Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions

Abstract

Synthetic hexaploid wheat (SHW), possesses numerous genes for drought that can help breeding for drought-tolerant wheat varieties. We evaluated 10 root traits at seedling stage in 111 F9 recombinant inbred lines derived from a F2 population of a SHW line (SHW-L1) and a common wheat line, under normal (NC) and polyethylene glycol-simulated drought stress conditions (DC). We mapped quantitative trait loci (QTLs) for root traits using an enriched high-density genetic map containing 120370 single nucleotide polymorphisms (SNPs), 733 diversity arrays technology markers (DArT) and 119 simple sequence repeats (SSRs). With four replicates per treatment, we identified 19 QTLs for root traits under NC and DC, and 12 of them could be consistently detected with three or four replicates. Two novel QTLs for root fresh weight and root diameter under NC explained 9 and 15.7% of the phenotypic variation respectively, and six novel QTLs for root fresh weight, the ratio of root water loss, total root surface area, number of root tips, and number of root forks under DC explained 8.5–14% of the phenotypic variation. Here seven of eight novel QTLs could be consistently detected with more than three replicates. Results provide essential information for fine-mapping QTLs related to drought tolerance that will facilitate breeding drought-tolerant wheat cultivars.

Full text

Journal of Integrative Agriculture 2020, 19(8): 1947–1960
RESEARCH ARTICLE
Available online at www.sciencedirect.com
ScienceDirect
Quantitative trait loci analysis for root traits in synthetic hexaploid
wheat under drought stress conditions
LIU Rui-xuan1, WU Fang-kun1, YI Xin2, LIN Yu1, WANG Zhi-qiang1, LIU Shi-hang1, DENG Mei1, MA Jian1,
WEI Yu-ming1, ZHENG You-liang1, LIU Ya-xi1
1 Triticeae Research Institute, Sichuan Agricultural University, Chengdu 611130, P.R.China
2 College of Environmental Sciences, Sichuan Agricultural University, Chengdu 611130, P.R.China
Abstract
Synthetic hexaploid wheat (SHW), possesses numerous genes for drought that can help breeding for drought-tolerant wheat
varieties. We evaluated 10 root traits at seedling stage in 111 F9 recombinant inbred lines derived from a F2 population
of a SHW line (SHW-L1) and a common wheat line, under normal (NC) and polyethylene glycol-simulated drought stress
conditions (DC). We mapped quantitative trait loci (QTLs) for root traits using an enriched high-density genetic map containing
120
370 single nucleotide polymorphisms (SNPs), 733 diversity arrays technology markers (DArT) and 119 simple sequence
repeats (SSRs). With four replicates per treatment, we identied 19 QTLs for root traits under NC and DC, and 12 of them
could be consistently detected with three or four replicates. Two novel QTLs for root fresh weight and root diameter under
NC explained 9 and 15.7% of the phenotypic variation respectively, and six novel QTLs for root fresh weight, the ratio of
root water loss, total root surface area, number of root tips, and number of root forks under DC explained 8.5–14% of the
phenotypic variation. Here seven of eight novel QTLs could be consistently detected with more than three replicates. Results
provide essential information for ne-mapping QTLs related to drought tolerance that will facilitate breeding drought-tolerant
wheat cultivars.
Keywords: synthetic hexaploid wheat, quantitative trait loci, drought stress, root traits
less predictable, causing rapid expansion of aridity around
the world (Ayalew et al. 2017). Consequently, drought has
become probably the major abiotic factor limiting crop yield,
thus making the development of crops that are high-yielding
under environmentally stressful conditions a top priority for
food security (Ergen and Budak 2009; Fleury et al. 2010).
Wheat (Triticum spp.) is the leading human food source
(Tao et al. 2018; Wang et al. 2019), accounting for more
than half of the total food consumption in the world (Qin
et al. 2016). Hence, cultivating high-yielding wheat cultivars
under drought stress conditions is a global target of the
highest priority. The wild relatives of wheat harbor valuable
genetic resources that can improve drought tolerance in
cultivated wheat (Ibrahim et al. 2012). Many wild species
also retain outstanding genetic resources that have not yet
Received 16 April, 2019 Accepted 17 September, 2019
LIU Rui-xuan, E-mail: 947130299@qq.com; Correspondence LIU
Ya-xi, Tel: +86-28-86290952, Fax: +86-28-82650350, E-mail:
liuyaxi@sicau.edu.cn, yaxi.liu@hotmail.com, ORCID: 0000-0001-
6814-7218
© 2020 CAAS. Published by Elsevier Ltd. This is an open
access article under the CC BY-NC-ND license (http://
creativecommons.org/licenses/by-nc-nd/4.0/).
doi: 10.1016/S2095-3119(19)62825-X
1. Introduction
Global climate change has made rains more erratic and

1948 LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
been investigated. One such species is Aegilops tauschii
Coss., the diploid D-genome progenitor of hexaploid wheat
(Triticum aestivum L.). Aegilops tauschii showed greater
drought tolerance than T. aestivum and wild emmer wheat
(Triticum dicoccoides (Körn. ex Asch. & Graebn.) Schweinf.),
and harbored drought tolerance traits that were lost during
breeding processes (Ashraf 2010).
Synthetic hexaploid wheat (SHW) obtained from the
distant hybridization of T. turgidum L. and Ae. tauschii is
a source of novel genetic variability associated with the D
genome of Ae. tauschii (Mares and Mrva 2008); SHW lines
show signicantly higher abiotic stress tolerance, disease
resistance, P-deciency tolerance (Wang et al. 2015; Wu
et al. 2017), and drought stress tolerance (Lopes and
Reynolds 2011; Ibrahim et al. 2012; Bhatta et al. 2018) than
the tetraploid and common wheat lines; additionally, they
show suitable quality and anti-sprouting ability (Lage et al.
2003; Trethowan and Mujeeb-Kazi 2008; Yang et al. 2009).
Hence, identifying QTLs associated with drought tolerance
in SHW should prove useful for developing drought-tolerant
wheat cultivars.
Several phenotypic drought-responsive traits in wheat
have been correlated with molecular markers, thereby
allowing precise mapping of their respective QTLs on
chromosomes (Ibrahim et al. 2012; Bhatta et al. 2018).
However, drought tolerance is a complex phenomenon,
as most of the traits associated with it are polygenic in
nature and understating the genetic architecture of drought
tolerance for crops is still under way (Bhatta et al. 2018).
