jighly •
Gouyou Ye •Livinus C. Emebiri •David Moody •
Omid Ansari •Francis C. Ogbonnaya
Received: 23 February 2012 / Accepted: 7 September 2012
ÓSpringer Science+Business Media B.V. 2012
Abstract Soilborne pathogens such as cereal cyst
nematode (CCN; Heterodera avenae) and root lesion
nematode (Pratylenchus neglectus; PN) cause sub-
stantial yield losses in the major cereal-growing
regions of the world. Incorporating resistance into
wheat cultivars and breeding lines is considered the
most cost-effective control measure for reducing
nematode populations. To identify loci with molecular
markers linked to genes conferring resistance to these
pathogens, we employed a genome-wide association
approach in which 332 synthetic hexaploid wheat lines
previously screened for resistance to CCN and PN
were genotyped with 660 Diversity Arrays Technol-
ogy (DArT) markers. Two sequence-tagged site
markers reportedly linked to genes known to confer
resistance to CCN were also included in the analysis.
Using the mixed linear model corrected for population
structure and familial relatedness (Q?Kmatrices), we
were able to confirm previously reported quantitative
trait loci (QTL) for resistance to CCN and PN in
bi-parental crosses. In addition, we identified other
significant markers located in chromosome regions
where no CCN and PN resistance genes have been
reported. Seventeen DArT marker loci were found to
be significantly associated with CCN and twelve to PN
resistance. The novel QTL on chromosomes 1D, 4D,
5B, 5D and 7D for resistance to CCN and 4A, 5B and
7B for resistance to PN are suggested to represent new
sources of genes which could be deployed in further
wheat improvement against these two important root
diseases of wheat.
Keywords Primary synthetic wheat Cereal cyst
nematode Root lesion nematode Genetic resistance
Linkage disequilibrium mapping Marker-assisted
selection
Electronic supplementary material The online version of
this article (doi:10.1007/s11032-012-9790-z) contains
supplementary material, which is available to authorized users.
M. A. Mulki A. Jighly F. C. Ogbonnaya (&)
International Center for Agricultural Research in the Dry
Areas (ICARDA), P.O. Box 5466, Aleppo, Syria
e-mail: francis.ogbonnaya@grdc.com.au
Present Address:
M. A. Mulki
Max Planck Institute for Plant Breeding Research, 50829
Cologne, Germany
G. Ye
International Rice Research Institute (IRRI), Los Banos,
Laguna, Philippines
L. C. Emebiri
EH Graham Centre for Agricultural Innovation, Pine
Gully Road, WaggaWagga, NSW 2650, Australia
D. Moody
InterGrain Pty Ltd, 19 Ambitious Link, Bibra Lake, WA
6163, Australia
O. Ansari F. C. Ogbonnaya
Grains Research and Development Corporation, P.O. Box
5367, Kingston, ACT 2604, Australia
123
Mol Breeding
DOI 10.1007/s11032-012-9790-z
Author's personal copy
Introduction
The cereal cyst nematode (CCN) caused by Hetero-
dera avenae and root lesion nematodes (RLN)
including Pratylenchus neglectus (PN) are widely
acknowledged to be economically important biotic
constraints in rainfed wheat production regions of
Australia, USA, China, India and several countries in
West Asia and North Africa (Nicol and Rivoal 2008).
Cereal cyst nematodes attack only members of the
grass family (Poaceae) while RLN have a broader host
range. For the former, economic damage can be
minimized by any rotation that includes at least one
growing season without cereals or grass weeds. For
RLN, management of crop damage by crop rotation is
possible only if resistant alternative crops are available
to growers. The use of genetic resistance remains one
of the best control strategies for the control of
soilborne pathogens in wheat. This is particularly
very important in developing countries where farming
practices remain largely peasant and chemical control
is prohibitive.
Sources of resistance to CCN include cultivated
wheat and its wild relatives such as Aegilops tauschii
(Coss.) [syn. Triticum tauschii (Coss.) Schmal.; syn.
Aegilops squarrosa auct., non L.] known as goat grass
(vanSlageren 1994) and Triticum turgidum. Previous
studies have reported the identification of CCN
resistance genes: Cre1 and Cre8 in T. aestivum
(Slootmaker et al. 1974; Williams et al. 2002), Cre2,
Cre5 and Cre6 in Ae. ventricosa (Delibes et al. 1993;
Jahier et al. 1996; Ogbonnaya et al. 2001a), Cre3 and
Cre4 in Ae. tauschii (Eastwood 1995; Eastwood et al.
