Introduction
Rice (Oryza sativa L.) is one of the most important
food crops in the world. In recent years, with global warming and environmental
deterioration, the frequency and range of droughts has increased. Drought as a
major challenge results in the reduction of large-scale rice production (Luo 2010). The most effective way to improve
drought tolerance of rice is to study physiological responses to drought
stress, to screen and identify drought-resistant genes, and to develop
drought-tolerant varieties (Luo and Zhang 2001).
The response of rice plants to drought stress involves many complex
physiological changes and QTLs. To date, more than 800 rice QTLs that respond
to drought tolerance have been identified. Among these lines, 338 were
associated with root development and tillering
under drought stress, 36 were associated with physiological traits (i.e., abscisic
acid, osmotic adjustment, and relative water content), and 435 were associated
with other traits (http://www.plantstress.com/biotech/index.asp?Flag=1).
A water stress experiment was performed on rice at two different periods of the
vegetative stage, which identified four drought-sensitive index QTLs using a
comparative study of plant height, tiller number, and root thickness (Hemamalini et al. 2000). Further study
of root traits revealed 8 drought tolerance QTLs under drought stress (Ali et al. 2000; Kamoshita et al. 2002;
Price et al. 2002), while under drought in rice at the seedling
and vegetative stages have identified another 18 drought tolerance QTLs (Champoux et al. 1995). Then, 2 novel
drought tolerance QTLs were identified at the seedling stage under drought
treatment (Teng et al. 2002). As
rice has a complex response to drought stress, it is difficult to
comprehensively characterize drought resistance by conducting drought treatment
experiments only on plants of a particular developmental stage or by studying a
limited number of traits under drought stress conditions. However, many
drought-responsive QTLs are strongly influenced by the environment, and these
QTLs are limited in plant cultivation.
Single segment substitution lines (SSSLs) are ideally
suited for QTL identification and have the advantages of line stability and
genetic background purity. SSSLs decompose QTLs controlling complex
traits into individual Mendelian factors and improve
the accuracy of QTL analysis.
To date, several rice SSSLs have been developed, and many QTLs underlying
traits of biological and economic interest have been detected (Liu et al. 2004, 2008; Ebitani et al.
2005; Xi et al. 2006; Zhu et al. 2009; Teng et al. 2012).
In this study, we used SSSLs as a population for the identification and
characterization of drought tolerance QTLs in yield-related traits. Through a
comparative analysis of QTLs of relative traits under drought stress, drought
tolerance QTLs with stable heredity and significant effect were screened to
provide a reference for molecular marker-assisted breeding of drought tolerance
varieties and identify drought tolerance genes.
Materials and Methods
Experimental materials
A set
of single segment substitution lines was derived from a cross of O. sativa indica
cv. 9311 as the recipient parent and O.
sativa japonica cv. Nipponbare as the donor
parent. Our population was composed of 123 lines. The average length of the set
lines was 5.58 Mb, covering 63.72% of the rice genome. In this study, 70
substitution lines with productive value were selected for the experimental
study.
Field experiment
A
drought stress experiment was implemented in an upland field with fertile soil and
without shade in winter at Sanya, Hainan. Seeds for
the experiment were dried for 48 h at a constant temperature
of 50°C followed by 48 h of imbibition, after which they were germinated for 24
h at 30 -35°C and sown. Sublines were sowed evenly in
wet seedbeds at 30 days. Thirty plants of each replicate were grown with a
single individual per hill in 16.67 × 23.33-cm plots. The experimental field
was divided into quarters. Three of these experimental sections contained
replicates of the drought treatment, while one-quarter contained a control
replicate that experienced regular irrigation. Quarters were isolated from one
another using a double layer of ridging film. When the transplanted seedlings
were completely green, irrigation was restricted in the treatment plots, while
normal irrigation continued in the control plots. This restriction was
continued until plant maturation. The 15-15-15NPK compound fertilizer (
The combined ability test for the drought-resistant lines was designed
according to NC II. The thermosensitive genic male sterile line Y58S and recessive genic male sterile line
ABCG15 were hybridized with SSSLs or 9311. Hybrids of each combination were planted
at the Wenjiang experimental farm of Sichuan
Agricultural University under normal management. Five plants of each
combination were harvested after maturity to investigate yield-related
agronomic characters.
Data survey and collection
Days to
heading, plant height at maturation of five representative plants was recorded.
After maturation, five plants of each line were harvested, threshed, and dried,
and the number of effective panicles and spikelets,
1000-grain weight, seed setting rate, and grain yield were scored.
Relative
trait values = trait values under drought stress / traits values under
irrigation.
Statistical analysis and QTL
detection
All statistical analyses were performed using SPSS 7.05
data analysis software. We adopted the method of Liu et al. (2004) to identify QTLs; that is, a t-test was used to analyze the differences between
SSSLs and the recipient parent (9311). In this instance, “P ≤0.00
Additive effect values = (homozygous phenotypic values of
SSSLs - phenotypic value of 9311)/2;
Additive effect (%) = (|value of additive effect|/value
of control) × 100
QTL substitution mapping methods
were performed as per He et al. (2005).
The substitution fragment length was calculated according to the method of
Young and Tanksley (1989).
QTL naming followed the rules of McCouch (2008).
Results
Performance of SSSLs and parents
under drought stress
Under
drought stress, plant height decreased and days to heading increased. Compared
with the control under normal irrigation, the spikelet number per panicle,
number of effective panicles, seed setting rate, and grain yield significantly
decreased both in the SSSLs and in parents exposed to drought stress (P < 0.01). Grain yield showed the
strongest effect followed by plant height, 1000-grain weight, number of effective panicles, seed setting
rate, spikelet number per panicle, and days to heading. Differences for drought
tolerance were represented for the same trait between SSSLs (Fig. 1). Trait
values and relative trait values of SSSLs were continuously
distributed, and the variation coefficient of grain yield—the trait that
differed the most strongly—was 29.05%, that of relative grain yield was 34.04%, and the transgressive phenomena appeared in SSSL traits. These
findings show that the genes controlling related traits and drought tolerance
were quantitative trait loci (QTLs).
