Identification and validation of a major chromosome region for high grain number per spike under meiotic stage water stress in wheat (Triticum aestivum L.)
Identification and validation of a major chromosome region for high grain number per spike under meiotic stage water stress in wheat (Triticum aestivum L.)
Ifeyinwa Onyemaobi 0 1
Habtamu Ayalew 0 1
Hui Liu 0 1
Kadambot H. M. Siddique 0 1
Guijun Yan 0 1
0 UWA School of Agriculture and Environment and The UWA Institute of Agriculture, The University of Western Australia , Perth WA , Australia
1 Editor: Meixue Zhou, University of Tasmania , AUSTRALIA
Grain number is a major trait for wheat yield under dryland farming. An International Triticeae Mapping Initiative (ITMI) mapping population comprising 105 recombinant inbred lines (RIL) developed from a cross between a Synthetic hexaploid wheat (Triticum aestivum) `W7984' and a spring wheat variety `Opata M85' was used to identify quantitative trait loci (QTL) associated with grain number per spike under two treatment conditions, normal watering and water stress during meiosis. Two major QTL for grain number per spike on the main stem Q.Gnu.uwa-5A-1 and Q.Gnu.uwa-5A-2 with phenotypic variations of 25.71% and 24.93%, respectively, were detected on the long arm of chromosome 5A when plants were exposed to water stress during meiosis. One QTL (Q.Gnu.uwa-2A) with a LOD score of 2.8 was detected on the long arm of chromosome 2A under normal watering condition. The alleles associated with higher grain number per spike under different treatment conditions came from the Synthetic W7984 parent. Two populations developed from crosses Synthetic W7984 × Lang and Synthetic W7984 × Westonia were used to validate the identified QTL under water stress during meiosis. SSR markers Xbarc230 and Xbarc319 linked with the identified QTL on chromosome 5AL were validated in the two F2:4 segregating populations. These closely linked SSR markers could potentially be utilized in marker-assisted selection to reduce yield loss in regions where water stress during meiosis occurs frequently. The identified QTL can be incorporated into elite lines / cultivars to improve wheat grain yield.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
Bread wheat (Triticum aestivum L.) is one of the most important food crops consumed by
]. It is grown on more than 220 million hectares of land worldwide and provides
about 20% of the global daily requirements for calories and proteins [
]. One of the primary
aims of plant breeding projects is to improve crop yield to ensure food security for the
everincreasing world population [
], especially under adverse environmental conditions, such as
dryland farming that generally leads to significant reductions in grain yield.
Grain yield is a complex trait that integrates many components and developmental
processes, with gene expressions that are often strongly influenced by the environment [
number per spike (GN) and thousand grain weight (TGW) are two of the main components of
grain yield in wheat [
]. The study of individual yield components would provide better insight
into the genetics of plant development and how it affects yield performance [
physiological maturity, final grain number determines grain yield in wheat, such that yield increases
have been directly associated with increases in grain number [8±12].
Water stress is a frequent abiotic stress under rainfed agriculture, which can significantly
reduce grain yield [
]. For example, water stress reduced wheat yield by 1.1 t/ha during the
2006/2007 cropping season in Australia, resulting in 46% yield reduction when compared to
the previous (2005/2007 cropping season) year record [
]. Water stress resistance can be
defined as the ability of plants to survive and produce measurable yield under periodic water
stress or limited water supply [
]. Therefore, it is essential to breed crop varieties that are
resistant to water stress and maintain yield under dryland farming.
In Mediterranean-type environments, crops often experience water stress during the
reproductive or grain-filling phases [
]. Meiosis is a brief, yet unique stage during the
reproductive processes in plants, and a short duration of water stress during this growth phase disrupts
grain yield. Water stress during meiosis results in fewer grains and consequently lower grain
]. Recent studies have indicated that yield reductions due to water stress during
meiosis in wheat arises from the loss of viability of both male and female reproductive parts
without any significant reduction in grain weight [19±21].
Grain number is a quantitative trait, and quantitative trait loci (QTL) analysis is an
important tool for determining the chromosomal regions or locating genes underlying its genetic
]. QTL studies will facilitate a better understanding of the relationship between
grain number and yield. Extensive studies have been conducted to identify the genetic basis of
grain number in diploid crops such as rice, maize, tomato and barley [
]. For example, the
loss of function of the gene OsCKX2 in rice contributes to higher grain number [
QTL clusters for yield and yield components have been identified on different
chromosomes scattered or clustered across the wheat genome [26±31]. S1 Table shows a list of
different yield-related QTL. Abiotic stress that coincides with the meiotic process can greatly reduce
grain number. However, most yield-trait related analyses have focused more on the
identification, validation and/or cloning of genes associated with grain weight while few studies have
focused on the identification of gene(s) or QTL influencing grain number in wheat, specifically
under water stress treatment during meiosis [32±36]. Identification of these chromosome
regions/gene(s) associated with grain number would enhance the efficiency of selection and
breeding of new wheat varieties with improved water stress resistance.
Our research focused on the genetic control and molecular basis of grain number per spike
in wheat plants exposed to water stress during meiosis. The objectives of this study were to
identify genome regions controlling grain number per spike under water stress during meiosis,
validate the identified chromosome regions using molecular markers in different mapping
populations and identify suitable wheat grain number-associated markers for marker assisted
Materials and methods
A recombinant inbred line (RIL) mapping population developed from a cross between the
Synthetic W7984 (Altar84/Aegilops tauschii (219) CIGM86.940, female) and Opata (Opata
M85, male) [
] along with the parents was used in this QTL identification study. A total of
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105 RIL lines were evaluated with seeds obtained from the Australian Grains Genebank. Two
F2:4 populations, Synthetic W7984 × Westonia, and Synthetic W7984 × Lang were generated
and used for QTL validation and detection of the chromosomal segments from the donor
parent (Synthetic W7984) into different progenies. Seventy lines from each validation population
were randomly selected for validation. The closely associated SSR markers to the identified
QTL were used to genotype segregating lines from the crosses mentioned above.
