




































Highlights in BioScience
ISSN:2682-4043
DOI:10.36462/H.BioSci.202405

Research Article

Open Access

1 International Center for Agricultural Research in

the Dry Areas (ICARDA), P.O. Box 114/5055,

Beirut, Lebanon.
2 International Maize and Wheat Improvement

Center (CIMMYT) Pakistan Office, CSI Build-

ing, NARC, Park Road, Islamabad 44000,

Pakistan.
3 Agricultural Genetic Engineering Research

Institute (AGERI), 9 Gamaa Street, Giza, Egypt.

* To whom correspondence should be
addressed: a.hamwieh@cgiar.org

Editor: Jean Legeay, University Mohammed VI
Polytechnic, Morocco.

Reviewer(s):
Ahmed Sallam, Leibniz Institute of Plant Genetics
and Crop Plant Research (IPK), Stadt Seeland,
Germany..

Clara R. Azzam, Cell Research Department, Field
Crops Research Institute, Agricultural Research
Center (ARC), Egypt.

Received: June 15, 2024

Accepted: September 9, 2024

Published: December 26, 2024

Citation: Hamwieh A, Muhammad I, Ahmed S,
Kababeh S, Alsamman AM, Istanbuli T. Exploring
QTL genes contribute to chickpea ascochyta blight
resistance across multiple environments using SSR,
DArT and SNP assays . 2024 Dec. 26;7:bs202405

Copyright: © 2024 Hamwieh et al.. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduc-
tion in any medium, provided the original author and
source are credited.
Data Availability Statement: All relevant data are
within the paper and supplementary materials.
Funding: This work was supported by Grains Re-
search and Development Corporation (GRDC), Aus-
tralia.
Competing interests: The authors declare that they
have no competing interests.

Exploring QTL genes contribute to chickpea ascochyta blight
resistance across multiple environments using SSR, DArT and SNP
assays

Aladdin Hamwieh1
>< �, Imtiaz Muhammad2

>< �, Seid Ahmed1
>< �, Siham

Kababeh1
>< �, Alsamman M. Alsamman1,3

>< �, Tawffiq Istanbuli1 ><�

Abstract

Chickpea (Cicer arietinum L.) occupies the third leading position among grain legumes
in cultivated area around the world. Ascochyta blight (AB) caused by Ascochytarabiei
(Pass.) Labr. is one of the most destructive foliar diseases of chickpea and can
cause complete crop failure in many chickpea growing regions around the world.
A recombinant inbred line (RIL) population, comprising 165 lines derived from the
cross FLIP98-1065 (R) ×ILC1929 (S),were evaluated in six environments over three
years (2008 – 2011) and three locations in Syria (field and greenhouse locations in Tel
Hadya “TH“ and a field location at Lattakia “Lat“). The greenhouse experiments were
conducted against AB pathotype II. ANOVA analysis indicated significant differences
both among the RILs and among the environments. We produced a total of 1398
(134 SSR, 652 DArTseq and 612 SNP) markers and developed a high-resolution
genetic map (1244 markers spanning 2503 cM on eight linkage groups). Three major
conserved quantitative trait loci (QTLs) that confer AB resistance were identified:
two on linkage group 2 (indicated as LG2-A and LG2-B) and one on linkage group
4 (indicated as LG4). These explain, respectively, a maximum of 18.5%, 11.1% and
25% of the total variation. In total, 18 predicted genes were located in LG4, and 9
and10 predicted genes, respectively, were located in LG2-A and LG2-B. This study
presents a first set of SNP markers located within genes associated with AB resistance
in chickpea, which could be applied in marker-assisted selection programs for breeding
AB-resistant chickpeas.

Keywords: Ascochyta blight, Ascochytarabiei, chickpea, AMMI analysis, QTL, SNP

Introduction
Ascochyta blight (AB) of chickpea, caused by Ascochyta rabiei (Pass.) Lab., is one of the

most important diseases in many chickpea production areas. Four pathotypes have been identified

within A.rabiei populations in Syria and other countries [1; 2; 3]. Chemical control of AB has

proved inefficient and very expensive; therefore, new varieties with improved resistance are needed

to manage the impact of the disease. Breeders using conventional methods have made considerable

progress toward developing chickpea varieties with increased AB resistance [4; 5]. Research teams

investigating AB of chickpea have identified 14 quantitative trait loci (QTLs) in eight linkage groups

(LGs) that each contribute to A. rabiei resistance [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. This indicates that

several mechanisms are likely responsible for AB resistance, but very little is known about the identity

and functions of the genes involved. Two major QTLs, located on LG2 close to the markers GA16

and TA37, control resistance to AB pathotype I [10], while another QTL contributing to resistance to

pathotype II is located on LG4 near simple sequence repeat (SSR) loci GAA47, TA130, TR20, TA72,

TS72, and TA2 [16; 9; 10]. [10] traced the AB resistance of the chickpea line FLIP84-92C to regions

on LG2 and LG4 associated with the resistance in FLIP84-92C to pathotype II. In that study, the

TA46 marker explained a maximum of 69% of that lines total AB resistance; furthermore, additional

markers (GAA47, GA24, and GA16) were identified on LG2, each explaining 10.4 – 19.3% of the

line's total AB resistance [10]. Additional markers on LG1 (TS12b, STMS28, and TS45) have been

reported to be associated with AB resistance under controlled conditions [7; 8].

Highlights in BioScience Page 1 of 11 December 2024|Volume 7

https://doi.org/10.36462/H.BioSci.202405
https://creativecommons.org/licenses/by/4.0/
mailto:a.hamwieh@cgiar.org
https://orcid.org/0000-0001-6060-5560
mailto:m.imtiaz@cgiar.org
mailto:s.a.kemal@cgiar.org
https://orcid.org/0000-0002-1791-9369
mailto:sihamkababe1989@gmail.com
mailto:smahmoud@ageri.sci.eg
https://orcid.org/0000-0002-7765-5035
mailto:T.ISTANBULI@cgiar.org
https://orcid.org/0000-0001-7450-6408
http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

A SCAR marker SCY17590 was reported as linked to AB re-
sistance [11], and this marker was applied successfully to selected
resistant genotypes in Australian chickpea breeding materials
[17]. This marker was further applied together with an allele-
specific primer for the gene CaETR-1 for genotyping QTLAR1
and QTLAR2 in chickpea germplasm [18; 19].

