ArticlePDF Available

Abstract and Figures

Yellow (stripe) rust, caused by Puccinia striiformis 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 resistance is the most economical and environment friendly approach to combating wheat rusts through the deployment of resistant cultivars. In this study, we report findings from an association mapping (AM) study of resistance to Pst in 167 facultative/winter elite wheat genotypes. The genotypes were evaluated for resistance to yellow rust (YR) at the adult plant stage and other agronomic traits for 2 yr (2011–2012) at ICARDA field station, Tel Hadya, Syria. The same genotypes 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 genotypes (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 identified and have been distributed to the National Agricultural Research System (NARS) for potential 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 discovery rate (FDR), identified five genomic regions located on wheat chromosomes 2BL, 4BS, 6AS, 6BL, and 7BL which are significantly 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 incorporating new YR resistance genes into locally adapted wheat cultivars.
Content may be subject to copyright.
crop scie nce, v ol. 54, marchapril 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-
nicant 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-
tied 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), identied ve genomic regions
located on wheat chromosomes 2BL, 4BS, 6AS,
6BL, and 7BL which are signicantly 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
wheat cultivars.
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: (
Abbreviations: AM, association mapping; CI, coecient 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).
doi: 10.2135/cropsci2013.05.0289
© 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
oers 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 specic virulence genes. On the other hand,
the development of molecular markers that are closely
linked with the respective resistance genes would facilitate
the eective pyramiding of dierent 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
identied 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 oers 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 articial 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 quantied using Bayesian
analysis, which has been eective for assigning individu-
als to subpopulations (Q matrix) using unlinked markers
(Pritchard et al., 2000). Other multivariate statistical analy-
ses such as classication (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).
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. Articial 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 modied 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 coecient 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 ( as
crop scie nce, v ol. 54, marchapril 2014 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
81/3/STEPHENS), and MÜFI
TBEY (NGDA146/4/
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 identied
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.
Population Structure
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).
Population Structure
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).
Linkage Disequilibrium
TASSEL 3.0 (Bradburyet al., 2007) was used to estimate LD
as squared allele frequency correlation estimates (R2) and to
measure the signicance of R2 at P values £ 0.01 for each pair
of loci on dierent chromosomes (interchromosomal LD) and
within the same chromosome (intrachromosomal LD). Only
DArT markers with known chromosomal position were used
in the estimation of LD.
Association Mapping
The 2-yr average phenotypic yellow rust coecient 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 signicantly 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 identied 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 signicant (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
name Pedigree
of origin
to YR
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// 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
˙TBEY NGDA146/4/YMH/TOB//MCD/3/LIRA/5/F130L1.12 Turkey 0
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
19 EKI
˙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
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
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, marchapril 2014 611
loci showing signicant LD (p < 0.01), 17957 (9.3%) of
which had R2 > 0.2. Of the intrachromosomal locus pairs,
26,144 (33.3%) had a signicant 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 specic 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.
No Variety/Pedigree
Yield under
Yield under
————————— 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
11 PLK/LIRA/5/NAI60/3/14.53/ODIN//[CI13441]/4/GRK79/
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 icantly 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 signicantly 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 signicant
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 .
of loci
of positions
————— 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, marchapril 2014 ww 613
Host plant resistance is the most economically eective
option to manage YR in developing countries. According
to Suenaga et al. (2003), there is signicant diversity for
genes that have minor to intermediate additive eects 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 eects, 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 signicant 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 identied and released under dier-
ent names in dierent 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.
Chr omosome
location Position P value FDRR2Allele 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 eective
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 signicant 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 selng 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 signicant 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 signicantly
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 eec-
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 Prolic). Yr7 is ineective 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 identied 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
genetic distance.
crop scie nce, v ol. 54, marchapril 2014 ww 615
This study was carried out through the nancial support from
the Government of Japan.
Benjamini, Y., and Y. Hochberg. 1995. Controlling the false dis-
covery rate: A practical and powerful approach to multiple
testing. J. R. Stat. Soc. B 57:289–300.
Boukhatem, N., P.V. Baret, D. Mingeot, and J.M. Jacquemin.
2002. Quantitative trait loci for resistance against yellow rust
in two wheat-derived inbred wheat line populations. Theor.