Plant growth and treatment
Plants were grown in polyvinyl chloride pots (9 cm diameter, 37 cm height), in a
controlledtemperature glasshouse where day and night temperatures were maintained at 22±2ÊC and 15
±2ÊC, respectively at The University of Western Australia (31Ê 570 S, 115Ê 470 E) from April to
September 2015. Each pot, with a 9-mm hole at the bottom to allow free drainage of water was
filled with 2.1 kg of sterilised soil mixture (5: 2: 3 fine composted pine barks: coco peat: brown
river sand, pH ~ 6.0). Three seeds were sown per pot for each RIL and later thinned to one
plant per pot after seven days. A randomized complete block design with three replications
and two treatments − normal watering (control) and water stress was used, where the locations
of the pots with respect to genotype, replicate and treatment were random within the blocks.
Scotts Peters1 Excel1 water soluble nutrient fertilizer with 15% nitrogen (11.6% as nitrate
nitrogen, 1.4% as ammoniacal nitrogen, 2.0% as urea nitrogen), 2.2% phosphorus (soluble in
neutral ammonium citrate and water), 12.4% potassium (as potassium nitrate), 5.0% calcium
(as calcium nitrate), 1.8% magnesium (as magnesium nitrate), 0.12% iron, 0.06% manganese,
0.02% boron, 0.015% copper, 0.015% zinc, and 0.010% molybdenum was supplied weekly
from 21 days after sowing. Nutrient fertilizer was not applied during the treatment period. For
the measurement of field (pot) capacity of the soil media, four free draining pots, each
containing 2.1 kg of sterilised soil mixture, were flooded with water and allowed to drain for 48h. Two
samples from each pot were taken, and their fresh weight and dry weight were measured using
a balance before and after oven-drying, respectively. The per cent water content of the soil
mixture at field capacity was calculated as 26% (w/w), using the following formula:
% soil water content
where, FW = fresh weight, and DW = dry weight of the samples. Each pot was weighed daily
to record the mass of water loss by evapotranspiration. By the next watering the minimum
field capacity of the pots was around 60%. Based on the amount of moisture lost, each pot
was watered to bring it up to 80% field capacity. When the auricle distance (AD)Ðthe
distance between the auricles of the flag leaf and the second last leaf of the plantsÐreached 0
cm, water stress was imposed by withholding water entirely for 7 days [
]. Wheat grain
yield per plant is determined mainly by the contribution of the main stem under rainfed
]. The stress treatment of each pot was based on the AD of the main stem of each
plant to monitor the effect of water stress on the total grain number on the main stem, which
hereafter is referred to as grain number per spike. Soil water content measurements were
conducted to determine plant water status during the stress treatment. During the imposed
water stress treatment, the pots were weighed daily to monitor the field capacity but were not
watered throughout the 7 days stress period. However, watering was resumed in the stress
group as per the normal watering group after the stress treatment. Field capacity at the end
of the stress period was between 40% - 45%. The daily pot weighing was used to record the
amount of water loss, corresponding to the daily transpiration of the plants or water use
during the stress period. The field capacity was maintained at about 80% for the whole plant life
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apart from the stress period until the stage of physiological maturity when watering was
The Synthetic W7984 × Opata M85 molecular linkage map constructed by [
] was used. The
map was downloaded from the GrainGenes website (https://wheat.pw.usda.gov/cgi-bin/GG3/
report.cgi?class=image;name=Wheat,+Synthetic+x+Opata,+BARC+markers,+5A, accessed 10
May 2017). The map had a total of 1,475 SSR and RFLP markers distributed across the 21
linkage groups. To ensure maximum genome coverage and reduce errors due to missing values,
1,017 markers were selected from the linkage analysis with an average marker density of 1 cM
after filtering for 60% missing values. The locations and effects of QTL were determined
following the composite interval mapping method (CIM). Windows QTL (WinQTL)
Cartographer v2.5 software was used to perform CIM analysis [
]. The CIM analysis was run using a
backward stepwise regression method of a window size of 10 cM and a step size of 1 cM. The
significant threshold LOD scores for QTL detection were determined based on 1,000
permutations at P 0.05 [
]. The logarithm of odds (LOD) peak location 2.5 was used to declare a
QTL for both water stress and normal growing conditions. For cM position, a 0 cM position
indicates the first (most distal) marker on the short arm of the chromosome. The confidence
intervals for the QTL was determined by locating the markers on both sides of the QTL peak
that correspond to a decrease in 1 LOD score relative to the peak marker [
]. Only SSR
markers within the 1-LOD support interval was used for validating the identified QTL. Adjacent
QTL on the same chromosome for the same trait was considered as different when the
intervals between them was not overlapping.
DNA isolation and PCR
Genomic DNA was extracted from the leaves of three-week-old seedlings of individual plants
from the parental lines of Synthetic W7984, Westonia and Lang, and each of the F2:4
populations (70 lines from Synthetic W7984 × Lang and 70 lines from Synthetic W7984 × Westonia)
using a modified CTAB method [
]. A NanoDrop ND-1000 (Thermo Fisher Scientific)
was used to measure the quality and quantity of the total DNA samples. The primers were
obtained from Sigma-Aldrich (Sigma-Aldrich Pty Ltd, NSW, Australia). Polymerase chain
reaction (PCR) contained 50 ng genomic DNA as template, 1× Bioline MyTaq™ reaction buffer
(containing 1mM dNTPs, 3mM MgCl2, stabilizers and enhancers), 0.20 μM of forward and
reverse primers and 1 U MyTaq™ DNA Polymerase (Bioline, NSW, Australia) in a total volume
of 15 μL. The PCR reactions were conducted using an EppendorfMaster cycler ep Gradient S
thermocycler (Eppendorf, NY, USA) programmed at: 94ÊC for 5 mins, 35 cycles of
denaturation at 94ÊC for 30 s, annealing at varying temperatures obtained from GrainGenes for the
different selected SSR markers for 30 s, elongation at 72ÊC for 45 s, and a final extension at
72ÊC for 5 mins. The primers were obtained from Sigma-Aldrich (Sigma-Aldrich Pty Ltd,
PCR was run for each marker and their products were analysed based on a previously
described method by [
] using a LabChip1 GX Touch 24 (PerkinElmer, Massachusettes,
Statistical data analysis
The main stems of the parents and each RIL were tagged, and at full physiological maturity,
grain number per spike was evaluated from the tagged stem only. The populations (F2:4) used
for the validation studies were F2:4 lines (derived from selfed F2 single seed descent method).