A study by [20] using an Fst genome scan and genome-wide
association methods indicated that a 100 kb region on chromo-
some 4 was significantly associated with AB resistance. This
region covered a large QTL interval of 7 Mb30 Mb, which had
been genotyped at relatively low density with SSR or single nu-
cleotide polymorphism (SNP) markers through previous mapping
population studies. [20] and colleagues validated this QTL re-
gion on LG4 in a genome-wide association study (GWAS) using
approximately 144,000 SNPs and a collection of 132 advanced
breeding lines from the Australian chickpea breeding program.
In total, 12 predicted genes were located in the region associated
with AB resistance, including sequences annotated as encoding
an NBS-LRR receptor-like kinase, a wall-associated kinase, a
zinc finger protein, and serine/threonine protein kinases. One
significant SNP located in the conserved catalytic domain of an
NBS-LRR receptor-like kinase led to an amino acid substitution
[20].

Researchers could use high-throughput genotyping systems
to identify markers such as single nucleotide polymorphisms
(SNP) markers and diversity array technology (DArT) markers.
Therefore, we aimed to study the interaction between different
environments and AB resistance, to produce a highly saturated
genetic map, and to identify markers closer to potential AB resis-
tance genes using a recombinant inbred line (RIL) population of
chickpea, enabling practical application of marker(s) in chickpea
breeding programs.

Materials and methods
Plant materials and DNA isolation

A mapping population consisting of 165 (F8) RILs derived
from an intra-specific cross between an AB-resistant chickpea,
FLIP98-1065, and an AB-susceptible chickpea, ILC1929, was
used in this study. The RILs were developed using the single seed
descent (SSD) method from F2 to F7. DNA was extracted from
fresh leaves of six-week-old seedlings using the cetyltrimethyl
ammonium bromide (CTAB) method [21; 22].

The sequences of chickpea SSR primers were obtained from
published papers [16; 23; 24]. The PCR mixture (20 µL) con-
tained 10 ng genomic DNA, 0.2 mM dNTP, 1 U Taq DNA poly-
merase (Invitrogen, Carlsbad, Calif.), 10 pmol of each forward
and reverse primer, and 1X PCR buffer (Invitrogen, Carlsbad,
Calif.). Polymerase chain reaction (PCR) was conducted using a
thermocycler (ABI GeneAmp2720) with the following program:
a denaturing cycle of 94oC for 3 minutes, followed by 35 cycles
of 94oC for 15 seconds (denaturation), a specific temperature
depending upon the primer pair for 15 seconds (annealing), and
72oC for 30 seconds (extension), followed by a final extension at

72oC for 5 minutes. The PCR products were separated on an 8%
polyacrylamide gel according to the electrophoresis user guide
(Invitrogen).

DArT and DArTseq
DArTseq is a genotyping by sequencing (GBS) platform de-

veloped by DArT PL, Canberra, Australia ( http://www.diversit-
yarrays.com/dart-application-dartseq). DArTseq is a combination
of complexity reduction methods that were initially developed for
array-based DArT with sequencing of resulting representations
on next-generation sequencing platforms.

Experimental Design and Locations
Four field experiments and two greenhouse experiments were

performed at different locations in Syria, enabling us to examine
disease susceptibility of the chickpea lines under six sets of
environmental conditions (Table 1). Field experiments were
performed in Lattakia, Syria in 2009 (Lat09) and at the Tel Hadya
station, Aleppo, Syria, in 2008, 2009, and 2010 (TH08, TH09,
and TH10). These locations differed in the average rainfall they
received during the experiments: in Lattakia, the average was
around 750 mm, but in Tel Hadya, the average was 350 mm.
Each experiment was laid out in an alpha lattice design with two
replications using multiple row plots. The length of each plot
was 5 m and rows were spaced 45 cm apart in all trials.

Two experiments were performed in the greenhouse at ICARDA
headquarters in Aleppo, Syria in 2009 and 2010 (PII-2009 and
PII-2010). In the greenhouse, five healthy seeds of each line were
planted in a pot (15 cm diameter). The experiments were con-
ducted in a growth chamber (temperature 22oC and 12 hours/12
hours light/dark). Locations, years, and planting dates for the six
experiments are summarized in (Table 1).

The AB cultures were obtained from the legume pathology
laboratory at ICARDA. The experiments were laid out in a ran-
domized complete block design with two replications. A spore
suspension of AB pathotype II (concentration of 105 spores/mL−1)
was prepared from 14-day old Ascochyta rabiei culture grown on
chickpea dextrose agar (4% chickpea flour, 2% dextrose, and 2%
agar in 1 liter of distilled water) and used for seedling inocula-
tions. The disease was scored when symptoms were observed on
the susceptible chickpea control (ILC-263).

Scoring Plants for AB Symptom Severity
Ascochyta blight symptom scoring was based on a nine-point

rating scale [25] in which plants are scored as follows: 1. immune,
no symptoms of disease; 2. few, very small lesions (<2 mm) on
leaves and stems (1 to 2% of the plant area infected); 3. many
small lesions (6 to 10% of the plant area infected); 4. many small
and large lesions (26 to 50% of the plant area infected); 5. many
small lesions on the stem (51 to 75% of the plant area infected);
6. many large lesions, lesions coalescing, stem girdled (76 to
90% of the plant area infected); 7. many small and large lesions,
lesions coalescing, girdling stem breakage (>90% of the plant
area infected); 8. plant is almost dead; and 9. plant is dead.

Highlights in BioScience Page 2 of 11 December 2024|Volume 7

http://www.diversityarrays.com/dart-application-dartseq
http://www.diversityarrays.com/dart-application-dartseq
http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

Table 1. Rainfall "mm", temperature "oC" (between brackets) and planting dates during 3 years (2008–2010) for experiments conducted in six environments in Syria
(three in the field and two in the greenhouse at Tel Hadya TH; one in the field at Lattakia "Lat").