Appl. Genet. 104:111–115. doi:10.1007/s001220200013
Bradbury, P.J., Z. Zhang, D.E. Kroon, T.M . Casstevens, Y. Ram-
doss, and E.S. Buckler. 2007. Tassel: Software for association
mapping of complex traits in diverse samples. Bioinformatics
23:26332635. doi:10.1093/bioinformatics/btm308
Breseghello, F., and M.E. Sorrells. 2006. Association mapping of ker-
nel size and milling quality in wheat (Triticum aestivum L.) cul-
tivars. Genetics 172:11651177. doi:10.1534/genetics.105.044586
Chao, S., W. Zhang, J. Dubcovsky, and M. Sorrell. 2007. Evalu-
ation of genetic diversity and genome-wide linkage disequi-
librium among U.S. wheat (Triticum aestivum L.) germplasm
representing dierent market classes. Crop Sci. 47:10181030.
Chen, X.M., S.S. Jones, and R.F. Line. 1996. Chromosomal loca-
tion of genes for resistance to Puccinia striiformis in seven wheat
cultivars with resistance genes at the Yr3 and Yr4 loci. Phyto-
pathology 86:12281233. doi:10.1094/Phyto- 86-1228
Chen, X.M., R.F. Line, and S.S. Jones. 1995. Chromosomal loca-
tion of genes for resistance to Puccinia striiformis in winter wheat
cultivars Heines VII, Clement, Moro, Tyee, Tres, and Daws.
Phytopathology 85:13621367. doi:10.1094/Phyto-85-1362
Christiansen, M.J., B. Feenstra, I.M. Skovgaard, and S.B. Andersen.
2006. Genetic analysis of resistance to yellow rust in hexaploid
wheat using a mixture model for multiple crosses. Theor. Appl.
Genet. 112:581591. doi:10.1007/s00122-005-0128-7
Crossa, J., J. Burgueno, S. Dreisigacker, M. Vargas, S.A. Herrera-
Foessel, M. Lillemo, et al. 2007. Association analysis of histor-
ical bread wheat germplasm using additive genetic covariance
of relatives and population structure. Genetics 177:18891913.
Detering, F., E. Hunter, G. Uszynski, P. Wen z l, and K. Andrzej.
2010. A consensus genetic map of wheat: Ordering 5000
wheat DArT markers. Paper presented at: 20th International
Triticeae Mapping Initiative and 2nd Wheat Genomics in
China Workshop, Beijing, China. 1–5 Sept., 2010.
Emebiri, L.C., J.R. Oliver, K. Mrva, and D. Mares. 2010. Associa-
tion mapping of late maturity -amylase (LMA) activity in a
collection of synthetic hexaploid wheat. Mol. Breed. 26:39
49. doi:10.1007/s11032- 009-9375-7
FAO. 2012. FAOSTAT agriculture data. Agricultural production
2009. FAO, Rome, Italy. (accessed 22
Apr. 2012).
Gupta, P. K ., R.K. Varsh ney, P.C. Sharma, and B. Ramesh.
1999. Molecular markers and their application in wheat
breeding. Plant Breed. 118:369390. doi:10.1046/j.1439-
Huang, X.Q., S.L.K. Hsam, F.J. Zeller, G. Wen z el, and V. Mohler.
2000. Molecular mapping of the wheat powdery mildew resis-
tance gene Pm24 and marker validation for molecular breeding.
Theor. Appl. Genet. 101:407414. doi:10.1007/s001220051497
Jannink, J.L., and J.B. Walsh. 2002. Association mapping in plant pop-
ulations. In: M. S. Kang, editor, Quantitative genetics genomics
and plant breeding. CABI, Wallingford, UK. p. 59–68.
Joukhadar, R., M. El-Bouhssini, A. Jighly, and F.C. Ogbonnaya.
2013. Genome-wide association mapping for ve major pest
resistances in wheat. Mol. Breed. 10.1007/s110320139924 -y.
Kang, H.M., J.H. Sul, S.K. Service, N.A. Zaitlen, S.Y. Kong, N.B.
Freimer, et al. 2010. Variance component model to account
for sample structure in genome-wide association studies. Nat.
Genet. 42:348354. doi:10.1038/ng.548
Kraakman, A.T.W. , R.E. Niks, P.M.M.M. Van den Berg, P. Stam,
and F.A. van Eeuwijk. 2004. Linkage disequilibrium mapping
of the yield stability in modern spring barley cultivars. Genet-
ics 168:435446. doi:10.1534/genetics.104.026831
Li, Y., Y.C . Niu, and X.M. Chen. 2009. Mapping a stripe rust
resistance gene YrC591 in wheat variety C591 with SSR
and AFLP markers. Theor. Appl. Genet. 118:339346.
Li, Z.F., T.C . Zheng, Z.H. He, G.Q. Li, S.C. Xu, X. P. Li, et al. 2006.
Molecular tagging of stripe rust resistance gene YrZh84 in Chinese
wheat line Zhou 8425B. Theor. Appl. Genet. 112:10891103.
