crop scie nce, v ol. 54, march–april 2014 www.c rops .org 607
W (Triticum aestivum L.) is the dominant crop in the
CWANA region with current annual production of 112
million tons in approximately 55 million hectares (FAO, 2012).
However, its productivity in the region is very low (2 t/ha) which
is below the global average yield (3 t/ha). This is mainly due to
the prevalence of severe drought and YR problems in the region.
In recent years, many countries in the region have reported sig-
nicant wheat yield losses ranging from 10 to 80% as a result of
YR epidemics (Solh et al., 2012).
The development and use of resistant cultivars is the most
economical and environment friendly solution to combating
wheat rusts. However, because of the co-evolution of the host
and pathogen, the deployment of single major genes leads to the
emergence of new virulent pathogen pathotypes, hence the ‘boom
and bust cycle’ of cultivar performance continues. This has been
clearly evident in wheat by the breakdown of YR resistance genes
Yr9 in cultivars derived from ‘Veery’ in the 1980s and Yr27 in
Association Mapping of Resistance to
Yellow Rust in Winter Wheat Cultivars
and Elite Genotypes
W. Tadesse,* F.C. Ogbonnaya, A. Jighly, K. Nazari, S. Rajaram, and M. Baum
Yellow (stripe) rust, caused by Puccinia striifor-
mis f.sp. tritici (Pst), is a destructive disease of
wheat (Triticum aestivum L.) all over the world,
particularly in the Central and West Asia and
North Africa (CWANA) region. Host plant resis-
tance is the most economical and environment
friendly approach to combating wheat rusts
through the deployment of resistant cultivars. In
this study, we report ndings from an associa-
tion mapping (AM) study of resistance to Pst in
167 facultative/winter elite wheat genotypes. The
genotypes were evaluated for resistance to yel-
low rust (YR) at the adult plant stage and other
agronomic traits for 2 yr (2011–2012) at ICARDA
eld station, Tel Hadya, Syria. The same geno-
types were genotyped using 3051 diversity array
technology (DArT) markers of which 1586 were
of known chromosome positions. Out of the 167
genotypes evaluated for YR resistance, 65 geno-
types (38.9%) were resistant, 20 genotypes (12%)
were moderately resistant, 30 genotypes (18%)
were moderately susceptible, and 52 genotypes
(31.1%) were susceptible. Elite genotypes with
high yield potential and YR resistance were iden-
tied and have been distributed to the National
Agricultural Research System (NARS) for poten-
tial direct release and/or use as parents after
local adaptation trials by the respective countries.
Further, AM analysis using a mixed linear model
(MLM), corrected for population structure and
kinship relatedness and adjusted for false dis-
covery rate (FDR), identied ve genomic regions
located on wheat chromosomes 2BL, 4BS, 6AS,
6BL, and 7BL which are signicantly associated
with genes conferring resistance to YR. The loci
located on chromosome 4BS appeared to be a
novel quantitative trait loci (QTL). These loci may
be useful for choosing parents and incorporat-
ing new YR resistance genes into locally adapted
W. Tadesse, A. Jighly, K. Nazari, S. Rajaram, and M. Baum, Interna-
tional Agricultural Research Center for Dry Areas (ICARDA), P.O.
Box 6299, Rabat, Morocco; F.C. Ogbonnaya, Grains Research and
Development Corporation (GRDC), Canberra, Australia. Received 4
May 2013. *Corresponding author: (email@example.com).
Abbreviations: AM, association mapping; CI, coecient of infection;
CWANA, Central and West Asia and North Africa; DArT, diversity
array technology; FDR, false discovery rate; LD, linkage disequilib-
rium; LR, leaf rust; M, intermediate; MLM, mixed linear model; MR,
moderately resistant; NARS, National Agricultural Research System;
Pst, Puccinia striiformis f.sp. tritici; QTL, quantitative trait loci; R, resis-
tant; S, susceptible; YR, yellow rust.
Published in Crop Sci. 54:607–616 (2014).
© Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA
All rights reserved. No part of this periodical may be reproduced or transmitted in any
form or by any means, electronic or mechanical, including photocopying, recording,
or any information storage and retrieval system, without per mission in writing from
the publisher. Permission for printing and for reprinting the material contained herein
has been obtained by the publisher.