1994), Cre7 in Ae. truincialis (Romero et al. 1998),
CreX and CreY in Ae.variabilis (Barloy et al. 2007)
and CreR in Secale cereale (Asiedu et al. 1990).
Similarly, sources of resistance to RLN have previ-
ously been reported in wheat and its wild relatives
including Ae. tauschii, T. urartu,T. monococcum and
T. turgidum (Thompson and Haak 1997; Sheedy 2004;
Sheedy et al. 2012). A survey of several thousand
Australia and overseas cultivars for resistance to PN in
Australia in the 1990s resulted in the identification of
superior resistance in cultivars Virest (Aus11984)
from Italy and Persia 20 (Aus5205) from Iran (Van-
stone et al. 2008). In recent studies, most bread wheat
varieties were reported to exhibit tolerance or partial
resistance to Pratylenchus spp. (Thompson 2008;
Thompson et al. 2008), necessitating the continuing
search for better sources of resistance. A single gene
for PN resistance, Rlnn1, was identified and mapped
on chromosome 7AL in a doubled-haploid (DH)
population of wheat (Williams et al. 2002), while in
another study three major quantitative trait loci (QTL)
for P.thornei were identified on chromosomes 2BS,
6DS and 6DL, and two for PN resistance on 2BS and
6DS were identified in a synthetic backcross-derived
population (Zwart et al. 2010). They also reported the
detection of additional QTL for PN resistance on 3DS,
4BL and 4DS.
The identification of CCN and RLN resistance QTL
in wild wheat relatives, especially Ae. tauschii, shifted
the focus of some recent studies on nematode resis-
tance towards synthetic hexaploid wheat (SHW,
2n =6x =42, AABBDD) lines. The SHWs are
obtained by crossing Ae. tauschii L. (2n =2x =14,
DD), donor of the D genome, with modern durum
wheat (T. turgidum L., 2n =4x =28, AABB), donor
of the AB genome, analogous to the evolution of
cultivated bread wheat, which took place about
10,000 years ago (van Ginkel and Ogbonnaya 2007).
In a number of studies, SHWs have been reported as
sources of novel genetic diversity for disease resis-
tance in wheat (Eastwood et al. 1994; Eastwood 1995;
Villareal et al. 1994; Mujeeb-Kazi et al. 1996; van
Ginkel and Ogbonnaya 2007; Xu et al. 2004; Friesen
et al. 2008). Similarly, SHWs possessing resistance to
PN have been reported (Ogbonnaya et al. 2008;
Thompson 2008; Zwart et al. 2004,2005,2010). In a
haplotype analysis of a large set of SHWs using the
diagnostic marker (Cre3spF/R) linked to the CCN
gene, Cre3, 20 % of the 384 SHWs screened pos-
sessed fragments associated with Cre3 gene (Wilson
and Ogbonnaya, unpublished data). Of these, 15 %
had same Ae. tauschii accessions consistent with the
presence of the Cre3 CCN resistance gene. Based on
these analyses, at least 85 % of the 384 SHWs would
be expected to harbor potentially new genes.
The conventional approach involved in exploiting
unadapted germplasm for crop improvement encom-
passes firstly the assemblage of genotypes or accessions,
evaluation of such accessions, and identification of
accession(s) with desirable traits. This is followed by
crossing to adapted germplasm most often deficient in
the trait of interest to develop inbred, DH or backcross
(BC) populations. The developed populations are then
phenotyped and genotyped to identify linked molecular
markers to facilitate the incorporation of desirable traits
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in breeding programs via marker-assisted selection
(Somers et al. 2007). Association mapping (AM) or
linkage disequilibrium (LD) mapping is an alternative
approach to phenotype–genotype association that does
not require development of bi-parental crosses or
screening generations of progeny. Association map-
ping was first successfully used for identification of
alleles at loci contributing to susceptibility to human
diseases (reviewed by Goldstein et al. 2003). In this
approach to mapping, a large collection of accessions
that are related, though independently derived, is
phenotyped and genotyped and subsequently trait
association is examined (Flint-Garcia et al. 2003;
Rafalski 2002).
AM has been suggested to be applicable to any set
of germplasm and to detect QTL for as many traits that
show variation (Roy et al. 2010). An increasing
number of studies are using AM in wheat to comple-
ment previous and ongoing QTL studies (Breseghello
and Sorrells 2006; Emebiri et al. 2010; Neumann et al.
2011; Ravel et al. 2006; Tommasini et al. 2007).
In this study, we analyzed the association of
Diversity Arrays Technology (DArT) markers with
resistance to CCN and PN in a collection of SHWs and
were able to identify new chromosomal regions
controlling resistance to the two types of nematodes.