Lin, F., and X.M. Chen. 2007. Genetics and molecular mapping of
genes for race- specic and all-stage resistance and non-spe-
cic high-temperature adult-plant resistance to stripe rust in
spring wheat cultivar Alpowa. Theor. Appl. Genet. 114:1277
1287. doi:10.1007/s00122-007-0518-0
Lynch, M., and B. Walsh. 1998. Genetics and analysis of quantita-
tive traits. Sinauer Associates, Sunderland, MA.
Maccaferri, M., M.C. Sanguineti, E. Natoli, J.L. Araus-Ortega,
M. Bensalem, J. Bort, et al. 2006. A panel of elite accessions
of durum wheat (Triticumdurum Desf.) suitable for association
mapping studies. Plant Genet. Resour. 4:7985. doi:10.1079/
PGR20 06117
McIntosh, R.A., K.M. Devos, J. Dubcovsky, W.J. Rogers, C.F.
Morris, R. Appels, et al. 2006. Catalogue of gene symbols for
wheat: 2006 supplement. Wheat Genetics Symposium, USDA,
Riverdale Park, MD.
awn/53/Textle/WGC.html (accessed 20 May 2013).
McIntosh, R.A., C.R. Wellings, and R.F. Park. 1995. Wheat
rusts: An atlas of resistance genes. Kluwer Academic Publish-
ers, Boston.
Mulk i, M.A., A. Jighly, G. Ye, L.C. Emebiri, D. Moody, O. Ansari,
et al. 2013. Association mapping for soilborne pathogen resis-
tance in synthetic hexaploid wheat. Mol. Breed. 31:299–311.
Ogbonnaya, F.C., M. Baum, and R. Brettell. 2010. Biotechnol-
ogy: Can it really solve the problems of food production? In:
B. Payne and J. Ryan, editors, The international dimension
of the American Society of Agronomy: Past and future. ASA,
CSSA, and SSSA, Madison, WI.
Ogbonnaya, F.C., I. Seah, A. Delibes, J. Jahier, I. Lopez-Brana,
R.F. Eastwood, et al. 2001. Molecular-genetic character-
ization of nematode resistance from Aegilops ventricosa and
its derivatives in wheat. Theor. Appl. Genet. 102:263269.
Pathan, A.K., and R.F. Park. 2006. Evaluation of seedling and
adult plant resistance to leaf rust in European wheat cultivars.
Euphytica 149:327342. doi:10.1007/s10681-005-9081-4
Peterson, R.F., A.B. Champbell, and A.E. Hannah. 1948. A dia-
grammatic scale for estimating rust intensity of leaves and stem
of cereals. Can. J. Res. 26:496500. doi:10.1139/cjr48c-033
616 www.c rops .org c rop s cien ce, vo l. 54, m arch april 2014
Pritchard, J.K., M. Stephen, and P. Donnely. 2000. Inference on
population structure using multi-locus genotype data. Genet-
ics 155:945959.
Ravel, C., S. Praud, A. Murigneux, L. Linossier, M. Dardevet, F.
Balfourier, et al. 2006. Identication of Glu - B1 –1 as a candi-
date gene for the quantity of high molecular-weight glutenin
in bread wheat (Triticum aestivum L.) by means of an associa-
tion study. Theor. Appl. Genet. 112:738743. doi:10.10 07/
Roelfs, A.P., R .P. Singh, and E.E. Saari. 1992. Rust diseases of
wheat: Concepts and methods of disease management. CIM-
MYT, D.F., Mexico.
Ryan, J., S. Masri, S. Garabet, J. Diekmann, and H. Habib. 1997.
Soil of ICARDA’s agricultural experiment stations and sites:
Climate, classication, physical, and chemical properties and
land use. International Center for Agricultural Research in
the Dry Areas, Aleppo, Syria.
Singh, R.P., J. Huerta-Espino, and H.M. William. 2005. Genetics
and breeding for durable resistance to leaf and stripe rusts in
wheat. Turk. J. Agric. For. 29:121127.
Solh, M., K. Nazari, W. Tadesse, and C.R. Wellings. 2012. The
growing threat of stripe rust worldwide. Paper presented at:
Borlaug Global Rust Initiative (BGRI) conference, Beijing,
China. 1–4 Sept. 2012.
Suenaga, K., R.P. Singh, J. Huerta-Espino, and H.M. William.
2003. Microsatellite Markers for genes Lr34/Yr18 and other
quantitative trait loci for leaf rust and stripe rust resistance
in bread wheat. Phytopathology 93:881890. doi:10.1094/
Tadesse W., A. I. Morgounov, H. J. Braun, B. Akin, M. Keser, Y.