QTLs identication for the molecular tracing of drought
tolerance is a great challenge due to the large number of
genes influencing the trait, the instability of some QTLs,
the large size of the wheat genome, and epistatic QTLs
interactions, among other constraints (Ashraf 2010; Kumar
et al. 2010; Sharma 2013; Mwadzingeni et al. 2016).
Therefore, there is a need to identify QTLs for drought
tolerance with molecular markers in wheat.
The improvement of drought tolerance of wheat seedlings
can overcome the negative impact of low soil water
availability (Zhang et al. 2013). Liu et al. (2013) and Zhang
et al. (2013) pointed out the pivotal role of the root system at
the seedling stage for wheat to tolerant drought stress. As
crucial for water absorption, the root plays a major role in
growth, development and ultimately, crop yield (Rogers and
Benfey 2015). Root architecture and adaptation to soil water
decit are determined by various important characteristics
(Meister et al. 2014). For example, the maximum root length
contributes the baseline for water acquisition; root number,
root diameter and root length density, which are strongly
correlated with root dry weight, root volume and root surface
area (Noordwijk et al. 1994; Courtois et al. 2009; Meister
et al. 2014; Colombi and Walter 2017), affect the intensity
of xation in the soil prole and the ability to absorb the soil
solution within the baseline (Courtois et al. 2009; Wasson
et al. 2012). Wasson et al. (2012) suggested an approach
for developing new varieties that make better use of deep-
stored soil water, via the identication and employment of
genetic diversity for superior wheat root traits (e.g., deep
and highly branched roots). However, the high complexity
of root phenotyping greatly hampers the incorporation of
root characteristics in plant breeding. Studies of genetic
variability and QTLs mapping of root traits are rare (Atkinson
et al. 2015; Lianne et al. 2017), particularly under drought
stress conditions, and most reports on root morphology
QTLs have focused on root length and root weight (Coudert
et al. 2010; Herder et al. 2010; Cao et al. 2014).
The goal of this study was to identify QTLs related with
drought stress tolerance for root traits in SHW to obtain
useful information for breeding wheat cultivars with drought
tolerance.
2. Materials and methods
2.1. Plant materials
This study used a total of 111 F9 recombinant inbred
lines (RILs) derived by single-seed descent from the F2
population of SHW-L1/Chuanmai 32. SHW-L1 is a SHW
line derived from a cross between T. turgidum ssp. turgidum
AS2255 (AABB) and Ae. tauschii ssp. tauschii AS60 (DD).
Chuanmai 32 is a commercial cultivar of hexaploid wheat
grown in winter in southwest of China (Yu et al. 2014).
2.2. Plant growth and experimental treatments
Together, RILs and parental lines were hydroponically
cultured for measuring root traits. Thirty uniformly sized
seeds from each line were surface-sterilized by soaking in
10% sodium hypochlorite for 5 min and rinsed three times
with deionized water. Seeds were then germinated on lter
paper in Petri dishes at (25±1)°C for 7 d. After removing
residual endosperm materials, two sets of four uniform
seedlings with a coleoptile were transplanted into a different
hydroponic system under normal condition (NC) for 4 d,
and then, one set was placed under polyethylene glycol-
simulated drought stress condition (DC) while the other was
kept under NC. Uniform seedlings of every strain of RILs
were placed in a completely randomized design with four
replications per treatment. Hydroponic tanks were lled with
standard Hoagland’s nutrient solution (1 mmol L–1 KH2PO4,
2 mmol L–1 MgSO4·7H2O, 4 mmol L–1 CaNO3·4H2O, 6 mmol
L–1 KNO3, 0.046 mmol L–1 H3BO3, 0.76 mol L–1 ZnSO4,
0.32 mol L–1 CuSO4·5H2O, 9.146 mol L–1 MnCl2, 0.0161 µmol
L–1 (NH4) 6MoO4·4H2O, and 100 µmol L–1 NaFeEDTA;

1949
LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
Hoagland and Arnon 1950), with or without 19.2%
polyethylene glycol (Qin et al. 2016) for DC and NC,
respectively. Seedlings were grown at a temperature of
(25/22±1)°C day/night, relative humidity of 65/85% day/
night, and a 16-h photoperiod under 500 mmol m–2 s–1 photon
flux density at the level of plant canopy. The hydroponic
system was formed by a cystosepiment substrate, which
was placed in plastic tanks (50 cm×40 cm×30 cm) lled with
21 L of modied Hoagland’s nutrient solution.
2.3. Collection and analysis of phenotypic data
Uniform seedlings were transferred to be harvested after
growing for 12 d. We used an Epson XL (11
000×) scanner
to evaluate with a WinRHizo Pro 2008a image analysis
system (Régent Instruments, Quebec, Canada) for the
following traits: total root length (TRL), total root surface
area (TSAR), root diameter (RD), total root volume (TRV),
number of root tips (RT), and number of root forks (RF).
We measured root fresh weight (RFW) and the maximum
root length (RL). To determine root dry weight (RDW), roots
were stored in paper bags, heated at 105°C for 30 min and
dried to constant mass at 75°C. Then, we calculated the
ratio of root water loss (RWL).
We calculated the mean value of root traits with four
replicates by using IBM SPSS Statistics for Windows 20.0.
Pearson correlation analysis and principal component
analysis were calculated using the mean values of each
trait (IBM Corp., Chicago, IL, USA). In addition, the root
trait values of each replicate, which were used for QTLs
detection, were calculated. Heritability was calculated as
follows (Smith et al. 1998):
H2=VG/(VG+VE)
where VG and VE represent estimates of genetic and
environmental variances, respectively.
2.4. QTLs mapping
An enriched high-density genetic map containing 120
370
single nucleotide polymorphisms (SNPs) (Axiom™ wheat
660k Arrays), 733 diversity arrays technology (DArT)
markers and 119 simple sequence repeats (121
222
markers in total) was used for QTLs analysis (Wu et al.
2017). The genetic map was 17
889.62 cM in length. The
average distance between markers was 0.148 cM, which
corresponds to 143 kb (wheat genome size according to
the International Wheat Genome Sequencing Consortium
(IWGSC) Database); QTLs were identied in the Biparental
Populations (BIP) module with the inclusive composite
interval mapping (ICIM) by IciMapping 4.1 (Meng et al.
2015). Logarithm of odds (LOD) threshold values for ICIM
were determined based on 1
000 permutations to declare
a signicant QTLs at P<0.05, and the QTLs with LOD
values<3.0 were excluded to ensure the authenticity and
reliability of reported QTLs.
