QTLs for earliness and yield-forming traits in the Lubuski × CamB barley RIL population under various water regimes

Article (PDF Available)inJournal of applied genetics 58(1) · August 2016with 172 Reads
DOI: 10.1007/s13353-016-0363-4
Cite this publication
Drought has become more frequent in Central Europe causing large losses in cereal yields, especially of spring crops. The development of new varieties with increased tolerance to drought is a key tool for improvement of agricultural productivity. Material for the study consisted of 100 barley recombinant inbred lines (RILs) (LCam) derived from the cross between Syrian and European parents. The RILs and parental genotypes were examined in greenhouse experiments under well-watered and water-deficit conditions. During vegetation the date of heading, yield and yield-related traits were measured. RIL population was genotyped with microsatellite and single nucleotide polymorphism markers. This population, together with two other populations, was the basis for the consensus map construction, which was used for identification of quantitative trait loci (QTLs) affecting the traits. The studied lines showed a large variability in heading date. It was noted that drought-treatment negatively affected the yield and its components, especially when applied at the flag leaf stage. In total, 60 QTLs were detected on all the barley chromosomes. The largest number of QTLs was found on chromosome 2H. The main QTL associated with heading, located on chromosome 2H (Q.HD.LC-2H), was identified at SNP marker 5880–2547, in the vicinity of Ppd-H1 gene. SNP 5880–2547 was also the closest marker to QTLs associated with plant architecture, spike morphology and grain yield. The present study showed that the earliness allele from the Syrian parent, as introduced into the genome of an European variety could result in an improvement of barley yield performance under drought conditions. Electronic supplementary material The online version of this article (doi:10.1007/s13353-016-0363-4) contains supplementary material, which is available to authorized users.
QTLs for earliness and yield-forming traits
in the Lubuski × CamB barley RIL population
under various water regimes
Piotr Ogrodowicz
&Tade u s z A d a m s ki
&Krzysztof Mikołajczak
&Anetta Kuczyńska
Maria Surma
&Aneta Sawikowska
&Andrzej G. Górny
Kornelia Gudyś
&Iwona Szarejko
&Justyna Guzy-Wróbelska
Karolina Krystkowiak
Received: 22 April 2016 /Revised: 4 July 2016 / Accepted: 14 July 2016
#The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Drought has become more frequent in Central
Europe causing large losses in cereal yields, especially of
spring crops. The development of new varieties with increased
tolerance to drought is a key tool for improvement of agricul-
tural productivity. Material for the study consisted of 100 bar-
ley recombinant inbred lines (RILs) (LCam) derived from the
cross between Syrian and European parents. The RILs and
parental genotypes were examined in greenhouse experiments
under well-watered and water-deficit conditions. During veg-
etation the date of heading, yield and yield-related traits were
measured. RIL population was genotyped with microsatellite
and single nucleotide polymorphism markers. This popula-
tion, together with two other populations, was the basis for
the consensus map construction, which was used for identifi-
cation of quantitative trait loci (QTLs) affecting the traits. The
studied lines showed a largevariability in heading date. It was
noted that drought-treatment negatively affected the yield and
its components, especially when applied at the flag leaf stage.
In total, 60 QTLs were detected on all the barley chromo-
somes. The largest number of QTLs was found on
chromosome 2H. The main QTL associated with heading,
located on chromosome 2H (Q.HD.LC-2H), was identified
at SNP marker 58802547, in the vicinity of Ppd-H1 gene.
SNP 58802547 was also the closest marker to QTLs associ-
ated with plant architecture, spike morphology and grain
yield. The present study showed that the earliness allele from
the Syrian parent, as introduced into the genome of an
European variety could result in an improvement of barley
yield performance under drought conditions.
Keywords Consensus map .Drought .Earliness .SNP
annotation .SNP markers .Spring barley .SSR markers
Barley (Hordeum vulgare L.) is not only one of the most
important crops from an economic point of view (FAOSTAT
2014), but it is also an excellent species for genome mapping
and map-based analyses (Costa et al. 2001; Mansour et al.
2014). Its diploid nature, low chromosome number and a high
degree of self-fertility mean that barley is a common subject
for genetic studies examining drought resistance of crops
(Tondelli et al. 2006; Talamè et al. 2007).
Several genetic maps based upon different genetic marker
techniques have been published (Wenzl et al. 2006;Zhouetal.
2015). Among various types of DNA markers, microsatellites
(SSR) and single nucleotide polymorphism (SNP) have been
widely used for genome analyses (Cockram et al. 2010;
Cuesta-Marcos et al. 2010;Honsdorfetal.2014; Varshney
et al. 2007). The first high-density gene map based on SNP
markers contained 2.943 SNP loci in 975 marker bins and
covered a genetic distance of 1099 cM (Close et al. 2009).
Communicated by: Andrzej Górny
Electronic supplementary material The online version of this article
(doi:10.1007/s13353-016-0363-4) contains supplementary material,
which is available to authorized users.
*Karolina Krystkowiak
Institute of Plant Genetics, Polish Academy of Sciences,
Strzeszyńska 34, 60-479 Poznań, Poland
Department of Genetics, Faculty of Biology and Environmental
Protection, University of Silesia, Jagiellońska 28,
40-032 Katowice, Poland
J Appl Genetics
DOI 10.1007/s13353-016-0363-4
The availability of high-throughput SNP genotyping has fa-
cilitated the genetic studies of agronomically important traits
(Close et al. 2004; Wang et al. 2010b). Recently, research
containing a detailed overview of the functional portions of
the barley genome has been published (International Barley
Genome Sequencing Consortium 2012). This highly resoluted
genetic map together with the sequencedata has a tremendous
potential for candidate gene discovery using conservation of
the grass genome synteny (Mayer et al. 2009).
Abiotic stresses reduce average yields for most crops
(Boyer 1982;Bray1997). Among the stresses, water deficit
is the most devastating on a global scale (Zhao and Runnings
2009). Aspinall et al. (1964)andSamarah(2005)havereport-
ed stage-specific drought responses in the crops. Appropriate
irrigation conditions during the stem elongation phase are in-
dispensable for the formation of fertile florets at anthesis as the
final number of grains is determined during this period
(Miralles and Slafer 1995). Water deprivation in this critical
developmental stage affects numerous aspects of plant metab-
olism leading to impairment of many biochemical pathways
(Moran et al. 1994; Loggini et al. 1999; Farooq et al. 2009).
The simplest solution to survive in dry environments is an
escape from drought (Passioura 1996;Richards1996). The
short life cycle of crop plants might be considered as an im-
portant trait related to water deficit adaptation (Araus et al.
2002). The majority of barley cultivars vary significantly in
their response to water scarcity (Zare 2012). Sources of
drought tolerance can be found in landraces from geographi-
cal regions with challenging climates close to their domesti-
cation origin (Ellis et al. 2000;Górny2001; Nevo and Chen
Heading date in barley depends on vernalisation require-
ments (Takahashi and Yasuda 1956; Sasani et al. 2012), pho-
toperiodic response (Roberts et al. 1988;Laurieetal.1995)
and earliness per se genes (Gallagher et al. 1991; Sameri et al.
2006). Quantitative trait loci (QTLs) associated with heading
date have been mapped on all barley chromosomes (e.g.
Hayesetal.1993; Laurie et al. 1995; Tinker et al. 1996;
Bezant et al. 1997;Qietal.1998;Pillenetal.2003) and many
allele-specific markers for some candidate genes controlling
these processes are known (Turner et al. 2005; Faure et al.
2007;Szűcs et al. 2007).
The major photoperiod response locus has been identified
by RFLP analysis on the short arm of chromosome 2H.
Dominant alleles at Ppd-H1 accelerate flowering under long
day conditions, whereas no effect has been detected under
short day conditions. Laurie et al. (1995) and Turner et al.
(2005) have shown that the late-flowering allele is recessive.
Two main single nucleotide polymorphisms have been detect-
ed which differentiate alleles involved in the plant sensitivity
to day length. Non-synonymous (G Ppd-H1/Appd-h1)
SNP within the CCT domain has been suggested as an expla-
nation for recessive form of the allelic variation (Turner et al.
2005). Another study revealed that a polymorphism in the
photoperiodic response in barley varieties might be associated
with the SNP48 situated in the exon 6 of the Ppd-H1 coding
region) (Jones et al. 2008). A second major photoperiodic
response locus (Ppd-H2) has been mapped to the chromosome
1H (Laurie et al. 1995). The Ppd-H2 affects the flowering time
under short day conditions. A candidate gene (HvFT3) for this
locus has been proposed by Faure et al. (2007).