Kaya, et al. 2013. Breeding progress for yield in winter wheat
genotypes targeted to irrigated environments of the CWANA
region. Euphytica 194:177185. doi:10.1007/s10681-013-0903-5
Tadesse, W., M. Schmolke, S.L.K. Hsam, V. Mohler, G. Wenzel,
and F.J. Zeller. 2007. Molecular mapping of resistance genes
to tan spot (Pyrenophoratritici-repentisrace 1) in synthetic wheat
lines. Theor. Appl. Genet. 114:855862. doi:10.1007/s00122-
Tena i l l on, M.I., M.C. Sawkins, A.D. Long, R.L. Gaut, J.F. Doebley,
and B.S. Gaut. 2001. Patterns of DNA sequence polymorphism
along chromosome 1 of m aize (Zea mays spp. mays. L.). Proc. Natl.
Acad. Sci. U. S. A. 98:91619166. doi:10.1073/pnas.151244298
White, J., J.R. Law, I. MacKay, K.J. Chalmers, J.S.C. Smith, A.
Kilian, and W. Powell. 2008. The genetic diversity of UK,
US, and Australian cultivars of Triticum aestivum measured
by DArT markers and considered by genome. Theor. Appl.
Genet. 116(3):439453. doi:10.1007/s00122-007-0681-3
Yu, J., G. Pressoir, W.H. Briggs, I. Vroh Bi, M. Yamasaki, et al.
2006. A unied mixed-model method for association map-
ping that counts for multiple level of relatedness. Nat. Genet.
28(2):203208. doi:10.1038/ng1702
Yu, L.X., A. Lorenz, J. Rutkoski , R.P. Singh, S. Bhavani, J.
Huerta-Espino, et al. 2011. Association mapping and gene–
gene interaction for stem rust resistance in CIMMYT spring
wheat germplasm. Theor. Appl. Genet. 123:1257–1268.
Zhang, D., G. Bai, C. Zhu, J. Yu, and B.F. Carver. 2010. Genetic
diversity, population structure, and linkage disequilibrium in
U.S. elite winter wheat. Plant Gen. 3:117127. doi:10.3835/
Zhu, C., M. Gore, E.S. Buckler, and J. Yu. 2008. Status and pros-
pects of association mapping in plants. Plant Gen. 1:5–20.
... The Warrior (PstS7) race was confirmed in 2013 in Morocco and continue to be spread widely in the Northern African countries, Europe and East Asia (Tadesse et al. 2014;Hovmøller et al. 2016). PstS10 known as Warrior(-) (virulence pattern: [1,2,3,4,6,7,9,17,25,32,Sp,AvS,) was also reported in Algeria and Spain in 2014 and perhaps in Morocco as well but no data is available to confirm (Hovmøller et al. 2018). ...
... Thus, the deployment of resistance genes in the development of effectively resistant cultivars is important for the ongoing control against stripe rust in wheat (Todorovska et al. 2009). Up to date, ICARDA continues to deliver germplasm that possesses adult plant resistance to stripe rust combining several genes with minor effects, mostly Yr18, Yr29 and Yr30 (Tadesse et al. 2014). SNINA cultivar, of ICARDA origin, has been recently released in Morocco and demonstrated its broad resistance to YR. ...
Full-text available
Yellow rust, caused by Puccinia striiformis f. sp. tritici, is a common and serious fungal disease of wheat (Triticum aestivum L.) all over the world and particularly in the Central and West Asia and North Africa region. To identify effective yellow rust resistance loci, genome-wide association study (GWAS) was performed using 196 bread wheat genotypes based on 10,477 single nucleotide polymorphisms markers. Adult plants were evaluated under field conditions for resistance to yellow rust for 2 years (2014–2015) at ICARDA station, Marchouch, Morocco. Out of the 196 genotypes, 85 genotypes (43.37%) were resistant, 22 genotypes (11.22%) were moderately resistant, 13 genotypes (6.63%) were moderately susceptible, 48 genotypes (24.49%) were moderately susceptible to susceptible and 28 genotypes (14.29%) were susceptible to Puccinia striiformis f. sp. tritici. GWAS using mixed linear model identified 23 markers on chromosomes 2A, 2B, 2D and 7B significantly associated with adult plant resistance at false discovery rate (FDR-adjusted P ≤ 0.05). Of which, three markers were within 10 wheat functional genes involved in several plant disease resistance and defense mechanism. Five of the reported functional genes are annotated as disease resistance proteins with nucleotide-binding site leucine repeat domains. BLAST analysis confirmed that YrR61 and Yr17 were mostly the candidate genes linked to the marker Tdurum_contig29983_490 on chromosome 2A. Moreover, markers identified on chromosome 7B and Kukri_c12648_434 on 2D were not mapped within any of previously reported gene/QTL region hence, representing novel resistance loci for Pst and needs to be confirmed using an allelism test.