608 www.c rops .org c rop s cien ce, vo l. 54, m arch –april 2014
the 2000s in widely grown cultivars derived from ‘Attila’
crosses such as PBW343 (India), Inquilab-91 (Pakistan),
Kubsa (Ethiopia), and others in almost all CWANA coun-
tries (Solh et al., 2012). The continuous search for new
sources of resistance to keep ahead of changing pathogens
oers opportunities to pyramid diverse genes with resis-
tance to YR into adapted wheat cultivars. However, the
selection of genotypes with such gene combinations via
classical genetics and breeding methods is very time con-
suming and may be impossible due to the lack of pathogen
isolates with specic virulence genes. On the other hand,
the development of molecular markers that are closely
linked with the respective resistance genes would facilitate
the eective pyramiding of dierent resistant genes suc-
cessfully (Gupta et al., 1999; Huang et al., 2000; Lin and
Chen, 2007; Tadesse et al., 2007; Ogbonnaya et al., 2010).
Molecular markers linked to traits of interest can be
identied either through biparental mapping or associa-
tion mapping approaches. Association mapping (AM) is a
good alternative to biparental (conventional linkage map-
ping) because it uses linkage disequilibrium (LD) between
alleles within diverse populations to identify marker–trait
associations, minimizing the time and cost of developing
biparental populations. Interchromosomal LD is deter-
mined by the physical distance of the loci across chromo-
somes and has proven useful for dissecting complex traits
because it oers ne-scale mapping due to the inclusion
of historical recombination (Lynch and Walsh, 1998).
However, false positive correlation between markers and
traits can arise in the absence of physical proximity due to
population structure caused by admixture, mating system,
genetic drift, or by articial or natural selection during
evolution, domestication, or plant improvement (Jannink
and Walsh, 2002). False associations can also be caused
by alleles occurring at very low frequencies in the initial
population (Breseghello and Sorrells, 2006). These factors
create LD between loci that are not physically linked and
cause a high rate of false positives when relating polymor-
phic markers to phenotypic trait variation. Thus, separat-
ing LD due to physical linkage from LD due to population
structure is a critical prerequisite in association analyses.
Population structure can be quantied using Bayesian
analysis, which has been eective for assigning individu-
als to subpopulations (Q matrix) using unlinked markers
(Pritchard et al., 2000). Other multivariate statistical analy-
ses such as classication (clustering) and ordination (scal-
ing) can also be used to account for population structure
(Kraakman et al., 2004). Association analysis has been suc-
cessfully applied to identify marker–trait associations in dif-
ferent crops using preexisting germplasm such as landraces,
modern cultivars, and advanced breeding lines (Zhu et al.,
2008). In wheat, an AM approach has been used to map
agronomic and quality traits such as kernel size and milling
quality (Breseghello and Sorrells, 2006), grain yield (Crossa
et al., 2007), high-molecular-weight glutenins (Ravel et
al., 2006), resistance to rusts (Crossa et al., 2007; Yu et al.,
2011), soil-borne pathogens (Mulki et al., 2013), and major
insect pest resistances in wheat (Joukhadar et al., 2013).
In this study, we investigated the association of approx-
imately 3051 polymorphic DArT markers with resistance
to YR in 167 winter facultative wheat genotypes to deter-
mine the genetic structure within these wheat genotypes
and identify closely associated markers with YR resistance
for possible use in marker-assisted selection (MAS).
MATERIALS AND METHODS
Germplasm Development and Phenotyping
One hundred and sixty seven facultative/winter wheat germ-
plasms were used in this study comprised of 123 elite genotypes
from the International Center for Agricultural Research in Dry
Areas (ICARDA) advanced yield trials and 44 cultivars from
the CWANA region. The 167 genotypes in this study were
planted using a lattice design in two replications in a plot size
of 5 m length, planted in six rows with 0.2 m spacing between
rows at Tel Hadya, Syria for 2 yr (2011–2012) under rain-fed
and irrigated conditions to identify genotypes with high yield
potential and drought tolerance. Yellow rust evaluation was
made only under irrigated conditions (500 mm). Trials were
managed as per the recommended management practices. Tel
Hadya is located in Northern Syria at 36° 16¢ N, 36° 56¢ E and
at an altitude of 284 m.a.s.l. with long-term average annual
rainfall of 350 mm. The soil at Tel Hadya is ne clay, thermic,
Chromic Calcixerert, merging into a Calcixerollic Xerochrept
in some areas (Ryan et al., 1997).