The vernalisation and photoperiodic pathways correspond
to each other to promote flowering in crops (Distelfeld et al.
2009). A study conducted using barley spring crosses revealed
loci for flowering time in the regions connected with
vernalisation response (Bezant et al. 1997). Three genes,
Vrn-H1,Vrn-H2 and Vrn-H3, located on the chromosomes
5H, 4H and 7H, respectively, have been proposed as the major
vernalisation response genes (Cockram et al. 2007).
The aim of the present study was to detect QTLs determin-
ing yield and yield-forming traits in a recombinant inbred line
(RIL) population developed from a hybrid between European
and Syrian genotypes (adapted to dry environments) under
optimal and water stress conditions, with special attention
being paid to earliness.
Materials and methods
Plant material
Material for the study covered RIL population of spring barley
(Hordeum vulagre L.) derived from the cross Lubuski ×Cam/
B1/CI08887//CI05761. The parent Cam/B1/CI08887//
CI05761 (hereafter referred as CamB) is the Syrian breeding
line supplied to Dr. A. Górny by Drs S. Grando and S.
Ceccarelli from ICARDA in Aleppo and Lubuski is an old
Polish cultivar derived from a Heines-Haisa/Skrzeszowicki
hybrid. The examined population was developed by means
of the single seed descent (SSD) technique (up to F
(Goulden 1939) associated with in vitro culture of immature
embryos (Surma et al. 2013). Out of 150 developed RILs 100
were randomly chosen for the present experiments.
Greenhouse experiments
The greenhouse experiments with the Lubuski× CamB pop-
ulation were conducted (was grown in three replicates) in two
growing seasons (2012, 2013) during AprilAugust. In both
years, three water regimes were applied: (1) C optimal water
supply for the whole vegetation period, (2) DI drought stress
beginning at the three-leaf stage (13 in the BBCH scale) and
maintained for 10 days, (3) DII drought stress beginning at
the flag leaf stage (37 in the BBCH scale) and maintained for
14 days, which created six environments denoted as: C 2012,
C 2013, DI 2012, DI 2013, DII 2012 and DII 2013. Ten plants
J Appl Genetics
were grown in pots containing 9 kg of soil. Air moisture and
temperature were monitored by special device (LOG32 -
Temperature-humidity logger with integrated USB-interface
and automatic PDF-creation). Control of the soil moisture
was provided by a hand-held device (FOM/mts) designed
for field measurements of the soil moisture and temperature
(Malicki et al. 1996). The weighing method was used as an
additional control of the irrigation system. The soil moisture
was kept at 2.2 and 3.2 pF in optimal and drought conditions,
respectively (ESM 1). Three groups of traits were observed:
associated with morphology of the main and lateral spikes
(grain weight per main spike - GWSm, number of grains per
main spike - NGSm, number of spikelets per main spike -
NSSm, length of main spike - LSm, grain weight per lateral
spike - GWSl, number of grains per lateral spike - NGSl,
number of spikelets per lateral spike- NSSl, length of lateral
spike - LSl), with plant architecture (length of main stem - LSt,
number of productive tillers per plant - NPT), and with grain
yield (1000-grain weight - TGW, Grain weight per plant -
GWP). Duration of the vegetative growth period was
expressed as the number of days from sowing to heading
(heading date - HD). The measured traits are listed in ESM 2.
In the present studies consensus map constructed by
Mikołajczak et al. (2016) was used for QTL analysis.
Briefly: A set of 78 barley SSR markers developed by
Varshney et al. (2007) was used in the experiment. SNP
genotyping was carried out at the Southern California
Consortium using the Illumina GoldenGate array 1 (Illumina
Inc., San Diego, CA) that analyses 1.536 genome-wide single
nucleotide polymorphisms; details of this array (BOPA - bar-
ley oligo pool assays) are described by Close et al. (2009).
JoinMap 3.0 software (Van Ooijen and Voorrips 2001)was
used for the map construction. Once the individual genetic
map was obtained, the consensus map was constructed. The
complete dataset consisted of 819 markers mapped in the
Maresi × CamB (MCam), Lubuski × CamB (LCam) and
Georgie × Harmal (GH) populations. Details on the develop-
ment of the map construction are given in Mikołajczak et al.
Statistical analysis
Observations for RILs were processed by analysis of variance
in a mixed model with fixed effects for year, drought and
year × drought interaction, and with random effects for line
and interaction of line with year and/or drought treatment.
The residual maximum likelihood (REML) algorithm was
used to estimate variance components for random effects
and the F-statistic was computed to assess the significance
of the fixed effects. Ordinary, mean values computed for
RILs in all specific (years × drought) - combinations were
used for construction of principal component biplots.
Pearson correlation coefficients between all the analysed traits
were calculated. QTL analysis was performed for the consen-
sus linkage map (Mikołajczak et al. 2016) with the mixed-
model approach described by Malosetti et al. (2013), includ-
ing optimal genetic correlation structure selection and the sig-
nificance threshold estimation. The interval mapping was con-
ducted with a step size of 2 cM by selecting the QTL candidate
and then using them iteratively as cofactors until the list of
QTL was not changed. The threshold for the log10(P-value)
statistic was computed by the method of Li and Ji (2005)to
ensure the genome-wide error rate was less than 0.01. The
windows for not selecting two close QTLs and for exclusion
of cofactors were set at 10 and 30 cM, respectively. Selection
of the set of QTL effects in the final model was performed at
P<0.05; the P-values for the Wald test were computed as the
mean from the values obtained by adding and dropping the
QTL main and interaction effects in the model. All the above
computations were performed in Genstat 16 (VSN Int. 2013).
QTL annotation
All SNP sequences taken from Close et al. (2009)
(Supplementary material file BOPA1 SNP 1471-2164-
10-582-S19.xls) were mapped using NCBI Blast for
Windows to barley genomic space in Ensembl Plants
ver. 2.28 (reference repeat masked sequence
Hordeum_vulgare.082214v1.28.dna_rm.toplevel.fa, maxi-
mum EValue = 1e-060, minimum 95 % identity of the
SNP sequence). The SNP mapping positions were used
to obtain a projection of two LOD QTL support intervals
(see Xu 2010) onto the genomic sequence; all genes lo-
cated in projected intervals were listed and annotated
using Gene Ontology (GO) terms. For QTL interpretation,
we applied a method similar to the one implemented by
Cantalapiedra et al. (2015).
Early and late heading subgroups of plants
According to SNP 58802547 segregation (Mansour et al.
2014; Muñoz-Amatriaín et al. 2011), RILs were divided into
two subgroupsearly heading (group A allele from CamB)
and late heading plants (group B allele from Lubuski).
Phenotypic evaluation
The average heading dates and the mean values of mor-
phological traits for parental cultivars and RILs in the six
environments are presented in ESM 3aandESM3bas
J Appl Genetics
supplementary material, respectively. Parental genotypes
are classified as early (CamB) and late (Lubuski) accord-
ing to the large differences between their heading dates
in all experiments. The heading (HD) of the Syrian ge-
notype grown under well-watered conditions was about
1519 days earlier than of the European one. The HD for
parents increased both in DI conditions (by about 3 -
6 days) and in DII conditions (by about 1 - 6 days) as
compared to the well-watered conditions. The data anal-
ysis across two years showed highly significant differ-
ences among lines for HD. RILs with longer vegetation
periods than the late-heading parent were noticed among
the studied population in all environments. For all stud-
ied traits, Lubuski showed significantly higher values
under well-watered conditions compared to CamB (with
the exception of LSt in 2012, 2013 and NPT in 2013).
The Syrian cultivar showed higher values for TGW than
the European parent in DI over the two years. On the
other hand, Lubuski showed higher GWP in all environ-
ments. The comparison of the parental genotypes for
traits connected with the plant architecture showed that
CamB formed more productive tillers under water-stress
conditions applied at the three-leaf stage.
In RILs, NPT increased both under drought I and drought II
conditions. For all observed traits (with the exceptions: HD
and NPT), greater decrease were noticed under drought II.
Lines of the LCam population were significantly dif-
ferentiated in terms of all analysed traits (Table 1). In all
case, the variance components for all types of
interactions were smaller than that for lines. For HD,
variance components were significant for all types of
interaction (i.e. for line × year, line × water regimes,
line × year × water regimes). On the other hand, no inter-
action component was significant for NGSm and LSl.