Environment code
Greenhouse Field

PJI-2009 PJI-2010 Lat09 TH08 TH09 TH10

Location Tel Hadya greenhouse Tel Hadya greenhouse Lattakia Tel Hadya greenhouse Tel Hadya greenhouse Tel Hadya greenhouse

Year 2009 2010 2009 2008 2009 2010

Planting date 15 Nov. 2008 13 Nov. 2009 22 Mar. 2009 11 Dec. 2007 12 Dec. 2008 9 Dec. 2009

February MI (21 řC) MI (21 řC) 98.4 (13.2 řC) 22.0 (14.3 řC) 84.3 (14.2 řC) 50.6 (15.2 řC)

March MI (21 řC) MI (21 řC) 93.3 (20.3 řC) 27.9 (22.9 řC) 36.2 (17.1 řC) 12.4 (20.8 řC)

April MI (21 řC) MI (21 řC) 56.6 (23.4 řC) 1.9 (27.9 řC) 23.4 (24.0 řC) 12.7 (25.8 řC)

May - - 34.6 (25.1 řC) 10.9 (29.2 řC) 4.0 (30.1 řC) 0.0 (31.7 řC)

Date of score - - 22/5/2009 25/5/2008 17/5/2009 16/5/2010

Total rainfall (mm) - - 282.9 62.7 147.9 75.7

MI: Mist irrigation used to keep relative humidity around 80% under controlled environments.

Statistical Analysis
The disease rating data collected from the experiments were

analyzed using GenStat 12th edition (VSNi, Hemel Hempstead,
UK). To evaluate AB scores, analysis of variance (ANOVA)
with multiplicative interactions analysis (AMMI) was performed.
AMMI analysis was described by [26] based on the following
formula:

Yi j = µ + gi + a j +

n∑
k=1

λk xiky jk + ri j + S̄ i j

where µ is the overall mean of the test; gi is the fixed effect
of genotype i (i = 1, 2, . . . , g); a j is the fixed effect of environ-
ment j ( j = 1, 2, . . . , a); Yi j is the mean response of genotype
i in environment j; λk is the singular value of the k-th IPCA,
(k = 1, 2, . . . , p, where p is the maximum number of estimable
principal components); xik is the singular value of the i-th geno-
type in the k-th IPCA; y jk is the singular value of the j-th en-
vironment in the k-th IPCA; ri j is the residue of the GEI or
AMMI residue (data noise); k is the characteristic non-zero root,
k = [1, 2, . . . ,min(g − 1, e − 1)]. The plot genotype and environ-
ment interactions were analyzed against four interaction principal
component axes (IPCAs). The ratio of the genetic variance to the
total phenotypic variance was calculated to estimate heritability.

Genetic mapping was conducted using JoinMap 4® software,
Kyazma [27]. Linkage groups were created based on a loga-
rithm of odds (LOD) score greater than 3 and a recombination
frequency below 0.45. Map distances in centimorgans (cM) were
estimated using the Kosambi function [28]. QTL analysis was
conducted using MapQTL 6® software, Kyazma [29], and the

fraction of the variation explained by the QTL and the additive ef-
fects were calculated. The potential QTLs were estimated based
on a 2,000 permutation test using LOD ≥2 as the significance
threshold. Predicted genes within the QTL regions were identi-
fied across the chickpea genome using the BLAST package [30],
and homologous protein-encoding genes were extracted from the
chickpea genome sequence. The SnpEff tool was used to detect
the genetic sequence effect for potential SNPs with a correlation
to Ascochyta blight resistance.

Results
Phenotypic characterization

We collected data from three growing seasons (2008 – 2010)
and from one greenhouse and two field locations; this allowed
us to analyze plants exposed to six different environmental con-
ditions. We observed significant differences (P < 0.001) in the
AB severity scores (on a scale from 1 through 9) among the
165 RILs and among plants exposed to the six environments.
However, the mean values of the AB severity scores showed a
continuous distribution from 3 to 9 (Figure 1 (A)) that fit the
normal distribution. To estimate the interactions between AB
resistance and the environments we tested, the AB scores were
further analyzed using the AMMI model, which combines fea-
tures of ANOVA and principal component analysis. The analysis
indicated that 25% of the total sum of squares (SS) was due
to environmental factors, and only 26.5% and 32.9%, respec-
tively, was due to genotypic and gene-environment interaction
(GEI) factors (Table 2). The percentage attributed to GEI was
about 1.2 times the percentage attributed to genotype, indicating
substantial differences in genotypic responses across environ-

Highlights in BioScience Page 3 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

Figure 1. (A) Frequency distributions of AB disease scores for 165chickpea

RILs derived from FLIP98-1065 (AB resistant) Œ ILC1929 (AB susceptible).

Arrows indicate disease score for each parental genotype. (B) AMMI model 2

biplot of AB resistance scores for 165 RILs and 6environments.

ments. However, the large SS value for environmental factors
indicated that diverse environments caused a significant portion
of the variation in the AB values. The first principal component
axis (PCA1) captured 40.11% of the GEI-SS, while the second
(PCA2) captured only 27.4%. The sum of squares for PCA1 and
PCA2 was 1484, which is smaller than the 1774 captured for
genotype. Since mean squares for both PCA1 and PCA2 were
significant at P < 0.001 and contributed about 67.52% of the total
GEI, the postdictive evaluation suggested that two axes (PCA1
and PCA2) were significant for the model with 334 degrees of
freedom (Table 2).

The AMMI 2 biplot analysis for the six environments is
shown in (Figure 1 (B)). The high rainfall environment in Lat-
takia (Lat09), which received 184.5 mm rainfall after planting,
was located in quadrant II. The environments TH08, TH09 and
TH10 were located in quadrants IV, III, and I, respectively. This
variation is expected because the Tel Hadya location received dif-
ferent amounts and distributions of rainfall in the three growing

Table 2. ANOVA (AMMI model) for AB score of 165 RILs evaluated in six
different environments in field and greenhouse.