Yellow Rust Assessment
The 167 geneotypes were evaluated against Yr27- virulent Pst
race (with avirulence/virulence formula: Yr1, Yr3, Yr4, Yr5, Yr8,
Yr10, Yr15, Yr32, YrSu, YrSD, YrND, YrSp/Yr2,Yr6, Yr7, Yr9,
Yr25, Yr27, YrA) at adult-plant stage. Articial inoculation was
performed at tillering stage using fresh urediniospores mixed
with talcum powder at a rate of 1:10 (v/v). Adult-plant responses
for the major infection types were recorded according to Roelfs
et al. (1992). Disease severity as a percentage of covered areas
was assessed following a modied Cobb’s scale (Peterson et al.,
1948). Field responses were recorded 2 to 3 times and the nal
scoring at soft-dough stage was considered for the AM analysis.
The data on disease severity and host reaction was combined
to calculate the coecient of infection (CI) following Pathan
and Park (2006), by multiplying the severity value by a value of
0, 0.2, 0.4, 0.6, 0.8, or 1.0 for host response ratings of immune
(I), resistant (R), moderately resistant (MR), intermediate (M),
moderately susceptible (MS), or susceptible (S), respectively.
Genomic DNA was extracted from 2-wk-old pooled leaf samples
collected from ve plants per line. The samples were frozen in liq-
uid nitrogen and stored at -80°C before DNA extraction. DNA
extraction was performed according to Ogbonnaya et al. (2001),
after which 10 L of a 100 ng l-1 DNA of each sample was sent
to Triticarte Pty. Ltd, Australia (http://www.triticarte.com.au) as
crop scie nce, v ol. 54, march–april 2014 www.crops.org 609
resistant, 20 genotypes (12%) were moderately resistant, 30
genotypes (18%) were moderately susceptible, and 52 geno-
types (31.1%) were susceptible to Puccinia striiformis f.sp. tritici
(Fig. 1) based on YR severity level. Twenty seven (61.4%)
of the currently grown CWANA cultivars were highly sus-
ceptible to YR (Table 1). The wheat cultivars CETINEL
2000 (MALCOLM/4/VPM 1/MOISSON 951//HILL
81/3/STEPHENS), and MÜFI
YMH/TOB//MCD/3/LIRA/5/F130L1.12) from Tur-
key were immune against the inoculated YR race during
both seasons. Bezostaya, the dominant winter wheat cul-
tivar in the region, showed a mean response level of 40 S.
Solh (OK82282//BOW/NKT), a facultative/winter wheat
cultivar, released in Afghanistan and Kyrgyzstan, showed
a moderate resistant level of response (20 MR). Simi-
larly, Kinaci-97 (YMH/TOB//MCD/3/LIRA), a variety
released in Turkey, Afghanistan and Uzbekistan, showed
moderate level of resistance. The other varieties such as Ger-
eek and Katya, were highly susceptible. Among the geno-
types in the resistant group, 22 genotypes were identied
as elite lines since they have combined YR resistance with
high yield potential and drought tolerance (Table 2). These
elite genotypes have been distributed to NARS through
international nurseries for local adaptation trials to identify
adapted genotypes for potential direct release or to be used
as parents in the wheat breeding programs.
As indicated in Fig. 2, the entire germplasm in this study
was clustered into k = 11 sub populations. Cluster 6 is the
largest with 27 genotypes accounting for approximately
16.2% of the total genotypes. In this group, commonly cul-
tivated cultivars such as Katya and Gereek were included.
The high yielding genotypes with drought tolerance such
a commercial service provider for whole genome scan using DArT
markers (White et al., 2008). Three thousand and fty one DArT
markers were used to genotype the 167 wheat genotypes. The
markers were integrated into a linkage map by inferring marker
order and position from Wheat Interpolated Maps V4 (Triticarte
Pty Ltd, Australia, personal communication, 2013).
The genetic structure of the 167 genotypes was investigated
using 50 unlinked DArT markers distributed across the wheat
genome with at least two loci on each wheat chromosome
(Pritchard et al., 2000). Genetic distance between pairs of cho-
sen markers on the same chromosome was more than 50 cM
to minimize LD caused by tightly linked markers. A Bayesian
clustering method was applied to identify clusters of genetically
similar individuals using the software STRUCTURE version
2.3 (Pritchard et al., 2000). To infer population structure, three
runs for each k value from 2 to 12 (k = the number of sub-
populations) was performed based on an admixture model and
correlated allele frequency. Both the length of burn-in period
and the number of iterations were set at 100,000. To reach the
appropriate k value, the estimated normal logarithm of the
probability of t provided in the STRUCTURE output was
plotted against k. This value reaches a plateau when the mini-
mal number of groups that best describe the population sub-
structure has been reached (Pritchard et al., 2000).