As shown in biplots (Fig. 1), RIL plants grown in drought
II were affected more than in drought I, as they are further
away from control plants superiorin both yearsby spike
morphology traits and LSt.
Correlations of all traits with HD were significant
(P< 0.001) in at least one environment, with no correlation
significant in DII 2012 (Table 2). The highest correlation co-
efficient was found for NSSl in drought I in 2012 (r = 0.762),
whereas the correlation between HD and NGSl (DII 2013)
was the weakest (r = 0.236). Significant negative correlations
between HD and NPT, revealed also in 2013 biplot, were
observed across three environments (DI 2012, DI 2013, C
2013) which indicates thatearly heading lines developed more
productive tillers, especially in DI conditions. No significant
association was found between days to heading and 1000-
grain weight, except for the control conditions in 2012 (neg-
ative correlation). Positive and significant correlations were
recorded between HD and spike traits: GWSm, NGSm,
NSSm, LSm, GWSl, NGSl, NSSl in both years in DI and C
conditions. This indicates that late heading lines developed
longer spikes with more spikelets andas a consequence
more grains. Moreover, a positive correlation was found be-
tween GWP and heading stage, which indicates that early
heading lines were characterised by lower yield.
Tabl e 1 ANOVA results and variance components estimates for agronomic traits observed in LCam population
Trait (abbrev.) P-values for significance
of effects of
Variance components and std. errors for
years (Y) treatment (D) Y × D
lines s.e. interaction
line x year
s.e. interaction line
x treatment
s.e. interaction line
x year x treatment
HD <0.001 < 0.001 < 0.001 11.8878* 2.4091 1.8182* 0.5891 8.3332* 1.1899 6.0901* 0.6216
TGW < 0.001 < 0.001 < 0.001 3.0051* 0.7274 1.4115* 0.4623 0.7955 0.4152 0.0146 0.5255
GWP < 0.001 < 0.001 < 0.001 0.0327* 0.0076 0.0216* 0.005 0.0013 0.0028 0.0005 0.004
LSt < 0.001 < 0.001 < 0.001 18.5399* 3.3819 5.0418* 1.3247 0.7975 0.8817 1.3372 1.2274
NPT < 0.001 < 0.001 < 0.001 0.0817* 0.0193 0.0075 0.01 0.0476* 0.0154 0.0228 0.0167
GWSm < 0.001 < 0.001 < 0.001 0.0073* 0.0013 0.0007 0.0003 0.0018* 0.0004 0.0008 0.0004
NGSm < 0.001 < 0.001 < 0.001 4.2621* 0.665 0.1455 0.0939 0.3227 0.1172 0.2625 0.1393
NSSm < 0.001 < 0.001 < 0.001 5.1929* 0.7881 0.1276 0.0845 0.2369 0.1022 0.387* 0.1242
LSm < 0.001 < 0.001 < 0.001 0.4565* 0.0709 0.0121 0.0097 0.0326 0.0126 0.06* 0.0147
GWSl < 0.001 < 0.001 < 0.001 0.0031* 0.0007 0.0013* 0.0004 0 0.0003 0.0008 0.0004
NGSl < 0.001 < 0.001 < 0.001 1.6608* 0.365 1.0565* 0.2214 0.0386 0.1002 0.2787 0.1471
NSSl < 0.001 < 0.001 < 0.001 3.3928* 0.5479 0.4478* 0.1179 0.1225 0.0816 0.0809 0.1104
LSl < 0.001 < 0.001 < 0.001 0.31* 0.0494 0.0223 0.0092 0.0191 0.0093 0.0173 0.0116
* variance component at least three times greater than its standard error
s.e.- standard error
J Appl Genetics
QTL analyses
A total of 60 QTLs were detected on all chromosomes
(Table 3). The largest number of QTLs were found on chro-
mosome 2H (23 QTLs). Only three QTLs were detected on
chromosome 1H. The largest number of QTLs were found for
NGSm and LSm (nine QTLs). The lowest number of QTLs
were found for GWP (one QTL). The QTL × E interaction
was found for 68 % of QTLs detected. All QTLs for HD
and NSSm showed QTL × E interaction (ESM 4).
QTLs for earliness and yield-forming traits
Four QTLs for heading date (HD) were found on chromo-
somes 2H (Q.HD.LC-2H), 3H (Q.HD.LC-3H.1), 5H
(Q.HD.LC-5H.3) and 7H (Q.HD.LC-7H.2). In the vicinity
of the Q.HD.LC-2H, 12 QTLs for plant architecture, spike
morphology and grain yield were detected (Q.GWSl.LC-2H-
1, Q.GWSm.LC-2H-1, Q.GWP.LC-2H, Q.HD.LC-2H,
Q.LSl.LC-2H, Q.LSm.LC-2H-1, Q.NGSl.LC-2H-1,
Q.NGSm.LC-2H-1, Q.NPT.LC-2H-1, Q.NSSl.LC-2H,
Q.NSSm.LC-2H-1 and Q.LSt.LC-2H) for the region on the
short arm of chromosome 2H, whereas one QTL (Q.HD.LC-
3H.1) was found for the region detected on chromosome 3H.
On chromosome 5H, close to the Q.HD.LC-5H.3, were found
QLSm.LC-5H.3, QLSl.LC-5H.3 and QGWSm.LC-5H.3 and
also three QTLs were identified in the vicinity of the
Q.HD.LC-7H.2: Q.LSm.LC-7H.2, Q.LSl.LC-7H.2 and
For HD the major was QTL on chromosome 2H, which
showed the most significant effect and explained a large pro-
portion of the phenotypic variation. This QTL was mapped at
the marker 58802547 at the position of 10.74 cM (Fig. 2). In
almost all environments the alleles from the Syrian parent
reduced days to heading; DII 2012 was an exception to this
rule, and in this environment the percentage of explained var-
iation was a low (9.55 %).
According to SNP 58802547 segregation RILs were di-
vided into two subgroups early heading (group A allele
from CamB) and late heading plants (group B allele from
Lubuski). The different developmental pattern for these sub-
groups was noticed in the stress conditions. An extreme delay
in heading was observed for early heading lines in DII condi-
tions (Fig. 3).
2012 2013
PC1 (34.3%) PC1 (35.0%)
PC2 (15.6%)
PC2 (15.3%)
Fig. 1 Principal component biplots, with dots corresponding to LCam recombinant inbred lines observed in drought DI (red), drought DII (yellow)and
in control conditions (green), and vectors corresponding to observed traits, made for data obtained in 2012 and 2013
Tabl e 2 Correlation coefficients between HD and yield forming traits
under well-watered and drought conditions
Trait Treatment
DI 2012 DI 2013 DII 2012 DII 2013 C 2012 C 2013
TGW n.s. n.s. n.s. n.s. 0.350 n.s.
GWP 0.520 0.295 n.s. n.s. 0.438 0.296
LSt n.s. 0.296 n.s. n.s. n.s. n.s.