... The Warrior (PstS7) race was confirmed in 2013 in Morocco and continue to be spread widely in the Northern African countries, Europe and East Asia (Tadesse et al. 2014;Hovmøller et al. 2016). PstS10 known as Warrior(-) (virulence pattern: [1,2,3,4,6,7,9,17,25,32,Sp,AvS,) was also reported in Algeria and Spain in 2014 and perhaps in Morocco as well but no data is available to confirm (Hovmøller et al. 2018). ...
... Thus, the deployment of resistance genes in the development of effectively resistant cultivars is important for the ongoing control against stripe rust in wheat (Todorovska et al. 2009). Up to date, ICARDA continues to deliver germplasm that possesses adult plant resistance to stripe rust combining several genes with minor effects, mostly Yr18, Yr29 and Yr30 (Tadesse et al. 2014). SNINA cultivar, of ICARDA origin, has been recently released in Morocco and demonstrated its broad resistance to YR. ...
... The races belonging to PstS2 and Warrior (PstS7) lineages are the most widely spread pathotypes of Pst, covering the geographical regions from Asia all the way to Northern Europe (Hovmøller et al., 2016;Tadesse et al., 2014). The PstS7 lineage was first discovered in the UK in 2011, and currently is the most prevalent race of Pst in Europe (Ali et al., 2017). ...
... Therefore, exploring new sources of resistance is still of paramount importance in rust affected zones to ensure maximum wheat productivity. PstS2 and Warrior are the two most widely spread pathotypes of Pst, which are virulent to several important Yr genes deployed in the affected regions (Hovmøller et al., 2016;Tadesse et al., 2014). Hence, the information identified here is of high significance, and should be further investigated through the development of functional molecular markers and validation of the QTL by using biparental populations. ...
Full-text available
Stripe or yellow rust, caused by Puccinia striiformis Westend. f. sp. tritici is a major threat to bread wheat production worldwide. The breakdown in resistance of certain major genes and newly emerging aggressive races of stripe rusts pose serious concerns in all main wheat growing areas of the world. To identify new sources of resistance and associated QTL for effective utilization in future breeding programs an association mapping (AM) panel comprising of 600 bread wheat landraces collected from eight different countries conserved at ICARDA gene bank were evaluated for seedling and adult plant resistance against the PstS2 and Warrior races of stripe rust at the Regional Cereal Rust Research Center (RCRRC), Izmir, Turkey during 2016, 2018 and 2019. A set of 25,169 informative SNP markers covering the whole genome were used to examine the population structure, linkage disequilibrium and marker-trait associations in the AM panel. The genome-wide association study (GWAS) was carried out using a Mixed Linear Model (MLM). We identified 47 SNP markers across 19 chromosomes with significant SNP-trait associations for both seedling stage and This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
... Additionally, fungicide application is also an effective way of controlling stripe rust; however, it is not the most economical and recommended method (Brar et al., 2018). The most effective strategy to control stripe rust outbreaks is the exploitation of genetic resistance and pyramiding of multiple minor and major stripe rust resistance genes conferring seedling and adult plant resistance (APR) (Chen et al., 2014;Tadesse et al., 2014;Muleta et al., 2017;Cobo et al., 2018). Most breeding programs in the world rely on two types of genetic resistance based on major and minor genes (Chen et al., 2014). ...
Full-text available
Stripe rust caused by Puccinia striiformis Westend. f. sp. tritici. is a major bread wheat disease worldwide with yield losses of up to 100% under severe disease pressure. The deployment of resistant cultivars with adult plant resistance to the disease provides a long-term solution to stripe rust of wheat. An advanced line from the International Winter Wheat Improvement Program (IWWIP) 130675 (Avd/Vee#1//1-27-6275/Cf 1770/3/MV171-C-17466) showed a high level of adult plant resistance to stripe rust in the field. To identify the adult plant resistance genes in this elite line, a mapping population of 190 doubled haploid (DH) lines was developed from a cross between line 130675 and the universal stripe rust-susceptible variety Avocet S. The DH population was evaluated at precision wheat stripe rust phenotyping platform, in Izmir during 2019, 2020, and 2021 cropping seasons under artificial inoculations. Composite interval mapping (CIM) identified two stable QTLs QYr.rcrrc-3B.1, and QYr.rcrrc-3B.2, which were detected in multiple years. In addition to these two QTLs, five more QTLs, QYr.rcrrc-1B, QYr.rcrrc-2A, QYr.rcrrc-3A, QYr.rcrrc-5A, and QYr.rcrrc-7D, were identified, which were specific to the cropping year (environment). All QTLs were derived from the resistant parent, except QYr.rcrrc-3A. The significant QTLs explained 3.4-20.6% of the phenotypic variance. SNP markers flanking the QTL regions can be amenable to marker-assisted selection. The best DH lines with high yield, end-use quality, and stripe rust resistance can be used for further selection for improved germplasm. SNP markers flanking the QTL regions can aid in identifying such lines.
