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QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
遗 传 学 报 Acta Genetica Sinica, September
2006, 33 (9)：824–832
ISSN 0379-4172
QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice YUE Bing1, XUE Wei-Ya1, LUO Li-Jun2, XING Yong-Zhong1,① 1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; 2. Shanghai Agrobiological Gene Center, Shanghai 201106, China Abstract: Photosynthesis of carbohydrate is the primary source of grain yield in rice (Oryza sativa L.). It is important to genetically analyze the morphological and the physiological characteristics of functional leaves, especially flag leaf, in rice improvement. In this study, a recombinant inbred population derived from a cross between an indica (O. sativa L. ssp. indica) cultivar and a japonica (O. sativa L. ssp. japonica) cultivar was employed to map quantitative traits loci (QTLs) for the morphological (i.e., leaf length, width, and area) and physiological (i.e., leaf color rating and stay-green) characteristics of flag leaf and their relationships with yield and yield traits in 2003 and 2004. A total of 17 QTLs for morphological traits (flag leaf length, width, and area), 6 QTLs for degree of greenness and 14 QTLs for stay-green-related traits (retention-degrees of greenness, relative retention of greenness, and retention of the green area) were resolved, and 10 QTLs were commonly detected in both the years. Correlation analysis revealed that flag leaf area increased grain yield by increasing spikelet number per panicle. However, the physiological traits including degree of greenness and stay-green traits were not or negatively correlated to grain yield and yield traits, which may arise from the negative relation between degree of greenness and flag leaf size and the partial sterility occurred in a fraction of the lines in this population. The region RM255-RM349 on chromosome 4 controlled the three leaf morphological traits simultaneously and explained a large part of variation, which was very useful for genetic improvement of grain yield. The region RM422-RM565 on chromosome 3 was associated with the three stay-green traits simultaneously, and the use of this region in genetic improvement of grain yield needs to be assessed by constructing near-isogenic lines. Key words: Oryza sativa L.; QTL mapping; flag leaf size; stage-green; grain yield
Grain yield is one of the important aims in conventional crop breeding. However, grain yield, as well as yield components, is an extremely complex trait, and genetic control of grain yield is realized through the control of a series of complex biochemical and physiological processes [1]. Photosynthesis is the primary source of grain yield in rice (Oryza sativa L.) [2]. The top three leaves on a stem, particularly the flag leaf, are the primary source of the carbohydrates production [3-5]. Some morphological traits, such as size and shape of the leaves, have been considered to be a major source of capacity-related traits in cereals [6, 7], and a number of physiological traits, such as chlorophyll content, photosynthesis capacity, and stay-green,
were also considered as important determinants of grain yield [2, 8]. Previous studies have mainly focused on few morphological traits, such as the size of leaves in rice, and the quantitative traits loci (QTLs) for these traits were co-located with some sink-related traits [9, 10]. Recently, the genetic basis of photosynthesis rate, chlorophyll content, and stomatal resistance has been studied in rice [8]. However, the relationships between these traits and grain yield are still unknown. Although the contribution of stay-green to maintain high and stable yield production under drought-prone conditions has been reported in sorghum [11], the genetic correlation between stay-green and yield has not been detected yet in rice [12,13].
Received: 2005-08-11; Accepted: 2005-09-08 This work was supported by the National Program on the Development of Basic Research (No.G1998010204) and the Rockefeller Foundation. ①
Corresponding author. E-mail: [email protected] ; Tel: +86-27-8728 1715, Fax: +86-27-8728 7092
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
In this study, QTLs for some morphological and physiological traits of flag leaf and their relationships with yield and yield components were analyzed in rice. The aim of this study was to understand the genetic basis of these traits and their possible role in genetic improvement of grain yield in rice.
1 1. 1
Materials and Methods Plant materials and growing conditions
A population consisting of 180 recombinant inbred lines (RILs at F9/F10 generation) was developed from the cross between a paddy rice (O. sativa L. ssp. indica) cultivar Zhenshan 97, and an upland rice (O. sativa L. ssp. japonica) cultivar IRAT109. Zhenshan 97 is the maintainer line for a number of elite hybrids widely grown in China, and IRAT 109 was introduced from Cote d’Ivoire. The RIL population, together with its parents, was directly planted in polyvinyl chloride (PVC) pipes with one plant per pipe in the experimental farm of Huazhong Agricultural University, Wuhan, China in the rice-growing season. The pipe was 20 cm in diameter and 1 m in length. The pipes were laid out in three blocks following a randomized complete block design with three replicates, two pipes per replicate for each genotype in May 2003, and one pipe per replicate for each genotype in May 2004. Each pipe was loaded with a plastic bag filled with 38 kg of thoroughly mixed soil that composed of two parts of clay and one part of river sand, to which 25 g fertilizers (including 4 g each of N, P2O5, and K2O) were added. Three to five germinated seeds were sown in each pipe and then thinned to single healthy plant 30 days after sowing. At the beginning of tillering stage, 1 g of urea (dissolved in water) was applied to each pipe. Plants were fully irrigated by watering every day till maturating. 1. 2
Traits and measurements
Three morphological traits, four physiological traits, and five yield traits were investigated for all plants that headed within 2 weeks, considering that these traits are sensitive to environments. The flag leaf length (FLL, cm) and flag leaf width (FLW, cm) were measured on the largest two tillers of
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all plants at maturing stage. One derived trait, the flag leaf area (FLA) = FLL × FLW, was calculated. The degree of greenness (DG) and some stay-green traits were also investigated for all plants following a method described by Jiang et al [13]. Using a Minolta Chlorophyll Meter SPAD-502 (Minolta Camera Co., Japan), the degrees of greenness of the flag leaves from the biggest two tillers were measured on the day of heading and again after 15 days. To ensure that the measurements were taken on the right day for the right tiller, tillers were tagged at the day of heading. The SPAD readings of the flag leaves measured on the day of heading were designated DG, and the SPAD values 15 days after heading were used as measurements for the retention-degrees of greenness, designated RDG. The ratios of RDG to DG were used as indexes for the relative retention of greenness, designated RRG. Another measurement of stay-green was an independent visual estimation of the retention of the green-area (RGA) for leaves 15 days after heading on a 1-5 scale, where 1 represents complete or nearly complete leaf death and 5 corresponds to a complete green leaves. Yield and its component traits were also examined for all plants, including grain yield per plant (Y, g), panicle number per plant (PN), number of spikelets per panicle (SN), 1000-grain-weight (GW, g), and spikelet fertility (SF, %). Spikelet fertility was measured as the number of grains divided by the total number of spikelets of a plant. 1. 3
QTL analysis
The genetic linkage map was constructed using a total of 245 SSR markers as described previously [14]. The means of each trait were used to identify QTL by the method of composite interval mapping (CIM) by using QTL Cartographer 2.0 software [15] with a threshold of LOD score 2.4 (selected by permutation test based on 1 000 runs, P=0.05).