3. Results
3.1. Variation of root traits in RILs
The values of RFW, RWL, TRL, TSAR, and RF for SHW-L1
were lower than those for Chuanmai 32 grown under NC,
while SHW-L1 showed signicantly (P<0.05 or P<0.01)
higher values for nine root traits, including RL, RFW, RDW,
TRL, TSAR, TRV, RD, RT, and RF, than those of Chuanmai
32, under DC, while RWL for SHW-L1 was signicantly
(P<0.05) lower than the corresponding value for Chuanmai
32 under DC (Table 1). Signicant (P<0.05) or highly
signicant differences (P<0.01) between NC and DC in
the RIL population under study were observed for all traits
(Table 2). On the other hand, RD showed a different trend,
as values were signicantly lower (P<0.01) in NC than in
DC. Heritability ranged from 0.41 to 0.77 under NC and from
0.33 to 0.78 under DC (Table 2). The frequency distribution
of all selected traits among the RILs was continuous under
both NC and DC (Fig. 1).
Table 1 Phenotypic variation for root traits in the parental lines
under the normal condition (NC) and the polyethylene glycol-
simulated drought stress condition (DC)
Trait1) Condition SHW-L1 Chuanmai 32 Signicance
RL (cm) NC 16.02 14.19 ns
DC 9.95 6.24 *
RFW (g) NC 0.19 0.20 ns
DC 0.19 0.12 *
RDW (g) NC 0.03 0.03 ns
DC 0.02 0.01 **
RWL (%) NC 82.14 88.03 ns
DC 85.22 95.32 *
TRL (cm) NC 114.74 129.75 ns
DC 65.99 42.42 *
TSAR (cm²) NC 15.20 16.12 ns
DC 9.28 7.36 **
RD (mm) NC 0.42 0.40 ns
DC 0.83 0.58 *
TRV (cm³) NC 0.16 0.16 ns
DC 0.16 0.09 *
RT (count) NC 142.08 139.06 ns
DC 68.78 35.17 *
RF (count) NC 226.33 311.33 ns
DC 172.67 93.11 *
1) RL, the maximum root length; RFW, root fresh weight; RDW,
root dry weight; RWL, the ratio of root water loss; TRL, total
root length; TSAR, total root surface area; RD, root diameter;
TRV, total root volume; RT, number of root tips; RF, number of
root forks.
The signicance level of P-value between two genotypes in the
parental lines. *, signicant at P<0.05; **, signicant at P<0.01; ns,
not signicant.

1950 LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
Table 2 Phenotypic variation for root traits in the SHW-L1/Chuanmai 32 recombinant inbred lines (RILs) population under normal
condition (NC) and the polyethylene glycol-simulated drought stress condition (DC)
Trait1) Condition Mean±SD CV (%) Signicance h2 2)
RL (cm) NC 18.42±3.61 19.58 ** 0.72
DC 9.35±1.73 18.46 0.66
RFW (g) NC 0.29±0.09 29.33 ** 0.77
DC 0.13±0.04 30.02 0.51
RDW (g) NC 0.02±0.004 21.69 ** 0.41
DC 0.01±0.002 20.61 0.33
RWL (%) NC 94.17±3.11 3.32 *0.56
DC 92.63±1.76 1.89 0.50
TRL (cm) NC 158.15±45.62 28.85 ** 0.76
DC 56.00±15.44 27.58 0.63
TSAR (cm²) NC 20.84±5.63 26.99 ** 0.73
DC 9.16±2.30 25.06 0.64
RD (mm) NC 0.43±0.03 6.96 ** 0.70
DC 0.55±0.05 9.60 0.68
TRV (cm³) NC 0.22±0.06 27.30 ** 0.69
DC 0.12±0.03 27.88 0.69
RT (count) NC 177.07±43.92 24.80 ** 0.74
DC 51.57±13.36 25.92 0.78
RF (count) NC 385.40±109.85 28.50 ** 0.71
DC 121.64±36.39 29.91 0.61
1) RL, the maximum root length; RFW, root fresh weight; RDW, root dry weight; RWL, the ratio of root water loss; TRL, total root length;
TSAR, total root surface area; RD, root diameter; TRV, total root volume; RT, number of root tips; RF, number of root forks.
2) h2, broad-sense heritability of the tested trait.
The signicance level of P-value between two treatments in the RILs population. *, signicant at P<0.05; **, signicant at P<0.01.
3.2. Correlations among traits
All 10 root traits were selected for Pearson correlation
analysis to identify relationships among them (Table 3).
The Pearson correlation matrix showed strong correlations
among most selected traits. We found 68 significant
(P<0.05) or highly significant correlation coefficients
(P<0.01) under NC and DC. RL, RFW, RDW, TRL, TSAR,
TRV, RT, and RF were signicantly (P<0.05) or highly
signicantly (P<0.01) correlated among them under both
NC and DC. RD showed a highly signicant negative
correlation with RF and RT (P<0.01) under NC; further, it
showed a highly signicant negative correlation with TRL
and RT (P<0.01), and a signicant negative one with RL
(P<0.05) under both NC and DC (Table 3).
3.3. Principal component analysis (PCA)
All root traits were subjected to PCA (Table 4). Three
principal components (PCs) identied with eigenvalues>1
accounted for 88.79 and 86.58% of the total variation in all
traits under NC and DC, respectively. The rst component
(PC1) represented 63.24 and 60.83% of the variability
in NC and DC, respectively, accounting primarily for the
following traits: RL, RFW, RDW, TRL, TSAR, TRV, RT, and
RF, both under NC and DC; PC2 represented 14.41 and
14.08% of the variation derived mainly from RD under NC
and DC, respectively. Finally, PC3 explained 11.14 and
11.67% of the variation under NC and DC, respectively, and
mainly reflected the contribution of RWL to the variation it
accounted for.
Genotype distribution of all selected traits, based on
PCA, is shown in Fig. 2. The relative distance among all
genotypes is displayed for each combination of selected
traits. Loading plots for PC1 vs. PC2 represented 77.65 and
74.91%, of the variability under NC and DC, respectively.
3.4. QTLs for measured traits
Seven QTLs for RFW, RDW, TRL, TSAR, and RD on
chromosomes (Chr.) 2B, 2D, 3A, and 5D were detected
under NC, and explained 8–15.7% of the phenotypic
variation, respectively. Twelve QTLs for RL, RFW, RWL,
TRL, TSAR, TRV, RT, and RF were identied under DC on
Chr. 2B, 2D, 3B, 3D, 4B, 5A, 6D, 7B, and 7D, explaining
8.5–16.1% of the phenotypic variation, respectively. Among
them, three QTLs (QTRL.RFW.TSAR.sicau-3A, QTRL.