... The aggressive, high temperature-adapted race PstS2 and the new virulent races PstS10 and PstS7 [Warrior (-), Warrior] are the most widely spread pathotypes of Pst covering geographical regions from Asia to Northern Europe (Hovmøller et al., 2016;Tadesse et al., 2014;Tehseen et al., 2021). The Warrior race was first discovered in the UK in 2011 and is currently the most prevalent race of Pst in Europe (Ali et al., 2017). ...
Full-text available
Novel resistance sources to the pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust (stripe rust), a widespread devastating foliar disease in wheat (Triticum aestivum L.), are in demand. Here, we tested two doubled haploid (DH) spring wheat populations derived from the genetic resources for resistance to yellow rust in field trials in Germany and Egypt. Additionally, we performed tests for all‐stage resistance (seedling resistance). We performed linkage mapping based on 15k Infinium SNP chip genotyping data that resulted in 3,567 and 3,457 polymorphic markers for DH Population 1 (103 genotypes) and DH Population 2 (148 genotypes), respectively. In DH Population 1, we identified a major and consistent quantitative trait locus (QTL) on chromosome 1B that explained up to 28 and 39% of the phenotypic variation in the field and seedling tests, respectively. The favorable allele was contributed by the line ‘TRI‐5645’, a landrace from Iran, and is most probably the yellow rust resistance (Yr) gene Yr10. In DH Population 2, the favorable allele of a major QTL on chromosome 6B was contributed by the line ‘TRI‐5310’, representing the variety ‘Eureke’ from France. This QTL was mainly effective in the German environments and explained up to 36% of the phenotypic variation. In Egypt, however, only a moderate resistance QTL was identified in the field tests and no resistance QTL was observed in the seedling tests. Our results demonstrate the usefulness of genetic resources to identify novel sources of resistance to yellow rust, including the “Warrior” race PstS10. Major QTLs for yellow rust resistance were discovered in genetic resources of wheat. Resistance patterns were environment‐specific and included resistance to the race “Warrior”. Physical regions on chromosomes 1B and 6B were evaluated for candidate genes. Resistance on chromosome 1B is most probably conferred by the gene Yr10.
... Jighly et al. (2015) genotyped 200 ICARDA wheat genotypes with 2,688 DArT and 4,252 SNP markers to identify QTL associated with stripe rust resistance in wheat. Furthermore, Tadesse et al. (2014) genotyped 167 facultative/winter elite wheat genotypes with 3,051 DArT markers and reported 10 markers associated with stripe rust resistance. ...
Full-text available
Septoria tritici blotch (STB) of wheat, caused by the ascomycete Zymoseptoria tritici (formerly Mycosphaerella graminicola), is one of the most important foliar diseases of wheat. In Morocco, STB is a devastating disease in temperate wheat-growing regions, and the yield losses can exceed up to 50% under favorable conditions. The aims of this study were to identify sources of resistance to STB in Septoria Association Mapping Panel (SAMP), which is composed of 377 advanced breeding lines (ABLs) from spring bread wheat breeding program of ICARDA, and to identify loci associated with resistance to STB at seedling (SRT) as well as at the adult plant (APS) stages using genome-wide association mapping (GWAM). Seedling resistance was evaluated under controlled conditions with two virulent isolates of STB (SAT-2 and 71-R3) from Morocco, whereas adult plant resistance was assessed at two hot spot locations in Morocco (Sidi Allal Tazi, Marchouch) under artificial inoculation with a mixture of STB isolates. At seedling stage, 45 and 32 ABLs were found to be resistant to 71-R3 and SAT-2 isolates of STB, respectively. At adult plant stage, 50 ABLs were found to be resistant at hot spot locations in Morocco. Furthermore, 10 genotypes showed resistance in both locations during two cropping seasons. GWAM was conducted with 9,988 SNP markers using phenotypic data for seedling and the adult plant stage. MLM model was employed in TASSEL 5 (v 5.2.53) using principal component analysis and Kinship Matrix as covariates. The GWAM analysis indicated 14 quantitative trait loci (QTL) at the seedling stage (8 for isolate SAT-2 and 6 for isolate 71-R3), while 23 QTL were detected at the adult plant stage resistance (4 at MCH-17, 16 at SAT-17, and 3 at SAT-18). SRT QTL explained together 33.3% of the phenotypic variance for seedling resistance to STB isolate SAT-2 and 28.3% for 71-R3, respectively. QTL for adult plant stage resistance explained together 13.1, 68.6, and 11.9% of the phenotypic variance for MCH-17, SAT-17, and SAT-18, respectively. Identification of STB-resistant spring bread wheat germplasm in combination with QTL detected both at SRT and APS stage will serve as an important resource in STB resistance breeding efforts.