Comparison of agronomic traits and combining ability of
drought-tolerant lines and 9311
Table
1: Agronomic characteristics of hybrids of drought-tolerant
lines combined with two male sterile lines
SSSLs (male) |
Male
sterile lines (female) |
Days
to heading (d) |
Plant
height (cm) |
Number
of effective panicles |
Spikelet
number per panicle |
1000-grain
weight (g) |
Seed
setting rate (%) |
Grain
yield per plant (g) |
9311(CK) |
ABCG15 |
116.3 |
115.55 |
6.1 |
261.31 |
26.23 |
72.45 |
29.94 |
9311(CK) |
Y58S |
109.1 |
110.17 |
7.0 |
192.48 |
26.81 |
80.07 |
26.49 |
X707 |
ABCG15 |
117.0 |
114.67 |
4.4 |
419.84 |
25.17 |
71.81 |
33.09 |
X699 |
ABCG15 |
113.3 |
118.80 |
5.1 |
282.23 |
26.55 |
80.67 |
30.47 |
X705 |
ABCG15 |
117.0 |
116.73 |
4.3 |
384.53 |
26.06 |
70.10 |
29.69 |
X707 |
Y58S |
108.0 |
108.00 |
6.8 |
180.70 |
26.01 |
85.41 |
27.00 |
X705 |
Y58S |
107.7 |
108.87 |
6.7 |
169.01 |
27.40 |
86.90 |
26.87 |
X699 |
Y58S |
107.7 |
114.20 |
8.5 |
128.37 |
26.99 |
86.93 |
25.74 |
Fig. 1: Variation
of seven traits of single segment substitution lines and parents under drought
stress (D) and irrigation conditions (I). The box plots overlapped
by trait values and normal lines of plant height (PH), days to heading (DH),
spikelet number per panicle (SN), effective panicles per plant (EP), seed
setting rate (SSR), 1000-grain weight (TGW) and grain yield per plant (GY) of
SSSLs and parents. ** Significant at the 0.01 probability level. Nipponbare trait data are not presented due to the abnormal
seed setting at the Hainan experimental site in winter
The
seed setting rate and grain yield per plant are sensitive indexes to drought
stress. The relative seed setting rate and relative grain yield per plant under
drought stress can indicate the drought resistance of SSSLs.
Seven drought-tolerant lines were identified compared with 9311 and
showed better drought tolerance in terms of the seed setting rate and grain
yield under drought stress. There was no obvious difference in the agronomic
characters in comparisons of lines X646, X699, X707 with 9311. The spikelet
number per panicle was significantly lower in X633 than 9311, and the weight
per panicle was decreased, which resulted in a significantly lower grain yield
than 9311. Moreover, the seed setting rate of each drought-resistant line was
more than 80% (Fig. 2A). To evaluate the breeding valuation, two male sterile
lines, the recessive genic male sterile line ABCG15 and the thermosensitive
male sterile line Y58S, were used to test their combining ability. The results
showed that the general combining ability (GCA) values of X705, X707 and X699
were all positive, and larger than or equal to 9311 (Fig. 2B). The combination
of ABCG15 × X707 was the best compared with other combinations in terms of yield,
and the grain yield reached 33.09 g per plant on average, which was 10.53%
higher than the contrasting combination of ABCG15 × 9311.
This combination was characterized by moderate days to heading and plant
height, with larger panicles reaching 419.84 spikelets
per panicle on average, but less effective panicles, a lighter 1000-grain
weight, and a lower seed setting rate compared with the control. A huge panicle
was clearly observed in the hybrid rice (Table 1). Other excellent combinations,
including ABCG15 × X699, Y58S × X707 and Y58S × X705,
showed 1.41–1.91% higher grain yield than the control.
In general, the GCA of the substitution lines X699, X705
and X707 was better than or equivalent to 9311, which had a higher seed setting
rate and grain yield under drought conditions; these features are helpful for
breeding high-quality and drought-tolerant hybrid rice.
QTLs for plant height and its
relative
In
response to drought stress, eight QTLs for plant height (PH) were detected
(Table 2), which were distributed across 14 substitution segments on 7
chromosomes (Chr.1, 4, 5, 7, 8, 9 and 12); qPH-9
was detected only in lines X703 and X713. Among these QTLs, 7 showed positive
additive effects (increased plant height), and the additive effect
contributions ranged from 37.84 to 13.77%. In contrast, qPH-5b showed negative additive effect (decreased plant height) and
contributed an additive effect of approximately 5.48%. Five QTLs for relative
plant height (RPH) were detected in 10 substitution segments on 5 chromosomes
(Chr. 5, 7, 8, 9 and 11); of these, qRPH-9
was detected only in X703, X707, and X709, and qRPH-5 was detected only in X682. Among these 5 QTLs, 4 showed
positive additive effects, and their contributions ranged from 9.72 to 7.64%.
In addition, we also found 2 substitution segments (RM4674-RM161 and RM410-RM201)
in which RPH and PH QTLs were detected simultaneously.