2
Results
2. 1 Phenotypic variation of the parents and RILs The phenotypic differences between parents as well as the variation in the RIL population are sum-
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marized in Table 1. Transgressive segregations were observed in the RIL population for all the traits investigated. ANOVA revealed that the difference from
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from genotypes for these traits was significant, whereas for the most of the cases the difference from replicates was not significant (Table 1).
Table 1 Measurements and ANOVA of the traits relative to flag leaf in the RIL population and the parents in 2003 and 2004 Traitsa) FLL
Zhenshan 97 25.7/27.2
IRAT109
b)
Mean of RILs
28.8/29.7 **
1.4/1.5
Range of RILs
ANOVA significant c) Genotype
Replication
28.8/28.1
(16.2-50.2)/(18.7-43.0)
**/**
Ns/Ns
1.5/1.4
(1.0-2.2)/(0.9-3.8)
**/**
Ns/Ns
FLW
1.3/1.4
FLA
33.4/38.1
40.3*/44.6*
41.9/40.7
(19.0-108.7)/(17.1-97.7)
**/**
Ns/Ns
DG
*
39.8 /40.5
36.8/39.4
39.5/40.2
(29.2-48.1)/(30.3-51.1)
**/**
**/Ns
RDG
33.8*/30.2
30.4/28.7
31.2/23.0
(17.2-43.9)/(10.7-35.4)
**/**
**/Ns
RRG
82.2/70.2
82.7/71.7
78.9/56.4
(51.1-98.4)/(29.8-75.0)
**/**
*/Ns
RGA
2.6/2.6
2.7/2.8
2.6/2.4
(1.0-4.7)/(1.0-3.6)
**/**
**/Ns
a)
FLL: flag leaf length; FLW: flag leaf width; FLA: flag leaf area; DG: degree of greenness; RDG: retention-degrees of greenness; RRG: relative retention of greenness; RGA: retention of the green area; b) The data on the left- and right-side of slash in each cell were the results obtained in 2003 and 2004, respectively. Data following the marks * or ** are significantly higher than the other parent at the 0.05 and 0.01 probability levels based on t-test; c) “*”, “**”, and “Ns” represent significant at P<0.05, P<0.01 level and no significant, respectively.
For traits FLL, FLW, and FLA, IRAT109 had higher values than Zhenshan 97 in both the years, and the differences of FLA between the parents in both the years and FLW in 2004 were significant. However, the values of DG and RDG of Zhenshan 97 were significantly higher than that of IRAT109 in 2003. For other traits, the difference between two parents was not significant in both the years. When data collected from the two years were compared, the mean values of RILs for traits RDG, RRG, and RGA were higher in 2003 than in 2004 (Table 1). This was caused by high temperature at
flowering stage in 2003 resulting in reduction of seed setting rate. 2. 2
Correlations of the traits The correlations among the flag leaf characteristics
are shown in Table 2. Three traits related to flag leaf area (FLL, FLW, and FLA) were highly intercorrelated, and so were the three stay-green-related traits, RDG, RRG, and RGA. Strong correlations were also detected between DG and RDG in both the years. Negative or small correlations were identified between flag leaf area and stay-green-related traits.
Table 2 Coefficients of pairwise correlations of the traits related to flag leaf investigated in 2003 and 2004 Traits a) FLW
FLL 0.42/0.22
FLW
FLA
DG
RDG
FLA
0.87/0.76
0.80/0.80
DG
-0.36/-0.54
-0.43/-0.43
-0.43/-0.61
RDG
-0.40/-0.19
-0.30/-0.13
-0.39/-0.19
0.59/0.60
RRG
-0.27/0.02
-0.07/0.08
-0.20/0.07
0.02/0.16
0.82/0.86
-0.21/0.21
-0.06/0.16
-0.16/0.20
0.05/0.00
0.72/0.66
RGA See the footnote
a), b)
RRG
b)
of Table 1 for explanation, the values in bold face are significantly at P<0.05.
0.86/0.86
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
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Correlations between yield, yield components,
were significant in at least one year. PN and GW had
and the traits related to flag leaf characteristics are
small or negative correlation with the traits related to
given in Table 3. In general, potential yield and SN
flag leaf characters. It was interesting to note that
were positively correlated with FLL, FLW, and FLA,
stay-green traits were significantly and negatively
and negatively correlated with DG, RDG, RRG, and
correlated with SF in 2003, but they were positively
RGA in both the years. Furthermore, the differences
associated with SF in 2004.
Table 3 Coefficients of pairwise correlations between yield related traits and other traits investigated in 2003 and 2004 Traits a) FLL
Y 0.41/0.31
b)
PN
SN
SF
GW
-0.14/-0.03
0.54/0.51
0.09/-0.13
0.03/-0.21
FLW
0.30/0.10
-0.26/0.01
0.44/0.18
0.13/-0.13
0.13/0.16
FLA
0.43/0.26
-0.22/-0.02
0.60/0.44
0.11/-0.16
0.07/-0.03
DG
-0.22/-0.26
-0.08/-0.14
-0.14/-0.35
-0.03/0.18
-0.11/0.17
RDG
-0.36/-0.16
-0.11/-0.23
-0.21/-0.07
-0.16/0.15
-0.08/-0.01
RRG
-0.31/-0.06
-0.08/-0.19
-0.19/0.06
-0.18/0.10
-0.02/-0.04
-0.37/-0.02
-0.08/-0.23
-0.13/0.16
-0.31/0.08
-0.10/-0.08
RGA
a),b)
See the footnote of Table 1 for explanation, the values in bold face are significantly at P<0.05. Y: yield; PN: panicle number per plant; SN: spikelet number per panicle; SF: spikelet fertility; GW: 1000 grain weight.