TASSEL 3.0 (Bradburyet al., 2007) was used to estimate LD
as squared allele frequency correlation estimates (R2) and to
measure the signicance of R2 at P values £ 0.01 for each pair
of loci on dierent chromosomes (interchromosomal LD) and
within the same chromosome (intrachromosomal LD). Only
DArT markers with known chromosomal position were used
in the estimation of LD.
The 2-yr average phenotypic yellow rust coecient of infection
(YR CI value) and the genotypic data were used for the AM. TAS-
SEL version 3.0 was used to perform association mapping analysis
using the mixed linear models (MLM) (Yu et al., 2006) which
takes into consideration kinship matrix (K) while implementing
the EMMA (Kang et al., 2010) and P3D algorithms (Zhang et
al., 2010) to reduce computing time. The MLM was again used
but after including population structure (Q) as a covariate to con-
trol both Type I and Type II errors (Benjamini and Hochberg,
1995). Marker alleles with FDR values £ 0.05 in both MLM and
MLM-Q models were declared signicantly associated with YR
resistance. However, because of the high stringency of the FDR
test, P values £ 0.005 in both MLM and MLM-Q were declared
as suggestive QTLs associated with YR resistance.
Response of Genotypes to Yellow Rust
The mean level of YR severity ranged from 0 (immune) to
100 S (highly susceptible) (Supplemental Table 1). Out of
the 167 genotypes tested for 2 yr, 65 genotypes (38.9%) were
Figure 1. Response of 167 winter/facultative wheat genotypes to
yellow rust at Tel Hadya, Syria, from 2011 to 2012.
610 www.c rops .org c rop s cien ce, vo l. 54, m arch –april 2014
as 4WON-IR-257//KS82117/MLT and PANTHEON/
BLUEGIL-2 were also clustered in this subpopulation.
Clusters 9 and 5 consisted of 23 (13.7%) and 21 (12.5%)
genotypes, respectively. Bezostaya, the most widely grown
cultivar in the CWANA region, is grouped in cluster 9.
Genetic variation among the 11 identied subpopulations
was tested using F-statistics estimated from pairwise com-
parisons as a measure of genetic distance between sub-
populations. F-statistics values between subpopulations
were signicant (P = 0.01) and ranged from 0.05 to 0.97,
supporting the existence of genetic structure.
Table 1. Mean response of 44 Central and West Asia and North Africa (CWANA) winter wheat cultivars to yellow rust (YR) at
Tel Hadya Syria, from 2011 to 2012.
Vari et y
1BE ZOSTAYA B E ZOSTAYA Russia 40S
2GEREK GEREK Turke y 80S
3Armcim 1D 13 .1/ M LT Armenia 40S
4Egemen BHR*5/AGA//SNI/3/TRK13 Kazakhstan 50S
5Konditerskaya NEELY/SPN//SPN/3/SPN//18.104.22.168/BEZ Kazakhstan 30MS
6Almira F.474 S10 .1 Kyrghyzstan 70S
7Zubkov 1D13.1/MLT//KAUZ Kyrghyzstan 70S
8Hans AGRI/NAC//ATTILA Kyrghyzstan 90S
9ALPU 2001 ID800994.W/VEERY Tu r key 40S
10 CETINEL 2000 MALCOLM/4/VPM 1/MOISSON 951//HILL 81/3/STEPHENS Tur k ey 0
11 IZGI CA8055/KUTLUK 94 Tur k ey 10R
12 SOYER ATAY 85/GALVEZ S 87 Tur k ey 50S
13 SU LTA N 95 AGRI/NACOZARI F 76 Turke y 90S
14 YILDIZ 98 55.1744/PULLMAN SELECTION 101//MAYA 74/3/MUSALA/PRIMO//MAYA 74/ALONDRA Turke y 90S
˙TBEY NGDA146/4/YMH/TOB//MCD/3/LIRA/5/F130L1.12 Turkey 0