NPT 0.583 0.444 n.s. n.s. n.s. 0.313
GWSm 0.658 0.523 n.s. n.s. 0.536 0.413
NGSm 0.755 0.618 n.s. n.s. 0.661 0.614
N.S.Sm 0.745 0.593 n.s. n.s. 0.656 0.581
LSm 0.630 0.426 n.s. n.s. 0.467 0.251
GWSl 0.627 0.357 n.s. n.s. 0.326 0.299
NGSl 0.717 0.461 n.s. 0.236 0.548 0.456
NSSl 0.762 0.572 n.s. 0.321 0.534 0.575
LSl 0.597 0.490 n.s. 0.287 n.s. 0.402
n.s.- not significant
Correlations shown are significant at the P < 0.001 level
J Appl Genetics
Tab l e 3 QTLs identified in the LCam population for the observed traits
Trait QTL ID Linkage
Marker Synonim
to QTL
Additive effect (b) Percent of variance explained by
QTL in years (%) (c)
2012 2012 2012 2013 2013 2013 2012 2012 2012 2013 2013 2013
Heading date Q.HD.LC-2H 2H 10.74 5880-2547 11_21015 57.33 0.00 1 6.32 0.81 6.41 6.18 0.99 5.40 99.02 9.55 75.84 102.96 25.77 112.57
Q.HD.LC-3H.1 3H.1 47.56 10353-119 11_10011 3.54 0.00 1 1.03 n.s. 2.01 n.s. n.s. n.s. 2.64 7.42 ––
Q.HD.LC-5H.3 5H.3 59.03 314-559 11_20487 6.23 0.00 1 0.77 1.18 1.62 n.s. n.s. n.s. 1.48 20.28 4.84 ––
Q.HD.LC-7H.2 7H.2 15.97 1213-1959 11_10056 7.31 0.00 1 n.s. 1.29 1.09 n.s. n.s. n.s. 24.11 2.19 ––
Q.TGW.LC-2H 2H 47.93 6384-866 11_21096 3.87 0.00 1 1.22 1.15 0.55 n.s. n.s. n.s. 11.06 12.73 3.81 ––
5H.3 66.97 ABC04352-
11_11092 3.17 0.00 0 0.68 0.68 0.68 0.68 0.68 0.68 3.47 4.48 5.86 4.54 7.45 3.42
Q.TGW.LC-6H 6H 48.08 4258-1498 11_20720 3.12 0.00 0 0.70 0.70 0.70 0.70 0.70 0.70 3.63 4.70 6.14 4.76 7.81 3.58
7H.2 15.97 1213-1959 11_10056 4.03 0.00 1 0.75 n.s. 0.90 n.s. n.s. n.s. 4.23 10.12 ––
grain weight
per plant
Q.GWP.LC-2H 2H 10.74 5880-2547 11_21015 8.00 0.00 1 0.21 0.15 0.17 0.09 0.07 0.13 41.04 31.71 28.63 13.56 8.98 14.77
Length of
main stem
Q.LSt.LC-1H.2 1H.2 32.59 5048-1685 11_10729 6.73 0.00 0 1.88 1.88 1.88 1.88 1.88 1.88 9.40 15.95 7.03 10.39 14.16 8.23
Q.LSt.LC-2H 2H 14.78 7747-1056 11_21261 10.14 1.90 1 2.00 n.s. n.s. 3.36 2.70 n.s. 10.71 ––33.26 29.25
Q.LSt.LC-3H.1 3H.2 36.57 2346-318 11_10283 3.60 0.00 1 n.s. 1.26 n.s. n.s. n.s. 2.62 7.16 –– 16.10
Number of
tillers per
Q.NPT.LC-2H-1 2H 10.74 5880-2547 11_21015 13.90 0.00 1 0.45 0.15 0.13 0.29 n.s. 0.10 45.52 6.55 7.26 30.47 4.71
Q.NPT.LC-2H-2 2H 74.42 6117-1507 11_10823 3.47 1.90 1 n.s. n.s. n.s. n.s. n.s. 0.17 ––––13.82
Q.NPT.LC-2H-3 2H 109.05 5088-59 11_10731 4.47 0.00 0 0.15 0.15 0.15 0.15 0.15 0.15 5.02 6.26 9.29 7.86 14.49 11.00
Q.NPT.LC-5H.3 5H.3 72.01 1306-408 11_10080 5.71 0.00 0 0.17 0.17 0.17 0.17 0.17 0.17 6.83 8.52 12.63 10.68 19.70 14.95
Q.NPT.LC-6H 6H 41.21 4191-268 11_20707 2.50 0.00 0 0.10 0.10 0.10 0.10 0.10 0.10 2.22 2.76 4.10 3.47 6.39 4.85
Grain weight
per main
2H 10.74 5880-2547 11_21015 14.08 0.00 1 0.10 0.06 0.11 0.11 0.05 0.07 61.23 38.72 63.96 71.98 49.71 26.55
2H 113.18 3000-1074 11_10404 2.40 0.00 1 0.03 n.s. 0.03 n.s. n.s. 0.03 5.08 1.32 5.53 ––4.42
2H 138.98 1344-930 11_10085 3.99 5.92 1 n.s.n.s.n.s.n.s.0.03 n.s. ––17.30
4H 45.08 3127-273 11_20482 2.44 0.00 0 0.02 0.02 0.02 0.02 0.02 0.02 2.24 4.45 2.03 2.14 8.88 1.79
5H.3 61.54 Consensus
11_11448 3.96 0.00 0 0.02 0.02 0.02 0.02 0.02 0.02 3.39 6.73 3.07 3.24 13.44 2.70
Number of
grains per
main spike
2H 10.74 5880-2547 11_21015 23.66 0.00 1 2.55 1.92 2.31 2.98 1.14 2.04 84.41 70.06 77.10 107.03 66.76 56.85
2H 140.95 1344-930 11_10085 4.14 3.95 1 n.s.n.s.n.s.n.s.0.51 0.48 ––13.61 3.14
4H 5.91 2533-773 11_10319 4.99 1.44 1 n.s. 0.61 0.56 0.53 n.s. n.s. 7.15 4.50 3.44 ––
4H 28.79 14765-388 11_20180 3.92 1.66 1 n.s.n.s.n.s.n.s.0.27 0.41 ––3.64 2.33
4H 45.08 3127-273 11_20482 4.19 0.00 0 0.47 0.47 0.47 0.47 0.47 0.47 2.87 4.23 3.19 2.67 11.39 3.03
4H 76.48 5245-304 11_10751 5.37 0.00 1 n.s. n.s. 0.66 0.65 n.s. 0.64 ––6.21 5.09 1.03 5.63
5H.3 70.25 Consensus
11_11441 2.03 1.25 1 0.96 n.s. 0.76 0.77 n.s. 0.83 11.84 8.43 7.15 9.45
J Appl Genetics
Tab l e 3 (continued)
Trait QTL ID Linkage
Marker Synonim
to QTL
Additive effect (b) Percent of variance explained by
QTL in years (%) (c)
2012 2012 2012 2013 2013 2013 2012 2012 2012 2013 2013 2013
6H 38.46 2176-891 11_10244 6.63 0.00 1 1.01 0.62 1.09 0.85 0.34 1.26 13.24 7.30 17.23 8.79 5.98 21.72
6H 60.09 3773-756 11_20620 4.51 0.00 1 0.47 0.82 0.47 0.85 0.27 0.89 2.90 12.82 3.24 8.70 3.77 10.75
Number of
per main
2H 10.74 5880-2547 11_21015 23.18 0.00 1 2.56 2.03 2.21 3.29 1.54 2.55 80.12 65.48 74.28 114.08 82.47 80.77
2H 140.95 1344-930 11_10085 3.43 3.95 1 n.s.n.s.n.s.n.s.0.61 n.s. ––13.12
5H.3 23.58 10669-188 11_10024 4.00 3.70 1 0.66 0.92 n.s. 0.93 0.48 1.34 5.30 13.48 9.04 7.85 22.31
5H.3 71.5 Consensus
11_11441 1.40 0.00 1 0.75 n.s. 0.73 n.s. n.s. 0.61 6.92 8.03 ––4.68
Q.NSSm.LC-6H 6H 38.46 2176-891 11_10244 7.07 0.00 1 1.08 0.54 0.92 0.64 n.s. 0.76 14.33 4.67 12.92 4.35 7.27
Length of
main spike
Q.LSm.LC-2H-1 2H 10.74 5880-2547 11_21015 24.73 0.00 1 0.86 0.56 0.78 1.00 0.34 0.76 101.39 64.31 95.69 99.29 38.20 64.78
Q.LSm.LC-2H-2 2H 113.18 3000-1074 11_10404 2.65 0.00 0 0.11 0.11 0.11 0.11 0.11 0.11 1.82 2.66 2.09 1.31 4.37 1.48
Q.LSm.LC-3H.1 3H.1 5.68 5945-748 11_21027 2.09 1.89 0 0.26 0.26 0.26 0.26 0.26 0.26 9.02 13.22 10.39 6.53 21.72 7.36
Q.LSm.LC-4H 4H 45.08 3127-273 11_20482 4.67 0.00 0 0.17 0.17 0.17 0.17 0.17 0.17 4.02 5.89 4.63 2.91 9.68 3.28
Q.LSm.LC-5H.3 5H.3 59.03 314-559 11_20487 14.80 0.00 1 0.19 0.21 0.25 0.46 n.s. 0.55 4.77 8.67 9.59 21.32 34.44
Q.LSm.LC-6H-1 6H 30.02 1588-537 11_10129 6.70 0.00 1 0.14 n.s. n.s. 0.16 0.09 0.25 2.75 ––2.55 2.61 6.79
Q.LSm.LC-6H-2 6H 60.09 3773-756 11_20620 4.92 0.00 1 0.13 0.19 0.16 0.23 0.10 0.31 2.31 7.40 3.85 5.25 3.53 10.58
Q.LSm.LC-6H-3 6H 72.83 2968-1066 11_10400 4.13 0.00 0 0.24 0.24 0.24 0.24 0.24 0.24 8.11 11.89 9.35 5.87 19.54 6.62
Q.LSm.LC-7H.2 7H.2 15.97 1213-1959 11_10056 6.61 0.00 0 0.21 0.21 0.21 0.21 0.21 0.21 5.81 8.51 6.69 4.20 13.99 4.74
Grain weight
per lateral
1H.2 27.76 3786-2204 11_20625 1.01 0.00 0 0.02 0.02 0.02 0.02 0.02 0.02 4.88 8.33 5.40 10.25 12.40 3.99
1H.2 32.59 5048-1685 11_10729 5.52 0.00 1 0.05 0.05 0.04 n.s. n.s. n.s. 19.89 32.18 12.80 ––
2H 10.74 5880-2547 11_21015 13.05 0.00 1 0.08 0.04 0.05 0.03 0.02 0.04 60.41 30.83 30.59 23.11 10.00 12.60
2H 124.23 4241-445 11_20715 4.35 1.46 1 0.03 0.03 0.03 n.s. n.s. n.s. 10.55 13.44 10.11 ––
Number of
grains per