2. 3
QTL mapping
Seven QTLs were resolved for flag leaf length in the two years; only one was detected in both the years, and individual QTL explained 5.3%–21.7% of phenotypic variation (Table 4). Four QTLs for flag leaf width were identified in 2003 and 2004 with individual QTL explained 4.4%–35.8% of phenotypic variation. However, only QFlw4 was detected in both years and had the largest additive effects. Alleles from IRAT109 at eight of the QTLs (72.7%) for FLL and FLW had positive effects, which was also coincided to the performance of both the parents for these traits. Six QTLs for FLA were detected, two of them were identified in both the years, and individual QTL explained 4.7%–26.8% of phenotypic variation. For the trait of DG, six QTLs were resolved in 2003 and 2004; two of them were detected in both the years, and individual QTL explained 4.5%–28.1% of phenotypic variation. Six QTLs for RDG were identified in 2003 and 2004 with individual QTL explained 6.8%–19.5% of phenotypic variation, and two of them were detected in both the years. Five QTLs for RGA were detected, only one of them was common in both the years, and individual QTL explained
10.0%–14.2% of phenotypic variation. For the trait of RRG, three QTLs were identified, whereas only one of them was detected in both the years. Individual QTL explained 7.9%–23.8% of phenotypic variation. In summary, a total of 17 QTL were resolved for the flag leaf size related traits (i.e., FLL, FLW, and FLA) including four commonly detected in both the years and 13 observed in one year. The region RM255-RM349 on chromosome 4 controlled the three traits simultaneously and detected in both years. For the stay-green-related traits (i.e., RDG, RGA, and RRG), 14 QTLs were resolved including four detected in both years and 10 identified only in one year. The region RM422-RM565 on chromosome 3 controlled the three traits simultaneously (Table 4, Fig. 1). 2. 4
Congruence of QTL
There were three regions congregated more than three QTLs and eight regions clustered two QTLs. For examples, the region RM422-RM565 on chromosome 3 clustered QTLs for the three stay-green traits, and the region RM255-RM349 on chromosome 4 overlapped the QTLs for FLL, FLW, FLA, and RGA (Fig. 1). Among them, five regions were associated with FLL, FLW, or FLA simultaneously, four
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Table 4 QTL for the traits related to flag leaf resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109 2003
Traits FLL
FLW
FLA
DG
RDG
RGA
RRG
b
2004 Addc
Var%d
2.52
19.69
3.0
1.93
11.98
RM588-RM589
6.2
2.89
21.68
QFlw4
RM255-RM349
3.9
0.14
16.50
5
QFlw5
RM274-RM480
3.1
0.11
10.08
10.60
2
QFla2
RM262-MRG0303 3.0
4.51
10.47
7.45
26.83
4
QFla4
RM255-RM349
5.2
5.92
17.68
4.1
4.43
9.22
6
QFla6
RM111-RM276
3.6
-5.70
11.80
RM111-RM276
2.5
-3.11
4.66
QFla10
RM596-RM271
3.2
-3.45
5.68
1
QDg1
RM302-RM472
5.8
-1.29
12.86
2
QDg2
RM29-RM341
3.0
-1.46
8.25
2
QDg2
RM29-RM341
8.9
-1.49
16.45
4
QDg4b MRG4503-RM255 6.9
-2.33
18.92
4
QDg4a
RM471-RM142
3.6
0.94
6.66
9
QDg9
RM434-RM257
5.6
2.04
16.68
4
QDg4b MRG4503-RM255
14.1
-1.92
28.09
Chr
a
c
d
Chr
a
QTL
Intervalb
QTL
Interval
LOD
Add
Var%
3
QFll3
RM523-RM231
4.0
-1.72
7.69
2
QFll2
4
QFll4
RM255-RM349
3.7
1.74
7.75
4
QFll4
RM255-RM349
5
QFll5
RM480-RM334
2.5
1.53
5.32
6
QFll6
7
QFll7
RM274-RM480
4.9
1.80
8.41
10
QFll10
RM596-RM271
3.1
-1.57
5.95
4
QFlw4
RM255-RM349
20.5
0.15
35.81
4
5
QFlw4
RM421-RM274
7.9
0.09
13.40
6
QFlw6
RM539-RM527
3.3
-0.05
4.41
3
QFla3
RM523-RM231
5.9
-4.65
4
Qfla4
RM255-RM349
12.8
5
QFla5
RM274-RM480
6
Qfla6
10
LOD
RM262-MRG0303 5.5
5
QDg5
RM161-RM421
3.0
0.77
4.46
1
QRdg1
RM302-RM472
2.5
-1.37
7.24
2
QRdg2
RM29-RM341
4.0
-2.53
19.51
3
QRdg3
RM422-RM565
5.4
1.88
13.60
3
QRdg3
RM422-RM565
3.1
1.94
11.12
4
QRdg4 MRG4503-RM255
4.1
-1.49
8.58
9
QRdg9
RM215-RM245
4.2
2.09
13.28
8
QRdg8
RM350-RM284
2.4
-1.34
6.76
9
QRdg9
RM215-RM245
2.5
1.16
5.15
3
Qrga3
RM422-RM565
5.4
0.29
13.18
2
Qrga2
MRG2762-RM526 2.9
-0.23
10.80
7
Qrga7
RM295-RM481
2.9
0.25
10.44
4
Qrga4
MRG4503-RM255 3.0
0.24
11.68
8
Qrga8
RM350-RM284
6.3
-0.35
14.24
8
Qrga8
2.7
-0.22
9.96
2
QRrg2b
RM497-RM240
2.7
3.06
7.92
2
QRrg2a MRG2762-RM526 4.3
-5.74
23.79
3
QRrg3
RM422-RM565
4.9
4.23
14.91
3
QRrg3
3.49
9.76
RM350-RM284
RM422-RM565
2.5
a,b
Chromosome number and marker intervals, the bold format means the QTLs were detected in two years; Additive effects, the positive values of additive effects indicate the alleles from IRAT109 with increasing effects; d Phenotype variation rate explained by detected QTLs. c
regions clustered stay-green traits, three regions overlapped with the QTL for DG and RDG. These results coincide with the correlations among these traits (Table 2).
with FLL and FLA in the present study, and the correlations between flag leaf characteristics and yield components revealed that large leaf length, leaf width, and leaf area contributed to increased spikelet number per pani-
3
Discussion
cle. Li et al. [9] concluded that leaf area was positively
Grain yield was positively and strongly correlated
related to grain yield, and QTL-influencing-related
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
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Fig. 1 Chromosomal locations of the QTLs for the flag leaf related traits The QTLs for all traits are shown on the left of the chromosomes. The QTLs in bold were detected in both years. The italic QTLs indicate that the alleles for increasing trait values were from Zhenshan 97. Distances are given in Kosambi centiMorgans.