Materials and methods
Genetic resources
A total of 332 SHWs obtained from CIMMYT and
Australia was selected for this study (Supplementary
Table S1). The SHWs consisted of interspecific
crosses between 223 Aegilops tauschii accessions
and 61 elite Triticum turgidum subsp. durum parents.
Phenotyping
The procedures used for CCN resistance evaluation
that include standard Australian checks are described
in Ogbonnaya et al. (2001a,2008). The nematode
reproduction values obtained were used to classify
plant resistance relative to the control varieties
Aus10894 (resistant), Frame (moderately resistant)
and Meering (susceptible). Mean values of less than
five and more than 30 cysts (range per plant root) were
classified as resistant and susceptible, respectively,
and those with 15–30 cysts were considered moder-
ately resistant.
The RLN test for PN was conducted according to
Williams et al. (2002). At least ten seeds were divided
into two tests of five single plant replicates. The
susceptible check varieties were Pugsley and Machete,
and the resistant check was the triticale cv. Abacus.
Raw data from five replicates in each test were used to
perform ANOVA. Experimental results were com-
pared with the check varieties, using the least signif-
icant difference (l.s.d.).
DNA extraction and DArT and STS marker
genotyping
DNA extraction was carried out according to
Ogbonnaya et al. (2001a), with 10 ll of 100 ng ll
-1
DNA of each sample being sent to Triticarte Pty. Ltd.
Australia (www.triticarte.com.au) for genotyping, as a
commercial service provider for DArT markers.
DArT is an array-based genotyping technology
which generates DNA markers that are binary and
dominant. The basis of polymorphisms is single
nucleotide polymorphisms (SNPs) and insertion/dele-
tions (InDels) at restriction enzyme cutting sites and
large InDels within restriction fragments (White et al.
2008). A high-density DArT array was used and 660
DArT markers were scored. The SHWs were also
genotyped using two sequence-tagged site (STS)
markers M19Cre1F/R and Cre3spF/R previously
reported to be linked to two CCN resistance genes
Cre1 and Cre3, respectively (de Majnik et al. 2003;
Ogbonnaya et al. 2001b).
Gene diversity, marker allele frequency
and construction of the genetic map
Gene diversity and marker allele frequency were
calculated using PowerMarker v3.25 (Liu and Muse
2005). DArT markers with minor allele frequency of
less than 5 % were culled from the data set to reduce
false positives. The remaining DArT markers were
integrated into a linkage map by inferring marker order
and position from a consensus genetic map of wheat
(Detering et al. 2010) (ordering 5,000 wheat DArT
markers); 446 of the scored 660 DArT markers were
assigned to their map position, in which 145, 241 and
60 markers were specific to the A, B and D genomes,
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respectively. The presentation of the consensus map
was displayed using MapChart (Voorrips 2002).
Population structure
The software program STRUCTURE v.2.2 (Pritchard
et al. 2000) was used to estimate the number of
subpopulations within SHWs. Genotype data of 49
unlinked DArT markers, chosen to cover the wheat
genome with at least two loci on each wheat chromo-
some, were used in the analysis. The genetic distance
between two chosen markers on the same chromosome
was at least 50 cM to avoid physical linkage.
STRUCTURE uses a model based on the Bayesian
clustering method to infer population structure, which
places individuals of the population into Kclusters,
where Kis chosen in advance but can be varied for
independent runs and assigns each genotype to
subpopulations. To infer population structure amongst
the SHWs, ten independent runs for each Kvalue from
2to15(K, the number of subpopulations) were
performed based on an admixture model and corre-
lated allele frequency. Both the length of burn-in
period and the number of iterations were set at
100,000. To reach the appropriate Kvalue, the
estimated normal logarithm of the probability of fit
(averaged for the ten runs), provided in the STRUC-
TURE output, was plotted against K. This value
reaches a plateau when the minimal number of groups
that best describes the population substructure has
been attained (Pritchard et al. 2000). An ad-hoc
quantity statistic (DK) based on the rate of change in
the log probability of data between successive Kvalues
(Evanno et al. 2005) was used to predict the real
number of subpopulations.
Linkage disequilibrium
Linkage disequilibrium (LD) between markers was
estimated as squared allele frequency correlation
estimates (r
2
) using TASSEL 2.1 (Bradbury et al.
2007)(http://www.maizegenetics.net); the compari-
son-wise significance values were computed with
1,000 permutations. Genome-wide LD was estimated
using all the 660 DArT markers. However, to estimate
LD between loci on different chromosomes (inter-
chromosomal LD) and within the same chromosome
(intra-chromosomal LD), only the 446 DArT markers
with known chromosomal position were considered.