2H 10.74 5880-2547 11_21015 19.03 0.00 1 2.16 1.31 1.54 1.48 0.94 1.43 73.12 44.55 64.41 58.53 35.28 39.42
2H 124.23 4241-445 11_20715 3.98 1.46 1 0.54 0.78 0.43 n.s. n.s. n.s. 4.61 15.88 5.08 ––
2H 140.95 1344-930 11_10085 3.12 3.95 0 0.55 0.55 0.55 0.55 0.55 0.55 4.68 7.72 8.12 8.02 11.82 5.80
3H.1 28.16 4593-2007 11_10672 2.24 1.74 1 0.68 0.70 0.60 n.s. n.s. n.s. 7.20 12.64 9.91 ––
Number of
per lateral
2H 10.74 5880-2547 11_21015 19.69 0.00 1 2.08 1.28 1.34 1.82 1.19 1.63 64.61 35.51 46.95 58.10 41.50 41.71
6H 38.46 2176-891 11_10244 2.06 0.00 0 0.53 0.53 0.53 0.53 0.53 0.53 4.13 5.98 7.24 4.88 8.14 4.33
6H 59.65 5187-752 11_20892 5.46 0.00 1 0.36 0.70 0.54 0.67 n.s. 0.73 1.94 10.68 7.56 7.90 8.37
2H 10.74 5880-2547 11_21015 20.68 0.00 1 0.68 0.43 0.41 0.82 0.50 0.68 84.33 58.61 42.83 99.56 73.11 81.43
J Appl Genetics
In the region of Q.HD.LC-2H, marked by SNP 58802547
(10.14 cM) and 77471056 (14.78 cM), QTLs for all yield-
related traits, except TGW, were found (Fig. 2). In all cases
they appeared to be the most significant QTLs for a particular
trait, with the LogP statistic ranging from 8.00 for GWP to
24.73 for LSm. All of them showed a significant interaction
with environment, but the sign of the allelic effects was con-
sistent over environments. For yield-forming traits, except
NPT, alleles contributed by Lubuski increased the traits.
Q.HD.LC-3H.1 on chromosome 3H at SNP 10353119
showed a minor positive effect contributed by a Syrian parent
allele. That QTL explained only 2.647.42 % of phenotypic
variance, and its additive effect was significant only in DI
2012 and C 2012. No QTL associated with other traits was
found in this region (Table 3).
The Q.HD.LC-5H.3 located on the linkage group 5H.3 at
SNP 314559 with CamB allele causing later heading was
significant only in 2012. In the region of this QTL, marked
by SNP 314-559 and ConsensusGBS0138-2, QTLs for LSm,
GWSm and LSl were also found, with alleles from Lubuski
increasingthe trait values, and the variance explained from 0.5
to 34.4 % (Table 3).
The Q.HD.LC-7H.2 was detected on chromosome 7H at
SNP 12131959. It explained from 2.19 to 24.11 % of the
phenotypic variation, with the Lubuski allele increasing the
number of days from sowing to heading. Effect of that QTL
was significant only in 2012 in DII and C conditions. At the
same position QTL for TGW (Q.TGW.LC-7H.2) was local-
ised and significant also only in 2012. That QTL explained
4.23-10.12 % of the phenotypic variance and allele contribut-
ed by Lubuski reduced the TGW. Near Q.HD.LC-7H.2 the
QTLs for LSm and LSl were also found. These QTLs
(Q.LSm.LC-7H.2 and Q.LSl.LC-7H.2, both with stabile ef-
fects) explained 4.2013.99 % and 5.6212.17 % of the phe-
notypic variation, respectively. Both QTLs were characterised
by Lubuski alleles increasing the traits (Table 3).
Functional annotation of QTLs
For a biological interpretation of the QTL regions identified
on the basis of linkage analysis, we refer to the annotation of
SNP using the Ensembl Plants barley gene space according
with the approach used in Mikołajczak et al. (2016). The anal-
ysis revealed two of the main GO biological processes (de-
fense response and protein ubiquitination) overrepresented in
the annotation genes for traits: grain weight per main spike,
grain weight per plant and lengthof main spike (Table 4). The
largest number of genes (15) annotated with the previously
mentioned terms was noticed for Bprotein ubiquitination^.
Functional annotation analysis also showed six other biolog-
ical processes overrepresented in the annotation of genes oc-
curring in the QTL regions (defense response, lipid transport,
metabolic process, oxidation-reduction process, protein
Tab l e 3 (continued)
Trait QTL ID Linkage
Marker Synonim
to QTL
Additive effect (b) Percent of variance explained by
QTL in years (%) (c)
2012 2012 2012 2013 2013 2013 2012 2012 2012 2013 2013 2013
Length of
Q.LSl.LC-5H.3 5H.3 61.54 Consensus
11_11448 9.29 0.00 1 0.17 0.19 0.23 0.30 n.s. 0.34 5.29 11.55 13.80 12.94 20.43
Q.LSl.LC-6H 6H 72.83 2968-1066 11_10400 5.56 0.00 0 0.27 0.27 0.27 0.27 0.27 0.27 13.02 22.44 18.35 10.37 20.36 12.45
Q.LSl.LC-7H.2 7H.2 15.97 1213-1959 11_10056 5.79 0.00 0 0.20 0.20 0.20 0.20 0.20 0.20 7.06 12.17 9.95 5.62 11.04 6.75
(a) QTL x E 1- significant, 0 not significant
(b) Additive effects: negativealleles increasing trait value from Lubuski. positivealleles increasing trait value from CamB
(c) Percentage of variance computed as the ratio of variance for effect and variance in given year; values larger than 100 % indicate overestimation of additive effect in the analysis based on six environments
in comparison to individual analyses
(d) QTL strength measured by the statistic Log10(P-value) with P-values for Wald test computed in accumulated analysis of variance
n.s. not significant
J Appl Genetics
6338-682 2964-382
5379-595 ABC12560-1-1-42147.4
6384-866 GBM145947.9
ConsensusGBS0 705-180.1
ConsensusGBS0 348-2100.0
ABC09941-1-1-100 3000-1074113.2
ConsensusGBS0 379-1113.5
ABC01791-1-1-110 HVM54114.4
2464-1228 6328-736115.3
2822-739 285-2932117.5
3608-2133 6990-661121.2
4240-749 252-556123.1
9910-427 6419-1680143.2
1449-1103 1344-930144.9
2 cM
HVM 36
Fig. 2 QTLs identified in Bhot spot^region on chromosome 2H
Number of days from sowing
DI 2012
DI 2013
DII 2012
DII 2013
C 2012
C 2013
group A group B
Fig. 3 Schematic representation
of mean heading dates observed
for two subgroups of RILs in
different water regimes and years.
Groups A, B homozygotes G/G
(CamB) and A/A (Lubuski) at
SNP 5880-2547 located in
linkage group 2H at 10.74 cM
J Appl Genetics
phosphorylation, protein ubiquitination, response to oxidative
stress, transmembrane transport (Table 5).
The present study examined the mapping population derived
from the cross Lubuski × Cam/B1/CI08887//CI05761 and
special attention was given to earliness. The Syrian genotype,
when compared to the European cultivar, was generally
characterised by earlier heading and lower yield. Drought
stress conditions caused the reduction of the studied traits
(with the exceptions: HD and NPT). Grain yield was the most
decreased under drought stress applied at the flag leaf stage.