16 SONMEZ BEZ//BEZ/TVR/3/KREMENA/LOV29/4/KATIA1 Tu r key 5R
17 BAGCI 02 HN7/OROFEN//BEIJING 8/3/SERI M 82/4/74CB462/TRAPPER//VONA Tur k ey 70S
18 GOKSU 99 AGRI/NACOZARI F 76 Turkey 70S
˙ZF8 85 K1.1/ S X L Tur k ey 15M R
20 KAT YA KAT YA Bulgaria 50S
21 OZCAN K8/MM2 Turk ey 90S
22 SAKIN PI/FUNO*2//VLD/3/CO723595 Turkey 50S
23 CANIK2003 ANZA/VRZ Turke y 20MR
24 GÜN 91 FUNDALEA 35.70/MOCHIS 73 Tur k ey 100 S
25 ALPASLAN TX69A509–2//BLUEBOY II/FOX Tur k ey 70S
26 DAPHAN JUP/4/CLLF/3/II14.53/ODIN//CI13431/WA00477 Tur key 20MR
27 KARASU 90 LOVRIN 11/BOLAL 2973//MIRONOVSKAYA 264 Tur k ey 100 S
28 NENEHATUN NORD DEPREZ/PULLMAN SELECTION 101//BLUEBOY Tur k ey 100 S
29 YILDIRIM ID800994.W/VEERY Tur key 15R
30 HANLI OK82282//BOW/NKT/3/F4105 Turke y 70S
31 BESKOPRU 362K2.111/6/NKT/5/TOB/CNO67//TOB/8156/3/CAL//BB/CNO67/4/TRM Turke y 70S
32 Grecum 2002 8 0 23 .16 .1.1/ K A UZ Uzbekistan 100S
33 TAL E38 SPN/NAC//ATTILA Azerbaijan 40MR
34 FATIM A F5H80/5/KVZ/3/BB/CHA//TOR/4/TEMU47 Azerbaijan 30MR
35 LOMTAGORA 9 SHARK/F4105W2.1 Georgia 80S
36 LOMTAGORA 12 3 FRTL/NEMURA Georgia 15M R
37 ORMON NW T/ 3/TAST/SPR W// TAW12 399.75 Tajikistan 20MR
38 NORMAN OR F1.158/FDL//BLO/3/SHI4414/CROW Tajikistan 30MR
39 ALEX PYN/BAU Tajikistan 30S
40 Pamir 94 YMH/TOB//MCD/3/LIRA Afghanistan 60S
41 Zare 130L1.11//F35.70/MO73/4/YMH/TOB//MCD/3/LIRA Iran 40M
42 Dostlik YMH/TOB//MCD/3/LIRA Uzbekistan 40M
43 ZARRIN NAI60/HEINE VII//BUC/3/F59.71/GHK Iran 90S
44 BITARAP SN64//SKE/2*ANE/3/SX/4/BEZ/5/SERI Turkmenistan 60M
† M, intermediate; MR, moderately resistant; R, resistant; S, susceptible.
crop scie nce, v ol. 54, march–april 2014 www.crops.org 611
loci showing signicant LD (p < 0.01), 17957 (9.3%) of
which had R2 > 0.2. Of the intrachromosomal locus pairs,
26,144 (33.3%) had a signicant LD of which 5724 (21.9%)
had R2 > 0.2. Intrachromosomal locus pairs had a higher
mean R2 value (0.16) than interchromosomal locus pairs
(0.09). The scatter plots of LD (R2) as a function of the
intermarker distance (cM) within the same chromosome
for all genotypes indicated a clear LD decay with genetic
distance (Fig. 3). The LDs with R2 > 0.2 extended to dis-
tances up to 35 cM suggesting that the mapping resolu-
tion using these genotypes would generally be well below
35 cM. Genome wide R2 estimates declined rapidly from
0.58 for markers with 0 interval distance to 0.22 within 5
cM of genetic distance across all chromosomes.
Marker Statistics and Linkage Disequilibrium
Of the 3051 DArT markers analyzed, only 1968 (64.5%)
were polymorphic and were used for the AM analysis. Of
these, 1586 (80.74%) were of known map position in the
consensus map (Detering et al., 2010) in which 607, 669,
and 310 were specic to the A, B, and D genomes. Another
92 markers were of known chromosomes but have no posi-
tion on the consensus map. Chromosomes with the largest
number of markers are 1B (182 markers) followed by 3B
(152 markers). Chromosomes 4D and 5D showed the least
number of loci, 3 and 8 markers, respectively (Table 3).
The LDs for locus pairs within the same chromosomes
and between chromosomes were calculated separately.