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These lines were also evaluated similarly. The mean values for the control and water-stressed
treatments were used for QTL analysis. As expected, the numbers of heterozygotes among the
F2:4 lines screened were few, hence they were excluded from the analysis. Only F2:4 plants
homozygous for the Synthetic W7984 marker allele and those homozygous for the Westonia/
Lang marker allele were used to validate the identified QTL. They were placed into two
separate allele groups: lines that were homozygous for Synthetic W7984 marker allele (group 1)
and lines with Westonia or Lang marker allele (group 2) for the different SSR marker and
validation population. Phenotypic statistical data analysis was conducted using Genstat statistical
software 17th edition [
]. Analysis of variance was conducted based on the following fixed
effects model: Yij = μ + gj + εij where Yij is observed mean, μ is population mean; gj is an effect
due to the jth genotype, and εij is random error. Heritability analysis was conducted using the
formula: h2 dg2=
dg2 d2 where dg2 and de2 are the estimated genotypic and error variances,
]. The genotypic and error variances were estimated as: dg2 MSg r MSe and de2
MSe where MSg is mean square of the RILs, MSe is the residual error, and r is the number of
Phenotypic variation of wheat grain number per spike
The phenotypic data analysis of variance showed significant (P < 0.01) differences for grain
number per spike among the 105 RILs of Synthetic W7984×Opata M85 under both normal
growing conditions (control) and water stress during meiosis (Table 1). The mean grain
number per spike was 39.7 under control and 26.5 under water stress during meiosis. The
frequency distribution of mean grain number per spike in the RIL population under normal
growing conditions and under water stress during meiosis are presented in Fig 1. The
Synthetic W7984 parent had higher grain numbers per spike under normal and water-stressed
conditions than the Opata M85 parent (S2 Table). Broad sense heritability was 51% for grain
number per spike under stress and 70% under normal growing conditions (Table 1). The high
broad sense heritability indicated that genetic factors strongly influenced grain number per
spike variation in the mapping population.
QTL analysis for grain number per spike
QTL for grain number per spike were detected under both normal and water stress during
meiosis conditions (Fig 2; Table 2). The only significant QTL for grain number per spike
under water stress during meiosis were identified on the long arm of chromosome 5A (Q.Gnu.
uwa-5A-1 and Q.Gnu.uwa-5A-2) with LOD scores of 5.2 and 6.2 respectivelyÐclose to SSR
markers Xbarc151, Xbarc230, Xgwm666 and Xbarc319Ðand explained 25.7% and 24.9% of
the phenotypic variation respectively. Q.Gnu.uwa-5A-1 and Q.Gnu.uwa-5A-2 were mapped
MSg: mean square of genotype; MSe: mean square of random error; dg2: estimated genetic variance; de2: estimated error variance; d2p: estimated phenotypic variance; H2:
broad sense heritability.
indicates significant difference at P < 0.01.
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Fig 1. Frequency distribution of phenotypic variation for wheat grain number per spike among 105 RILs under (a) control (normal watering) and (b) water
stress during meiosis. Grain number per spike for Synthetic W7984 and Opata M85 parental lines under control and water-stressed conditions are indicated by arrows.
Values shown are means.
6.5 cM apart. One QTL was detected on chromosome 2A (Q.Gnu.uwa-2A) for grain number
per spike under normal watering conditions, which explained 8% of the phenotypic variation
and had a LOD score of 2.8. Synthetic W7984 parent contributed high grain number alleles
under both normal and water stress during meiosis conditions.
Validation of the QTL for grain number per spike under water stress
Four SSR markersÐXbarc151, Xbarc230, Xbarc319 and Xgwm666Ðwere tightly linked with the
two identified QTL for grain number per spike under water stress during meiosis. The peak
position of the Q.Gnu.uwa-5A-1 was 77.7 cM while Q.Gnu.uwa-5A-2 was 84.2 cM; the selected four
markers span between 64.0 cM and 86.4 cM on wheat chromosome 5A. Only two markers,
Xbarc230 and Xbarc319, were polymorphic between the parental lines of Synthetic
W7984 × Lang and Synthetic W7984 × Westonia and were therefore used for the QTL validation.
Plants possessing different alleles of markers Xbarc230 (closest to Q.Gnu.uwa-5A-1) and
Xbarc319 (closest to Q.Gnu.uwa-5A-2) were separated into allele group 1 and allele group 2
using LabChip1. The fragment sizes in Table 3 were used to score the randomly selected 70
lines from each of the F2:4 populations (Synthetic W7984 × Westonia and Synthetic W7984 ×
Lang) into different groups based on the allele combination observed for the different
genotypes. The mean performance of genotypes based on two types of allele combination groups
(1 and 2) were used to calculate the phenotypic effect of the identified QTL under water stress
during meiosis. Generally, plants in group 1 had higher grain numbers per spike compared
with those in group 2 (Fig 3). Fig 4A and 4B shows the electropherogram of PCR products
for F2:4 individual plants derived Synthetic W7984 × Westonia amplified by Xbarc230 and
Xbarc319, respectively. The two-sample t-test revealed that under water stress during meiosis,
the mean difference in grain number per spike between the allele combination groups in the
different F2:4 validation populations was statistically significant (P 0.05) for both Xbarc230
and Xbarc319 (Fig 3).