Table 2: QTLs for plant height and relative plant height under
drought stress
QTL |
Chr. |
SSSL |
Introgression segment
marker |
Segment length (Mb) |
Plant height |
Relative plant height |
|||
Additive
effect |
Additive
effect contribution (%) |
Additive
effect |
Additive
effect contribution (%) |
||||||
qPH-1 |
|
1 |
X638,X641,X649 |
RM297-RM302-RM319-RM5811 |
7.62 |
14.92 |
23.94 |
|
|
qPH-4 |
|
4 |
X672 |
RM518-RM3471 |
8.24 |
18.34 |
29.42 |
|
|
qPH |
|
5 |
X669,X678,X679 |
RM3348-RM274-RM480 |
2.8 |
15.08 |
24.20 |
|
|
qPH-5b |
qRPH-5† |
5 |
X682,X734 |
RM4674-RM39-RM3351-RM161 |
2.32 |
-3.42 |
5.48 |
-0.05 |
6.94 |
qPH-7 |
|
7 |
X738 |
RM320-RM432-RM11-RM10-RM455 |
11.54 |
8.58 |
13.77 |
|
|
|
qRPH-7 |
7 |
X697,X735 |
RM427 |
0.22 |
|
|
0.07 |
9.72 |
qPH-8 |
|
8 |
X702 |
RM344-RM331-RM42 |
10.82 |
23.58 |
37.84 |
|
|
|
qRPH-8 |
8 |
X699,X701 |
RM25-RM72 |
5.9 |
|
|
0.07 |
9.72 |
qPH-9 |
qRPH-9 |
9 |
X703,X707,X709,X713 |
RM410-RM257-RM6543-RM278-RM242-OSR28-RM107-RM201 |
4.16 |
17.96 |
28.81 |
0.06 |
7.64 |
|
qRPH-11 |
11 |
X718,X719 |
RM4-RM167 |
5.43 |
|
|
0.06 |
8.33 |
qPH-12 |
|
12 |
X729 |
RM1261-RM519-RM3331 |
9.48 |
15.58 |
25.00 |
|
|
†QTL for relative plant height
Fig. 2: A.
Comparison of major agronomic characteristics between drought-tolerant lines
with 9311 planted under normal conditions. B. GCA comparison of major agronomic
characteristics of drought tolerance lines. *,**
Significant compared with 9311 at the 0.05 and 0.01 probability level,
respectively, based on LSD-t tests
QTLs for days to heading and its relative days
Five
QTLs for days to heading (DH) were detected in 7 substitution segments on 5
chromosomes (Chr.4, 5, 7, 9 and 10) under drought stress (Table 3). Among these
QTLs, 4 loci were associated with lower DH, and the additive effect ranged from
1.92 to 1.57%. One locus (qDH-10) was
associated with a higher DH contribution to the additive effect of 2.43%. Six
QTLs for relative days to heading (RDH) were detected in 10 substitution
segments on 6 chromosomes (Chr. 3, 4, 5, 7, 9 and 10), and their additive
effect contributions ranged from 1.92 to 1.44%. Furthermore, 3 substitution
segments (RM16792-RM185, RM505 and RM410-RM201) were found in which QTLs for DH
and RDH were detected simultaneously.
QTLs for spikelet number per panicle
Under
drought conditions, four QTLs for spikelet number per panicle (SN) were
detected in 4 substitution segments on 3 chromosomes (Chr. 1, 4 and 5) (Table
4). Three QTLs were associated with a decrease in the SN value, and their
additive effect contributions ranged from 9.35 to 10.96%. One QTL (qSN-4) was associated with an increase
in the SN value, and it had a positive effect of approximately 17.53%. No QTLs
for relative spikelet number were detected.
QTLs for the number of effective
panicle and their relative number
Eighteen QTLs for the number of effective panicles (EP) were detected
under drought stress and were determined to be located in 26 substitution
segments on 11 chromosomes (Table 5). The results showed that all QTLs had
negative additive effects, and their additive effect contributions ranged from
9.09 to 21.21%. Among the detected QTLs, qEP-7b showed the largest contribution. Ten QTLs for the relative number of
effective panicles (REP) were detected in 16 substitution segments on 8
chromosomes. Nine QTLs showed negative additive effects, and their additive
effect contributions ranged from 18.62 to 8.51%, with qREP-4,
qREP-5, and qREP-12a providing the greatest contribution. The qREP-1alocus had a positive additive
effect and contributed 11.17% to the combined additive effect. EP and REP QTLs
were detected simultaneously in 9 substitution
fragments.
QTLs for seed setting rate and
relative seed setting rate
The seed setting rate is an
important index to evaluate plant stress tolerance. Four QTLs associated with a
lower seed setting than 9311 were found and were determined to be located in 5
substitution segments on 4 chromosomes (Chr.3, 5, 9 and 12) (Table 6). The
contributions of these QTLs showed a negative effect,
with the combined additive effect ranging from 19.42 to 18.74%. The qSSR-3 locus showed the largest
contribution. Four QTLs were shown to have a negative effect on the relative
seed setting rate (RSSR), and these were detected in 5 substitution fragments
on 4 chromosomes. Three of these QTLs were located in the same fragment as the
SSR QTLs.