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source-sink traits were mapped to similar genomic locations and showed consistent gene actions. Therefore it is possible to improve grain yield by genetic improvement of FLL, FLW, and FLA with the aid of using molecular markers. Comparison with the QTL detected in another population with one common parent, Zhenshan 97, the QTL for FLA (QFla6) on chromosome 6 in the present study corresponded to the QTL for flag leaf area reported by Cui et al [10]. Moreover, the region RM255-RM349 on chromosome 4 was responsible for the three leaf morphological traits simultaneously and was detected in both years with large additive effects. This region is potential for genetic improvement of grain yield, and its contribution to grain yield could be assessed in field tests using near-isogenic lines. High chlorophyll content and delaying senes-
tive in 2004 (Table 3). This result also explained that
cence of the leaves have been considered to be a fa-
able that flag leaf area is highly associated with yield
[2,8,11]
partial sterility affected the correlation between grain yield and stay-green traits. Cha et al. [12] identified a stay-green gene (sgr) on chromosome 9 corresponding to the QTL for retention of degree of greenness (QRdg9) in this study, which delays the progress of yellowing but not functionally keeping the photosynthetic capability. This kind of genes may also have partly contribution to the negative correlation between grain yield and stay-green as observed in this population. It is widely believed that yield gains are most likely to be achieved by simultaneously increasing both source (photosynthetic rate) and sink (partitioning to grain) strengths. Flag leaf area is an important factor which determines yield potential through affecting photosynthetic rate. Thus, it is very reason-
.
and yield traits in this study. While partial sterility of
However, degree of greenness (corresponding to leaf
some lines in the population could limit the transpor-
chlorophyll content) and stay-green traits had no cor-
tation of assimilates from stem and leaves to grain.
relation or negatively associated with potential yield
This case could be a noise to discover the real rela-
and yield components in this study. Negative correla-
tionship between leaf stay-green duration and yield.
tion between degree of greenness and grain yield can
After removal of the partial sterility lines, the correla-
be explained by the fact that degree of greenness was
tion (data not shown) between flag leaf traits and
significantly and negatively associated with flag leaf
yield traits is reanalyzed, and the relationship keeps
size (Table 2), which was positively correlated with
highly similar. At the QTL mapping level, some
yield and yield parameters. The lack of correlation
QTLs for flag leaf area are located to the similar re-
between stay-green and yield was also found in maize
gions where QTL for yield and yield traits are de-
and rice [13, 16]. A possible explanation for the lack of,
tected, whereas no QTL for stay-green are located in
or negative, correlation between stay-green traits and
the yield QTL regions (data not shown). This situa-
grain yield is that the experimental population was
tion also supports the relationships between flag leaf
derived from an inter-subspecific cross and the partial
associated traits and yield traits in this study.
sterility occurred in a fraction of the lines. Partial ste-
Some of the QTLs for stay-green detected in the present study appear to match the stay-green QTLs obtained from other populations. These include the genomic regions RM29-RM341 and RM497-RM240 on chromosome 2, and RM293-RM571 on chromosome 3 [13, 17]. The use of the stay-green traits to delay leaf senescence as a means to increase crop production has remained an attractive strategy. These common stay-green QTLs and the QTL region (RM422-RM565 on chromosome 3 controlling the three stay-green traits may be used as targets for
vorable characteristic in crop production
rility would result in less portion of nutrient transport from leaves to the developing seeds, which causes lower seed-setting rate accompanied by greener leaves with higher chlorophyll content. In addition, the extremely high temperature (above 38℃ for 1 week) at reproductive stage in 2003 resulted in higher degree of spikelet fertility (data not shown); and the correlations between spikelet fertility and the physiological traits were negative in 2003 but slightly posi-
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
construction near isogenic lines, and the contribution of QTLs and domains to grain yield necessitates to be assessed in field conditions.
831
related physiological traits in rice (Oryza sativa L.). Euphytica, 2004, 135 : 1-7. [9] Li Z, Pinson S R M, Stansel J W, Paterson A H. Genetic dissection of the source-sink relationship affecting fecun-
References: [1] Ashraf M, Akbar M, Salim M. Genetic improvement in physiological traits of rice yield. In: Slafer G A, eds. Genetic Improvement of Field Crops. Marcel Dekker Incorporates, New York, 1994, 413-455. [2] Chen Wen-Fu, Xu Zheng-Jin, Zhang Long-Bu. Physiological bases of super high yield breeding in rice. Shenyang: Liaoning Science and Technology Publishing Company, 1995(in Chinese). [3] Sicher R C. Assimilate partitioning within leaves of small grain cereals. In: Yash P A, Prasanna M, Govindjee D, eds. Photosynthesis Photoreactions to Plant Productivity. Kluwer Academic Publishers, Dordrecht, Netherlands, 1993, 351-360. [4] Foyer C H. The basis for source-sink interaction in leaves. Plant Physiol Biochem, 1987, 25 : 649-657. [5] Gladun I V, Karpov E A. Distribution of assimilates from the flag leaf of rice during the reproductive period of development. Russ J Plant Physiol, 1993, 40 : 215-219. [6] Hirota O, Oka M, Takeda T. Sink activity estimation by sink size and dry matter increase during the ripening stage of barley (Hordeum vulgare) and rice (Oryza sativa). Ann Bot, 1990, 65 : 349-354. [7] Kholupenco I P, Burundukova O L, Zhemchugova V P, Voronkova N M, Chernoded G K. Source-sink relations in Far-Eastern rice cultivars as related to their productivity. Russ J Plant Physiol, 1996, 43 : 141-148. [8] Teng S, Qian Q, Zeng D, Kunihiro Y, Fujimoto K, Huang D, Zhu L. QTL analysis of leaf photosynthetic rate and
dity and yield in rice (Oryza sativa L.). Mol Breed, 1998, 4 : 419-426. [10] Cui K H, Peng S B, Xing Y Z, Yu S B, Xu C G, Zhang Q. Molecular dissection of the genetic relationships of source, sink and transport tissue with yield traits in rice. Theor Appl Genet, 2003, 106 : 649-658. [11] Rosenow D T, Quisenberry J E, Wendt C W, Clark L E. Drought-tolerant sorghum and cotton germplasm. Agric Water Manage, 1983, 7 : 207-222. [12] Cha K W, Koh H J, Lee Y J, Lee B M, Nam Y W, Paek N C. Isolation, characterization, and mapping of the stay-green mutant in rice. Theor Appl Genet, 2002, 104 : 526-532. [13] Jiang G H, He Y Q, Xu C G, Li X H, Zhang Q. The genetic basis of stay-green in rice analyzed in a population of doubled haploid lines derived from an indica by japonica cross. Theor Appl Genet, 2004, 108 : 688-698. [14] Yue B, Xiong L Z, Xue W Y, Xing Y Z, Luo L J, Xu C G. Genetic analysis for drought resistance of rice at reproductive stage in field with different types of soil. Theor Appl Genet, 2005, 111 : 1127-1136. [15] Zeng Z B. Precision mapping of quantitative trait loci. Genetics, 1994, 136 : 1457-1468. [16] Bolanos J, Edmeades G O. The importance of the anthesis-silking interval in breeding for drought tolerance in tropical maize. Field Crop Res, 1996, 48 : 65-80. [17] Abdelkhalik A F, Shishido R, Nomura K, Ikehashi H. QTL-based analysis of leaf senescence in an indica/japonica hybrid in rice (Oryza sativa L.). Theor Appl Genet, 2005, 110 : 1226-1235.