The two major QTL identified in this researchÐQ.Gnu.uwa-5A-1 and
Q.Gnu.uwa-5A-2Ðcontributing to variations in grain number per spike under water stress during meiosis were both
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Fig 2. QTL identified in the Synthetic W7984 × Opata M85 RIL population which were associated with grain number per spike
(a) under water stress during meiosis, located on chromosome 5A and (b) under normal watering, located on chromosome 2A
using composite interval mapping. The horizontal bars indicate the significant LOD thresholds for the QTL detection which was set
at 2.5 for both treatments.
located on the long arm of wheat chromosome 5A, a region known to carry major genes
influencing adaptability and productivity [
26, 47, 48
]. The genomic region harbouring these two
closely-liked major QTL explained 25.7% and 24.9% of the total phenotypic variation,
respectively. Wheat chromosome 5A is known to carry several gene(s) influencing adaptability and
productivity [29, 48±50]. Chromosome 5A also plays a crucial role in drought resistance. For
example, Quarrie et al. [
] identified a QTL on chromosome 5A with a major effect on
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drought-induced ABA accumulation in wheat. Genomic regions and gene(s) of agronomic
importance have been mapped on the long arm of wheat chromosome 5A, with examples
including genes for reduced vernalisation response (Vrn-A1), ear emergence time, spike
morphology, and awn development [
Vernalisation, the initiation of flowering by prolonged exposure to cold temperatures, is a
major determinant of flowering time. BoÈrner et al [
] already identified flowering time QTL
in Synthetic W7984 and Opata M85 RIL mapping population on chromosomes 2D, 3A and
5D. The two major QTLÐQ.Gnu.uwa-5A-1 and Q.Gnu.uwa-5A-2Ðcontributing to variations
in grain number per spike under water stress during meiosis, were found to be located close to
the vernalisation gene Vrn-A1 based on the physical positions (obtained by blasting against the
wheat reference genome) of the gene and the identified QTL (https://urgi.versailles.inra.fr).
However, the QTL results for final auricle distance (measured at the end of the stress period),
number of days from final AD measurement to anthesis, the number of days from sowing to
anthesis, tiller number per plant, and water use during the stress period suggested that the 5AL
QTL were not due to a staging artefact caused by Vrn-A1 segregation, because the Vrn-A1
alleles segregating in the population were not functionally different with regard to flowering
time under the experimental conditions.
SSR markers act as anchors in genetic mapping and are potentially useful for MAS [
Xbarc230 and Xbarc319 could be potential markers for plant breeding to reduce yield loss in
regions where water stress during meiosis occurs often. Whether the identified QTL region in
chromosome 5AL contains one or more key genes for water stress resistance during the
meiotic process requires further investigation through generating near isogenic lines and fine
mapping of Q.Gnu.uwa-5A-1 and Q.Gnu.uwa-5A-2 with more DNA markers.
QTL for final auricle distance (measured at the end of the stress period), number of days
from final AD measurement to anthesis, number of days from sowing to anthesis, tiller
number per plant, and water use during the stress period were mapped on almost all the
chromosomes except 2A, 3B, 4B and 7B [S3 Table; S4 Table; S5 Table; S6 Table; S7 Table; S8 Table].
None of the identified QTL associated with these developmental traits occurred in the same
chromosome region as the identified QTL for grain number per spike on chromosome 5AL
Fragment size (bp)
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Fig 3. Validation of SSR markers Xbarc230 and Xbarc319 associated with the identified QTL for grain number per spike
under water stress during meiosis. (a) and (c) indicate F2:4 lines homozygous for Xbarc230 marker alleles from Synthetic W7984
and Lang parental lines, (b) and (d) indicate F2:4 lines homozygous for Xbarc319 marker alleles from Synthetic W7984 and
Westonia parental lines. Values shown are means and error bars are standard errors of the means. Two-sample t-test revealed that
under water stress during meiosis, the mean difference in grain number per spike between the allele combination groups in
different F2:4 validation populations was statistically significant (P 0.05) for both Xbarc230 and Xbarc319 marker alleles.
(Table 2; S3 Table). However, a region on chromosome 2D contained QTL effects for both
number of days from the final AD measurement to anthesis and number of days from sowing
to anthesis. By blasting against the wheat reference genome (https://urgi.versailles.inra.fr), it
was found that photoperiod sensitivity (Ppd_D1) gene is closely located to the identified 2D
QTL (Q.Ndan.uwa-2D and Q.Snan.uwa-2D). Therefore, Ppd_D1 might have influence on the
QTL for number of days from the final AD measurement to anthesis and number of days from
sowing to anthesis. Chromosome 5AL has also been reported to harbour the most repeatable
grain yield QTL across different mapping populations, environments and treatments
]. Cuthbert et al. [
] detected two grain yield QTL and eight yield-related QTL on
the long arm of chromosome 5A. QTL that control both water stress resistant related traits
and yield component traits have been detected in this chromosome region [
]. In this study,
we showed that chromosome 5AL affects grain number per spike. Such unique attributes
highlight the significant contributions of the long arm of chromosome 5A to yield improvements
and some of those QTL may coincide with the QTL detected in this study.
A QTL, Q.Gnu.uwa-2A, relating to grain number per spike under normal water conditions,
was detected on chromosome 2AL. SSR markers Xgwm312 and Xbarc353 were linked to the
identified QTL. Q.Gnu.uwa-2A is in a similar position to a QTL for grain number per ear
reported by Huang et al. [
]. In other studies, QTL associated with yield traits such as relative
water content, plant height, days to heading, spikelets per spike and awn length have been
detected on chromosome 2A [
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Fig 4. Electropherogram of PCR products of marker Xbarc230 and Xbarc319 from F2:4 Synthetic W7984 × Westonia lines exposed to water stress during meiosis.