Table 3: QTLs for
days to heading and relative days to heading under drought stress
QTL |
Chr. |
SSSL |
Introgression segment marker |
Segment length (Mb) |
Heading date |
Relative days to heading |
|||
Additive
effect |
Additive
effect contribution (%) |
Additive
effect |
Additive
effect contribution (%) |
||||||
|
qRDH-3† |
3 |
X656 |
RM6266-RM426-RM168 |
5.8 |
|
|
-0.02 |
1.92 |
qDH-4 |
qRDH-4 |
4 |
X668 |
RM16792-RM6314-RM185 |
3.63 |
-1.92 |
1.8 |
-0.02 |
1.92 |
|
qRDH-5 |
5 |
X669,X676,X678,X679 |
RM3348-RM274-RM480 |
2.8 |
|
|
-0.02 |
1.92 |
qDH-5 |
|
5 |
X678,X679 |
RM26-RM31 |
1.56 |
-2.04 |
1.92 |
|
|
qDH-7 |
qRDH-7 |
7 |
X698 |
RM505 |
0.72 |
-1.66 |
1.57 |
-0.02 |
1.44 |
qDH-9 |
qRDH-9 |
9 |
X712,X713 |
RM410-RM257-RM6543-RM278-RM242-OSR28-RM107-RM201 |
4.16 |
-1.66 |
1.57 |
-0.02 |
1.44 |
|
qRDH-10 |
10 |
X714 |
RM3451-RM333-RM496 |
2.12 |
|
|
-0.02 |
1.44 |
qDH-10 |
|
10 |
X716,X717 |
RM258-RM171 |
3.22 |
2.58 |
2.43 |
|
|
†QTL for relative days to heading
Table 4: QTLs for
spikelet number per panicle under drought stress
QTL |
Chr. |
SSSLs |
Introgression segment marker |
Segment length (Mb) |
Additive effect |
Additive effect contribution
(%) |
qSN |
1 |
X630 |
RM1-RM283-RM8146 |
1.65 |
-15.5 |
10.96 |
qSN-1b |
1 |
X648 |
RM5497-RM443 |
4.01 |
-13.23 |
9.35 |
qSN-4 |
4 |
X672 |
RM3471 |
5.56 |
24.8 |
17.53 |
qSN-5 |
5 |
X678 |
RM26-RM31 |
1.56 |
-14 |
9.9 |
Table 5: QTLs for
number of effective panicles and relative number of effective panicles under
drought stress
QTL |
Chr. |
SSSL |
Introgression segment marker |
Segment length (Mb) |
Number of effective panicles |
Relative number of effective panicles |
|||
Additive
effect |
Additive
effect contribution (%) |
Additive
effect |
Additive
effect contribution (%) |
||||||
qEP |
qREP |
1 |
X633 |
RM3682-RM11356 |
2.24 |
-0.6 |
9.09 |
0.11 |
11.17 |
qEP-1b |
qREP-1b |
1 |
X638,X639,X641,X649 |
RM297-RM302-RM319-RM5811 |
7.62 |
-1.1 |
16.67 |
-0.12 |
12.23 |
qEP-2 |
|
2 |
X655 |
RM213-RM208-RM406-RM266-RM138 |
1.17 |
-1.1 |
16.67 |
|
|
qEP |
|
4 |
X668 |
RM16792-RM6314-RM185 |
3.63 |
-0.8 |
12.12 |
|
|
qEP-4b |
qREP-4 |
4 |
X671,X672 |
RM2416-RM518 |
3.35 |
-1.2 |
18.18 |
-0.18 |
18.62 |
qEP |
|
5 |
X676,X678 |
RM274-RM480 |
1.36 |
-1.2 |
18.18 |
|
|
|
qREP-5 |
5 |
X669,X676 |
RM421 |
2.12 |
|
|
-0.18 |
18.62 |
qEP-5b |
|
5 |
X678,X681 |
RM31 |
0.91 |
-1 |
15.15 |
|
|
qEP-6 |
|
6 |
X695 |
RM340-RM412-RM345-RM141-RM494 |
3.51 |
-0.8 |
12.12 |
|
|
qEP |
|
7 |
X698 |
RM505 |
0.72 |
-0.9 |
13.64 |
|
|
qEP-7b |
qREP-7 |
7 |
X738 |
RM320-RM432-RM11-RM10-RM455 |
11.54 |
-1.4 |
21.21 |
-0.14 |
14.89 |
qEP-8 |
qREP-8 |
8 |
X702 |
RM25-RM72 |
5.9 |
-1.2 |
18.18 |
-0.08 |
8.51 |
qEP-9 |
|
9 |
X712,X713 |
RM410-RM257-RM6543-RM278-RM242-OSR28-RM107-RM201 |
4.16 |
-1.2 |
18.18 |
|
|
qEP |
|
10 |
X714 |
RM3451-RM333-RM496 |
2.12 |
-0.85 |
12.88 |
|
|
qEP-10b |
qREP-10 |
10 |
X716,X717 |
RM258-RM171 |
3.22 |
-1 |
15.15 |
-0.11 |
11.17 |
qEP |
|
11 |
X718 |
RM4-RM167 |
5.43 |
-1 |
15.15 |
|
|
qEP-11b |
qREP-11 |
11 |
X723 |
RM5590-RM5857-RM21-RM1355-RM209-RM229 |
8.76 |
-0.6 |
9.09 |
-0.09 |
9.57 |
qEP |
qREP |
12 |
X729 |
RM1261-RM519 |
6.95 |
-1 |
15.15 |
-0.18 |
18.62 |
qEP-12b |
qREP-12b |
12 |
X733 |
RM17 |
0.78 |
-1.2 |
18.18 |
-0.12 |
12.23 |
†QTL for relative effective panicle
Table 6: QTLs for
seed setting rate and relative seed setting rate under drought stress
QTL |
Chr. |
SSSLs |
Introgression segment marker |
Segment length (Mb) |
Seed setting rate |
Relative seed setting rate |
|||
Additive
effect |
Additive
effect contribution (%) |
Additive
effect |
Additive
effect contribution (%) |
||||||
qSSR-3 |
|
3 |
X656 |
RM6266-RM426-RM168 |
5.8 |
-14.98 |
19.42 |
|
|
qSSR-5 |
qRSSR-5† |
5 |
X669,X679 |
RM421-RM3348-RM274-RM480-RM26 |
5.57 |
-14.94 |
19.38 |
-0.09 |
10.46 |
|
qRSSR-6 |
6 |
X685 |
RM141 |
0.11 |
|
|
-0.12 |
13.37 |
qSSR-9 |
qRSSR-9 |
9 |
X703 |
RM410-RM257-RM6543-RM278-RM242-OSR28-RM107-RM201 |
4.16 |
-14.46 |
18.74 |
-0.13 |
15.12 |
qSSR-12 |
qRSSR-12 |
12 |
X730 |
RM1261-RM519-RM3331 |
9.48 |
-14.84 |
19.25 |
-0.12 |
13.37 |
†QTL for relative seed setting rate
Table 7: QTLs for
grain yield and relative grain yield under drought stress
QTL |
Chr. |
SSSLs |
Introgression segment marker |
Segment length (Mb) |
Grain yield |
Relative grain yield |
|||
Additive
effect |
Additive
effect contribution (%) |
Additive
effect |
Additive
effect contribution (%) |
||||||
|
qRGY-1† |
1 |
X633 |