There were 193,416 (16.4%) interchromosomal pairs of
Table 2. Mean yellow rust (YR) response and grain yield performance of elite facultative/winter wheat genotypes at Tel Hadya,
Syria, from 2011 to 2012.
————————— k g / h a —————————
1JI5418/MARAS//SHARK/F4105W2.1 5R 4941 5372 5157
2Solh (Check) 10MR 4872 6365 5619
3Cham 8 (local check) 100S 4149 5088 4618
44WON-IR-257/5/YMH/HYS//HYS/TUR3055/3/DGA/4/VPM/MOS 5R 5098 7514 6306
5ER Y T 78 3 – 96/ S H A R K-1 5R 4 477 5908 5192
6FRET2/TUKURU//FRET2 10MR 4902 5642 5272
7SHI#4414/CROWS”//GK SAGVARI/CA8055 5R 4676 7910 6293
8CAR422/ANA//YACO/3/KAUZ*2/TRAP//KAUZ/4/BUCUR/5/BUCUR 10MR 5381 6540 5961
9ZANDER-6/5/YE2453/4/KS831024/3/AUR/LANC//NE7060 5R 5566 6219 5892
10R 470 0 6681 5691
10MR 4354 8116 6235
12 ZANDER//ATTILA/3*BCN 5R 5754 7974 6864
13 BOH4/7/WA476/3/391//NUM/5/W22/5/ANA/6/TAM200/KASYAN 5R 4640 7159 5900
14 RANA96/3/RSK/CA8055//CHAM6 10MR 4705 5115 4910
15 PANTHEON/BLUEGIL-2 5R 5235 8127 6681
16 BLUEGIL-2/CAMPION 5MR 3665 6884 5274
17 DAGDAS/APCB-40 10MR 4162 7095 5629
18 TAM200/KAUZ/4/CHAM6//1D13.1/MLT/3/SHI4414/CROW 5R 4101 7046 5574
19 SPN/NAC//ATTILA/3/TRAKIA 5R 5047 6379 5713
20 KARL/NIOBRARA//TAM200/KAUZ/3/TAM200/KAUZ 5R 4421 8354 6388
21 CADET/6/YUMAI13/5/NAI60/3/14.53/ODIN//CI13441/CANON 5R 4452 6 871 5661
5R 4727 7735 6231
23 NWT/3 / TAST/ SPR W//TAW123 99.75/6/ VEE /TS I//G R K /3/
5R 4414 6482 5448
24 Bezostaya (Check) 40 MR 3596 5559 4577
P value < 0.001 0.05 < 0.001
CV‡ (%) 12.9 7. 0 9 11.14
SE§ (kg) 602.1 479.2 636.2
LSD¶ at 5% (kg) 1066 8 4 8 .1 1034
† M, intermediate; MR, moderately resistant; R, resistant; S, susceptible.
‡ Coefﬁcient of variation.
§ Standard error of the mean.
¶ Least signiﬁcant difference.
612 www.c rops .org c rop s cien ce, vo l. 54, m arch –april 2014
Association Analysis of Resistance
to Yellow Rust
For the entire data set, 10 markers representing 5 genomic
regions were found to be sign icantly associated (p < 0.005)
with YR resistance (Table 4). These markers were located
on chromosomes 2BL, 4BS, 6AS, 6BL, and 7BL. All of
the markers accounted for > 5% of the phenotypic varia-
tion. When combined, the ve reported QTLs explained
phenotypic variation of more than 33.4% (Table 4; Fig. 4).
Out of the 10 signicantly associated markers, only two
markers, wPt-6192 on chromosome 2BL and wPt-732183
on chromosome 6AS, passed the FDR test. The marker
wP t - 6192 was present on 122 genotypes (73.1%), of which
97 genotypes (79.5%) showed resistance/moderately resis-
tant response to YR. Marker wP t-732183 was present in
81 genotypes (48.6%), of which only 48 genotypes (59.3%)
were resistant/moderately resistant. Of the ten signicant
markers, the markers with the highest proportion of pres-
ence in the lines evaluated were wPt- 8554 on chromo-
some 6BL and wPt-4025 on chromosome 7BL. Marker
wP t - 8554 was present in 142 genotypes (85.1%), of which
109 genotypes (76.8%) showed resistance response to YR,
and wPt-4025 was present in 141 genotypes (84.4%), of
which only 99 genotypes (70.2%) showed the resistance
response (supplemental Table 1).