The calculated molecular weight (bp) of PCR product (peaks) is displayed, the black arrows point to amplified fragment sizes (a) indicates an F2:4 plant with the alleles of
both Synthetic W7984 and Westonia parental lines for SSR marker Xbarc230 (b) indicates an F2:4 plant amplified by SSR marker Xbarc319 and possessing the allele of
Lang parental line alone.
Clusters of yield QTL have also been identified on different wheat chromosomes, either
controlling yield itself or a yield component. The results of this study agreed with the report by
Peng et al. [
] on the presence of highly significant (P 0.001) yield-related QTL on both
wheat chromosomes 2A and 5A. The favourable allele for grain number per spike under normal
growing conditions and water stress during meiosis was contributed by the Synthetic W7984
parent. In the study by BoÈrner et al. [
], two major QTL with LOD > 3.0 and seven minor
QTL with LOD between 2 and 3, detected for grain yield and yield-related traits across different
growing seasons and environments, all came from the Synthetic W7984 parent. The present
finding is consistent with other research that identified drought-tolerant genes and adaptive
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traits in Synthetic W7984 and other Synthetic wheat lines derived from the diploid wild goat
grass Aegilops tauschii [58±60], which suggests that the Synthetic hexaploid wheat W7984 could
harbour a suite of yield-related adaptive features that should be further explored. Fine mapping
of the QTL loci on the long arm of chromosome 5A is now in progress in our laboratory.
Grain number is a quantitative trait influenced by both genetic and environmental factors.
Water stress is a yield-limiting factor and the most intense effects on yield have been recorded
when stress coincides with the period between the onset of meiosis and early grain initiation
]. In major wheat-growing areas, particularly those with a Mediterranean climate, water
stress during the meiotic process occurs often. Successful completion of meiosis results in
viable gamete production which will ensure grain number production. Making direct selections
for grain yield components and breeding high-yielding wheat cultivars with high grain
numbers will positively influence grain yield . In the present study, identifying grain number
per spike QTL under water stress during meiosis and its effect on grain yield portrays how the
selection and genetic analysis of a single yield component could be used to potentially increase
grain yield. Consequently, the identified and validated favourable alleles could be transferred
into wheat cultivars used in dryland farming which often experience water stress during the
meiotic process, to make them more tolerant to drought and increase grain yield.
S1 Table. Summary of yield-related QTL identified for water-stress resistance in wheat.
S2 Table. Mean grain number per spike for Synthetic W7984 parent, Opata M85 parent
and 105 recombinant inbred lines (RILs) of Synthetic W7984×Opata M85 under both
normal watering (control) and water stress during meiosis.
S3 Table. QTL associated with different developmental traits under water stress during
meiosis (S) and under control (C) condition using Synthetic W7984 × Opata M85 RIL
population. QTL was detected by composite interval mapping. QTL peak position (cM) is based
on linkage between markers from the Synthetic W7984 × Opata M85 molecular linkage map
constructed by Song et al., 2005. All QTL were detected at LOD 2.5 threshold following
1,000 permutations, the percent phenotypic variance (R2%) and the additive allele effect are
S4 Table. Mean final auricle distance (AD) measurement (cm) for Synthetic W7984 parent,
Opata M85 parent and 105 recombinant inbred lines (RILs) of Synthetic W7984×Opata
M85 under both normal watering (control) and water stress during meiosis.
S5 Table. Mean number of days from final auricle distance (AD) measurement to anthesis
for Synthetic W7984 parent, Opata M85 parent and 105 recombinant inbred lines (RILs)
of Synthetic W7984×Opata M85 under both normal watering (control) and water stress
S6 Table. Mean number of days from sowing to anthesis for Synthetic W7984 parent,
Opata M85 parent and 105 recombinant inbred lines (RILs) of Synthetic W7984×Opata
M85 under both normal watering (control) and water stress during meiosis.
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S7 Table. Mean tiller number per plant for Synthetic W7984 parent, Opata M85 parent
and 105 recombinant inbred lines (RILs) of Synthetic W7984×Opata M85 under both
normal watering (control) and water stress during meiosis.
S8 Table. Mean amount of water use (corresponding to the daily transpiration of the pots)
which was determined by weighing the pots daily (kg) under water stress treatment during
meiosis for Synthetic W7984 parent, Opata M85 parent and 105 recombinant inbred lines
(RILs) of Synthetic W7984×Opata M85.
data collection and harvesting.
Ifeyinwa Onyemaobi acknowledges Robert Creasy and other glasshouse staff for their help
with the experiment set-up, Rodrigo Pires and Victoria Francisca Figueroa for assistance with
Conceptualization: Ifeyinwa Onyemaobi, Hui Liu, Guijun Yan.
Data curation: Ifeyinwa Onyemaobi, Habtamu Ayalew.
Formal analysis: Ifeyinwa Onyemaobi, Hui Liu.
Funding acquisition: Kadambot H. M. Siddique, Guijun Yan.
Investigation: Ifeyinwa Onyemaobi, Hui Liu.
Methodology: Ifeyinwa Onyemaobi, Guijun Yan.
Project administration: Hui Liu, Guijun Yan.
Supervision: Hui Liu, Guijun Yan.
Validation: Ifeyinwa Onyemaobi.
Writing ± original draft: Ifeyinwa Onyemaobi.