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水稻剑叶部分形态生理特性 QTL 分析以及它们与产量、产量 性状的关系 岳 兵 1，薛为亚 1，罗利军 2，邢永忠 1 1. 华中农业大学作物遗传改良国家重点实验室，武汉 430070； 2. 上海市农业基因中心，上海 201106 摘 要：光合产物是水稻产量的主要来源，因此对水稻后期功能叶片尤其是剑叶形态生理性状的遗传分析对水稻高产育种 很重要。利用来源于籼/粳交后代的重组自交系群体为材料对水稻剑叶形态（叶片长、宽、面积）和生理性状（叶绿度、持 绿性）进行了 QTL 定位，并对这些性状与产量、产量性状的相关性进行了分析。两年分别定位了 17、6 和 14 个与剑叶形 态性状、叶绿度和持绿性有关的 QTL，其中 10 个 QTL 在两年中共同检测到。相关分析表明，较大的剑叶可以增加穗粒数 并显著增加产量，然而叶绿度和持绿性与产量、产量性状无关或呈显著负相关。叶绿度与剑叶大小呈显著负相关以及籼/ 粳交群体后代半不育是叶绿度和持绿性与产量、产量性状无关或呈显著负相关的可能原因。染色体 4 上的 RM255-RM349 区域同时控制 3 个剑叶形态性状并且解释的变异也较大，该区域可用于遗传改良以提高水稻产量。染色体 3 上的 RM422-RM565 区域重叠了 3 个与持绿性有关的 QTL，它们对产量的贡献有待于通过构建近等基因系进行深入研究。 关键词：水稻；QTL 定位；剑叶大小；持绿性；产量 作者简介：岳兵，男（1972-），博士，研究方向：水稻分子遗传，现工作单位：湖北省农业科学研究院粮食作物研究所。 E-mail: [email protected]
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2006, 33 (9)：824–832
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QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice YUE Bing1, XUE Wei-Ya1, LUO Li-Jun2, XING Yong-Zhong1,① 1. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; 2. Shanghai Agrobiological Gene Center, Shanghai 201106, China Abstract: Photosynthesis of carbohydrate is the primary source of grain yield in rice (Oryza sativa L.). It is important to genetically analyze the morphological and the physiological characteristics of functional leaves, especially flag leaf, in rice improvement. In this study, a recombinant inbred population derived from a cross between an indica (O. sativa L. ssp. indica) cultivar and a japonica (O. sativa L. ssp. japonica) cultivar was employed to map quantitative traits loci (QTLs) for the morphological (i.e., leaf length, width, and area) and physiological (i.e., leaf color rating and stay-green) characteristics of flag leaf and their relationships with yield and yield traits in 2003 and 2004. A total of 17 QTLs for morphological traits (flag leaf length, width, and area), 6 QTLs for degree of greenness and 14 QTLs for stay-green-related traits (retention-degrees of greenness, relative retention of greenness, and retention of the green area) were resolved, and 10 QTLs were commonly detected in both the years. Correlation analysis revealed that flag leaf area increased grain yield by increasing spikelet number per panicle. However, the physiological traits including degree of greenness and stay-green traits were not or negatively correlated to grain yield and yield traits, which may arise from the negative relation between degree of greenness and flag leaf size and the partial sterility occurred in a fraction of the lines in this population. The region RM255-RM349 on chromosome 4 controlled the three leaf morphological traits simultaneously and explained a large part of variation, which was very useful for genetic improvement of grain yield. The region RM422-RM565 on chromosome 3 was associated with the three stay-green traits simultaneously, and the use of this region in genetic improvement of grain yield needs to be assessed by constructing near-isogenic lines. Key words: Oryza sativa L.; QTL mapping; flag leaf size; stage-green; grain yield
Grain yield is one of the important aims in conventional crop breeding. However, grain yield, as well as yield components, is an extremely complex trait, and genetic control of grain yield is realized through the control of a series of complex biochemical and physiological processes [1]. Photosynthesis is the primary source of grain yield in rice (Oryza sativa L.) [2]. The top three leaves on a stem, particularly the flag leaf, are the primary source of the carbohydrates production [3-5]. Some morphological traits, such as size and shape of the leaves, have been considered to be a major source of capacity-related traits in cereals [6, 7], and a number of physiological traits, such as chlorophyll content, photosynthesis capacity, and stay-green,
were also considered as important determinants of grain yield [2, 8]. Previous studies have mainly focused on few morphological traits, such as the size of leaves in rice, and the quantitative traits loci (QTLs) for these traits were co-located with some sink-related traits [9, 10]. Recently, the genetic basis of photosynthesis rate, chlorophyll content, and stomatal resistance has been studied in rice [8]. However, the relationships between these traits and grain yield are still unknown. Although the contribution of stay-green to maintain high and stable yield production under drought-prone conditions has been reported in sorghum [11], the genetic correlation between stay-green and yield has not been detected yet in rice [12,13].
Received: 2005-08-11; Accepted: 2005-09-08 This work was supported by the National Program on the Development of Basic Research (No.G1998010204) and the Rockefeller Foundation. ①
Corresponding author. E-mail: [email protected] ; Tel: +86-27-8728 1715, Fax: +86-27-8728 7092
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
In this study, QTLs for some morphological and physiological traits of flag leaf and their relationships with yield and yield components were analyzed in rice. The aim of this study was to understand the genetic basis of these traits and their possible role in genetic improvement of grain yield in rice.