TSAR.sicau-2B, and QDCTSAR.RL.RF.sicau-2D) showed
pleiotropism (Table 5; Fig. 3). Under NC, QTRL.RFW.
TSAR.sicau-3A, QRFW.sicau-5D, QTRL.TSAR.sicau-2B,
and QRD.sicau-2B could be consistently detected with three
or four replicates; under DC, QDCTSAR.RL.RF.sicau-2D,
QDCRFW.sicau-2B, QDCRFW.sicau-7D, QDCTRL.sicau-
4B, QDCTSAR.sicau-2B, QDCTRV.sicau-7B, QDCRT.sicau-

1951
LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
Fig. 1 Frequency distribution of root traits in the SHW-L1/Chuanmai 32 recombinant inbred lines (RILs) population under normal
condition (NC) and the polyethylene glycol-simulated drought stress condition (DC). The horizontal axis indicates root traits value,
the ordinate axis indicate frequency. RL, the maximum root length; RFW, root fresh weight; RDW, root dry weight; RWL, the ratio
of root water loss; TRL, total root length; TSAR, total root surface area; RD, root diameter; TRV, total root volume; RT, number of
root tips; RF, number of root forks.
NC-RL
353025201510
20
15
10
5
0
NC-RFW
0.600.500.400.300.200.10
20
15
10
5
0
NC-RDW
0.0350.0300.0250.0200.0150.0100.005
30
20
10
0
NC-RWL
0.9750.9500.9250.9000.8750.850
40
30
20
10
0
NC-TRL
35030025020015010050
20
15
10
5
0
NC-TSAR
403020100
20
15
10
5
0
NC-RD
0.550.500.450.400.35
20
15
10
5
0
NC-TRV
0.400.350.300.250.200.150.10
20
15
10
5
0
NC-RT
35030025020015010050
25
20
15
10
5
0
NC-RF
800600400200
25
20
15
10
5
0
DC-RL
15.0012.5010.007.505.00
Frequency
25
20
15
10
5
0
DC-RFW
0.350.300.250.200.150.100.05
25
20
15
10
5
0
DC-RDW
0.0200.0150.0100.0050.000
25
20
15
10
5
0
DC-RWL
0.960.940.920.900.880.86
20
15
10
5
0
DC-TRL
12010080604020
25
20
15
10
5
0
DC-TSAR
17.5015.0012.5010.007.505.00
20
15
10
5
0
DC-RD
0.800.700.600.500.40
20
15
10
5
0
DC-TRV
0.300.250.200.150.100.05
25
20
15
10
5
0
DC-RT
80604020
20
15
10
5
0
DC-RF
250200150100500
20
15
10
5
0
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency
Frequency

1952 LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
3D, and QDCRF.sicau-2D could be consistently detected
with three or four replicates, and all of them were considered
reliable in different replicates. Among all 12 reliable QTLs,
novel QTLs for RFW (QRFW.sicau-5D) and RD (QRD.sicau-
2B) were identied under NC and respectively explained
9 and 15.7% of the phenotypic variation; under DC, we
detected ve novel QTLs for RFW (QDCRFW.sicau-2B
and QDCRFW.sicau-7D), TSAR (QDCTSAR.sicau-2B),
RT (QDCRT.sicau-3D), and RF (QDCRF.sicau-2D) that
explained 8.5–14% of the phenotypic variation, respectively
(Table 5; Fig. 3). Additionally, we detected one novel QTL
for RWL (QDCRWL.sicau-5A) that explained 11.5% of the
phenotypic variation. Six among the seven reliable and
novel QTLs, namely, QRD.sicau-2B, QRFW.sicau-5D,
Table 3 Genetic correlation among selected traits under normal condition (NC, above the diagonal) and the polyethylene glycol-
simulated drought sress condition (DC, bellow the diagonal)
Trait1) RL RFW RDW RWL TRL TSAR RD TRV RT RF
RL 1 0.58** 0.45** –0.17 0.64** 0.64** –0.19*0.58** 0.66** 0.51**
RFW 0.52** 1 0.74** 0.13 0.83** 0.91** 0.07 0.92** 0.70** 0.77**
RDW 0.55** 0.76** 1 –0.10 0.60** 0.68** 0.12 0.71** 0.52** 0.60**
RWL 0.15 0.42** –0.19 1 0.12 0.12 –0.01 0.12 0.09 0.15
TRL 0.75** 0.68** 0.68** 0.16 1 0.96** –0.34** 0.84** 0.86** 0.92**
TSAR 0.72** 0.77** 0.76** 0.17 0.93** 1 –0.09 0.95** 0.81** 0.90**
RD –0.21*0.06 0.02 0.02 –0.37** –0.06 1 0.20*–0.34** –0.25**
TRV 0.56** 0.74** 0.71** 0.17 0.70** 0.91** 0.32** 1 0.70** 0.80**
RT 0.64** 0.58** 0.54** 0.21*0.80** 0.77** –0.30** 0.61** 1 0.89**
RF 0.58** 0.71** 0.66** 0.21*0.83** 0.88** –1.70 0.77** 0.88** 1
1) RL, the maximum root length; RFW, root fresh weight; RDW, root dry weight; RWL, the ratio of root water loss; TRL, total root length;
TSAR, total root surface area; RD, root diameter; TRV, total root volume; RT, number of root tips; RF, number of root forks.
*, signicant at P<0.05; **, signicant at P<0.01.
Table 4 Principal component analysis (PCA) for root traits in the SHW-L1/Chuanmai 32 recombinant inbred lines (RILs) population
under normal condition (NC) and the polyethylene glycol-simulated drought stress condition (DC)1)
Treatment Variable2) Characteristic vector
PC1 PC2 PC3
NC RL 0.70 –0.03 –0.34
RFW 0.92 0.18 0.22
RDW 0.72 0.49 –0.26
RWL 0.29 –0.47 0.81
TRL 0.96 –0.19 –0.04
TSAR 0.98 0.04 0.07
RD –0.15 0.86 0.4
TRV 0.92 0.29 0.19
RT 0.88 –0.25 –0.15
RF 0.92 –0.14 –0.03
Eigenvalues 6.32 1.44 1.11
Contribution (%) 63.24 14.41 11.14
Cumulative contribution (%) 63.24 77.65 88.79
DC RL 0.76 –0.20 –0.03
RFW 0.83 0.25 0.21
RDW 0.80 0.19 –0.42
RWL 0.22 0.06 0.96
TRL 0.93 –0.27 –0.04
TSAR 0.97 0.07 –0.05
RD –0.12 0.96 0
TRV 0.86 0.44 –0.04
RT 0.85 –0.27 0.07
RF 0.92 –0.07 0.03
Eigenvalues 7.72 1.68 1.22
Contribution (%) 60.83 14.08 11.67
Cumulative contribution (%) 60.83 74.91 86.58
1) PC, principal component.