RM3682-RM11356 |
2.24 |
|
|
0.22 |
30.98 |
qGY |
|
4 |
X668 |
RM16792-RM6314-RM185 |
3.63 |
-4.164 |
27.39 |
|
|
qGY-4b |
|
4 |
X671 |
RM2416-RM518 |
3.35 |
-3.262 |
21.45 |
|
|
qGY-5 |
qRGY-5 |
5 |
X679 |
RM3348-RM480-RM26 |
3.44 |
-3.624 |
23.83 |
-0.18 |
24.65 |
qGY-6 |
qRGY-6 |
6 |
X685,X692 |
RM141 |
0.11 |
-3.522 |
23.16 |
-0.17 |
23.94 |
qGY-7 |
qRGY-7 |
7 |
X735 |
RM427 |
0.22 |
-4.522 |
29.74 |
-0.18 |
26.06 |
qGY |
qRGY-11 |
11 |
X722 |
RM167-RM3701 |
6.05 |
-3.342 |
21.98 |
-0.15 |
21.13 |
qGY-11b |
|
11 |
X724 |
RM229-RM26890-RM187 |
6.38 |
-2.956 |
19.44 |
|
|
qGY-12 |
qRGY-12 |
12 |
X728,X729,X730 |
RM1261-RM519-RM3331 |
9.48 |
-4.136 |
27.2 |
-0.16 |
23.24 |
†QTL for relative grain yield
QTLs for grain yield and
relative grain yield
Compared
with other indexes, grain yield was the most important comprehensive index for
the evaluation of drought tolerance. We found that 8 QTLs had a negative effect
on grain yield (Table 7). These QTLs were located in 11 substitution segments
on 6 chromosomes, and their additive effect contributions ranged from 29.74 to
19.44%, in which qGY-7 was the locus
with the largest effect. The 6 QTLs associated with relative grain yield (RGY)
that were closely related to drought tolerance in 9 substitution segments on 6
chromosomes. Of these QTLs, qRGY-5, qRGY-6, qRGY-7, qRGY-11, and qRGY-12 had negative additive effects,
and their contributions ranged from 26.06 to 21.13%. They were located in the
same regions as 5 of the QTLs for GY. Finally, one QTL (qRGY-1) was found to have a positive effect, contributing 30.98%.
Fig. 3: Chromosomal
locations of QTLs for plant height, days to heading, spikelet number per
panicle, effective panicle, seed setting rate, grain yield, and relative traits
under drought stress
Discussion
Breeding
highly resistant varieties with a higher yield under normal cultivation, less
yield loss under drought stress and wide adaptability is an important tactic
for rice production to resist adversity. In this study, 25 SSSLs derived from
the backbone parent 9311 of two-line hybrid rice lines were found to be more drought
tolerant than the recurrent parent 9311, and their comprehensive agronomic
characteristics and combining ability were very good. Among them, the excellent
drought-tolerant lines X699, X705 and X707 had fine agronomic characteristics
and a higher general combining ability of yield than 9311. Specifically, the
combination ABCG15 × X707 had great potential to increase production, which
was 10.53% higher than the control. These drought resistant lines can be used
as parents for breeding two-line hybrid rice with higher yield, wider
adaptability and greater drought tolerance.
Discovery of hidden alleles for
drought tolerance
Present
study found that - SSSLs using Nipponbare
as the donor parent and 9311 as the recipient parent - plant height decreased, the number of days before heading increased and the seed setting and yield
decreased under drought treatment. Compared with 9311, a
majority of the SSSLs showed greater variation after exposure
to drought stress. Most of the detected QTLs and relative QTLs had negative effect,
meaning that the drought tolerance of the SSSLs declined compared with 9311.
However, some segments were also identified that showed enhanced drought tolerance
in the SSSLs, such as the RM3682-RM11356 segment on Chr.1. In this
region, we found the positive effect loci qREP-1a
and qRGY-1 and may indicate the
presence of a drought tolerance gene in this region in the Nipponbare
donor parent. The phenomenon whereby SSSLs show phenotypes that surpass those
of their parents also demonstrates that many genes have cryptic recessive
alleles; such alleles have been found to affect drought tolerance,
water-logging, salt, cold tolerance, and disease resistance (Xu et al. 2005a; Ali et al. 2006; Lafitte
et al. 2006). Moreover, hidden alleles cannot be expressed in
their genetic background, where they may be subject to epistatic
interference, but can be expressed in specific backcrossed populations or
chromosomal introgression lines. In most cases, they are greatly influenced by
the genetic background.
Of the 78 QTLs detected in this study (Fig. 3), 56 were
found in the same locus or region as reported QTLs (http://www.gramene.org/).
Of these 56 QTLs, 36 were found to respond to drought stress (Hemamalini et al. 2000; Babu et al.
2003; Mei et al. 2003; Lafitte et al. 2004; Liu et al.