Source D.F. S.S. M.S. F Explained (%)

Genotypes 164 1774 10.82 10.37*** 26.53

Environments 5 1673 334.58 95.99*** 25.02

Interactions 819 2198 2.68 2.57*** 32.87

IPCA 1 168 882 5.25 5.03*** 40.13

IPCA 2 166 602 3.63 3.48*** 27.39

IPCA 3 164 293 1.79 1.71*** 13.33

IPCA 4 162 225 1.39 1.33 -

Error 978 1020 1.04 - -

Total 1979 6686 3.38 - -

*** Highly significant at the 0.001 probability level; D.F: degree of freedom, F:
tabulated frequency.

seasons, notably during February and March when the tempera-
ture is around 20 oC in Tel Hadya (TH). These conditions provide
the best environment for AB disease development. The environ-
ments (PII-2009, PII-2010, TH08 and TH09) were located close
to the biplot origin. However, one environment, TH10 (located
in quadrant I) received 75.7 mm of rain in 2010, only 12.4 mm
of which was received in March, the most important time for the
AB disease development. This amount is 50% lower than the
amounts received in 2008 and 2009.

Mapping and QTL analyses
Of the 650 SSR primer pairs we tested, only 134 (20.6%)

of the primer pairs produced different PCR products for the
two parental lines, FLIP98-1065 and ILC1929. A total of 652
DArTseq markers and 612 SNPs obtained from DArT PL were
merged with 134 SSR markers and used to develop the genetic
map. The linkage map comprised 1244 markers spanning 2503
cM on eight LGs (Table 3). The distance between the markers
was 2 cM on average; however, 156 (11.4%) of the markers
remained unlinked.

The QTL analysis identified three major QTL regions, one on
LG4 and two on LG2 (referred to as LG2-A and LG2-B; (Figure
2). The QTL on LG4 was consistently identified in all six envi-
ronments used in this study and was considered a major QTL,
explaining a maximum of 25% of the total variation at marker drt-
100004682. The two QTLs on LG2 (LG2-A and LG2-B) were
identified in only the three TH field environments (TH08, TH09,
and TH10) and explained a maximum of 18.5% of the total varia-
tion at marker sn-100021968. On LG4, 36 markers were located
within the QTL region, and fifteen of them were tightly linked to
15 genes within a span of 430,944 bp (Figure 3 (A)). The QTL
LG2-A included 53 markers, and 12 of them were tightly linked
to genes (Figure 3 (B)). The QTL LG2-B comprised 31 mark-
ers, seven of which were linked to genes. The most important
genes identified in the three QTL regions are listed in (Table
4), such as serine/threonine protein kinase BLUS1, coiled-coil
domain-containing protein, transcription factor bHLH157, metal-

Highlights in BioScience Page 4 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

nicotianamine transporter YSL1, leucine-rich repeat receptor-like
tyrosine-protein kinase PXC3, TMV resistance protein N-like,
chitin elicitor receptor kinase 1-like, and SNF1-related protein
kinase regulatory subunit beta-1. One SSR marker within LG2-B
(GA16) was linked to an uncharacterized protein and explains a
maximum of 16% of the total variation (Figure 3 (C)).

Table 3. Summary of the distribution of markers on different linkage groups in
chickpea.

LG SSR SNP DART Total Length (cM)

LG1 11 214 274 499 1010.1

LG2 5 83 109 197 201.8

LG3 0 29 0 29 166.5

LG4 34 34 48 116 287.0

LG5 6 38 41 85 156.8

LG6 7 48 59 114 180.3

LG7 19 40 55 114 293.0

LG8 12 28 50 90 207.7

Total 94 514 636 1244 2503.0

Discussion
Ascochyta blight (AB), which is caused by Ascochyta rabiei

(Pass.) Labr., is a major disease that limits chickpea production
in cool and humid environments and is the largest contributor to
high yield gaps in chickpea production in many countries [31; 4].

We expected to observe significant G × E in this study, as
AB severity in chickpea is greatly affected by environmental
conditions [32]. A study by Pande and colleagues [33] revealed
significant genotypic effects and G × E interactions in AB sever-
ity. However, very few studies regarding G × E interactions in
AB susceptibility of chickpea in multi-environments have been
reported. [11] evaluated 106 F 6:7 RIL population over two
cropping seasons under field conditions, and they identified a
significant RIL × year interaction, which they attributed to differ-
ences between the two years in environmental conditions, fungus
pathotype, or both.

For this study, we generated data from six different environ-
ments by conducting four field and two greenhouse experiments
over the course of three years (2008 – 2010) in two locations
(Tel Hadya and Lattakia). These environments provided different
moisture conditions (rainfall varied in amount and distribution
among the field experiments). Therefore, our results will con-
tribute to the field’s understanding of gene-environment interac-
tions in AB disease susceptibility.

As indicated by [34] and [35], the results of the AMMI anal-
ysis can be predicted by using the first two PCAs. Conversely,
the G × E-SS (sum of squares) in our study was about 1.2 times
higher than that reported for the genotypes, and this indicates
substantial differences in genotypic reactions across the environ-
ments we studied.

The results from our previous study on drought tolerance
performance of 181 chickpea RILs in nine environments found
that the GEI-SS was 2.3 times more than that obtained for geno-

type [22]. The AMMI analysis showed significant differences
for all genotypes, environments, and GEI. Furthermore, the GEI
revealed that the IPCA (1-3) together with the SS (1,777) was
slightly larger than what was obtained for the genotypes (1,774),
suggesting that the AMMI model excluded most of the actual
data noise. The broad-sense heritability of AB resistance in the
six environments in our study indicates high heritability (0.75),
which shows that AB is controlled by major genes with large
effect size in this study. However, this is much higher than the
heritability values (0.38-0.43) calculated in a study of three F2
populations derived from crosses of genotypes that are moder-
ately resistant to AB [13]. This may be attributed to our use of
multi-environment data in our study.

Although several mapping populations have been developed,
few maps have been constructed using single nucleotide polymor-
phism (SNP) markers in chickpea. In this study, 1244 markers
spanning 2503 cM were mapped; this is much higher than the
number of markers used by [36], who developed a high-density
map using 150 RIL lines (Lasseter × ICC3996) and 504 poly-
morphic SSRs and SNPs.