1 1. 1
Materials and Methods Plant materials and growing conditions
A population consisting of 180 recombinant inbred lines (RILs at F9/F10 generation) was developed from the cross between a paddy rice (O. sativa L. ssp. indica) cultivar Zhenshan 97, and an upland rice (O. sativa L. ssp. japonica) cultivar IRAT109. Zhenshan 97 is the maintainer line for a number of elite hybrids widely grown in China, and IRAT 109 was introduced from Cote d’Ivoire. The RIL population, together with its parents, was directly planted in polyvinyl chloride (PVC) pipes with one plant per pipe in the experimental farm of Huazhong Agricultural University, Wuhan, China in the rice-growing season. The pipe was 20 cm in diameter and 1 m in length. The pipes were laid out in three blocks following a randomized complete block design with three replicates, two pipes per replicate for each genotype in May 2003, and one pipe per replicate for each genotype in May 2004. Each pipe was loaded with a plastic bag filled with 38 kg of thoroughly mixed soil that composed of two parts of clay and one part of river sand, to which 25 g fertilizers (including 4 g each of N, P2O5, and K2O) were added. Three to five germinated seeds were sown in each pipe and then thinned to single healthy plant 30 days after sowing. At the beginning of tillering stage, 1 g of urea (dissolved in water) was applied to each pipe. Plants were fully irrigated by watering every day till maturating. 1. 2
Traits and measurements
Three morphological traits, four physiological traits, and five yield traits were investigated for all plants that headed within 2 weeks, considering that these traits are sensitive to environments. The flag leaf length (FLL, cm) and flag leaf width (FLW, cm) were measured on the largest two tillers of
825
all plants at maturing stage. One derived trait, the flag leaf area (FLA) = FLL × FLW, was calculated. The degree of greenness (DG) and some stay-green traits were also investigated for all plants following a method described by Jiang et al [13]. Using a Minolta Chlorophyll Meter SPAD-502 (Minolta Camera Co., Japan), the degrees of greenness of the flag leaves from the biggest two tillers were measured on the day of heading and again after 15 days. To ensure that the measurements were taken on the right day for the right tiller, tillers were tagged at the day of heading. The SPAD readings of the flag leaves measured on the day of heading were designated DG, and the SPAD values 15 days after heading were used as measurements for the retention-degrees of greenness, designated RDG. The ratios of RDG to DG were used as indexes for the relative retention of greenness, designated RRG. Another measurement of stay-green was an independent visual estimation of the retention of the green-area (RGA) for leaves 15 days after heading on a 1-5 scale, where 1 represents complete or nearly complete leaf death and 5 corresponds to a complete green leaves. Yield and its component traits were also examined for all plants, including grain yield per plant (Y, g), panicle number per plant (PN), number of spikelets per panicle (SN), 1000-grain-weight (GW, g), and spikelet fertility (SF, %). Spikelet fertility was measured as the number of grains divided by the total number of spikelets of a plant. 1. 3
QTL analysis
The genetic linkage map was constructed using a total of 245 SSR markers as described previously [14]. The means of each trait were used to identify QTL by the method of composite interval mapping (CIM) by using QTL Cartographer 2.0 software [15] with a threshold of LOD score 2.4 (selected by permutation test based on 1 000 runs, P=0.05).
2
Results
2. 1 Phenotypic variation of the parents and RILs The phenotypic differences between parents as well as the variation in the RIL population are sum-
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marized in Table 1. Transgressive segregations were observed in the RIL population for all the traits investigated. ANOVA revealed that the difference from
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from genotypes for these traits was significant, whereas for the most of the cases the difference from replicates was not significant (Table 1).
Table 1 Measurements and ANOVA of the traits relative to flag leaf in the RIL population and the parents in 2003 and 2004 Traitsa) FLL
Zhenshan 97 25.7/27.2
IRAT109
b)
Mean of RILs
28.8/29.7 **
1.4/1.5
Range of RILs
ANOVA significant c) Genotype
Replication
28.8/28.1
(16.2-50.2)/(18.7-43.0)
**/**
Ns/Ns
1.5/1.4
(1.0-2.2)/(0.9-3.8)
**/**
Ns/Ns
FLW
1.3/1.4
FLA
33.4/38.1
40.3*/44.6*
41.9/40.7
(19.0-108.7)/(17.1-97.7)
**/**
Ns/Ns
DG
*
39.8 /40.5
36.8/39.4
39.5/40.2
(29.2-48.1)/(30.3-51.1)
**/**
**/Ns
RDG
33.8*/30.2
30.4/28.7
31.2/23.0
(17.2-43.9)/(10.7-35.4)
**/**
**/Ns
RRG
82.2/70.2
82.7/71.7
78.9/56.4
(51.1-98.4)/(29.8-75.0)
**/**
*/Ns
RGA
2.6/2.6
2.7/2.8
2.6/2.4
(1.0-4.7)/(1.0-3.6)
**/**
**/Ns
a)
FLL: flag leaf length; FLW: flag leaf width; FLA: flag leaf area; DG: degree of greenness; RDG: retention-degrees of greenness; RRG: relative retention of greenness; RGA: retention of the green area; b) The data on the left- and right-side of slash in each cell were the results obtained in 2003 and 2004, respectively. Data following the marks * or ** are significantly higher than the other parent at the 0.05 and 0.01 probability levels based on t-test; c) “*”, “**”, and “Ns” represent significant at P<0.05, P<0.01 level and no significant, respectively.
For traits FLL, FLW, and FLA, IRAT109 had higher values than Zhenshan 97 in both the years, and the differences of FLA between the parents in both the years and FLW in 2004 were significant. However, the values of DG and RDG of Zhenshan 97 were significantly higher than that of IRAT109 in 2003. For other traits, the difference between two parents was not significant in both the years. When data collected from the two years were compared, the mean values of RILs for traits RDG, RRG, and RGA were higher in 2003 than in 2004 (Table 1). This was caused by high temperature at
flowering stage in 2003 resulting in reduction of seed setting rate. 2. 2
Correlations of the traits The correlations among the flag leaf characteristics
are shown in Table 2. Three traits related to flag leaf area (FLL, FLW, and FLA) were highly intercorrelated, and so were the three stay-green-related traits, RDG, RRG, and RGA. Strong correlations were also detected between DG and RDG in both the years. Negative or small correlations were identified between flag leaf area and stay-green-related traits.