Full-text available
We are pleased to present this book entitled “Recent Advances in Agricultural Science and Technology for Sustainable India”. Ratnesh Kumar Rao, Secretary, Mahima Research Foundation and Social Welfare are not new to Agriculture students. With his vast experience in Academic activities, he has dealt this complex subject and edited, with practical approach and simple language, to meet the requirement of the students and teachers of Agriculture. The large gap between potential and current crop yields makes increased food production attainable. India’s low agricultural productivity has many causes, including scarce and scant knowledge of improved practices, low use of improved seed, low fertilizer use, inadequate irrigation, conflict, absence of strong institutions, ineffective policies, lack of incentives and prevalence of diseases. Climate change could substantially reduce yields from rainfed agriculture in some countries. With scarcity of land, water, energy, and other natural resources, meeting the demands for food and fiber will require increases in productivity. Though this book is mainly deals with the agriculture research and education, it will also be very handy for those who desire to start Agricultural Research in Science and Technology. We are sure this will be accepted very much by the students, teachers, scientists and Stakeholders of Agriculture all over the India. We solicit your encouragement in this endeavour.
Full-text available
Sugar beet (B. vulgaris), a root crop grown in Europe, North America, the Middle East, Egypt, India, Chile, Japan, and China, is an important source of sugar. In temperate climates, sugar beet is the sole sucrose-storing crop. It produces approximately a third of the world's yearly sugar, with pulp and molasses being utilised for animal feed and methane production. Sugar beets are typically planted in the spring and harvested in the vegetative stage before the winter season. Because of the pre-winter development and increased growth in spring, cultivating sugar beet as a winter crop, by sowing in October and harvesting the following year, might enhance beet yields by up to 26%. (Jaggard and Werker, 1999; Hoffmann and Kluge-Severin, 2011). Winter beets have progressed to the point where they can be harvested and beet campaigns can begin early. As a result, one of the primary goals of sugar beet breeding is to produce winter beets. The control of bolting after the winter is a challenge in winter agriculture.
Full-text available
Marker assisted selection (MAS) is 'smart breeding' or fast track plant breeding technology. It is one tool utilized in breeding companies and research institutes for fast development of improved varieties, giving possibility to select desirable traits more directly using DNA markers. In this review, we discussed the use of MAS in biotic, abiotic, quality and other agronomic traits. Besides, we emphasized the importance of MAS at ICARDA and underlined the successful application of MAS in the last 10 years. The use of molecular markers makes the process of selecting parental lines more efficient based on genetic diversity analysis. It can aid the conventional breeding, especially for certain biotic and abiotic traits laborious to manage. Still, MAS contributed very little to the release of improved cultivars with greater tolerance to abiotic stresses, with only a few exceptions. MAS was extensively used to improve rice varieties, mainly resistant to bacterial blight and blast disease and was applied in drought tolerance along with GPC (Grain protein content) in quality traits. MAS at ICARDA is used to characterize new parental materials for disease resistance genes as well as in screening advanced lines with a focus on association mapping and identification of new QTLs. The application of MAS increased in the last decade. It is more and more used in different crops. However, rice is still the dominant crop in terms of number of publications using MAS.