We identified three major QTL regions in this study. One of
these, the QTL on LG4, was consistent across the six environ-
ments used in this study and considered a major QTL/gene(s) that
explained a maximum of 25% of the total variation at marker drt-
100004682. A QTL on LG4 that confers AB resistance has been
reported by several previous studies, all of which used relatively
low-density maps with SSR or SNP markers [12; 13; 37; 36]. For
example, [36] used SNP markers to identify a QTL conferring
AB resistance; this QTL explained 14-45% of phenotypic vari-
ation and spanned around 13 Mb between markers SSR TA146
and SNP_40000185 on LG4.

We identified two QTL regions located on LG2 (LG2-A and
LG2-B), but they were consistent only among the TH environ-
ments across three years (TH08, TH09, and TH10). This may
indicate that they contain genes whose impact is dependent on
environment (temperature/humidity), pathotype differences, or
both, as the plants in our greenhouse experiments were inocu-
lated with Ascochyta rabiei pathotype II. However, this may also
explain why many research teams have failed to report the QTLs
we found on LG2. In this study, LG2-A and LG2-B contained
33 and 23 markers spanning 28 cM and 63.6 cM, respectively.
Based on GA13 as an anchor SSR marker, LG2-B corresponds
with the QTL that was previously reported by [38], [10], and [9].
This QTL in this study could explain a relatively large percentage
(18.5%) of the total estimated phenotypic variation in suscepti-
bility to AB. Many other QTLs associated with AB resistance
have been reported on linkage groups LG3 [13], LG5 [37], LG6
[13; 37], and LG8 [12] but were not identified in our study.

A total of eighteen predicted genes were located in LG4, and 9
and 10 predicted genes, respectively, were located in LG2-A and
LG2-B. A co-dominant SCAR marker SCY17590 tightly linked
to QTLAR2 on LG4 was reported by [11] and was successfully
used to characterize AB source [17]. However, no genes have

Highlights in BioScience Page 5 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

Figure 2. Linkage map of LG2 and LG4 depicting QTLs for AB resistance detected in a RIL (FLIP98-1065 Œ ILC1929) mapping population in chickpea. TH: Tel

Hadya.

Highlights in BioScience Page 6 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

Figure 3. (A) Genetic and physical maps of markers on LG4. The estimated genetic distances are in centimorgans (cM) (left). The physical locations of mapped

markers on chromosome 4 are shown in base pairs (bp) (right). (B) Genetic and physical maps of markers on LG2-A, one of the two major QTL regions conferring

AB resistance in chickpea. (C) Genetic and physical maps of markers on LG2-B, the second major QTL region conferring AB resistance in chickpea.

Highlights in BioScience Page 7 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

Table 4. Markers on LG4 significantly associated with AB resistance across six environments.

Marker / sequence position Env. Code LOD Variance % Expl. Additive Marker gene linkage
sn-100024987 Lat-09 7.3 2.37 18.4 -0.73 Serine/threonine protein kinase BLUS1

PII-2009 5.97 0.86 15.3 -0.40 (SNP is located within the gene)
PII-2010 4.16 2.04 11.0 -0.50
TH-08 3.08 0.77 8.2 -0.26
TH-09 7.03 1.49 17.8 -0.57
TH-10 2.14 2.98 5.8 -0.43

sn-5825578 Lat-09 7.28 2.37 18.4 -0.73 Coiled-coil domain-containing protein
PII-2009 5.91 0.87 15.2 -0.39 (SNP is located within the gene)
PII-2010 4.14 2.04 10.9 -0.50
TH-08 3.08 0.77 8.2 -0.26
TH-09 7.02 1.49 17.8 -0.57
TH-10 2.15 2.97 5.8 -0.43

sn-5825139 Lat-09 7.12 2.38 18.0 -0.72 Transcription factor bHLH157
PII-2009 5.12 0.89 13.3 -0.37 (SNP is located within the gene)
PII-2010 4.02 2.05 10.6 -0.49
TH-08 3.29 0.76 8.8 -0.27
TH-09 6.94 1.50 17.6 -0.57
TH-10 2.36 2.96 6.4 -0.45

drt-100004682 Lat-09 10.52 2.17 25.5 0.87 NA
PII-2009 7.20 0.84 18.2 0.43
PII-2010 4.66 2.02 12.2 0.53
TH-08 3.92 0.75 10.4 0.30
TH-09 7.55 1.47 19.0 0.59
TH-10 2.38 2.96 6.4 0.45

sn-5826150 Lat-09 6.95 2.39 17.6 -0.72 NA
PII-2009 5.25 0.88 13.6 -0.37
PII-2010 4.10 2.05 10.8 -0.50
TH-08 3.44 0.76 9.1 -0.28
TH-09 6.90 1.50 17.5 -0.56
TH-10 2.17 2.97 5.9 -0.43

NA: gene/position is not available

Table 5. Sequence and position on LG4 of SNP markers significantly associated with AB resistance.

Marker SNP Position Sequence

sn-100024987 50:T>G 4113008
TGCAGAACAAGAAGCAATATCTCAGGTATAACTTATATTCAATTTTTATC(T/

G)GTCATAAAATGTGCTATA

sn-5825578 24:T>C 3997422
TGCAGTGCTTCCTAAATTCGAAGA(C/

T)CCTGTTTCTGTTCCTGAGCCTGAACCTGAAACTCAACCTAAGGA

sn-5825139 55:G>A 4048029
TGCAGAAAGAAAGGAAATTCTCAAGTAATAAGTGAACTAAAAACAAGAG(A/

G)AATTGAAATATAAAATTAG

sn-5826150 6:C>G 4113005
TGCAGA(G/

C)GGCATTCTCTTCAGTGCTAGTTGTGCAGCATCTTTACAGATCGGAAGAGCGGTTTCAGCAGGA

sn-100053539 5:T>C 4168916
TGCAG(C/

T)GGGTGACGCAGTTATAGTTTACAACGTTATGGTGTTAGTGAATGGTTGAAATTATTTTACAGA

sn-5825294 11:C>G 4167416
TGCAGCAAACT(G/

C)TGAAAAAATAAAGTAAGAGAAGTTCTAAGGTTCTATGAAAAAATAATAATGTATATT

been reported as genes associated with AB resistance on LG4.
In this study, we found the genes annotated as leucine-rich re-
peat receptor-like tyrosine-protein kinase PXC3, TMV resistance
protein N-like, chitin elicitor receptor kinase 1-like, histidine ki-
nase 2, probable L-type lectin-domain containing receptor kinase
S.7, uridine kinase-like protein 4, and serine/threonine protein
kinase BLUS1. Very recent research by [20] validated a QTL
similar to the one we observed on chromosome 4 using GWAS
with a collection of 132 advanced breeding lines from the Aus-
tralian chickpea breeding program and 144,000 SNP markers.
The study predicted 12 genes located on a QTL region in LG4,

including those annotated as an NBS-LRR receptor-like kinase, a
wall-associated kinase, a zinc finger protein, and serine/threonine
protein kinases. However, only one significant SNP from that
study, located in the conserved catalytic domain of an NBS-LRR
receptor-like kinase, led to an amino acid substitution.