Each pair of loci were considered to be in significant
LD at P\0.001. The 95th percentile of square-root-
transformed r
2
values of unlinked loci was considered
to be the population-specific critical value of r
2
,
beyond which LD was likely to be caused by genetic
linkage (Breseghello and Sorrells 2006). Significant r
2
values of intra-chromosomal LD were plotted against
map distance in centiMorgans and a second-degree
LOESS curve (Cleveland 1979) was drawn using R
software (http://www.r-project.org). The intersection
of the critical value baseline with the LOESS curve
was considered to be the estimate of the extent of LD
in the chromosome (Breseghello and Sorrells 2006).
Association analysis
TASSEL was used to perform association analysis
using the general linear model (GLM) and the mixed
linear model (MLM) functions. In GLM, a single
factor analysis of variance (SFA) that did not consider
population structure was first carried out using each
marker as the independent variable and comparing the
mean performance of each allelic class. GLM was
further performed with population structure (Q matrix)
integrated as covariate to correct for the effects of
population substructure. Finally, the MLM accounting
for both Q and family structure matrix (Kinship, K
matrix) to control both Type I and Type II errors (Yu
et al. 2006) was performed. To correct for multiple
testing, a false discovery rate (FDR) method (Benja-
mini and Hochberg 1995) was used to calculate
marker-specific B–H critical values at the significance
level of 0.05 (Steinberg and Kuang 2002). Marker
alleles with P\B–H critical value were declared
to be significantly associated with CCN and PN
resistances.
Results
Phenotypic variation for soilborne pathogen
resistance
Figure 1summarizes the results of the evaluation of
332 SHWs included in the current study for their
reaction to CCN and PN. The percentage of SHWs that
displayed complete resistant reactions varied from
17 % for CCN to 2 % for PN. In all cases, the SHWs
classified as resistant to both diseases demonstrated
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resistances that were superior to the existing standard
resistant check cultivars in bread wheat (data not
shown). Details have previously been reported in
Ogbonnaya et al. (2008).
Gene diversity, marker coverage and population
structure
The gene diversity of the 660 DArT markers used to
genotype the SHWs ranged between 0.18 and 0.50
with an average gene diversity of 0.41. DArT markers
integrated into the framework genetic map (446
markers) covered a total genetic distance of
2,607 cM, with an average density of one marker per
6 cM. The number of markers per chromosome ranged
between four (chromosomes 4D and 5D) and 54
(chromosome 3B) with an average of about 21 marker
loci per chromosome.
Analysis of population structure showed that the
logarithm of the data likelihood (ln P(D)) on average
continued to increase with increasing numbers of
assumed subpopulations (K) from 2 to 15. Differences
between ln P(D) values at two successive Kvalues
became non-significant after K=7 (Supplementary
Fig. S1A). The ad-hoc quantity based on the second-
order rate of change in the log probability (DK)
showed a clear peak at K=7 (Supplementary Figs
S1B, C and D), which confirmed that a Kvalue of
seven was the most probable prediction for the number
of subpopulations. The seven subpopulations con-
sisted of 22–90 SHW lines.
Linkage disequilibrium
LD was estimated by r
2
at PB0.001 from all pairs of
the 660 DArT markers. On a genome-wide level,
almost 25 % of all pairs of loci were in significant LD.
The average r
2
of genome-wide LD was 0.13. DArT
markers assigned to their map position were further
used to estimate inter- and intra-chromosomal LD.
About 25 % of inter-chromosomal pairs of loci were
in significant LD, with an average r
2
of 0.09, while
45 % of intra-chromosomal pairs of loci were in
significant LD with an average r
2
of 0.24.
The extent and distribution of LD were graphically
displayed by plotting intra-chromosomal r
2
values for
loci in significant LD at PB0.001 against the genetic
distance in centiMorgans and a second-degree LOESS
curve was fitted (Fig. 2a). The critical value for
significance of r
2
was estimated at 0.22 according to
Breseghello and Sorrells (2006), and thus all values of
r
2
[0.22 were estimated to be due to genetic linkage.
The baseline intersection with the LOESS curve was at
9 cM, which was considered as the estimate of the
extent of LD in the SHW population (Fig. 2b),
although in a few cases high levels of LD were
observed over longer distances (r
2
=1 at a genetic
distance of 149 cM). Thus the map coverage of 6 cM
was deemed appropriate to perform a genome-wide
association analysis on the SHW population.