Writing ± review & editing: Ifeyinwa Onyemaobi, Hui Liu, Kadambot H. M. Siddique, Guijun
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compare QTLs for grain yield across a range of environments. Theor Appl Genet. 2005; 110: 865±880.
https://doi.org/10.1007/s00122-004-1902-7 PMID: 15719212
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1. Monneveux P , Jing R , Misra SC . Phenotyping for drought adaptation in wheat using physiological traits . Front Physiol . 2012 ; 3 : 429 . https://doi.org/10.3389/fphys. 2012 .00429 PMID: 23181021
2. Shiferaw B , Smale M , Braun H-J , Duveiller E , Reynolds M , Muricho G . Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security . Food Secur . 2013 ; 5 ( 3 ): 291 ± 317 . https://doi.org/10.1007/s12571−013−0263−y
3. Food and Agricultural Organization . Sustainable agriculture for biodiversity. Biodiversity for sustainable agriculture . FAO , Rome 2016. Available from http://www.fao. org/3/a−i6602e.pdf. Accessed 18 July 2017
4. Passioura JB . The yield of crops in relation to drought . In: Boote KJ , Bennett JM , Sinclair TR , Paulsen GM , editors. Physiology and determination of crop yield. ASA , CSSA, SSSA , Wisconsin, 1994 . pp 343± 359 .
5. Griffiths S , Wingen L , Pietragalla J , Garcia G , Hasan A , Miralles D , et al. Genetic dissection of grain size and grain number trade-offs in CIMMYT wheat germplasm . PLoS ONE . 2015 ; 10 :e0118847. https://doi. org/10.1371/journal.pone. 0118847 PMID: 25775191
6. Bezant J , Laurie D , Pratchett N , Chojecki J , Kearsey M. Mapping QTL controlling yield and yield components in a spring barley (Hordeum vulgare L) cross using marker regression . Mol Breeding . 1997 ; 3 : 29 ± 38 . https://doi.org/10.1023/A:1009648220852
7. Heidari B , Sayed-Tabatabaei BE , Saeidi G , Kearsey M , Suenaga K. Mapping QTL for grain yield, yield components, and spike features in a doubled haploid population of bread wheat . Genome . 2011 ; 54 : 517 ± 527 . https://doi.org/10.1139/g11-017 PMID: 21635161
8. Fischer RA . The importance of grain or kernel number in wheat: A reply to Sinclair and Jamieson . Field Crop Res . 2008 ; 105 : 15 ± 21 . https://doi.org/10.1016/j.fcr. 2007 . 04 .002
9. Reynolds M , Foulkes MJ , Slafer GA , Berry P , Parry MAJ , Snape JW , et al. Raising yield potential in wheat . J Exp Bot . 2009 ; 60 : 1899 ± 1918 . https://doi.org/10.1093/jxb/erp016 PMID: 19363203
10. Dolferus R , Ji X , Richards RA . Abiotic stress and control of grain number in cereals . Plant Sci . 2011 ; 181 : 331 ± 341 . https://doi.org/10.1016/j.plantsci. 2011 . 05 .015 PMID: 21889038
11. Powell N , Ji X , Ravash R , Edlington J , Dolferus R . Yield stability for cereals in a changing climate . Funct Plant Biol . 2012 ; 39 : 539 ± 552 . https://doi.org/10.1071/fp12078
12. Guo Z , Schnurbusch T. Variation of floret fertility in hexaploid wheat revealed by tiller removal . J Exp Bot . 2015 ; 66 : 5945 ± 5958 . https://doi.org/10.1093/jxb/erv303 PMID: 26157170
13. ABS 2012. Year book Australia , 2012 . Year book Australia , 2012 Available: http://www.abs.gov.au/ ausstats/abs@. nsf/mf/1301 .0.
14. Turner NC . Drought resistance and adaptation to water deficits in crop plants . New York: John Wiley & Sons; 1979 .
15. Farooq M , Hussain M , Siddique KHM . Drought stress in wheat during flowering and grain-filling periods . Crit Rev Plant Sci . 2014 ; 33 : 331 ± 349 . https://doi.org/10.1080/07352689. 2014 .875291
16. Siddique KHM , Kirby EJM , Perry MW . Ear: Stem ratio in old and modern wheat varieties; relationship with improvement in number of grains per ear and yield . Field Crops Res . 1989 ; 21 : 59 ± 78 . https://doi. org/10.1016/ 0378 − 4290 ( 89 ) 90041 − 5
17. Fang X , Turner NC , Yan G , Li F , Siddique KHM . Flower numbers, pod production, pollen viability, and pistil function are reduced and flower and pod abortion increased in chickpea (Cicer arietinum L) under terminal drought . J Exp Bot . 2010 ; 61 : 335 ± 345 . https://doi.org/10.1093/jxb/erp307 PMID: 19854801
18. Saini HS , Aspinall D . Effect of water deficit on sporogenesis in wheat (Triticum aestivum L) . Ann Bot. 1981 ; 48 : 623 ± 633 . https://doi.org/10.1093/oxfordjournals.aob.a086170
19. Ji X , Shiran B , Wan J , Lewis DC , Jenkins CLD , Condon AG , et al. Importance of pre-anthesis anther sink strength for maintenance of grain number during reproductive stage water stress in wheat . Plant Cell Environ . 2010 ; 33 : 926 ± 942 . https://doi.org/10.1111/j.1365- 3040 . 2010 . 02130 . x PMID : 20199626
20. Passioura JB . Phenotyping for drought tolerance in grain crops: when is it useful to breeders? Funct Plant Biol . 2012 ; 39 : 851 ± 859 . https://doi.org/10.1071/fp12079
21. Onyemaobi I , Liu H , Siddique KHM , Yan G . Both male and female malfunction contributes to yield reduction under water stress during meiosis in bread wheat . Front Plant Sci . 2017 ; 7:2071 . https://doi. org/10.3389/fpls. 2016 . 02071 PMID: 28119733