In our study, 12 markers on the QTL region on LG4 were con-
sistent across environments and could be used for MAS in Cicer
arietinum. These include the markers sn-100024987, sn-5825578,
sn-5825139, and drt-5824787, which are located, respectively,
within four genes (serine/threonine protein kinase BLUS1, coiled-
coil domain-containing protein, transcription factor bHLH157,

Highlights in BioScience Page 8 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

and metal-nicotianamine transporter YSL1) (Table 5).
Unfortunately, little research has been conducted on these

genes in Cicer arietinum. However, [20] reported that NBS-
LRR receptor-like kinase and several serine/threonine protein
kinases on chromosome 4 were predicted genes associated with
AB resistance. These genes are already known to have a role
in plant disease resistance. For example, the network of protein
serine/threonine kinases in plant cells appears to act as a central
processor unit that plays a role in the resistance of tomato to
disease caused by Pseudomonas syringae pv. tomato [39].

Yellow-leaf-specific 9 (YSL9 / NHL10) is a transmembrane
gene related to plant defense response to virus. The gene prod-
uct is localized to the chloroplast and the mRNA is cell-to-cell
mobile. In Arabidopsis, the expression of this gene is induced
by virus infection and in senescing leaves [40; 41]. On the other
hand, BLUS1 (Blue Light Signaling1 or Ser/Thr protein kinase)
mediates a primary step for phototropin signaling in guard cells
and is essential for stomatal opening, and it also has a role in
the stress-activated protein kinase signaling cascade [42]. The
coiled-coil (CC) domain is implicated in specific interactions with
other proteins that enhance plant disease resistance. For example,
coiled-coil nucleotide binding site-leucine rich repeat (CC-NB-
LRR) protein Rx in potato has been found to interact with the
potato virus X (PVX) coat protein, conferring PVX resistance
[43]. Transcription factor bHLH157 (basic/helix-loop-helix) is
a master-switch gene in plants [44] and plays an important role
in disease resistance, high temperature-mediated adaptations,
and phytochrome signaling [45; 46; 47]. The DREB/CBF were
also regulated by bHLH-type transcription factor, ICE1 [48],
and recent studies demonstrate that the basic helix-loop-helix
(bHLH) transcription factor AtMYC2 plays a role in multiple
hormone signaling pathways [49]. The regulation of flavonoid
biosynthetic gene expression by the cooperation of R2R3 MYB
and basic bHLH transcription factors provides one of the best
described examples of combinatorial gene regulation in plants
[50]. The transcription activator-like effector (TALE) protein was
shown to induce bHLH transcription factors that activate a pectate
lyase and contribute to water soaking in bacterial spot of tomato
[51]. Cicer arietinum genome annotation [52] has been used as
an annotation database for the SnpEFF tool in order to detect
the possible effect of sn-5825578, sn-100024987, sn-5826150,
sn-100053539, sn-5825294, and sn-5825139. These SNPs have
shown a modifier effect on YLS9, coiled-coil domain-containing
protein 12, BLUS1, and transcription factor bHLH157. Some
genes, such as BLUS1 and methionine gamma-lyase-like, are
affected by two SNPs, while others are affected by only one SNP.

In summary, it is not yet clear how the predicted genes located
in the QTL regions we identified play a role in AB resistance
in Cicer arietinum. Therefore, further research into the poten-
tial roles of these genes is recommended. However, this study
presents the first set of functional SNP markers in chickpea (Ta-
ble 5), which could be converted to Kompetitive Allele Specific
PCR (KASP) markers and applied in MAS breeding programs

for AB resistance in chickpea.

Reference
1. Udupa SM, Weigand F, Saxena MC, Kahl G. Genotyping

with RAPD and microsatellite markers resolve pathotype
diversity in the ascochyta blight pathogen of chickpea. The-
oretical and Applied Genetics. 1998;97:299-307.

2. Imtiaz M, Abang MM, Malhotra RS, Ahmed S, Bayaa B,
Udupa SM, et al. Pathotype IV, a new and highly virulent
pathotype of Didymellarabiei, causing Ascochyta blight in
chickpea in Syria. Plant Disease. 2011;95(9):1192-2.

3. Atik O, Ahmed S, Abang MM, Imtiaz M, Hamwieh A, Baum
M, et al. Pathogenic and genetic diversity of Didymella rabiei
affecting chickpea in Syria. Crop protection. 2013;46:70-9.

4. Pande S, Siddique KH, Kishore G, Bayaa B, Gaur P, Gowda
C, et al. Ascochyta blight of chickpea (Cicer arietinum L.):
a review of biology, pathogenicity, and disease management.
Australian Journal of Agricultural Research. 2005;56(4):317-
32.

5. Li H, Rodda M, Gnanasambandam A, Aftab M, Redden R,
Hobson K, et al. Breeding for biotic stress resistance in
chickpea: progress and prospects. Euphytica. 2015;204:257-
88.

6. Tekeoglu M, Rajesh P, Muehlbauer F. Integration of se-
quence tagged microsatellite sites to the chickpea genetic
map. Theoretical and Applied Genetics. 2002;105(6-7):847-
54.

7. Flandez-Galvez H, Ades PK, Ford R, Pang ECK, Taylor
PWJ. QTL analysis for Ascochyta blight resistance in an
intraspecific population of chickpea (Cicer arietinum L.).
Theoretical and Applied Genetics. 2003;107(7):1257-65.