Figure 2. Population structure among genotypes. Plot of the average logarithm of the probability of data likelihood [Ln P(D)], as a function
of the number of assumed subgroups (k), with k allowed to range from 2 to 12.
Table 3. Genetic marker statistics; number of markers, num-
ber of markers with position on the consensus diversity array
technology (DArT) map and the average distance between
each two adjacent markers for each chromosome; and linkage
disequilibrium (LD) decay based on speciﬁc chromosomes .
————— c M ——————
1A 135 133 1.1 16.5
1B 182 105 1.0 14.3
1D 70 39 3.4 14.0
2A 78 75 1.6 16.0
2B 122 121 1.1 28.2
2D 72 68 1.5 11.5
3A 73 73 2.5 25.3
3B 152 150 0.9 26.6
3D 103 69 2.2 7.1
4A 74 67 1.5 10.1
4B 36 35 3.4 22.4
4D 3 3 0.0 0.0
5A 31 29 3.6 13 .1
5B 94 89 1.8 34.6
5D 8 4 51.0 –
6A 149 14 3 0.8 15.4
6B 98 97 1.4 16.0
6D 17 15 9.5 13.4
7A 91 87 2.0 25.4
7B 76 72 2.3 11. 5
7D 114 112 1.6 5.5
crop scie nce, v ol. 54, march–april 2014 ww w.crops.org 613
Host plant resistance is the most economically eective
option to manage YR in developing countries. According
to Suenaga et al. (2003), there is signicant diversity for
genes that have minor to intermediate additive eects on
YR resistance. Many YR resistant genes have been identi-
ed and mapped. Most of the spring bread wheat germ-
plasm at CIMMYT and ICARDA possesses adult plant
resistance to YR and leaf rust (LR) based on several genes
with minor eects, mostly Yr18 on 7DS (pleiotropic or
closely linked to Lr34), Yr29 on 1BL (pleiotropic or closely
linked to Lr46), and Yr30, which have been deployed to
provide a more durable solution against YR (Singh et al.,
2005). Structure analysis of the 167 winter/facultative
genotypes and cultivars in this study grouped them into 11
clusters indicating the presence of signicant genetic varia-
tion among the population. Principally, the wheat breed-
ing program at ICARDA utilizes parents originated from
ICARDA, CIMMYT, and from a wide range of geneti-
cally unrelated winter wheats from Turkey, Iran, Russia,
Ukraine, Romania, Bulgaria, Hungary, and the United
States of America. The utilization of such diverse parents
in the breeding program has contributed to the reported
genetic variation. Some genotypes of International Win-
ter Wheat Improvement Program (IWWIP) origin, such
as OK82282//BOW/NKT and YMH/TOB//MCD/3/
LIRA, have been identied and released under dier-
ent names in dierent countries indicating their broad
Figure 3. Decline of linkage disequilibrium (LD) as measured by R2 against genetic distance.
Table 4. Chromosome location, P values, R2, allele number, and effect of signiﬁcantly associated diversity array technology
(DArT) markers with yellow rust (YR) resistance.
location Position P value FDR†R2Allele Effect
wPt-2600 2BL 58.6 0.00310 0.0003 5.6 0-20.8 72.5
wPt-5556 2BL 60.6 0.00113 0.0001 6.6 1-21.6 71.9
wP t-6199 2BL 61.7 0.00191 0.0002 6.2 0-21.1 71.9
wP t-6192 2BL 63.2 0.000062 0.000063 7.1 1-22.4 73 .1
wP t-4125 2BL 63.2 0.00276 0.0003 5.6 1-20.7 73.1
wPt-744595 4BS 15.0 0.00178 0.0002 6.0 121.5 33.5
wP t-1272 4BS 16.6 0.00178 0.0002 6.0 121.5 33.5
wP t-732183 6AS 52.3 0.00002 0.00003 8.3 0-19.9 4 6 .1
wPt-8554 6BL 67. 8 0.00161 0.0001 6.7 1-26.4 85.6
wPt-4025 7BL 146. 4 0.00446 0.0003 5.4 122.0 85.0
† False discovery rate.
‡ Coefﬁcient of infection.
614 www.c rops .org c rop s cien ce, vo l. 54, m arch –april 2014
adaptation. The former is released in Afghanistan and Kyr-
gyzstan while the latter (Kinaci 97) has been released in
Turkey, Afghanistan, and Uzbekistan (Tadesse et al., 2013).