22. Kearsey MJ , Pooni HS . The genetical analysis of quantitative traits . Cheltenham: Stanley Thornes; 1998
23. Gupta PK , Rustgi S , Kumar N. Genetic and molecular basis of grain size and grain number and its relevance to grain productivity in higher plants . Genome . 2006 ; 49 : 565 ± 571 . https://doi.org/10.1139/g06- 063 PMID: 16936836
24. Kim D−M , Lee H−S , Kwon S−J , Fabreag ME , Kang J−W , Yun Y−T, et al. High−density mapping of quantitative trait loci for grain−weight and spikelet number in rice . Rice . 2014 ; 7 : 14 . https://doi.org/10. 1186/s12284-014 -0014-5 PMID: 26055996
25. Ashikari M , Sakakibara H , Lin S , Yamamoto T , Takashi T , Nishimura A , et al. Cytokinin oxidase regulates rice grain production . Science . 2005 ; 309 : 741 ± 745 . https://doi.org/10.1126/science.1113373 PMID: 15976269
26. BoÈrner A , Schumann E , FuÈrste A , CoÈster H , Leithold B , RoÈder MS ,et al. Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat (Triticum aestivum L) . Theor Appl Genet . 2002 ; 105 : 921 ± 936 . https://doi.org/10.1007/s00122-002 -0994-1 PMID: 12582918
27. Groos C , Robert N , Bervas E , Charmet G . Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat . Theor Appl Genet . 2003 ; 106 : 1032 ± 1040 . https://doi.org/10. 1007/s00122-002 -1111-1 PMID: 12671751
28. Quarrie SA , Steed A , Calestani C , Semikhodskii A , Lebreton C , Chinoy C , et al. A high-density genetic map of hexaploid wheat (Triticum aestivum L) from the cross Chinese Spring × SQ1 and its use to
29. Kirigwi FM , Van Ginkel M , Brown-Guedira G , Gill BS , Paulsen GM , Fritz AK . Markers associated with a QTL for grain yield in wheat under drought . Mol Breeding . 2007 ; 20 : 401 ± 413 . https://doi.org/10.1007/ s11032−007−9100−3
30. Cuthbert JL , Somers DJ , BruÃleÂ-Babel AL , Brown PD , Crow GH . Molecular mapping of quantitative trait loci for yield and yield components in spring wheat (Triticum aestivum L) . Theor Appl Genet . 2008 ; 117 : 595 ± 608 . https://doi.org/10.1007/s00122-008 -0804-5 PMID: 18516583
31. Bennett D , Reynolds M , Mullan D , Izanloo A , Kuchel H , Langridge P , et al. Detection of two major grain yield QTL in bread wheat (Triticum aestivum L) under heat, drought and high yield potential environments . Theor Appl Genet . 2012 ; 125 : 1473 ± 1485 . https://doi.org/10.1007/s00122-012 -1927-2 PMID: 22772727
32. Varshney RK , Prasad M , Roy JK , Kumar N , Harjit S , Dhaliwal HS , et al. Identification of eight chromosomes and a microsatellite marker on 1AS associated with QTL for grain weight in bread wheat . Theor Appl Genet . 2000 ; 100 : 1290 ± 1294 . https://doi.org/10.1007/s001220051437
33. Ammiraju JSS , Dholakia BB , Santra DK , Singh H , Lagu MD , Tamhankar SA , et al. Identification of inter simple sequence repeat (ISSR) markers associated with seed size in wheat . Theor Appl Genet . 2001 ; 102 : 726 ± 732 . https://doi.org/10.1007/s001220051703
34. Sun X−Y , Wu K , Zhao Y , Kong F−M , Han G−Z, Jiang H−M , et al. QTL analysis of kernel shape and weight using recombinant inbred lines in wheat . Euphytica . 2009 ; 165 : 615 ± 624 . https://doi.org/10. 1007/s10681−008−9794−2
35. Nezhad KZ , Weber WE , RoÈder MS , Sharma S , Lohwasser U , Meyer RC , et al. QTL analysis for thousand-grain weight under terminal drought stress in bread wheat (Triticum aestivum L) . Euphytica . 2012 ; 186 : 127 ± 138 . https://doi.org/10.1007/s10681−011−0559−y
36. Zhang P , He Z , Tian X , Gao F , Xu D , Liu J , et al. Cloning of TaTPP−6AL1 associated with grain weight in bread wheat and development of functional marker . Mol Breeding . 2017 ; 37 : 78 . https://doi.org/10. 1007/s11032−017−0676−y
37. Song QJ , Shi JR , Singh S , Fickus EW , Costa JM , Lewis J , et al. Development and mapping of microsatellite (SSR) markers in wheat . Theor Appl Genet . 2005 ; 110 : 550 ± 560 . https://doi.org/10.1007/s00122- 004 -1871-x PMID : 15655666
38. Elhani S , Martos V , Rharrabti Y , Royo C , Gracia del Moral LF . Contribution of main stem and tillers to durum wheat (Triticum turgidum L. var. durum) grain yield and its components grown in Mediterranean environments . Field Crops Res . 2007 ; 103 : 25 ± 35 . https://doi.org/10.1016/j.fcr. 2007 . 05 .008
39. Wang S , Basten CJ , Zeng ZB . Windows QTL Cartographer 2.5 . Department of Statistics, North Carolina State University, Raleigh, NC. Available from: http://statgen.ncsu.edu/qtlcart/WQTLCart.htm. 2012 .
40. Doerge RW , Churchill GA . Permutation tests for multiple loci affecting a quantitative character . Genetics . 1996 ; 142 : 285 ± 294 . PMID: 8770605
41. Collard BCY , Jahufer MZZ , Brouwer JB , Pang ECK . An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts . Euphytica 2005 ; 142 : 169 ± 196 . https://doi.org/10.1007/s10681-005-1681-5
42. Rogers SO , Bendich AJ . Extraction of DNA from plant tissues . In: Gelvin SB , Schilperoort RA , Verma DPS , editors. Plant molecular biology manual Vol A6 . The Netherlands: Kluwer Academic; 1988 . pp 1± 10 .