2) RL, the maximum root length; RFW, root fresh weight; RDW, root dry weight; RWL, the ratio of root water loss; TRL, total root length;
TSAR, total root surface area; RD, root diameter; TRV, total root volume; RT, number of root tips; RF, number of root forks.

1953
LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
QDCRFW.sicau-2B, QDCRF.sicau-2D, QDCRT.sicau-3D,
and QDCRFW.sicau-7D, were contributed by positive alleles
from SHW-L1 (Table 5; Fig. 3), explaining 8.5–15.7% of the
phenotypic variation; among these QTLs, QDCRF.sicau-2D,
QDCRT.sicau-3D, QRFW.sicau-5D, and QDCRFW.sicau-7D,
were detected in the D-genome.
4. Discussion
4.1. Analysis of root-trait variation response to
drought at the seedling stage
Seedling establishment is considered the most critical stage
for wheat growth and development, especially under drought
stress conditions (Liu et al. 2013). Drought-stress tolerance
is a complex trait chiefly resulting from the interaction of root
traits (Ludlow and Muchow 1990; Richards et al. 2002).
Therefore, researching root traits at the seedling stage is
vital for developing wheat cultivars with a high degree of
drought tolerance (Liu et al. 2013; Zhang et al. 2013). Ye
et al. (2018) indicated that the interaction of root traits, such
as root length density, and root surface area and volume,
among others, might enhance plant water uptake efciency
in soils with low water availability. In our research, Pearson
correlation indicated that most root traits had a signicant
(P<0.05) or a highly signicant (P<0.01) correlation among
them under DC (Table 3), which in turn indicated that root
system traits might exert reciprocal, profound influences
during wheat growth under drought stress conditions,
thereby increasing water uptake efciency to resist drought
stress.
Drought stress decreased plant height, total dry matter
production, the maximum root length, root length to shoot
length ratio, and other root traits (Djanaguiraman et al.
2019). In the present study, root traits, including RL, RFW,
RDW, RWL, TRL, TSAR, TRV, RT, and RF, for the RIL
population were signicantly (P<0.05) or highly signicantly
(P<0.01) lower under DC than under NC (Table 2), indicating
that most tested traits were highly affected by drought.
Energy required by growing plants is used to produce
biomass containing different proportions of proteins, lipids
and carbohydrates (Ludlow and Muchow 1990). Crop plants
need to maintain high water use efciency (WUE) rates
during growth (Nicotra and Davidson 2010).
Therefore, we hypothesized that wheat would reduce root
growth rates to reduce energy consumption and still show
adequate WUE levels when faced with a drought stress.
However, RD was higher in seedlings grown under DC than
under NC (Table 2), indicating that RD played a special role
during drought stress. Further, RD showed signicantly
(P<0.05) or highly signicantly (P<0.01) negative correlation
with RL, RT and TRL under DC (Table 3). PCA showed that
the major contributor to the variability explained by PC2
under DC was RD (Table 4; Fig. 2).
SHW seemed to adapt to simulated drought stress by
increasing root diameter, possibly due to the fact that coarse
roots tend to turnover more slowly, and thus reduce energy
consumption. Similar to what Wu et al. (2017) reported,
SHW resisted P deciency by increasing root diameter,
because coarse roots tend to have a lower turnover rate
and save energy. Qiu et al. (2013) reported that the survival
ability of roots increases with increasing RD. The increase
of RD also indicated that the size of xylem and phloem
increased, which promoted nutrient uptake (Zhao et al.
2005), thereby enhancing drought tolerance.
Fig. 2 Scatter plots of the rst two principal components (PCs) from principal component analysis (PCA) for normal condition (NC)
and the polyethylene glycol-simulated drought stress condition (DC). RD, root diameter; RDW, root dry weight; TRV, total root
volume; RFW, root fresh weight; TSAR, total root surface area; RL, the maximum root length; RF, number of root forks; TRL, total
root length; RT, number of root tips; RWL, the ratio of root water loss.
NC-PC1
1.00.50.0–0.5–1.0
NC-PC2
1.0
0.5
0.0
–0.5
–1.0
RF
RT
TRV
RD
TSAR
TRL
RWL
RDW
RFW
RL
DC-PC1
1.00.50.0–0.5–1.0
DC-PC2
1.0
0.5
0.0
–0.5
–1.0
RF
RT
TRV
RD
TSAR
TRL
RWL RDW
RFW
RL

1954 LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
4.2. SHW incorporated
drought tolerance
from the wheat wild
relative Ae. tauschii
Numerous traits
of high agronomic
interest are found in
Ae. tauschii; such as
insect, disease and
drought tolerance, as
well as yield, among
others (Cox et al. 1994;
Cox and Hatchett 1994;
Ma et al. 1995; Assefa
2000; Aghaee-Sarbarzeh
et al. 2002). The wheat
genome can incorporate
Ae. tauschii’s genes
via intergenic crossing
(Valkoun et al. 1990; Cox
et al. 1992; Li et al. 2006;
Zhang and Ma 2008).
Artificial hybridization
between tetraploid wheat
and Ae. tauschii has
resulted in allohexaploid
wheat lines known
as ‘resynthesized’ or
‘synthetic hexaploid’
wheat (SHW) (Mujeeb-
Kazi et al. 1996) that
show predominance
in some domains. For
example, Chuanmai 42
is a kind of synthetic
hexaploid wheat showing
considerable resistance
to new races of stripe rust
in China (Li et al. 2006).