2005; Wang et al. 2005; Xu et al. 2005b; Yue et al. 2005; Zou
et al. 2005; Yue et al. 2006; Bernier et al. 2007; Cui et
al. 2008; Subashri et al. 2008; Yue et al. 2008; Zhao et
al. 2008). Twelve QTLs of relative traits representing drought
tolerance were found at the same locus or chromosomal region as the reported
drought tolerance QTLs (Champoux et al.
1995; Teng et al. 2002; Yue et al. 2005, 2006; Zhang et al.
2006). Novel QTLs first identified herein include qEP-1a, qREP-1a, and qRGY-1 in the RM3682-RM11356 region on
Chr.1, qGY-4a, qGY-4b, qRDH-4, and qREP-4 in the RM16792-RM185 and
RM2416-RM518 regions on Chr.4, and qGY-6,
qRSSR-6, and qRGY-6 in the region flanked by RM141 on Chr.6. In addition, qREP-5, qRDH-5, qRPH-5, qRDH-7, qRPH-7,
qRGY-7, qREP-8, qRPH-8, qREP-10, qRDH-10, qREP-11,
and qREP-12b were first identified on
chromosomes 5, 7, 8, 10, 11, and 12.
Differences in relative trait values in response to
drought treatment may explain how rice plants tolerate drought (Lafitte et al. 2004; Yue et al. 2005,
2006, 2008). Notably, the newly detected QTLs qRSSR-6 and qRGY-6, both
of which were found to be associated with drought tolerance, contributed the
largest additive effects (13.37 and 29.94%). Moreover, these QTLs were located
in the 0.11-Mb genetic region and flanked by RM141 on Chr.6. We also
found that many QTLs including qREP-5,
qRDH-5, qRSSR-5 and qRGY-5 in the
RM3348-RM26 region, qRPH-9, qRDH-9 and qRSSR-9 in the RM410-RM201 region, and qREP-12a, qRSSR-12 and qRGY-12 in the RM1261-RM3331 region,
were associated with drought resistance, as reported in the same region (Champoux et al. 1995; Teng et al. 2002;
Xu et al. 2005b; Zhang et al. 2006; Bernier et al. 2007;
Zhou et al. 2013).
Of the traits examined, seed setting and rice grain yield
were the most sensitive to drought stress. QTLs affecting the relative seed
setting rate were detected in the same four chromosomal segments (RM141,
RM3348-RM26, RM410-RM201, and RM1261-RM3331), and QTLs for
relative grain yield were found in three of these segments (RM141, RM3348-RM26,
and RM1261-RM3331). Moreover, the contributions of these regions to the additive
effect for RSSR were all more than 10%, and their additive effect contributions
to RGY were all more than 20%. Finally, all of these drought-resistant QTLs
were repeatedly detected in the same fragment of different SSSLs. Thus, they
were found to have high reproducibility and reliability and, thereby, may be
promising candidates for future selective breeding.
In the substituted segments of SSSLs, we found multiple
drought-tolerant QTLs clustered in the same region of the chromosomes, and we also detected
other stress resistance QTLs (e.g.,
for cold stress or salt stress). The QTLs qREP-5,
qRDH-5, qRSSR-5, and qRGY-5 were
found to be associated with drought resistance and were detected in the
RM3348-RM26 region. These QTLs were found in the same region as the
salt-resistant QTLs (Lin et al. 2004)
and cold-resistant QTLs at the plumule stage (Zhou et al. 2013). In addition, qRPH-9, qRDH-9, and qRSSR-9 were
detected in the RM410-RM201 region, which is the same area as salt resistance
sensitivity QTLs (Lin et al. 2004)
and cold resistance QTLs (Andaya and Mackill
2003; Zhou et al. 2013).
These findings indicate considerable genetic overlap
between drought-resistant QTLs and other QTLs, including those for salt
resistance, cold resistance, and other stress resistances; this phenomenon is
common and has been observed in many other organisms (Hu et al. 2006; Karaba et al. 2007; Xiang et al.
2008; Huang et al. 2009). We hypothesize that the genetic overlap
areas described in this report are critical stress-resistant areas; they may
regulate plant responses to adversity and may jointly regulate plant stress resistance. Examples of
such genes include the zinc regulation transporter gene ZIP5, the auxin-induced protein genes iaa19 and iaa18 (which are found in the RM3348-RM26 region), the auxin-induced protein gene iaa26, the ADH activity regulation gene RAD (associated
with pollen fertility), and the leaf evergreen gene sgr (which is found in the RM410-RM201region). The discovery of
stress-resistance QTLs may improve our understanding of plant responses to
adversity, and they may prove useful for the development of new rice cultivars
with improved stress tolerance.
Conclusion
In this study, seven SSSLs were
identified to be more resistant to drought than 9311 in seed setting rate and
grain yield. Lines X699, X705 and X707 with good comprehensive agronomic
characteristics had stronger GCA for yield than 9311. A total of 78 QTLs were
detected under drought stress, which were located in 30 regions of 12
chromosomes in rice. Drought-tolerance QTLs qREP4,
qREP5, qREP12.1, qRSSR9, qRGY1, qRGY5, qRGY6, qRGY7, qRGY11 and qRGY12 were the main effective
drought-resistant QTLs, exhibiting an additive effect contribution of more than
15%. Some drought-tolerant QTLs clustered in certain regions on chromosome 5,
6, 9 and 12, which may be the key locus for enhancing drought tolerance in
plants.
Author Contributions
Conceptualization, Yong Zhou and Shigui Li; Data curation, Yinghai Wei and Chuncao Song; Formal analysis, Yong Zhou; Funding
acquisition, Shigui Li, Shijun
Huang and Mei Tang; Investigation, Yong Zhou, Yinghai
Wei and Ying Zheng; Project administration, Shigui Li and Shijun Huang; Resources, Shigui Li; Software, Yanjie Peng;
Writing original draft, Yong Zhou;
Writing, review and editing, Yanjie Peng.