Association mapping has been reported as an eective
strategy to identify linked markers with disease resistance
for possible marker assisted selection. However, careful
analysis of LD is critically important to detect the rate of
false positives (Crossa et al., 2007). In this study, both LD
analysis and association analysis were performed simul-
taneously. For LD, we used 1586 DArT markers with
known chromosome positions to calculate LD statistics
(R2) between DArT markers. However, the D genome,
in particular chromosomes 3D, 4D, and 5D, possessed the
least number of markers per chromosome. In contrast, the
chromosomes where signicant markers linked to YR
resistance had good marker coverage and therefore reli-
able LD decay estimates. A scatter plot of R2 values versus
genetic distances between all markers across the genome
abruptly declined to 0.2 within 5 cM when all mapped
DArT markers with chromosome position were analyzed.
This result is expected for self-pollinated crop species such
as wheat. A rapid rate of inbreeding with selng results in
a low recombination frequency in self-pollinated species
(Zhang et al., 2010). In the previous studies, the estimated
LD decay of wheat was 0.5 to 40 cM (Maccaferri et al.,
2006; Chao et al., 2007; Emebiri et al., 2010), which is rel-
atively high when compared with cross-pollinated crops
such as maize (200 to 2000 bp) (Tenaillon et al., 2001).
The estimated genome-wide LD decay in this study
ranged from 0 to 34.6 cM (Table 3). It is important to
note that the quality of the LD value is highly conditioned
by the distribution of markers. The estimated LD decay
values also varied according to wheat types and marker
systems (e.g., microsatellites and DArT) used. Therefore,
LD decay values should not be compared and general-
ized. The LD decay from 10 to 40 cM was detected when
advanced breeding lines or wild wheat populations were
analyzed by microsatellite and DArT markers (Chao et al.,
2007; Crossa et al., 2007; Emebiri et al., 2010).
In a recent study, several signicant DArT markers
were found in regions where no YR resistance genes or
QTL had been reported previously (Crossa et al., 2007). In
this study, we applied a genome-wide AM approach and
found 10 DArT markers located on wheat chromosomes
2BL, 4BS, 6AS, 6BL, and 7BL that were signicantly
associated with resistance to YR. From the current study,
the locus located on chromosome 4BS appeared to be a
novel QTL and the other four are probably associated with
known YR resistance genes. Yr5, the gene that is eec-
tive against the race we used for eld inoculation in the
current study, is located on 2BL. This gene is allelic with
Yr7 and YrSp (Spalding Prolic). Yr7 is ineective against
the Yr27 virulent race but YrS p is resistant against this race
(McIntosh et al., 1995; 2006). Considering this, it can be
speculated that the markers located on 2BL may be associ-
ated with the resistance conferred by Yr5 and/or YrSp. This
needs to be further investigated. Other YR genes includ-
ing Yr43, Yr44, and Yr53 (McIntosh et al., 1995; 2006), and
QTLs (Boukhatem et al., 2002) have been located on 2BL.
On chromosome 7BL, a YR QTL linked to the marker
Xcfa2040, was previously reported in the cultivar Zhou
8425B (Li et al., 2006). The same marker was reported to
be linked to YrC591 (Li et al., 2009).
Two YR resistant genes were reported on chromo-
some 6A, YrD and YrDru2, without tagging them with any
molecular marker (Chen et al., 1995, 1996). In this study,
marker wP t-732183 on chromosome 6AS is identied to
be associated to YR resistance. Moreover, Christiansen et
al. (2006) reported a novel QTL on the long arm of chro-
mosome 6B between markers Xwmc105a and Xwmc397
(36.6– 52.2 cM on their map) and closer to the marker
Xwmc397. Based on the comparative mapping analysis,
this region is supposed to be similar to the region 53.3 to
68.7 cM on the wheat DArT consensus map that we used
in this study. The marker wP t -8554 on chromosome 6BL
at position 67.8 cM is associated with YR resistance in
the facultative/winter wheat genotypes used in this inves-
tigation. This suggests that genetic regions around these
loci may be useful for choosing parents and incorporat-
ing new YR resistance genes into adapted wheat cultivars.
However, it is, essential to validate these QTLs by using
biparental populations or near-isogenic lines (NILs) and
testing them across multiple environments.
Figure 4. Decline of disequilibrium (LD) as measured by R2 against
crop scie nce, v ol. 54, march–april 2014 ww w.crops.org 615
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