8. Flandez-Galvez H, Ford R, Pang ECK, Taylor PWJ. An
intraspecific linkage map of the chickpea (Cicer arietinum
L.) genome based on sequence tagged microsatellite site and
resistance gene analog markers. Theoretical and Applied
Genetics. 2003;106(8):1447-56.

9. Udupa SM, Baum M. Genetic dissection of pathotype-
specific resistance to ascochyta blight disease in chickpea
(Cicer arietinum L.) using microsatellite markers. Theoreti-
cal and Applied Genetics. 2003;106(7):1196-202.

10. Cho S, Chen W, Muehlbauer FJ. Pathotype-specific genetic
factors in chickpea (Cicer arietinum L.) for quantitative resis-
tance to Ascochyta blight. Theoretical and Applied Genetics.
2004;109(4):733-9.

11. Iruela M, Rubio J, Barro F, Cubero JI, Millán T, Gil J. Detec-
tion of two quantitative trait loci for resistance to Ascochyta
blight in an intra-specific cross of chickpea (Cicer arietinum

Highlights in BioScience Page 9 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

L.): development of SCAR markers associated with resis-
tance. Theoretical and Applied Genetics. 2006;112(2):278-
87.

12. Lichtenzveig J, Bonfil DJ, Zhang HB, Shtienberg D, Abbo
S. Mapping quantitative trait loci in chickpea associated
with time to flowering and resistance to Didymella rabiei the
causal agent of Ascochyta blight. Theoretical and Applied
Genetics. 2006;113:1357-69.

13. Taran B, Warkentin T, Tullu A, Vandenberg A. Genetic rela-
tionships among Chickpea (Cicer arietinum L.) genotypes
based on the SSRs at the quantitative trait Loci for resistance
to Ascochyta Blight. European Journal of Plant Pathology.
2007;119(1):39-51.

14. Anbessa Y, Taran B, Warkentin TD, Tullu A, Vandenberg
A. Genetic analyses and conservation of QTL for ascochyta
blight resistance in chickpea (Cicer arietinum L.). Theoreti-
cal and Applied Genetics. 2009;119(4):757-65.

15. Kottapalli P, Gaur PM, Katiyar SK, Crouch JH, Buhariwalla
HK, Pande S, et al. Mapping and validation of QTLs for
resistance to an Indian isolate of Ascochyta blight pathogen
in chickpea. Euphytica. 2009;165(1):79-88.

16. Winter P, Benko-Iseppon AM, Hüttel B, Ratnaparkhe M,
Tullu A, Sonnante G, et al. A linkage map of the chickpea
(Cicer arietinum L.) genome based on recombinant inbred
lines from a C. arietinum Œ C. reticulatum cross: localiza-
tion of resistance genes for Fusarium wilt races 4 and 5.
Theoretical and Applied Genetics. 2000;101(7):1155-63.

17. Imtiaz M, Materne M, Hobson K, Van Ginkel M, Malhotra
RS. Molecular genetic diversity and linked resistance to
Ascochyta blight in Australian chickpea breeding materials
and their wild relatives. Australian Journal of Agricultural
Research. 2008;59(6):554-60.

18. Bouhadida M, Benjannet R, Madrid E, Amri M, Kharrat
M. Efficiency of marker-assisted selection in detection of
ascochyta blight resistance in Tunisian chickpea breeding
lines. Phytopathologia Mediterranea. 2013;52(1):202-11.

19. Bouhadida M, Benjannet R, Madrid E, Amri M, Kharrat
M. Efficiency of marker-assisted selection in detection of
ascochyta blight resistance in Tunisian chickpea breeding
lines. Phytopathologia Mediterranea. 2013;52(1):202-11.

20. Li Y, Ruperao P, Batley J, Edwards D, Davidson J, Hobson K,
et al. Genome analysis identified novel candidate genes for
Ascochyta blight resistance in chickpea using whole genome
re-sequencing data. Frontiers in Plant Science. 2017;8:359.

21. Rogers SO, Bendich AJ. Extraction of DNA from milligram
amounts of fresh, herbarium and mummified plant tissues.
Plant molecular biology. 1985;5:69-76.

22. Hamwieh A, Imtiaz M, Hobson K, Ahmed S. Genetic di-
versity of microsatellite alleles located at quantitative resis-
tance loci for Ascochyta blight resistance in a global collec-
tion of chickpea germplasm. Phytopathologia Mediterranea.
2013:183-91.

23. Lichtenzveig J, Scheuring C, Dodge J, Abbo S, Zhang HB.
Construction of BAC and BIBAC libraries and their appli-
cations for generation of SSR markers for genome analysis
of chickpea, Cicer arietinum L. Theoretical and Applied
Genetics. 2005;110(3):492-510.

24. Gujaria N, Kumar A, Dauthal P, Dubey A, Hiremath P,
Bhanu Prakash A, et al. Development and use of genic
molecular markers (GMMs) for construction of a transcript
map of chickpea (Cicer arietinum L.). Theoretical and Ap-
plied Genetics. 2011;122:1577-89.

25. Singh K, Reddy M. Sources of resistance to ascochyta blight
in wild Cicer species. Netherlands Journal of Plant Pathology.
1993;99:163-7.

26. Duarte JB, Vencovsky R. Interacao GenotiposŒAmbientes:
Uma Introducao a analise AMMI. vol. 9 of Serie Mono-
grafias. Ribeirao Preto: Sociedade Brasileira de Genetica;
1999.

27. Ooijen V. JoinMap® 5, Software for the calculation of
genetic linkage maps in experimental populations of diploid
species. Kyazma BV, Wageningen, Netherlands. 2018.

28. Kosambi DD. The estimation of map distance from recom-
bination values. Annals of Eugenics. 1944;12:172-5.

29. Alonso-Blanco C, Koornneef M, van Ooijen JW. QTL anal-
ysis. Arabidopsis protocols. 2006:79-99.

30. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z,
Miller W, et al. Gapped BLAST and PSI-BLAST: a new
generation of protein database search programs. Nucleic
Acids Research. 1997;25(17):3389-402.