Acknowledgements
This work was supported by the
National Science Fund for Distinguished Young Scholars of China (31025017), the
Science and Technology Planning of Sichuan Province of China (2016NYZ0028).
References
Ali AJ, JL Xu, AM Ismail, BY Fu, CHM Vijaykumar,
YM Gao, J Domingo, R Maghirang, SB Yu, G Gregorio, S Yanaghihara, M Cohen, B
Carmen, D Mackill, ZK Li (2006). Hidden diversity for abiotic and biotic stress
tolerances in the primary gene pool of rice revealed by a large backcross
breeding program. Field Crops Res 97:66‒76
Ali ML, MS Pathan, J Zhang, G Bai, S Sarkarung, HT
Nguyen (2000). Mapping QTLs for root traits in a recombinant inbred population
from two indica ecotypes in rice. Theor Appl Genet 101:756‒766
Andaya VC, DJ Mackill (2003). QTLs conferring cold
tolerance at the booting stage of rice using recombinant inbred lines from a japonica X indica cross. Theor Appl
Genet 106:1084‒1090
Babu RC, BD Nguyen, V Chamarerk, P
Shanmugasundaram, P Chezhian, P Jeyaprakash, SK Ganesh, A Palchamy, S Sadasivam,
S Sarkarung, LJ Wade, HT Nguyen (2003). Genetic analysis of drought resistance
in rice by molecular markers association between secondary traits and field
performance. Crop Sci 43:1457‒1469
Bernier J, A Kumar, V Ramaiah, D Spaner, G Atlin
(2007). A large-effect QTL for grain yield under reproductive-stage drought
stress in upland rice. Crop Sci 47:507–516
Champoux MC, G Wang, S Sarkarung, DJ Mackill, JC
O'Toole, N Huang, SR McCouch (1995). Locating genes associated with root
morphology and drought avoidance in rice via
linkage to molecular markers. Theor Appl
Genet 90:969‒981
Cui K, J Huang, Y Xing, S Yu, C Xu, S Peng (2008).
Mapping QTLs for seedling characteristics under different water supply
conditions in rice (Oryza sativa). Physiol Plantarum 132:53‒68
Ebitani T, Y Takeuchi, Y Nonoue, T Yamamoto, K
Takeuchi, M Yano (2005). Construction and evaluation of chromosome segment
substitution lines carrying overlapping chromosome segments of indica rice
cultivar ‘kasalath’in a genetic background of japonica elite cultivar ‘Koshihikari’.
Breed Sci 55:65‒73
Eshed Y, D Zamir (1995). An introgression line
population of Lycopersicon pennellii
in the cultivated tomato enables the identification and fine mapping of
yield-associated QTL. Genetics 141:1147‒1162
He FH, ZY Xi, RZ Zeng, A Talukdar, GQ Zhang
(2005). Mapping of heading date QTLs in rice (Oryza sativa L.) using
single segment substitution lines. Sci
Agric Sin 38:1505‒1513
Hemamalini GS, HE Shashidhar, S Hittalmani (2000).
Molecular marker assisted tagging of morphological and physiological traits
under two contrasting moisture regimes at peak vegetative stage in rice (Oryza sativa L.). Euphytica 112:69‒78
Hu H, M Dai, J Yao, B Xiao, X Li, Q Zhang, L Xiong (2006). Overexpressing a Nam, Ataf, and Cuc (Nac) transcription factor enhances drought resistance and
salt tolerance in rice. Proc Natl Acad
Sci USA 103:12987‒12992
Huang XY, DY Chao, JP Gao, MZ Zhu, M Shi, HX Lin
(2009). A previously unknown zinc finger protein, dst, regulates drought and
salt tolerance in rice via stomatal
aperture control. Genes Dev 23:1805‒1817
Kamoshita A, J Zhang, J Siopongco, S Sarkarung, H
Nguyen, LJ Wade (2002). Effects of phenotyping environment on identification of
quantitative trait loci for rice root morphology under anaerobic conditions. Crop Sci 42:255‒265
Karaba A, S Dixit, R Greco, A Aharoni, KR
Trijatmiko, N Marsch-Martinez, A Krishnan, KN Nataraja, M Udayakumar, A Pereira
(2007). Improvement of water use efficiency in rice by expression of hardy, an Arabidopsis drought and salt tolerance
gene. Proc Natl Acad Sci USA 104:15270‒15275
Lafitte HR, Z Li, CHM Vijayakumar, YM Gao, Y Shi,
J Xu, B Fu, SB Yu, J Ali, J Domingo, R Maghirang, R Torres, D Mackill (2006).
Improvement of rice drought tolerance through backcross breeding: Evaluation of
donors and selection in drought nurseries. Field
Crops Res 97:77‒86
Lafitte HR, AH Price, B Courtois (2004). Yield
response to water deficit in an upland rice mapping population: Associations
among traits and genetic markers. Theor
Appl Genet 109:1237‒1246
Lin HX, MZ Zhu, M Yano, JP Gao, ZW Liang, WA Su,
XH Hu, ZH Ren, DY Chao (2004). QTLs for Na+ and K+ uptake
of the shoots and roots controlling rice salt tolerance. Theor Appl Genet 108:253‒260
Liu G, Z Zhang, H Zhu, F Zhao, X Ding, R Zeng, W
Li, G Zhang (2008). Detection of QTLs with additive effects and
additive-by-environment interaction effects on panicle number in rice (Oryza sativa L.) with single-segment substitution lines. Theor Appl Genet 116:923‒931
Liu GM, WT Li, RZ Zeng, ZM Zhang, GQ Zhang (2004).