In this study, SHW-L1
showed outstanding
performance under
simulated drought stress
conditions. Measured
values for root traits in
SHW-L1 seedlings grown
under DC, including RL,
RFW, RDW, TRV, TRL,
TSAR, RD, RT, and
RF, were significantly
(P<0.05) or highly
Table 5 All QTLs for root traits identied in the SHW-L1/Chuanmai 32 recombinant inbred lines (RILs) population under normal condition (NC) and the polyethylene glycol-simulated
droght stress condition (DC)1)
Trait2) QTL3) Chr. Interval position (cM) Left marker Right marker LOD PVE (%) Source Reference
NC
RFW QRFW.sicau-2B 2B 868.39–875.95 AX-109416598 AX-111482498 3.99 12.1 CM32 Marjaei et al. (2014)
*QTRL.RFW.TSAR.sicau-3A 3A 52.76–53.12 AX-110434946 AX-94933737 3.12 8.0 CM32 Al-Chaarani et al. (2005)
*QRFW.sicau-5D 5D 425.81–426.17 AX-109509287 AX-111505386 3.49 9.0 SHW-L1
RDW QRDW.sicau-2B 2B 757.77–761.29 AX-95257452 AX-109285972 3.68 12.1 CM32 Zhang et al. (2013)
QRDW.sicau-2D 2D 317.19–332.10 AX-111922990 AX-95098390 3.20 11.4 CM32 Bai et al. (2013)
TRL *QTRL.TSAR.sicau-2B 2B 903.95–904.32 AX-110669593 AX-109955639 4.99 13.6 CM32 Kabir et al. (2015)
*QTRL.RFW.TSAR.sicau-3A 3A 52.76–53.12 AX-110434946 AX-94933737 5.08 14.0 CM32 Al-Chaarani et al. (2005)
TSAR *QTRL.TSAR.sicau-2B 2B 903.95–904.32 AX-110669593 AX-109955639 4.06 13.6 CM32 Kabir et al. (2015)
*QTRL.RFW.TSAR.sicau-3A 3A 52.76–53.12 AX-110434946 AX-94933737 3.68 12.5 CM32 Al-Chaarani et al. (2005)
RD *QRD.sicau-2B 2B 533.7–534.07 AX-111110588 AX-108954344 3.89 15.7 SHW-L1
DC
RL QDCRL.sicau-2B 2B 776.96–788.2 AX-109273291 AX-110508395 4.49 12.9 CM32 Zhou et al. (2005)
*QDCTSAR.RL.RF.sicau-2D 2D 36.64–53.78 AX-94971066 AX-109930650 4.19 13.8 SHW-L1 Ayalew et al. (2017)
QDCRL.sicau-6D 6D 192.86–197.75 AX-110119937 AX-94781191 3.52 10.1 CM32 Zhang et al. (2013)
RFW *QDCRFW.sicau-2B 2B 565.92–567.05 AX-109855033 AX-109393034 6.99 14.0 SHW-L1
QDCRFW.sicau-3B 3B 489.22–493.13 AX-110514951 AX-111056869 5.38 9.8 CM32 Zhang et al. (2013)
*QDCRFW.sicau-7D 7D 392.6–395.68 AX-109963284 AX-110925908 4.40 8.5 SHW-L1
RWL QDCRWL.sicau-5A 5A 671.98–672.34 AX-110040791 AX-108728176 3.93 11.5 CM32
TRL *QDCTRL.sicau-4B 4B 198.39–204.08 AX-111613161 AX-109364483 3.09 12.6 CM32 Anna et al. (2017)
TSAR *QDCTSAR.sicau-2B 2B 570.11–572.4 AX-109508485 AX-109824897 4.00 10.3 CM32
*QDCTSAR.RL.RF.sicau-2D 2D 36.64–53.78 AX-94971066 AX-109930650 5.95 16.1 SHW-L1 Ayalew et al. (2017)
TRV *QDCTRV.sicau-7B 7B 705.87–716.55 AX-95662355 AX-111601478 3.32 15.0 SHW-L1 Anna et al. (2017)
RT *QDCRT.sicau-3D 3D 183.45–184.22 wPt-6066 wPt-740598 3.79 10.7 SHW-L1
RF *QDCTSAR.RL.RF.sicau-2D 2D 36.64–53.78 AX-94971066 AX-109930650 5.23 13.2 SHW-L1 Ayalew et al. (2017)
*QDCRF.sicau-2D 2D 694.95–697.66 AX-108940963 AX-94650361 3.99 9.6 SHW-L1
1) LOD, logarithm of odds; PVE (%), percentage of explained phenotypic variation. Source, the source of positive alleles.
2) RFW, root fresh weight; RDW, root dry weight; TRL, total root length; TSAR, total root surface area; RD, root diameter; RL, the maximum root length; RWL, the ratio of root water loss; TRV,
total root volume; RT, number of root tips; RF, number of root forks.
3) * indicates that QTLs could be also detected with three or four replicates; QTLs-underlined were identied to be novel.

1955
LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
Fig. 3 Chromosomal locations of quantitative trait loci for root traits and associated markers in the SHW-L1/Chuanmai 32 recombinant
inbred lines (RILs) population under normal condition (NC) and the polyethylene glycol-simulated drought stress condition (DC). The
nearest flanking markers of QTL was marked on the graphs. and indicate QTL identied only in NC and only in DC, respectively.
QRD.sicau-2B
QDCRFW.sicau-2BQDCTSAR.sicau-2B
wPt-7672
AX-111496247
AX-111015749
3.52
AX-111489223
1.89
AX-95236522
1.5
AX-108746671
AX-110943121
0.73
AX-111110588
0.36
AX-108954344
0.37
AX-111255952
3.08
AX-111123423
1.11
AX-109872505
0.73
AX-111282596
3.13
AX-110400505
0.74
AX-111145873
0.36
AX-110095509
4.34
AX-109351901
2.67
AX-111025427
0.36
AX-110379057
2.69
AX-109905369
2.69
AX-108792697
0.74
AX-109295083
0.36
AX-94750236
2.67
AX-109855033
6.18
AX-109393034
1.13
AX-110046291
0.37
AX-109508485
2.69
AX-109824897
2.29
Chr. 2B-1
QRDW.sicau-2BQDCRL.sicau-2BQRFW.sicau-2BQTRL.TSAR.sicau-2B
AX-109449320
AX-109980104
2.29
AX-95257452
0.36
AX-109285972
3.52
AX-111683395
6.64
AX-110539255
0.37
AX-109344750
0.37
AX-110095950
2.71
AX-111198536
4.86
AX-110935082
0.36
AX-109273291
0.36
AX-110508395
11.24
AX-111011161
4.81
AX-108981299
AX-110418693
2.33
AX-109416598
0.36
AX-111482498
7.56
AX-111684920
2.27
AX-111567244
4.77
AX-111046634
6.70
AX-110720347
3.55
AX-111658368
7.61
AX-110669593
3.10
AX-109955639
0.37
AX-94761209
Chr. 2B-2
QRDW.sicau-2D
Chr. 2D
QDCTSAR.RL.RF.sicau-2D
QDCRF.sicau-2D
AX-109839696
AX-94956890
3.08
AX-108926113
AX-94971066
8.11
AX-109930650
17.14
AX-111088802
3.20
AX-109125278
AX-110597395
1.49
AX-111652465
0.74
AX-111922990
0.36
AX-95098390
14.91
AX-108966787
AX-94926459
3.94