Marker–trait associations
Markers associated with resistance to CCN and PN
were identified in SHWs by AM analysis using both
GLM and MLM. Comparison between results of all
three models used in this study (GLM, GLM?Q,
MLM?Q) showed that correction for population
structure (Q matrix) decreased the total number of
markers significantly associated (P\0.05) with
resistance by 27 and 54 % for CCN and PN, respec-
tively (data not shown). Controlling for familial
relatedness (Kmatrix) had a much smaller effect, as
most of the significant markers were common before
and after considering the Kmatrix. QTL for CCN
resistance were identified in all wheat chromosomes
except for 7A. Similarly, QTL for PN were identified
in all wheat chromosomes except for 2A and 6D.
For this paper, the results of the MLM analysis that
accounted for both Qand Kmatrices were adopted
after using the cutoff value of the FDR statistic at the
0
0.1
0.2
0.3
0.4
0.5
0.6
SMSMRR
Percent of SHWs
CCN
PN
Fig. 1 Results of the evaluation of the 332 SHWs included in
the study according to their reaction to cereal cyst nematode
(CCN) and P. neglectus (PN). Ssusceptible, MS moderately
susceptible, MR moderately resistant, Rresistant
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significance level of 0.05 (Benjamini and Hochberg
1995). The Cre3 diagnostic marker (Ogbonnaya et al.
2001b) and 17 DArT markers were significantly
associated with resistance to CCN (Table 1a). The
Cre3 diagnostic marker explained about 9 % of the
phenotypic variation (R
2
) for resistance to CCN. More
than one-fifth of the SHWs carried the Cre3spF/R
allele associated with CCN resistance gene Cre3,
0 50 100 150 200
0.0 0.2 0.4 0.6 0.8 1.0
Distance in cM
R.2
0 1020304050
0.0 0.2 0.4 0.6 0.8 1.0
Distance in cM
R.2
A
B
Fig. 2 Decline of linkage disequilibrium (LD) estimated by r
2
against genetic distance in synthetic hexaploid wheat showing
ascatter plot of estimates of r
2
for pairs of DArT markers in
significant LD (P\0.001) across the wheat genome; bdetailed
view of LD decline within the first 50 cM. Horizontal blue line
indicates the 95th percentile of the distribution of unlinked r
2
.
The red curve is the second-degree LOESS approximation of
mean LD
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while none of them possessed the DNA fragment
associated with the CCN resistance gene Cre1. The
significant DArT markers explained between 3 and
7 % of the phenotypic variation for resistance to CCN.
Marker alleles associated with resistance had a
frequency of 0.30–0.79. Based on the DArT consensus
map (Detering et al. 2010), nine of the 17 DArT
markers linked to CCN resistance were assigned to
chromosomes 1D, 4D, 5B, 5D and 7D (Fig. 3). The
DArT marker wPt-9997 was assigned to chromosome
2D based on the Wheat DArT maps Version 1.2
(Fig. 3). Two pairs of unmapped markers, wPt-4320/
wPt-9105 and wPt-3825/wPt-4150, were in significant
(P\0.001) and strong LD, with an r
2
of 0.60 and 0.99
respectively.
Twelve markers were significantly linked to PN
resistance in SHWs (Table 1b). The percentage of
phenotypic variation explained (R
2
) by the markers for
resistance to PN ranged from 3 to 5 %. The frequency
of significant marker alleles ranged between 0.20 and
0.84. Five of the significant markers mapped to loci on
chromosomes 2B, 3D, 4A, 5B and 7B (Fig. 3). The
marker wPt-0941 was assigned to chromosome 4D
based on the Wheat DArT maps Version 1.2, while the
remaining six unmapped markers were linked to each
other (0.27 \r
2
\0.94, P\0.001) and to wPt-7433
(0.29 \r
2
\0.99, P\0.001), which mapped to
chromosome 7A (Fig. 3).
Discussion
Variation for resistance to soilborne pathogens
in SHWs
The SWHs screened in this study exhibited varying
levels of resistance to CCN and PN, suggesting that the
resistance in SHWs consists of either major genes with
strong or weak expression, or a series of minor genes.
Given the limited number of known genes for
resistance to CCN and PN that are available in bread
wheat, the SHWs represent potentially new and
diverse sources of resistance against two important
soilborne pathogens.
To date, 11 CCN resistance genes conferring either
dominant or partial resistance have been catalogued.