This was due to reduced spikelets and grain numbers per
spike. These results are in agreement with the results
reported by Zinselmeier et al. (1999)andSamarahetal.
(2009) who demonstrated the impact of drought during the
flowering period on grain yield.
The effect of water scarcity on yield varies depending on
the plant development stage. This is why we noted different
mean values for traits observed in drought I and drought II. It
is noteworthy that in DI and DII treatments plants were ob-
served to have more tillers than in the well-watered condi-
tions. It may be explained by the emergence of new tillers
during re-watering period. A similar phenomenon has also
been noticed in other works, e.g. by Aspinall et al. (1964)
and Loss and Siddique (1994), but in most studies a signifi-
cant decrease of the number of productive tillers under
drought conditions has been observed (Samarah 2005;
Shirazi et al. 2010; Tsenov et al. 2015).
In our study, stress conditions caused a delay in heading.
These findings are in agreement with other studies (Winkel
et al. 1997; Wopereis et al. 1996;Farooqetal.2011)but,on
the other hand, our results are also in contrast to the results
Tabl e 4 GO biological process
terms over represented in the
annotation of genes occurring in
the QTL regions for a trait
GO term Trait QTL ID No. of
List of genes (MLOC)
Defense response grain weight per
main spike
Q.GWSm.LC-2H-2 7 MLOC_14076
Q.GWSm.LC-2H-2 MLOC_76088
Q.GWSm.LC-2H-2 MLOC_5583
Q.GWSm.LC-2H-2 MLOC_69392
Q.GWSm.LC-2H-2 MLOC_25677
Q.GWSm.LC-2H-2 MLOC_16581
Q.GWSm.LC-5H.3 MLOC_77713
Defense response grain weight per
Q.GWP.LC-2H 1 MLOC_69399
Protein ubiquitination length of main
Q.LSm.LC-2H-2 15 MLOC_40031
Q.LSm.LC-2H-2 MLOC_54978
Q.LSm.LC-2H-2 MLOC_679
Q.LSm.LC-2H-2 MLOC_60024
Q.LSm.LC-2H-2 MLOC_8581
Q.LSm.LC-2H-2 MLOC_81408
Q.LSm.LC-2H-2 MLOC_63051
Q.LSm.LC-2H-2 MLOC_39480
Q.LSm.LC-2H-2 MLOC_63511
Q.LSm.LC-2H-2 MLOC_38436
Q.LSm.LC-3H.1 MLOC_69418
Q.LSm.LC-3H.1 MLOC_68550
Q.LSm.LC-3H.1 MLOC_68553
Q.LSm.LC-3H.1 MLOC_64722
Q.LSm.LC-3H.1 MLOC_3103
Q.LSm.LC-3H.1 MLOC_6570
Q.LSm.LC-5H.3 MLOC_4665
Q.LSm.LC-6H-1 MLOC_58751
Q.LSm.LC-6H-1 MLOC_68356
J Appl Genetics
showed by Desclaux and Romet (1996), Slafer et al. (2005)
and Richards (2006), where the drought conditions caused an
acceleration of plant growth and development. In the present
studies the highest delay in the appearance of the heading was
triggered by drought stress conditions II, especially for early
heading lines. This phenomenon can be associated with the
survival strategies of this group of plants, which were, in gen-
eral, at an advanced stage of development at the time of stress
application. The results of the study confirmed the assumption
that the drought escape can be an effective strategy only when
a plant has completed its life cycle before the environment
conditions become unfavourable.
QTLs for earliness
Earliness affects the plant adaptation to the environmental
changes and it is a trait affected by numerous QTLs (Yano
et al. 2000;Samerietal.2011). In our study, four QTLs for
earliness were detected on chromosomes 2H, 3H, 5H and 7H.
The localisation of these QTLs on barley chromosomes is
consistent with previously identified QTLs (Hayes et al.
1993;Laurieetal.1995; Thomas et al. 1995;Bezantetal.
1996; Tinker et al. 1996;Qietal.1998; Pillen et al. 2003).
Some of them were found in genomic regions that have been
reported to harbour genes involved in flowering time regula-
tion. In our study, the main effects were shown by QTL de-
tected on chromosome 2H at SNP 58802547 (11_21015).
On the short arm of that chromosome the major photoperiod
response locus (Ppd-H1) which causes early flowering under
day length has been mapped (Laurie et al. 1995). The 2HS
region association with the earliness was also observed in
numerous other studies. Ren et al. (2010) identified three
QTLs determining the heading date on chromosomes 2H
(and also on 3H and 7H), which is in agreement with our
results. QTL analysis of the Steptoe/ Morex population con-
ducted by Mansour et al. (2014)revealedtheQTLalsolocated
at SNP 11_21015. All these findings support the notion that
the region on the short arm of chromosome 2H is tightly
associated with heading date. SNP 11_21015 has been
mapped close to markers 12_30871 and 12_30872 (Muñoz-
Amatriaín et al. 2011) which are SNPs in Ppd-H1.Borrás-
Gelonch et al. (2012), following a series of experiments in-
volving environments with artificially extended photoperiod,
reported the two QTLs affected earliness on chromosome 2H.
Tabl e 5 GO biological process terms over represented in the annotation of genes occurring in the regions of QTLs
GO biological
process term
QTL ID Total number
of genes in
the QTL region
Number of genes
annotated with
the term
List of genes (MLOC)
Defense response Q.GWSm.LC-2H-2 246 6 MLOC_14076, MLOC_76088, MLOC_5583,
MLOC_69392, MLOC_25677, MLOC_16581
Q.NGSm.LC-2H-2 147 2 MLOC_63489, MLOC_20004
Lipid transport Q.LSt.LC-1H.2 227 5 MLOC_70721, MLOC_52372, MLOC_55993,
MLOC_64544, MLOC_46285
Q.NPT.LC-5H.3 77 2 MLOC_42618, MLOC_16268
Q.NGSm.LC-5H.3 162 3 MLOC_42618, MLOC_57612, MLOC_38396
Metabolic process Q.HD.LC-2H 47 4 MLOC_44360, MLOC_12202, MLOC_51066,
Q.LSt.LC-1H.2 227 8 MLOC_70129, MLOC_70743, MLOC_70745,
MLOC_17987, MLOC_5359, MLOC_62584,
MLOC_67176, MLOC_14711
Oxidation-reduction process Q.NGSm.LC-2H-1 47 3 MLOC_52158, MLOC_44360, MLOC_25950
Protein phosphorylation Q.GWSm.LC-2H-1 47 5 MLOC_39533, MLOC_61989, MLOC_38009,
MLOC_80756, MLOC_63818
Q.LSm.LC-3H.1 86 13 MLOC_55753, MLOC_55752, MLOC_36868,
MLOC_36867, MLOC_10272, MLOC_40282,
MLOC_6370, MLOC_67657, MLOC_55684,
MLOC_66868, MLOC_73709, MLOC_14788,
Protein ubiquitination Q.LSm.LC-2H-2 246 9 MLOC_40031, MLOC_54978, MLOC_679,
MLOC_60024, MLOC_8581, MLOC_81408,
MLOC_63051, MLOC_39480, MLOC_63511
Response to oxidative stress Q.NSSm.LC-2H-2 147 5 MLOC_54892, MLOC_54893, MLOC_65477,
MLOC_72076, MLOC_57664
Transmembrane transport Q.LSl.LC-7H.2 71 6 MLOC_2098, MLOC_76366, MLOC_36691,
MLOC_12388, MLOC_44081, MLOC_9846
J Appl Genetics
The regions on 2H were also the main determinantsof heading
date in autumn-sown experiments conducted using mapping
populations grown under Mediterranean conditions (Moralejo
et al. 2004; Cuesta-Marcos et al. 2008). Comadran et al.
(2011) reported five QTLs for heading date (located on 1H,
2H, 3H and 5H). Their research revealed that the highest effect
was shown by two QTLs detected in the centromeric region of
2H, where another major gene affecting heading date (eam6)
Although our study was based on the analysis of a popula-
tion derived from spring barley parents, QTL analysis re-
vealed some associations with chromosome regions
harbouring genes related to vernalisation requirements.
Vernalisation response has been shown to be strongly influ-
enced by photoperiod (Roberts et al. 1988;Wangetal.
2010b). Epistatic interaction among major loci of
vernalisation response, photoperiod reaction and earliness
per se may be responsible for the fact that a large number of
genomic regions have been identified as determinants of head-
ing date (Karsai et al. 2001). In our study Q.HD.LC-3H.1 was
detected on chromosome 3H at SNP 10353119 and in the
vicinity (0.24 cM) of microsatellite Bmag603. Wang et al.