AX-108940963
2.33
AX-94650361
2.71
AX-94684313
0.36
AX-95161017
AX-111347722
8.70
AX-109386522
AX-110596529
4.77
AX-110499811
0.37
AX-109356270
0.36
AX-111482488
0.38
AX-111552829
0.37
AX-109283109
0.36
AX-109485490
2.27
AX-110176693
1.11
AX-110549861
AX-110434946
2.27 AX-94933737
0.36 AX-109846008
2.72 AX-108941632
3.12 AX-111256529
0.75 AX-111595574
4.41 AX-109861514
7.56
AX-110970163
7.61
AX-109848595
3.94
wPt-2478
2.85
AX-94394356
9.19
AX-111017489
0.36
AX-109862339
3.1
AX-94490447
7.12
AX-109441469
3.10
AX-109397205
3.10
AX-109102233
0.37
AX-110031124
0.73
AX-108780906
0.36
AX-109407434
0.37
AX-111497403
5.3
AX-109314625
3.94
AX-111082491
3.1
AX-109951727
0.36
AX-94833664
AX-108759089
1.12
AX-108840126
7.12
AX-109542246
5.30
AX-109573280
0.37
AX-110385981
0.36
AX-111129342
0.75
AX-110407495
1.13
AX-110950123
0.38
AX-108904379
0.74
AX-109652275
8.26
AX-111825737
4.89
AX-110591605
4.44
AX-111657192
5.09
AX-108815310
2.67
Chr. 3A
QTRL.RFW.TSAR.sicau-3A
QDCRFW.sicau-3B
AX-111565550
AX-111200115
2.71
AX-109371108
0.77
AX-89333132
0.38
AX-109864067
AX-110514951
0.37
AX-111056869
3.91
AX-111224110
10.13
AX-110439869
0.73
wPt-742172
AX-94808186
5.48
AX-94612918
2.85
AX-110971050
6.00
Chr. 3B Chr. 3D
wPt-732889
AX-108973500
2.71
AX-109366390
1.88
AX-110911706
1.88
AX-111374308
0.73
AX-110995970
0.36
AX-109377228
wPt-671740
4.67
wPt-6066
0.4
wPt-740598
0.77
wPt-671560
3.66
wPt-741796
1.56
wPt-740691
1.14
AX-94771710
AX-94942251
5.30
AX-110387514
9.14
AX-108862484
20.83
AX-111616186
0.73
AX-110916769
7.61
QDCRT.sicau-3D
AX-110467582
AX-110504006
0.76
AX-109411791
AX-109580703
1.59
AX-110620358
0.38
AX-111613161
1.41
AX-109364483
5.69
AX-110540306
2.77
AX-110655231
0.18
AX-110430004
0.18
AX-110047726
1.61
AX-110389131
wPt-732448
4.21
Chr. 4B
QDCTRL.sicau-4B
wPt-1200
AX-110102371
2.34
AX-111461330
0.73
AX-89737283
0.36
AX-94422894
0.37
AX-110990493
AX-111115088
1.11
AX-110040791
0.73
AX-108728176
0.36
AX-108987176
1.5
AX-110551412
0.36
AX-111041163
1.88
AX-108911445
6.12
AX-109490942
AX-111037220
3.97
Chr. 5A
QDCRWL.sicau-5A
AX-94408170
AX-109500865
14.91
AX-109965818
26.39
AX-111170347
0.36
AX-109477622
1.88
AX-110145713
1.49
AX-109353713
AX-109509287
5.67
AX-111505386
0.36
AX-111629269
6.59
Chr. 5D
QRFW.sicau-5D
AX-110923547
AX-111228393
0.74
AX-109428010
1.1
AX-108812802
AX-95632861
1.12
AX-110472185
1.11
AX-95071176
0.38
AX-109317109
1.59
AX-110119937
0.36
AX-94781191
4.89
AX-110919787
1.14
AX-111486762
0.38
AX-109972428
1.14
AX-111598010
AX-110931925
5.29
Chr. 6D
QDCRL.sicau-6D
AX-108910859
AX-89418651
3.94
AX-111455372
7.62
AX-109911525
3.93
AX-111619283
2.29
AX-95200970
3.10
AX-86173016
1.12
AX-95662355
AX-111601478
10.68
Chr. 7B
QDCTRV.sicau-7B
AX-95251028
AX-94969349
2.69
AX-108915925
0.37
AX-110581495
AX-108808666
0.73
AX-111151622
1.88
AX-109963284
5.22
AX-110925908
3.08
AX-95175718
7.55
AX-111547281
AX-109912794
6.13
AX-111724244
15.57
Chr. 7D
QDCRFW.sicau-7D
cM
cM
cM cM cM cM
cM cM cM
cM cM cM

1956 LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
signicantly (P<0.01) greater than those for Chuanmai 32,
while RWL for SHW-L1 was signicantly (P<0.05) lower than
that for Chuanmai 32 (Table 1). Kramer (1969) proposed
that one of the essential characteristics of drought tolerance
must be a “deep, wide-spreading, much-branched root
system”. Further, it has been reported (Blum 2009; Lopes
and Reynolds 2010; Wasson et al. 2012) that wheat varieties
with a deeper root system (denser roots at depth rather
than at the surface), have higher yields across a range of
environments, from rainfed systems where crops rely on
deep water for grain lling, to more favorable environments.
Hence, we deemed that drought tolerance had been
incorporated into SHW from Ae. tauschii as a highly
desirable trait to pass onto cultivated wheat. Therefore,
we identied QTLs in SHW with high drought tolerance that
could be used as promising resources to widen the genetic
diversity of cultivated wheat, and thus, to shorten the time
required to breed for drought tolerance.
4.3. Novel QTLs correlated with drought stress
tolerance from D genome in SHW
We used an enriched high-density genetic map containing
a total of 121
222 markers to identify QTLs in 111 RILs, with
the aim to enhance the resolution of QTL mapping. For QTL
mapping, high marker density and large population size are
both important for improving the power and precision. A
larger population size would increase the number of effective
recombinant events in the process of QTL ne-mapping
(Zhang et al. 2019). For a given trait in a population of
limited size, marker density may be a key factor for improving
the power and precision of QTL mapping. Yu et al. (2011)
reported that increasing marker density can increase the
resolution of the genetic map, thus enhancing the precision
of QTL mapping. Previously, some researchers used high-
density genetic markers to identify QTLs in the population
that were not large enough (around 100 lines) (Atkinson
et al. 2015; McCartney et al. 2016; Su et al. 2018; Yao et al.