In some cases, the level of resistance varies depending
on the pathotypes present. The Cre1 gene confers
resistance to Australian and several European cyst
nematode pathotypes. Cre2 exhibits a high level of
resistance to populations of H. avenae, Ha71 (Span-
ish), Ha11 (British), and Ha12 and Ha41 (French), but
proved ineffective against HgI–HgIII (Swedish) and
the Australian Ha13 (Delibes et al. 1993; Ogbonnaya
et al. 2001a). Cre3 and Cre6 provide better resistance
than Cre1 against the Ha13 pathotype but they are
susceptible to the European pathotypes Ha11 and
Ha12 (Ogbonnaya et al. 2001a). Cre5 confers partial
resistance to French (Ha12 and Ha41) and Australian
(Ha13) pathotypes (Rivoal et al. 1993; Jahier et al.
2001; Ogbonnaya et al. 2001a). Cre1,Cre3 and Cre6
are inherited as single dominant genes against the
Ha13 pathotype while wheat cultivars carrying Cre2,
Cre4,Cre5 and Cre8 exhibit partial resistance and
tolerance against Ha13.
Resistance to PN is partial and quantitative and
only one gene, Rlnn1, has been catalogued to date
though several QTL have been reported. Furthermore,
Zwart et al. (2005) identified two QTL, QRlnn.lrc-
6D.1 (6DS) and QRlnn.lrc-4D.1 (4DS), for PN
resistance in a CPI33872 9Janz population. Using
the same population, Zwart et al. (2010) reported the
detection of consistent QTL for PN in chromosomes
2BS and 6DS over two seasons, in addition to those on
3DS, 4BL and 4DL detected in one of the 2 years only.
Population structure and LD within the SHWs
The power of association studies depends on levels of
genetic variation, LD and population structure. Iden-
tifying and taking into consideration population
structure (Qmatrix) as a fixed effect and differences
in genetic relatedness among lines within the sub-
populations (Kinship or Kmatrix) as random effects
reduces the number of false positives (Yu et al. 2006).
Our results indicate that seven substructures were
appropriate in delineating the population structure
within the SHWs used in this study. The assignment of
the SHWs to the seven subgroups was largely in
agreement with their Ae. tauschii parent and less so
with the durum parent. The frequency of Ae.tauschii
accessions amongst the SHW varied from one to a
maximum of five while the durum elite lines ranged
from 1 to 45, an indication of the complexity of the
crosses. It has been suggested that the STRUCTURE
algorithm does not converge to an optimal Kwhen com-
plex genetic structures exist, such as strong relatedness
within some germplasm (Camus-Kulandaivelu et al.
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2007). Our results are within the range of the results
obtained by Emebiri et al. (2010) who reported that
values of K=8 and/or 9 were sufficient in the
grouping of 91 SHW genotypes.
Linkage disequilibrium is influenced by recombi-
nation rate, allele frequency, population structure and
selection (Flint-Garcia et al. 2003). Numerous studies
suggest that LD is not consistent across the whole
genome, or along single chromosomes. LD can occur
over large distances but may also decrease for nearby
loci (Neumann et al. 2011). In this study, the LD
generally decreased with the increase of genetic
distance with very strong LD between pairs of loci
observed at genetic distances of up to 9 cM, sugges-
tive of LD maintained by genetic linkage. Our results
are consistent with those reported by previous studies
in wheat. In a similar study using a subset of 91 SHW,
Emebiri et al. (2010) reported that the general trend
Table 1 (a) List of markers significantly associated with resistance to CCN detected using the MLM (Q?K) model; (b) List of
markers significantly associated with resistance to PN detected using the MLM (Q?K) model
Chromosome Locus Position (cM) Pvalue (Q?K)R
2
Frequency of
resistant allele
a
1D wPt-3855 21.39 3.51E-04 0.03 0.37
1D wPt-7828 39.62 2.37E-07 0.07 0.56
1D wPt-8678 99.09 1.29E-04 0.04 0.47
2D wPt-9997 39.50 1.29E-06 0.06 0.58
2D Cre3 80.00 2.65E-09 0.09 0.21
4D wPt-5184 1.94 3.98E-05 0.04 0.77
4D wPt-4106 103.71 1.39E-04 0.04 0.37
5B wPt-4677 44.73 4.29E-04 0.03 0.30
5D wPt-1210 4.39 7.70E-05 0.04 0.41
7D wPt-3328
a
37.25 4.77E-04 0.03 0.79
7D wPt-1100
a
37.25 4.19E-04 0.03 0.79
– wPt-0695 – 4.20E-05 0.04 0.64
– wPt-9480 – 1.72E-04 0.04 0.47
– wPt-9105
b
– 1.58E-04 0.04 0.34
– wPt-4320
b
– 1.23E-06 0.06 0.43
– wPt-3825
c
– 2.11E-06 0.06 0.55
– wPt-4150
c
– 2.85E-07 0.07 0.53
– wPt-1105 – 4.04E-07 0.07 0.56
b
2B wPt-1634 4.21 3.08E-04 0.04 0.20
3D wPt-7847 59.86 3.25E-04 0.04 0.74
4A wPt-9675 104.60 2.50E-04 0.04 0.74
4D wPt-0941 0.00 4.78E-04 0.03 0.75
5B wPt-5092 59.80 5.02E-05 0.05 0.50
7B wPt-4025 146.43 4.40E-04 0.04 0.73
7A* wPt-5758
a