43. Allen GC , Flores-Vergara MA , Krasynanski S , Kumar S , Thompson WF . A modified protocol for rapid DNA isolation from plant tissues using cetyltrimethylammonium bromide . Nat Protoc . 2006 ; 1 : 2320 ± 2325 . https://doi.org/10.1038/nprot. 2006 .384 PMID: 17406474
44. Castanha ER , Swiger RR , Senior B , Fox A , Waller LN , Fox KF . Strain discrimination among B. anthracis and related organisms by characterization of bclA polymorphisms using PCR coupled with agarose gel or microchannel fluidics electrophoresis . J Microbiol Methods . 2006 ; 64 : 27 ± 24 . https://doi.org/10.1016/ j.mimet. 2005 . 04 .032 PMID: 15992950
45. VSN International. Genstat for windows 17th edition . VSN International, Hemel Hempstead, UK. www. GenStat.co.uk. 2014 .
46. Nyquist WE , Baker RJ . Estimation of heritability and prediction of selection response in plant populations . Crit Rev Plant Sci . 1991 ; 10 : 235 ± 322 . https://doi.org/10.1080/07352689109382313
47. Dubcovsky J , Lijavetzky D , Appendino L , Tranquilli G . Comparative RFLP mapping of Triticum monococcum genes controlling vernalization requirement . Theor Appl Genet . 1998 ; 97 : 968 ± 975 . https://doi. org/10.1007/s001220050978
48. Kato K , Miura H , Sawada S. Mapping QTLs controlling grain yield and its components on chromosome 5A of wheat . Theor Appl Genet . 2000 ; 101 : 1114 ± 1121 . https://doi.org/10.1007/s001220051587
49. Snape JW , Law CN , Parker BB , Worland AJ . Genetical analysis of chromosome 5A of wheat and its influence on important agronomic characters . Theor Appl Genet . 1985 ; 71 : 518 ± 526 . https://doi.org/10. 1007/BF00251199 PMID: 24247464
50. Edae EA , Byrne PF , Haley SD , Lopes MS , Reynolds MP . Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes . Theor Appl Genet . 2014 : 127 : 791 ± 807 . https://doi.org/10.1007/s00122-013 -2257-8 PMID: 24408378
51. Quarrie SA , Gulli M , Calestani C , Steed A , Marmiroli N. Location of a gene regulating drought-induced abscisic acid production on the long arm of chromosome 5A of wheat . Theor Appl Genet . 1994 ; 89 : 794 ± 800 . https://doi.org/10.1007/BF00223721 PMID: 24178027
52. Kato K , Miura H , Sawada S. QTL mapping of genes controlling ear emergence time and plant height on chromosome 5A of wheat . Theor Appl Genet . 1999 ; 98 : 472 ± 477 . https://doi.org/10.1007/ s001220051094
53. Torada A , Koike M , Mochida K , Ogihara Y. SSR-based linkage map with new markers using an intraspecific population of common wheat . Theor Appl Genet . 2006 ; 112 : 1042 ± 1051 . https://doi.org/10. 1007/s00122-006 -0206-5 PMID: 16450184
54. Huang XQ , Kempf H , Ganal MW , RoÈder MS. Advanced backcross QTL analysis in progenies derived from a cross between a German elite winter wheat variety and a synthetic wheat (Triticum aestivum L) . Theor Appl Genet . 2004 ; 109 : 933 ± 943 . https://doi.org/10.1007/s00122-004 -1708-7 PMID: 15243706
55. Marza F , Bai GH , Carver BF , Zhou WC . Quantitative trait loci for yield and related traits in the wheat population Ning7840 × Clark . Theor Appl Genet . 2006 ; 112 : 688 ± 698 . https://doi.org/10.1007/s00122- 005 -0172-3 PMID: 16369760
56. Peng J , Ronin Y , Fahima T , RoÈder MS , Li Y , Nevo E , et al. Domestication quantitative trait loci in Triticum dicoccoides, the progenitor of wheat . P Natl Acad Sci USA . 2003 ; 100 : 2489 ± 2494 . https://doi.org/ 10.1073/pnas.252763199 PMID: 12604784
57. Yao J , Wang L , Liu L , Zhao C , Zheng Y. Association mapping of agronomic traits on chromosome 2A of wheat . Genetica . 2009 ; 137 : 67 ± 75 . https://doi.org/10.1007/s10709-009 -9351-5 PMID: 19160058
58. Reynolds M , Dreccer F , Trethowan R . Drought-adaptive traits derived from wheat wild relatives and landraces . J Exp Bot . 2007 ; 58 : 177 ± 186 . https://doi.org/10.1093/jxb/erl250 PMID: 17185737
59. Sohail Q , Inoue T , Tanaka H , Eltayeb AE , Matsuoka Y , Tsujimoto H . Applicability of Aegilops tauschii drought tolerance traits to breeding of hexaploid wheat . Breed Sci . 2011 ; 61 : 347 ± 357 . https://doi.org/ 10.1270/jsbbs.61.347 PMID: 23136471
60. Ayalew H , Liu H , Yan G . Identification and validation of root length QTLs for water stress resistance in hexaploid wheat (Triticum aestivum L) . Euphytica . 2017 ; 213 : 126 . https://doi.org/10.1007/s10681−017 − 1914 ± 4
61. Saini H.S. Effect of water stress on male gametophyte development in plants . Sex Plant Reprod . 1997 ; 10 , 67 ± 73 .
62. BarnabaÂs B , JaÈger K , FeheÂr A . The effect of drought and heat stress on reproductive processes in cereals . Plant Cell Environ . 2008 ; 31 : 11 − 38 . https://doi.org/10.1111/j.1365- 3040 . 2007 . 01727 . x PMID : 17971069
63. Peltonen-Sainio P , Kangas A , Salo Y , Jauhiainen L . Grain number dominates grain weight in temperate cereal yield determination: Evidence based on 30 years of multi-location trials . Field Crops Res . 2007 ; 100 : 179 ± 188 . https://doi.org/10.1016/j.fcr. 2006 . 07 .002