(2010b) revealed that a flowering time candidate gene
(HvFT2) had been located 3 cM from this SSR marker.
Another locus connected with heading date was found in our
study on chromosome 5H at SNP 314559 positioned at
59.03 cM (Q.HD.LC-5H.3). This QTL was located in a sim-
ilar position as QTL for heading date reported by Marquez-
Cedillo et al. (2001) and Thomas et al. (1995)andthe
vernalisation response gene (Vrn- H 1 ) found by Laurie et al.
(1995). According to Muñoz-Amatriaín et al. (2011), Vrn-H1
contains SNP 12_30883 and is mapped on the long arm of
chromosome 5H between SNPs 11_21247 (7639-122) and
11_11080 (ABC03900-1-2-406), the latter being located in
the consensus map used in our studies in the distance of
0.6 cM from SNP 314-559. Our data also showed that in the
vicinity of SNP 314559 another SNP 7523440 (11_21241)
was located, which was linked to the locus Vrn- H1 in the study
conducted by Malosetti et al. (2011).
QTLs for agronomic traits
Several yield-related QTLs have been mapped to the short arm
of chromosome 2H, including plant height (Karsai et al.
1997), kernel weight (Han and Ullrich 1993), number of seeds
per spike (Kjaer et al. 1991) and tiller number (Eshghi et al.
2011). In the present study, QTLs with large effects for yield,
plant height, number of productive tillers, length of spike,
spikelet number and number and weight of grain were found
near SNP 58802547 on chromosome 2H. Results obtained in
numerous studies have shown that loci associated with the
length of spikes are placed on all the barley chromosomes
(Horietal.2003;Samerietal.2006; Baghizadeh et al.
angetal.2010a,b). The localisation of the QTL for
earliness on chromosome 2H coincided with QTLs for spike
morphology. The QTL for the length of the main spike (LSm)
was discovered in genomic regions associated with earliness,
except the one which was found on chromosome 3H.
Interestingly, the QTL for LSm was found in our study both
on chromosome 5H (Q.LSm.LC-5H.3) and on chromosome
7H (Q.LSm.LC-7H.2), where were identified regions related
to heading stage. In the present study, the QTL for number of
grains per main spike was mapped at the marker 58802547
on chromosome 2H. The QTLs affecting the number of grains
per spike on chromosome 2H have been reported by
Mohammadi and Baum (2008) and Mehravaran et al.
(2014), and in our investigation, SNP 58802547 was also
the nearest marker for QTLs related to grain weight per
main and lateral spike and grain weight per plant. These
results are in agreement with the findings of Peighambari
et al. (2005) who found the QTL for grain yield on chromo-
some 2H. In other studies QTLs for grain yield were identified
on almost all the barley chromosomes (Cuesta-Marcos et al.
2009; Mansour et al. 2014; Mehravaran et al. 2014). Stem
length is an important morphological character directly linked
with the productive potential of barley plants. In the present
study, we did not detect any QTL for the length of stem close
to SNP 58802547 associated with earliness. However, the
QTL analysis revealed Q.LSt.LC-2H at SNP 7747-1056,
2.2 cM shifted from from SNP 58802547. In the region of
QTL for HD detected on 3H no QTL for stem length was
found. It should be noted that on 3H sdw1/denso locus causing
reduction of plant height was localized and several studies
revealed that this locus may also be associated with flowering
time (Barua et al. 1993;Laurieetal.1994;Bezantetal.1996;
Kuczyńska et al. 2013,2014).
In our experiment, two QTLs associated with the numbers
of spikelets per spikes (Q.NSSm.LC-2H and Q.NSSl.LC-2H)
were found on chromosome 2H at the SNP 58802547. These
results are in agreement with the QTL localisation previously
reported by Li et al. (2005) and Baghizadeh et al. (2007).
Additionally, these authors revealed QTLs for these traits also
on chromosomes 1H, 5H and 7H.
In the current study, we have identified locus associ-
ated with the number of productive tillers on 2H
(Q.NPT.LC-2H) at the same position as the main QTL
for heading date position 10.7 cM). Tiller number is a
key component of barley grain yield (Sakamoto and
Matsuoka 2004). Fertile tillers contribute significantly
to grain yield improvement, but those tillers without fer-
tile spikes decrease the harvest index (Mäkelä and
Muurinen 2011). In our study we noticed an increase in
the number of productive tillers triggered by drought
conditions which could be explained by the secondary
tiller development process, commonly observed in the
field conditions (Aspinall et al. 1964).
J Appl Genetics
QTLs related to drought
A recent study revealed that QTLs related to drought stress
tolerances were placed on chromosome 2H and 5H (Fan et al.
2015). The pivotal importance of the genomic regions for
drought tolerance was also reported by Mehravaran et al.
(2014) on chromosomes 2H, 5H and 7H. The authors sug-
gested that these regions may be used as an important target
for improving drought tolerance of barley.
The association of heading date and drought tolerance has
been reported by Xu et al. (2005), Araus et al. (2002), Kigel
et al. (2011), Schmalenbach et al. (2014). Similar results were
obtained in the present studies. We identified QTLs related to
heading date on chromosomes 2H, 5H and 7H, where QTLs
for drought tolerance have been reported in other studies.
QTLs connected with yield structure were found near QTLs
identified for earliness, which is also in agreement with other
studies (Wang et al. 2010a;Honsdorfetal.2014;Mansour
et al. 2014;Mehravaranetal.2014).
In the present study, early heading barley plants did not
realise the drought escape strategy, and other mechanisms also
associated with water scarcity tolerance appeared to be inef-
fective. On the other hand, we observed an increase in pro-
ductive tillers forming after drought during re-watering, espe-
cially in the Syrian parent. As early heading lines tend to have
low quality yield, the enhancement of productive tillers seems
to be a promising strategy.
Functional annotations
The overrepresentation of genes annotated as Bdefense
response^for traits: grain weight per main spike and grain
weight per plant did not allow for an unambiguous inter-
pretation. This GO term was descript by QuickGO
(http://www.ebi.ac.uk/QuickGO)asBreactions, triggered
in response to the presence of a foreign body or the
occurrence of an injury, which result in restriction of
damage to the organism attacked or prevention/recovery
from the infection caused by the attack^, which can be
assigned to a every type of plant reaction associated with
biotic or abiotic stresses. Noteworthy, the second type of
overrepresented GO term was related to protein
ubiquitination as Bthe process in which one or more ubiq-
uitin groups are added to a protein^. Ubiquitin is well
established as the major modifier of signalling in eukary-
otes. The main characteristic of ubiquitination is the con-
jugation of ubiquitin onto lysine residues of acceptor pro-
teins (Stone and Callis 2007). In most cases, the targeted
protein is degraded by the 26S proteasome, the major
proteolysis machinery in eukaryotic cells. The ubiquitin
proteasome system is responsible for removing most ab-
normal peptides and short-lived cellular regulators. This
allows cells to respond rapidly to intracellular signals and
changing environmental conditions. These types of bio-
logical processes are crucial to sustain cellular functions
under drought. In Arabidopsis thaliana more than 1400
genes encode components of the ubiquitin/26S protea-
some (Ub/26S) pathway (Smalle and Vierstra 2004).
Approximately 90 % of these genes encode subunits of
the E3 ubiquitin ligases, which confer substrate specificity
to the pathway. This mechanism can be observed in the
gibberellin-dependent signalling pathway that regulates
the flowering process (Cheng et al. 2004). Gibberellins
(GAs), one kind of endogenous growth regulator, play
an essential role not only in reproductive development
of plants but also in stem and spike growth regulation
(Kumar et al. 2003; Tyagi and Singh 2006;Janowska
and Andrzejak 2010). Moreover, treatment of GA causes
stem elongation, expansion and proliferation and cell wall
thickening increased cell division and cell elongation
(Taiz and Zeiger 1998). Similar processes may be ob-
served in spike growth and development. Our plant mate-
rial was differential in terms of spike length both in well-
watered and drought conditions, which may suggest that
the effect of GA can be a major factor related to spike
growth irrespective of irrigation conditions.