Identification of QTLs on substituted segments in single segment substitution
lines of rice. Acta Agron Sin 31:1395‒1400
Liu HY, GH Zou, GL Liu, SP Hu, MS Li, XQ Yu, HW
Mei, LJ Luo (2005). Correlation analysis and QTL identification for canopy
tem-perature, leaf water potential and spikelet fertility in rice under
contrasting moisture regimes. Chin Sci
Bull 50:317‒326
Luo LJ (2010). Breeding for water-saving and
drought-resistance rice (Wdr) in China. J
Exp Bot 61:3509‒3517
Luo LJ, QF Zhang (2001). The status and strategy
on drought resistance of rice (Oryza sativa L.). Chin J Rice Sci 15:209‒214
McCouch SR (2008). Gene nomenclature system for
rice. Rice 1:72‒84
Mei HW, LJ Luo, CS Ying, YP Wang, XQ Yu, LB Guo,
AH Paterson, ZK Li (2003). Gene actions of QTLs affecting several agronomic
traits resolved in a recombinant inbred rice population and two testcross
populations. Theor Appl Genet 107:89‒101
Price AH, KA Steele, BJ Moore, RGW Jones (2002).
Upland rice grown in soil-filled chambers and exposed to contrasting
water-deficit regimes: II. Mapping quantitative trait loci for root morphology
and distribution. Field Crops Res 76:25‒43
Subashri M, S Robin, KK Vinod, S Rajeswari, K
Mohanasundaram, TS Raveendran (2008). Trait identification and QTL validation
for reproductive stage drought resistance in rice using selective genotyping of
near flowering rils. Euphytica 166:291‒305
Teng B, R Zeng, Y Wang, Z Liu, Z Zhang, H Zhu, X
Ding, W Li, G Zhang (2012). Detection of allelic variation at the wx locus with
single-segment substitution lines in rice (Oryza
sativa L.). Mol Breed 30:583–595
Teng S, Q Qian, DL Zeng, Y Kunihiro, K Fujimoto,
DN Huang, LH Zhu (2002). Analysis of gene loci and epistasis for drought
tolerance in seedling stage of rice (Oryza
sativa L.). Acta Genet Sin 29:235‒240
Wang XS, J Zhu, L Mansueto, R Bruskiewich (2005).
Identification of candidate genes for drought stress tolerance in rice by the
integration of a genetic (QTL) map with the rice genome physical map. J Zhej Univ Sci B 6:382‒288
Xi ZY, FH He, RZ Zeng, ZM Zhang, XH Ding, WT Li,
GQ Zhang (2006). Development of a wide population of chromosome single-segment
substitution lines in the genetic background of an elite cultivar of rice (Oryza sativa L.). Genome 49:476‒484
Xiang Y, N Tang, H Du, H Ye, L Xiong (2008).
Characterization of Osbzip23 as a key player of the basic leucine zipper
transcription factor family for conferring abscisic acid sensitivity and
salinity and drought tolerance in rice. Plant
Physiol 148:1938‒1952
Xu JL, YM Gao, BY Fu, ZK Li (2005a).
Identification and screening of favorable genes from rice germplasm in
backcross introgression populations. Mol
Plant Breed 3:619‒628
Xu JL, HR Lafitte, YM Gao, BY Fu, R Torres, ZK Li
(2005b). QTLs for drought escape and tolerance identified in a set of random
introgression lines of rice. Theor Appl
Genet 111:1642‒1650
Young ND, SD Tanksley (1989). RFLP analysis of the
size of chromosomal segments retained around the Tm-2 locus of tomato during backcross breeding. Theor Appl Genet 77:353‒359
Yue B, W Xue, L Luo, Y Xing (2008). Identification
of quantitative trait loci for four morphologic traits under water stress in
rice (Oryza sativa L.). J Genet Genom 35:569‒575
Yue B, W Xue, L Xiong, X Yu, L Luo, K Cui, D Jin,
Y Xing, Q Zhang (2006). Genetic basis of drought resistance at reproductive
stage in rice: separation of drought tolerance from drought avoidance. Genetics 172:1213‒1228
Yue B, L Xiong, W Xue, Y Xing, L Luo, C Xu (2005).
Genetic analysis for drought resistance of rice at reproductive stage in field
with different types of soil. Theor Appl
Genet 111:1127‒1136
Zhang X, S Zhou, Y Fu, Z Su, X Wang, C Sun (2006).
Identification of a drought tolerant introgression line derived from dongxiang
common wild rice (O. Rufipogon
Griff.). Plant Mol Biol 62:247‒259
Zhao XQ, JL Xu, M Zhao, R Lafitte, LH Zhu, BY Fu,
YM Gao, ZK Li (2008). QTLs affecting morph-physiological traits related to
drought tolerance detected in overlapping introgression lines of rice (Oryza sativa L.). Plant Sci 174:618‒625
Zhou Y, XB Zhu, H Yuan, Y Zheng, P Qin, YH Wei, YP
Wang, SJ Huang, SG Li (2013). Characterization of cold tolerance and
identification of cold tolerance QTLs for rice single segment substitution
lines at plumule and seedling stages. Chin
J Rice Sci 27:381‒388
Zhu W, J Lin, D Yang, L Zhao, Y Zhang, Z Zhu, T
Chen, C Wang (2009). Development of chromosome segment substitution lines
derived from backcross between two sequenced rice cultivars, indica recipient
93-11 and japonica donor nipponbare. Plant
Mol Biol Rep 27:126‒131
Zou GH, HW Mei, HY Liu, GL Liu, SP Hu, XQ Yu, MS
Li, JH Wu, LJ Luo (2005). Grain yield responses to moisture regimes in a rice
population: association among traits and genetic markers. Theor Appl Genet 112:106‒113