– 2.62E-04 0.04 0.84
7A* wPt-8462
a
– 2.46E-04 0.04 0.75
7A* wPt-0203
a
– 2.20E-04 0.04 0.75
7A* wPt-9271
a
– 9.29E-05 0.04 0.74
7A* wPt-4779
a
– 3.34E-05 0.05 0.75
7A* wPt-2196
a
– 2.94E-05 0.05 0.79
* Marker assignment to chromosomes was based on LD estimates. a, b, c: DArT markers with the same letter are in strong LD at
P\0.001
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was high LD up to 15 cM, and a decline thereafter. LD
was estimated to extend to about 10 cM among 43
United States bread wheat elite cultivars and breeding
lines (Chao et al. 2007). In a global collection of barley
cultivars, Malysheva-Otto et al. (2006) identified
genomic regions where LD extended up to 50 cM,
while Crossa et al. (2007) reported that some LD
blocks extended up to 87 cM in a set of 170 bread
wheat breeding lines. Breseghello and Sorrells (2006)
suggested that LD may differ among populations and
may need to be evaluated for each population on a
case-by-case basis.
Significant markers linked to previously identified
genes for CCN and RLN resistance
Some of the markers significantly associated with
CCN and PN resistance were found in regions where a
candidate gene or QTL has previously been reported.
A major drawback in AM is the level of Type 1 error
(false positive), which occurs when a marker is
incorrectly declared as being associated with the
observed phenotype (Breseghello and Sorrells 2006).
In this study, using both the GLM and MLM approach
proved useful in confirming already known loci that
are associated with CCN and PN resistance. For
example, the STS diagnostic marker (Cre3spF/R)
linked to the CCN resistance gene Cre3 explained
about 9 % of the phenotypic variation for CCN
resistance in the SHWs: a positive demonstration of
the ability of AM analysis to identify loci conferring
resistance to CCN. Putative resistance QTL
(P\0.05) were also identified on all wheat chromo-
somes except chromosomes 7A for CCN and 2A and
6D for PN. These QTL explained about 1–3 and
1–4 % of observed phenotypic variation for resistance
against CCN and PN, respectively. However, some of
the QTL failed the stringent FDR test. For instance,
Williams et al. (2006) reported the identification of
major and minor QTL on chromosomes 1A, 2A and
6B for CCN resistance in a Trident/Molineux DH
population. This was consistent with the results from
this study in the absence of correction for false
discovery rate. Singh et al. (2010) also reported the
identification of QTL for CCN resistance on chromo-
somes 1A and 2A. Although an adjustment for
multiple comparisons seems to be necessary for
association mapping analysis to eliminate the false
positives (Sabatti et al. 2003), a high stringent FDR
threshold can lead to unexpected false-negative errors
as well (Park and Mori 2010).
Seventeen DArT markers were found to be signif-
icantly associated with CCN resistance. Based on
mapping information and LD estimates, 10 of the
significant markers were assigned to their exact map
position. The marker wPt-9997, which explained 6 %
of the observed phenotypic variation for CCN resis-
tance, mapped to chromosome 2D. The CCN resis-
tance gene Cre3 was previously reported on the long
arm of the same chromosome (Martin et al. 2004;de
Majnik et al. 2003). The LD analysis showed that wPt-
9997 and Cre3 diagnostic marker are not in LD,
suggesting that wPt-9997 is probably linked to another
new QTL for CCN resistance on chromosome 2D,
distal to the Cre3 resistance gene. On chromosome
1D, three significant markers, wPt-3855, wPt-7828
and wPt-8678, mapped to three distinct regions across
the short arm (wPt-3855 and wPt-7828) and the long
arm (wPt-8678) of the chromosome. The markers
explained 3, 7 and 4 % of the observed phenotypic
variation for resistance to CNN respectively. No
significant LD was observed among these markers,
suggesting that three putative QTL for resistance to
CCN lie on chromosome 1D. Similarly, wPt-5184 on
the distal end of the short arm of chromosome 4D and
wPt-4106 on the long arm of the same chromosome
were not in LD and each explained about 4 % of
resistance to CCN in the SHW. No previous studies
have reported any major genes or QTL for resistance
to CCN at these genomic regions. Markers wPt-4677
and wPt-1210 on chromosomes 5B and 5D, respec-
tively, explained 3 and 4 % of the phenotypic variance
for resistance to CCN. These regions, together with a
region harboring two linked significant markers, wPt-
3328 and wPt-1100 on 7D, could represent potentially
new QTL for resistance to CCN. Based on LD data, the
seven unmapped significant DArT markers are prob-
ably located in five different chromosomal regions that
confer resistance to CCN.