2019). The size of F9 RILs (111) used in the current study
was not large enough, which would lower the power of QTL
detection. However, the enriched high-density genetic map
containing a total of 121
222 markers in this study would
enhance the power for QTL identication and accuracy of
QTLs to a great extent.
We identied 19 QTLs for root traits in the current study.
Twelve of them could consistently be detected with three
or four replicates, indicating that these 12 QTLs were
reliable (Table 5). Here eight reliable QTLs were detected
only under DC; these loci might harbor alleles specically
expressed under water deciency. Kathiresan et al. (2006)
used DNA expression microarray technology to determine
that various stress-related genes were induced by different
forms of stress. Similarly, Liu et al. (2013) detected a QTL
for root traits located on chromosome 5B only under water
stress conditions. Yang et al. (2018) identied four QTLs for
root traits only under P-deciency treatment. In rice, Kumar
et al. (2014) identied large-effect QTLs for grain yield only
under drought and veried that a higher yield increase under
drought was obtained through the use of these QTLs. Due
to the drought stress tolerance induced by these QTLs, they
will be useful in marker-assisted selection of drought-stress-
tolerant wheat cultivars. Among 12 reliable QTLs, ve of
them (QTRL.TSAR.sicau-2B, QTRL.RFW.TSAR.sicau-3A,
QDCTSAR.RL.RF.sicau-2D, QDCTRL.sicau-4B, and
QDCTRV.sicau-7B) might be co-localized to previously
reported QTLs (Al-Chaarani et al. 2005; Kabir et al. 2015;
Anna et al. 2017; Ayalew et al. 2017). Previous studies
identied QTLs for RFW, TSAR, RT, and RF on Chr. 2BS,
2DS and 3DL (Ibrahim et al. 2012; Ren et al. 2012; Liu et al.
2013; Sun et al. 2013; Pu et al. 2018). By comparison,
QRFW.sicau-5D and QRD.sicau-2B, which were identied
only under NC, and QDCRFW.sicau-2B, QDCRFW.sicau-7D,
QDCTSAR.sicau-2B, QDCRT.sicau-3D, and QDCRF.
sicau-2D, which were detected only under DC, are novel
QTLs (Table 5). For example, a QTL for RT was identied
on Chr. 3DS (QDCRT.sicau-3D) in this study, whereas Ren
et al. (2012) found it on Chr. 3DL; similarly, two QTLs for RF
were previously detected on Chr. 2DS (Ibrahim et al. 2012;
Pu et al. 2018), while we found a QTL for RF on Chr. 2DL
(QDCRF.sicau-2D). Among them, QRFW.sicau-5D,
QRD.sicau-2B, QDCRFW.sicau-2B, QDCRFW.sicau-7D,
QDCRT.sicau-3D, and QDCRF.sicau-2D, consistently
detected with more than three replicates, were contributed
by positive alleles from SHW-L1 (Table 5), but only two other
QTLs were contributed by positive alleles from Chuanmai 32,
indicating that SHW contained more new genes correlated
with drought tolerance than Chuanmai 32 did.
Previous studies reported some QTLs for drought stress
tolerance in the D genome from wheat. For instance,
Quarrie et al. (2005) mapped QTLs for yield and yield
components that correlated with drought tolerance in
hexaploid wheat, on Chr. 2D and 3D, and found that three
yield QTL clusters were coincident with Vrn-D1 on Chr. 5DL.
Zhang et al. (2013) found two QTLs for RL associated with
drought tolerance on Chr. 6D in two F8:9 recombinant inbred
line populations. Qin et al. (2016) identied seven signicant
loci on Chr. 2D and one significant locus on Chr. 2D,
and identied loci for RL-DI on Chr. 6D; additionally, they
found two loci related to RD-SC and RD-DI on Chr. 7D.
Here, four among six reliable and novel QTLs that
were contributed by positive alleles from SHW-L1 were
mapped in the D genome (QRFW.sicau-5D, QDCRT.
sicau-3D, QDCRFW.sicau-7D, and QDCRF.sicau-2D).
Thus, the presence of novel QTLs detected in the D genome

1957
LIU Rui-xuan et al. Journal of Integrative Agriculture 2020, 19(8): 1947–1960
indicated that some unknown genes for drought stress
tolerance were brought into SHW, and correlated with the
D genome from Ae. tauschii. As previously reported, our
results indicated that the D genome could play a key role
in drought stress tolerance. Further, we found another QTL
for RD on 2B (QRD.sicau-2B) in NC, and for RFW on 2B
(QDCRFW.sicau-2B) under DC, and we suspected that they
were brought into SHW from T. turgidum (AABB).
Undoubtedly, numerous important genes were lost during
domestication of common wheat. For example, according
to Ma et al. (2017), during the formation of hexaploid wheat,
genes upregulated in the root were prone to be lost. SHW
contains some new genes obtained from Ae. tauschii, the
progenitor with the D genome. Therefore, we may detect
new drought-tolerant genes in SHW.
All in all, we identied two novel QTLs in NC, and ve
novel QTLs in DC, detected with more than three replicates
in this study. The reliable QTLs detected under DC required
mapping renement to allow QTL cloning for improving root
traits to enhance drought stress tolerance for wheat through
molecular breeding. In the present study, efforts were made
to saturate these loci to facilitate ne-mapping.
5. Conclusion
Genetics of drought/water stress tolerance in wheat has
become a priority area of research. Identifying QTLs associated
with drought tolerance from SHW is necessary for developing
drought-tolerant wheat cultivars. In this study, we identied
six novel QTLs for root traits under normal and drought
stress conditions (QRD.sicau-2B, QDCRFW.sicau-2B,
QDCRF.sicau-2D, QDCRT.sicau-3D, QRFW.sicau-5D, and
QDCRFW.sicau-7D), contributed by positive alleles from
SHW-L1, and all of them could be consistently detected with
three or four replicates. Our data provide new useful insights
into the genetic basis of root traits in SHW under different
water conditions; furthermore, they provide important
information for a sound foundation for QTL cloning and
developing stress-tolerant wheat cultivars.
Acknowledgements
This study was supported by the National Natural Science
Foundation of China (31771794, 91731305 and 31560388),
the outstanding Youth Foundation of the Department
of Science and Technology of Sichuan Province, China
(2016JQ0040), the Key Technology Research and
Development Program of the Department of Science and
Technology of Sichuan Province, China (2016NZ0057),
and the International Science & Technology Cooperation
Program of the Bureau of Science and Technology of
Chengdu, China (2015DFA306002015-GH03-00008-HZ).
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Executive Editor-in-Chief ZHANG Xue-yong
Managing editor WANG Ning