Table 2 Coefficients of pairwise correlations of the traits related to flag leaf investigated in 2003 and 2004 Traits a) FLW
FLL 0.42/0.22
FLW
FLA
DG
RDG
FLA
0.87/0.76
0.80/0.80
DG
-0.36/-0.54
-0.43/-0.43
-0.43/-0.61
RDG
-0.40/-0.19
-0.30/-0.13
-0.39/-0.19
0.59/0.60
RRG
-0.27/0.02
-0.07/0.08
-0.20/0.07
0.02/0.16
0.82/0.86
-0.21/0.21
-0.06/0.16
-0.16/0.20
0.05/0.00
0.72/0.66
RGA See the footnote
a), b)
RRG
b)
of Table 1 for explanation, the values in bold face are significantly at P<0.05.
0.86/0.86
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
827
Correlations between yield, yield components,
were significant in at least one year. PN and GW had
and the traits related to flag leaf characteristics are
small or negative correlation with the traits related to
given in Table 3. In general, potential yield and SN
flag leaf characters. It was interesting to note that
were positively correlated with FLL, FLW, and FLA,
stay-green traits were significantly and negatively
and negatively correlated with DG, RDG, RRG, and
correlated with SF in 2003, but they were positively
RGA in both the years. Furthermore, the differences
associated with SF in 2004.
Table 3 Coefficients of pairwise correlations between yield related traits and other traits investigated in 2003 and 2004 Traits a) FLL
Y 0.41/0.31
b)
PN
SN
SF
GW
-0.14/-0.03
0.54/0.51
0.09/-0.13
0.03/-0.21
FLW
0.30/0.10
-0.26/0.01
0.44/0.18
0.13/-0.13
0.13/0.16
FLA
0.43/0.26
-0.22/-0.02
0.60/0.44
0.11/-0.16
0.07/-0.03
DG
-0.22/-0.26
-0.08/-0.14
-0.14/-0.35
-0.03/0.18
-0.11/0.17
RDG
-0.36/-0.16
-0.11/-0.23
-0.21/-0.07
-0.16/0.15
-0.08/-0.01
RRG
-0.31/-0.06
-0.08/-0.19
-0.19/0.06
-0.18/0.10
-0.02/-0.04
-0.37/-0.02
-0.08/-0.23
-0.13/0.16
-0.31/0.08
-0.10/-0.08
RGA
a),b)
See the footnote of Table 1 for explanation, the values in bold face are significantly at P<0.05. Y: yield; PN: panicle number per plant; SN: spikelet number per panicle; SF: spikelet fertility; GW: 1000 grain weight.
2. 3
QTL mapping
Seven QTLs were resolved for flag leaf length in the two years; only one was detected in both the years, and individual QTL explained 5.3%–21.7% of phenotypic variation (Table 4). Four QTLs for flag leaf width were identified in 2003 and 2004 with individual QTL explained 4.4%–35.8% of phenotypic variation. However, only QFlw4 was detected in both years and had the largest additive effects. Alleles from IRAT109 at eight of the QTLs (72.7%) for FLL and FLW had positive effects, which was also coincided to the performance of both the parents for these traits. Six QTLs for FLA were detected, two of them were identified in both the years, and individual QTL explained 4.7%–26.8% of phenotypic variation. For the trait of DG, six QTLs were resolved in 2003 and 2004; two of them were detected in both the years, and individual QTL explained 4.5%–28.1% of phenotypic variation. Six QTLs for RDG were identified in 2003 and 2004 with individual QTL explained 6.8%–19.5% of phenotypic variation, and two of them were detected in both the years. Five QTLs for RGA were detected, only one of them was common in both the years, and individual QTL explained
10.0%–14.2% of phenotypic variation. For the trait of RRG, three QTLs were identified, whereas only one of them was detected in both the years. Individual QTL explained 7.9%–23.8% of phenotypic variation. In summary, a total of 17 QTL were resolved for the flag leaf size related traits (i.e., FLL, FLW, and FLA) including four commonly detected in both the years and 13 observed in one year. The region RM255-RM349 on chromosome 4 controlled the three traits simultaneously and detected in both years. For the stay-green-related traits (i.e., RDG, RGA, and RRG), 14 QTLs were resolved including four detected in both years and 10 identified only in one year. The region RM422-RM565 on chromosome 3 controlled the three traits simultaneously (Table 4, Fig. 1). 2. 4
Congruence of QTL
There were three regions congregated more than three QTLs and eight regions clustered two QTLs. For examples, the region RM422-RM565 on chromosome 3 clustered QTLs for the three stay-green traits, and the region RM255-RM349 on chromosome 4 overlapped the QTLs for FLL, FLW, FLA, and RGA (Fig. 1). Among them, five regions were associated with FLL, FLW, or FLA simultaneously, four
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Table 4 QTL for the traits related to flag leaf resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109 2003
Traits FLL
FLW
FLA
DG
RDG
RGA
RRG
b
2004 Addc
Var%d
2.52
19.69
3.0
1.93
11.98
RM588-RM589
6.2
2.89
21.68
QFlw4
RM255-RM349
3.9
0.14
16.50
5
QFlw5
RM274-RM480
3.1
0.11
10.08
10.60
2
QFla2
RM262-MRG0303 3.0
4.51
10.47
7.45
26.83
4
QFla4
RM255-RM349
5.2
5.92
17.68
4.1
4.43
9.22
6
QFla6
RM111-RM276
3.6
-5.70
11.80
RM111-RM276
2.5
-3.11
4.66
QFla10
RM596-RM271
3.2
-3.45
5.68
1
QDg1
RM302-RM472
5.8
-1.29
12.86
2
QDg2
RM29-RM341
3.0
-1.46
8.25
2
QDg2
RM29-RM341
8.9
-1.49
16.45
4
QDg4b MRG4503-RM255 6.9
-2.33
18.92
4
QDg4a
RM471-RM142
3.6
0.94
6.66
9
QDg9
RM434-RM257
5.6
2.04
16.68
4
QDg4b MRG4503-RM255
14.1
-1.92
28.09
Chr
a
c
d
Chr
a
QTL
Intervalb
QTL
Interval
LOD
Add
Var%
3
QFll3
RM523-RM231
4.0
-1.72
7.69
2
QFll2
4
QFll4
RM255-RM349
3.7
1.74
7.75
4
QFll4
RM255-RM349
5
QFll5
RM480-RM334
2.5
1.53
5.32
6
QFll6
7
QFll7
RM274-RM480
4.9
1.80
8.41
10
QFll10
RM596-RM271
3.1
-1.57
5.95
4
QFlw4
RM255-RM349
20.5
0.15
35.81
4
5
QFlw4
RM421-RM274
7.9
0.09
13.40
6
QFlw6
RM539-RM527
3.3
-0.05
4.41
3
QFla3
RM523-RM231
5.9
-4.65
4
Qfla4
RM255-RM349
12.8
5
QFla5
RM274-RM480
6
Qfla6
10
LOD
RM262-MRG0303 5.5
5
QDg5
RM161-RM421
3.0
0.77
4.46
1
QRdg1
RM302-RM472
2.5
-1.37
7.24
2
QRdg2
RM29-RM341
4.0
-2.53
19.51
3
QRdg3
RM422-RM565
5.4
1.88
13.60
3
QRdg3
RM422-RM565
3.1
1.94
11.12
4
QRdg4 MRG4503-RM255
4.1
-1.49
8.58
9
QRdg9
RM215-RM245
4.2
2.09
13.28
8
QRdg8
RM350-RM284
2.4
-1.34
6.76
9
QRdg9
RM215-RM245
2.5