Twelve DArT markers were identified as being
significantly associated with PN resistance. Six of the
markers were assigned to their map positions on
chromosomes 2BS, 3DS, 4A, 4DS, 5B and 7B, while
LD estimates revealed that the remaining six
unmapped markers probably map to chromosome
7A. The genomic regions on chromosome 2BS, 3DS,
4DS and 7A identified in this study have been
previously reported as harboring loci that confer
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1D
2B
Cre3
2D
3D
4A
4D
5B
7A
7B
7D
Mol Breeding
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resistance to PN and P. thornei (Williams et al. 2002;
Zwart et al. 2005,2006,2010). The marker wPt-1634
on 2BS, which explains about 4 % of the phenotypic
variation for PN, mapped to a region where Zwart et al.
(2010) recently identified QTL for resistance to PN in
a synthetic backcross derived bi-parental population.
In the same study, another QTL was reported on 3DS.
According to the results of the current study, wPt-7847
mapped close to the reported region on 3DS and
explained about 4 % of the observed phenotypic
variance for resistance to PN. For both QTL, Zwart
et al. (2010) found the donor parent to be the SHWs.
On 4DS, wPt-0941, which explained about 3 % of the
variation, also closely mapped to a previously reported
QTL for resistance to P. thornei (Zwart et al. 2006).
Based on LD estimates, six unmapped markers,
significantly associated with PN resistance, were in
strong LD with the marker wPt-7433 which mapped to
chromosome 7A. These markers are possibly linked to
the same QTL on chromosome 7A. Williams et al.
(2002) reported the first mapping of the PN resistance
gene designated Rlnn1 on chromosome 7AL in wheat.
In addition to the previously known loci for resistance
to PN confirmed in this study, three new loci which
explained between 4 and 5 % of the phenotypic
variation for resistance to PN were also identified on
chromosomes 4A, 5B and 7B.
The results of our study demonstrate the value of
genome-wide association mapping for identifying
QTL linked to CCN and PN resistance using genetic
resources such as the SHWs. Given the diversity of
QTL identified, the SHWs possessing potentially
novel resistance alleles at different QTL could be
used as parents in a marker-assisted backcrossing
scheme to develop genotypes carrying resistance
alleles at different loci in elite wheat backgrounds.
Williams et al. (2006) reported that the effects of
combining partial resistance for CCN in the Trident/
Molineux DH population were additive when QTL on
1B and 6B were pyramided together. However, most
of the significant loci explain only a small fraction of
the observed phenotypic variation. This is probably
due to the quantitative basis of resistance for both
CCN and PN in wheat. Another possible explanation
could be the effect of differences of allele frequencies
among the subpopulations, which would reduce the
statistical power of AM in detecting rare variants or
genes that are variable between populations, but are
nearly fixed within subpopulations (Breseghello and
Sorrells 2006). For potentially new loci conferring
resistance to the two soilborne pathogens, the devel-
opment of appropriate genetic stocks using bi-parental
populations, backcross families, near-isogenic lines
and physical and chemical mutagenesis would enable
appropriate delineation of the importance of these loci
in conferring resistance to soilborne pathogens.
Despite their relatively small phenotypic effect, com-
bining and pyramiding of these potential loci for
resistance into wheat breeding lines already harboring
other sources of resistance to CCN and PN would
considerably elevate levels of resistance in the field.
The DArT markers linked to these new loci could
readily be converted into STS markers which would
facilitate such incorporation of the resistance loci into
elite wheat germplasm.
Acknowledgments We thank all our colleagues from the
Australian wheat community for their collaborative spirit and
their willingness to share the data consistent with the aims of the
synthetic evaluation project. We thank Grain Research and
Development Corporation (GRDC), Australia, Department of
Primary Industries, Horsham, Victoria and ICARDA, Syria for
their financial support. The technical assistance of Jayne
Wilson, DPI, Victoria and Professor Rudi Appels for critical
review and editorial assistance with the manuscript are
gratefully acknowledged.
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