The annotation of QTL regions by genes occurring in the
projected support intervals showed the six other biological
processes, one of which may play a key role in the drought
stress. BResponse to oxidative stress^was annotated for five
genes occurring in the regions of identified QTLs. Prolonged
drought stress results in oxidative damage due to the over
production of reactive oxygen species (ROS) (Smirnoff
1993). ROS seem to have a dual effect under drought stress
conditions that depend on their overall cellular amount. If kept
at relatively low levels they are likely to function as compo-
nents of a stress-signalling pathway, triggering stress defense/
acclimation responses. However, when reaching a certain lev-
el of phytotoxicity, ROS become damaging, initiating unwell-
watered led oxidative cascades that harm cellular membranes
and other cellular components resulting in oxidative stress and
eventually cell death (Dat et al. 2000)andas a conse-
quencethe wilting process noticed in water scarcity
Four QTLs for HD were detected on chromosomes 2H, 3H,
5H and 7H. Hence, the major was QTL located on the short
arm of 2H chromosome at SNP marker 58802547, in the
vicinity of Ppd-H1 gene. In the region of SNP 58802547
QTLs associated with plant architecture, spike morphology
and grain yield were localised. The present study showed that
the earliness allele from the Syrian parent, as introduced into
the genome of a European variety could result in an
J Appl Genetics
improvement of barley yield performance under drought con-
ditions. Screening barley cultivars for growth duration under
terminal drought stress is needed to evaluate drought escape in
barley grown under such conditions. In order to use these
QTLs for improvement of agronomic traits, further comple-
mentary studies in different environments and genetic con-
texts should be performed.
Acknowledgments This work was supported by the European
Regional Development Fund through the Innovative Economy
Programme 20072013, project WND-POIG.01.03.0100101/08
POLAPGEN-BD BBiotechnological tools for breeding cereals with in-
creased resistance to drought^.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link
to the Creative Commons license, and indicate if changes were made.
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Supplementary resources

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  • Article
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  • Article
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    Rhizomes of Zantedeschia, 15-18 cm in circumference with leaf buds 0.5-1 cm in length, were planted into 20-cm pots on 15 May 2006 into a medium consisting of peat with a pH of 6.2, enriched with a slow-release fertiliser Osmocote Plus (3-4M) and mixed with fresh shredded pine bark at a rate of 3:1 (v:v). Before planting, the rhizomes were soaked for 20 minutes in a water solution of gibberellic acid at a concentration of 150 mg x dm-3, or sprayed with the same concentration once, twice or three times, each time after thorough drying. For each round of spraying, 100 ml of the solution were used. Gibrescol 10 MG was applied. Gibberellic acid had a similar effect, whether applied in the form of a solution for soaking or spraying of rhizomes. The number of sprayings of rhizomes had no effect on the effectiveness of gibberellic acid. In the cultivar 'Black Magic', rhizome spraying caused earlier flowering, while in 'Cameo' a similar effect was obtained by soaking the rhizomes in gibberellic acid. Gibberellic acid had no effect on the quality of flowers and the number of leaves. Gibberellic acid had a good effect on the index of leaf greenness in the cultivars under study.
  • Article
    Full-text available
    Genetic maps in barley are usually constructed from a limited number of molecular markers such as SSR (simple sequence repeat) and DarT (diversity arrays technology). These markers must be first developed before being used for genotyping. Here, we introduce a new strategy based on sequencing progeny of a doubled haploid population from Baudin × AC Metcalfe to construct a genetic map in barley. About 13,547 polymorphic SNP tags with >93% calling rate were selected to construct the genetic map. A total of 12,998 SNP tags were anchored to seven linkage groups which spanned a cumulative 967.6 cM genetic distance. The high-density genetic map can be used for QTL mapping and the assembly of WGS and BAC contigs. The genetic map was evaluated for its effectiveness and efficiency in QTL mapping and candidate gene identification. A major QTL for plant height was mapped at 105.5 cM on chromosome 3H. This QTL with LOD value of 13.01 explained 44.5% of phenotypic variation. This strategy will enable rapid and efficient establishment of high-density genetic maps in other species.
  • Article
    Full-text available
    TSENOV, N., D. ATANASOVA, I. STOEVA and E. TSENOVA, 2015. Effects of drought on grain productivity and quality in winter bread wheat. Bulg. J. Agric. Sci., 21: 589–595 Background and Aims: The aim of the study is to determine the influence of strong and continuous drought on yield and grain quality of bread wheat in Dobrudzha region. The levels of yield and grain quality through their components and parameters generated in contrast to both drought and favorable years are directly compared. Methods: In field conditions in Competitive yield trails grain yield and some traits, directly or indirectly associated with it are analyzed, and the most important parameters characterizing the quality of grain, too. Basis for comparison data from a favorable yield and quality of grain in 2006 was used. A detailed analysis of any change of trait of productivity and quality index of 65 advanced breeding lines was made. Specific change of individual indexes of quality of grain depends directly on the genetic nature of the studied genotypes. Key results: Prolonged drought reduces most grain yield, resulting in reduction of all traits associated with it. The most conservative trait is 1000 kernel weight, which changes the least, while most reduce the number of productive tillers. In grain quality the drought changes inadequate individual trait performance. Specific weight increases, and the protein content and sedimentation value is not substantially altered. All other indexes are changed in fairly negatively to varying degrees. Conclusions: The highest grain yield under drought in Dobrudzha has breeding lines that fail to form the highest number of grains per spike. Notwithstanding the terms of the year grain yield is determined most strongly by the trait number of kernels per spike. Under drought the most negatively affected performance have the strength of the dough and the quality of the grain-dough stability, including valorimetric value and loaf volume. When changing the quality of grain as lower the genetic quality talents of a variety, so the values of its parameters are changed slightly. This explains the wide variation in quality of wheat due to strong differences in terms of year conditions.
  • Article
    We analyzed antioxidative defenses, photosynthesis, and pigments (especially xanthophyll-cycle components) in two wheat (Triticum durum Desf.) cultivars, Adamello and Ofanto, during dehydration and rehydration to determine the difference in their sensitivities to drought and to elucidate the role of different protective mechanisms against oxidative stress. Drought caused a more pronounced inhibition in growth and photosynthetic rates in the more sensitive cv Adamello compared with the relatively tolerant cv Ofanto. During dehydration the glutathione content decreased in both wheat cultivars, but only cv Adamello showed a significant increase in glutathione reductase and hydrogen peroxide-glutathione peroxidase activities. The activation states of two sulfhydryl-containing chloroplast enzymes, NADP⁺-dependent glyceraldehyde-3-phosphate dehydrogenase and fructose-1,6-bisphosphatase, were maintained at control levels during dehydration and rehydration in both cultivars. This indicates that the defense systems involved are efficient in the protection of sulfhydryl groups against oxidation. Drought did not cause significant effects on lipid peroxidation. Upon dehydration, a decline in chlorophylla, lutein, neoxanthin, and β-carotene contents, and an increase in the pool of de-epoxidized xanthophyll-cycle components (i.e. zeaxanthin and antheraxanthin), were evident only in cv Adamello. Accordingly, after exposure to drought, cv Adamello showed a larger reduction in the actual photosystem II photochemical efficiency and a higher increase in nonradiative energy dissipation than cv Ofanto. Although differences in zeaxanthin content were not sufficient to explain the difference in drought tolerance between the two cultivars, zeaxanthin formation may be relevant in avoiding irreversible damage to photosystem II in the more sensitive cultivar.
  • Article
    After reproduction is initiated in plants, subsequent reproductive development is sometimes interrupted, which decreases the final number of seeds and fruits. We subjected maize (Zea mays L.) to low water potentials (ψw) that frequently cause this kind of failure. We observed metabolite pools and enzyme activities in the developing ovaries while we manipulated the sugar stream by feeding sucrose (Suc) to the stems. Low ψw imposed for 5 d around pollination allowed embryos to form, but abortion occurred and kernel number decreased markedly. The ovary contained starch that nearly disappeared during this abortion. Analyses showed that all of the intermediates in starch synthesis were depleted. However, when labeled Suc was fed to the stems, label arrived at the ovaries. Solute accumulated and caused osmotic adjustment. Suc accumulated, but other intermediates did not, showing that a partial block in starch synthesis occurred at the first step in Suc utilization. This step was mediated by invertase, which had low activity. Because of the block, Suc feeding only partially prevented starch disappearance and abortion. These results indicate that young embryos abort when the sugar stream is interrupted sufficiently to deplete starch during early ovary development, and this abortion results in a loss of mature seeds and fruits. At low ψw, maintaining the sugar stream partially prevented the abortion, but invertase regulated the synthesis of ovary starch and partially prevented full recovery.