31. Kaur M, Singh N. Characterization of protein isolates from
different Indian chickpea (Cicer arietinum L.) cultivars. Food
chemistry. 2007;102(1):366-74.

32. Lichtenzveig J, Shtienberg D, Zhang HB, Bonfil DJ,
Abbo S. Biometric analyses of the inheritance of resis-
tance to Didymella rabiei in chickpea. Phytopathology.
2002;92(4):417-23.

33. Pande S, Sharma M, Gaur PM, Basandrai AK, Kaur L,
Hooda KS, et al. Biplot analysis of genotype Œ environment
interactions and identification of stable sources of resistance
to Ascochyta blight in chickpea (Cicer arietinum L.). Aus-
tralasian Plant Pathology. 2013;42:561-71.

Highlights in BioScience Page 10 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/


Hamwieh et al., 2024 Exploring QTL genes contribute to chickpea ascochyta blight resistance

34. Gauch GH, Zobel RW. AMMI analysis of yield trials. In:
Kang MS, Gauch HG, editors. Genotype by Environment
Interaction. Boca Raton, FL: CRC Press; 1996. p. 85-122.

35. Yan W, Rajcan I. Biplots analysis of the test sites and trait
relations of soybean in Ontario. Crop Science. 2002;42:11-
20.

36. Stephens A, Lombardi M, Cogan NOI, Forster JW, Hobson
K, Materne M, et al. Genetic marker discovery, intraspecific
linkage map construction and quantitative trait locus analysis
of Ascochyta blight resistance in chickpea (Cicer arietinum
L.). Molecular Breeding. 2014;33:297-313.

37. Sabbavarapu MM, Sharma M, Chamarthi SK, Swapna N,
Rathore A, Thudi M, et al. Molecular mapping of QTLs for
resistance to Fusarium wilt (race 1) and Ascochyta blight in
chickpea (Cicer arietinum L.). Euphytica. 2013;193:121-33.

38. Cobos MJ, Rubio J, Strange RN, Moreno MT, Gil J, Millan
T. A new QTL for Ascochyta blight resistance in an RIL
population derived from an interspecific cross in chickpea.
Euphytica. 2006;149(1):105-11.

39. Martin GB, Brommonschenkel SH, Chunwongse J, Frary A,
Ganal MW, Spivey R, et al. Map-based cloning of a protein
kinase gene conferring disease resistance in tomato. Science.
1993;262:1432-6.

40. Zheng MS, Takahashi H, Miyazaki A, Hamamoto H, Shah
J, Yamaguchi I, et al. Up-regulation of Arabidopsis thaliana
NHL10 in the hypersensitive response to Cucumber mosaic
virus infection and in senescing leaves is controlled by sig-
nalling pathways that differ in salicylate involvement. Planta.
2004;218(5):740-50.

41. Yoshida S, Ito M, Nishida I, Watanabe A. Isolation and
RNA gel blot analysis of genes that could serve as poten-
tial molecular markers for leaf senescence in Arabidopsis
thaliana. Plant and Cell Physiology. 2001;42(2):170-8.

42. Takemiya A, Sugiyama N, Fujimoto H, Tsutsumi T, Ya-
mauchi S, Hiyama A, et al. Phosphorylation of BLUS1
kinase by phototropins is a primary step in stomatal opening.
Nature Communications. 2013;4:2094.

43. Bendahmane A, Kohn BA, Dedi C, Baulcombe DC. The
coat protein of potato virus X is a strain-specific elicitor of
Rx1-mediated virus resistance in potato. Plant J. 1995;8:933-
41.

44. Gurr SJ, Rushton PJ. Engineering plants with increased
disease resistance: how are we going to express it? Trends
in Biotechnology. 2005;23(6):283-90.

45. Duek PD, Fankhauser C. bHLH class transcription factors
take centre stage in phytochrome signalling. Trends in plant
science. 2005;10(2):51-4.

46. Cheng Q, Dong L, Gao T, Liu T, Li N, Wang L, et al. The
bHLH transcription factor GmPIB1 facilitates resistance to
Phytophthora sojae in Glycine max. Journal of experimental
botany. 2018;69(10):2527-41.

47. Koini MA, Alvey L, Allen T, Tilley CA, Harberd NP, White-
lam GC, et al. High temperature-mediated adaptations in
plant architecture require the bHLH transcription factor PIF4.
Current Biology. 2009;19(5):408-13.

48. Chinnusamy V, Ohta M, Kanrar S, Lee BH, Hong X, Agar-
wal M, et al. Complex regulation of cold-responsive gene
expression in Arabidopsis through genetic and epigenetic
mechanisms. Plant J. 2003;33:334-42.

49. Fujita M, Fujita Y, Noutoshi Y, Takahashi F, Narusaka Y,
Yamaguchi-Shinozaki K, et al. Crosstalk between abiotic
and biotic stress responses: a current view from the points
of convergence in the stress signaling networks. Current
Opinion in Plant Biology. 2006;9(4):436-42.

50. Feller A, Machemer K, Braun EL, Grotewold E. Evolu-
tionary and comparative analysis of MYB and bHLH plant
transcription factors. The Plant Journal. 2011;66(1):94-116.

51. Schwartz AR, Morbitzer R, Lahaye T, Staskawicz BJ. TALE-
induced bHLH transcription factors that activate a pec-
tate lyase contribute to water soaking in bacterial spot of
tomato. Proceedings of the National Academy of Sciences.
2017;114(5).

52. Varshney RK, Song C, Saxena RK, Azam S, Yu S, Sharpe
AG, et al. Draft genome sequence of chickpea (Cicer ari-
etinum) provides a resource for trait improvement. Nature
Biotechnology. 2013;31(3):240.

Highlights in BioScience Page 11 of 11 December 2024|Volume 7

http://bioscience.highlightsin.org/

	Abstract
	Introduction
	Materials and methods
	Plant materials and DNA isolation
	DArT and DArTseq
	Experimental Design and Locations
	Scoring Plants for AB Symptom Severity
	Statistical Analysis

	Results
	Phenotypic characterization
	Mapping and QTL analyses

	Discussion