Full-text available
The international winter wheat improvement program (IWWIP), an alliance between Turkey– CIMMYT–ICARDA, has distributed improved germ-plasm to different National Agricultural Research Systems (NARS) partners through international nurseries and yield trials for the last 25 years. This study was carried out in order to determine the rate of breeding progress for yield and yield related traits at IWWIP using data of the international winter wheat yield trials (IWWYT), IWWYT 1–13, collected from 1997 to 2010 in irrigated environments across different countries. The relative grain yield of the best line expressed as percent of the best check (Kinaci-97), widely grown cultivar (Bezostaya) and trial mean (TM) increased at a rate of 0.6, 1.6 and 0.2 %/year, each non-significant (P [ 0.05), respectively. Regression analysis indicated that TM has increased at a rate of 91.9 kg/ha/year (P = 0.007). The net realized breeding progress was estimated by accounting the variability due to management and weather conditions using surrogate variables such as integrated biological indices taken as means of common checks. The net realized gain for the BL was 66.2 ± 19.7 kg/ha/year (P = 0.01). Success rate of the BL, per cent of sites where the BL exceeds the local check in grain yield, ranged from 50 to 87 % across trials. To date, more than 55 varieties of IWWIP origin have been released in 10 countries of Central and West Asia including Afghanistan, Turkey, Iran, Uzbekistan and Tajikistan. Some varieties, such as Solh and Kinaci-97, have been released under different names in different countries indicating their broad adaptation. Cluster analysis of IWWYT sites indicated that IWWIP sites in Turkey and Syria are associated with most of the testing sites in Central and West Asia and North Africa (CWANA) region. The recently identified high yielding genotypes are recommended for direct release and/or parental purposes by the respective NARS.
Full-text available
As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure 1,2. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.
Full-text available
Information on genetic diversity and population structure of elite wheat ( L.) breeding lines promotes effective use of genetic resources. We analyzed 205 elite wheat breeding lines from major winter wheat breeding programs in the USA using 245 markers across the wheat genomes. This collection showed a high level of genetic diversity as reflected by allele number per locus (7.2) and polymorphism information content (0.54). However, the diversity of U.S. modern wheat appeared to be lower than previously reported diversity levels in worldwide germplasm collections. As expected, this collection was highly structured according to geographic origin and market class with soft and hard wheat clearly separated from each other. Hard wheat accessions were further divided into three subpopulations. Linkage disequilibrium (LD) was primarily distributed around centromere regions. The mean genome-wide LD decay estimate was 10 cM ( > 0.1), although the extent of LD was highly variable throughout the genome. Our results on genetic diversity of different gene pools and the distribution of LD facilitates the effective use of genetic resources for wheat breeding and the choice of marker density in gene mapping and marker-assisted breeding.
The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from
Singh, R.P., J. Huerta-Espino and H.M. William. 2004. Genetics and breeding for durable resistance to leaf and stripe rusts in wheat. Turk. J. Agric. For. 28: xxx-xxx. Yellow (or stripe) and leaf (or brown) rusts, caused by Puccinia striiformis and P. triticina, respectively, are important diseases of wheat worldwide. Growing resistant cultivars is the most economical and environmentally safe control measure and has no cost to growers. Wheat (Triticum aestivum) cultivars that have remained resistant for a long time, or in other words carry durable or race-nonspecific resistance, are known to occur. Inheritance of resistance indicates that these cultivars often carry a few slow rusting genes that have small-to-intermediate, but additive, effects. Our genetic studies show that a high level of resistance (approaching immunity) to both rusts could be achieved by accumulating from 4 to 5 such genes. We recommend that a group of winter and spring wheat cultivars known to carry adequate levels of durable resistance to yellow and/or leaf rusts are assembled and further evaluated in the region to identify those cultivars that show resistance stability. Resistance from these cultivars should then be transferred in a planned manner to the susceptible but locally adapted cultivars through a 'Single Backcross Breeding Approach', that allows the simultaneous accumulation of desired number of slow rusting genes with increased grain yield potential and other traits.
Insect pests cause substantial damage to wheat production in many wheat-producing areas of the world. Amongst these, Hessian fly (HF), Russian wheat aphid (RWA), Sunn pest (SP), wheat stem saw fly (WSSF) and cereal leaf beetle (CLB) are the most damaging in the areas where they occur. Historically, the use of resistance genes in wheat has been the most effective, environmentally friendly, and cost-efficient approach to controlling pest infestations. In this study, we carried out a genome-wide association study with 2518 Diversity Arrays Technology markers which were polymorphic on 134 wheat genotypes with varying degrees of resistance to the five most destructive pests (HF, RWA, SP, WSSF and CLB) of wheat, using mixed linear model (MLM) analysis with population structure as a covariate. We identified 26 loci across the wheat genome linked to genes conferring resistance to these pests, of which 20 are potentially novel quantitative trait loci with significance values which ranged between 5 × 10−3 and 10−11. We used an in silico approach to identify probable candidate genes at some of the genomic regions and found that their functions varied from defense response with transferase activity to several genes of unknown function. Identification of potentially new loci associated with resistances to pests would contribute to more rapid marker-aided incorporation of new and diverse genes to develop new varieties with improved resistance against these pests.