1.16
5.15
3
Qrga3
RM422-RM565
5.4
0.29
13.18
2
Qrga2
MRG2762-RM526 2.9
-0.23
10.80
7
Qrga7
RM295-RM481
2.9
0.25
10.44
4
Qrga4
MRG4503-RM255 3.0
0.24
11.68
8
Qrga8
RM350-RM284
6.3
-0.35
14.24
8
Qrga8
2.7
-0.22
9.96
2
QRrg2b
RM497-RM240
2.7
3.06
7.92
2
QRrg2a MRG2762-RM526 4.3
-5.74
23.79
3
QRrg3
RM422-RM565
4.9
4.23
14.91
3
QRrg3
3.49
9.76
RM350-RM284
RM422-RM565
2.5
a,b
Chromosome number and marker intervals, the bold format means the QTLs were detected in two years; Additive effects, the positive values of additive effects indicate the alleles from IRAT109 with increasing effects; d Phenotype variation rate explained by detected QTLs. c
regions clustered stay-green traits, three regions overlapped with the QTL for DG and RDG. These results coincide with the correlations among these traits (Table 2).
with FLL and FLA in the present study, and the correlations between flag leaf characteristics and yield components revealed that large leaf length, leaf width, and leaf area contributed to increased spikelet number per pani-
3
Discussion
cle. Li et al. [9] concluded that leaf area was positively
Grain yield was positively and strongly correlated
related to grain yield, and QTL-influencing-related
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
829
Fig. 1 Chromosomal locations of the QTLs for the flag leaf related traits The QTLs for all traits are shown on the left of the chromosomes. The QTLs in bold were detected in both years. The italic QTLs indicate that the alleles for increasing trait values were from Zhenshan 97. Distances are given in Kosambi centiMorgans.
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2006
source-sink traits were mapped to similar genomic locations and showed consistent gene actions. Therefore it is possible to improve grain yield by genetic improvement of FLL, FLW, and FLA with the aid of using molecular markers. Comparison with the QTL detected in another population with one common parent, Zhenshan 97, the QTL for FLA (QFla6) on chromosome 6 in the present study corresponded to the QTL for flag leaf area reported by Cui et al [10]. Moreover, the region RM255-RM349 on chromosome 4 was responsible for the three leaf morphological traits simultaneously and was detected in both years with large additive effects. This region is potential for genetic improvement of grain yield, and its contribution to grain yield could be assessed in field tests using near-isogenic lines. High chlorophyll content and delaying senes-
tive in 2004 (Table 3). This result also explained that
cence of the leaves have been considered to be a fa-
able that flag leaf area is highly associated with yield
[2,8,11]
partial sterility affected the correlation between grain yield and stay-green traits. Cha et al. [12] identified a stay-green gene (sgr) on chromosome 9 corresponding to the QTL for retention of degree of greenness (QRdg9) in this study, which delays the progress of yellowing but not functionally keeping the photosynthetic capability. This kind of genes may also have partly contribution to the negative correlation between grain yield and stay-green as observed in this population. It is widely believed that yield gains are most likely to be achieved by simultaneously increasing both source (photosynthetic rate) and sink (partitioning to grain) strengths. Flag leaf area is an important factor which determines yield potential through affecting photosynthetic rate. Thus, it is very reason-
.
and yield traits in this study. While partial sterility of
However, degree of greenness (corresponding to leaf
some lines in the population could limit the transpor-
chlorophyll content) and stay-green traits had no cor-
tation of assimilates from stem and leaves to grain.
relation or negatively associated with potential yield
This case could be a noise to discover the real rela-
and yield components in this study. Negative correla-
tionship between leaf stay-green duration and yield.
tion between degree of greenness and grain yield can
After removal of the partial sterility lines, the correla-
be explained by the fact that degree of greenness was
tion (data not shown) between flag leaf traits and
significantly and negatively associated with flag leaf
yield traits is reanalyzed, and the relationship keeps
size (Table 2), which was positively correlated with
highly similar. At the QTL mapping level, some
yield and yield parameters. The lack of correlation
QTLs for flag leaf area are located to the similar re-
between stay-green and yield was also found in maize
gions where QTL for yield and yield traits are de-
and rice [13, 16]. A possible explanation for the lack of,
tected, whereas no QTL for stay-green are located in
or negative, correlation between stay-green traits and
the yield QTL regions (data not shown). This situa-
grain yield is that the experimental population was
tion also supports the relationships between flag leaf
derived from an inter-subspecific cross and the partial
associated traits and yield traits in this study.
sterility occurred in a fraction of the lines. Partial ste-
Some of the QTLs for stay-green detected in the present study appear to match the stay-green QTLs obtained from other populations. These include the genomic regions RM29-RM341 and RM497-RM240 on chromosome 2, and RM293-RM571 on chromosome 3 [13, 17]. The use of the stay-green traits to delay leaf senescence as a means to increase crop production has remained an attractive strategy. These common stay-green QTLs and the QTL region (RM422-RM565 on chromosome 3 controlling the three stay-green traits may be used as targets for
vorable characteristic in crop production
rility would result in less portion of nutrient transport from leaves to the developing seeds, which causes lower seed-setting rate accompanied by greener leaves with higher chlorophyll content. In addition, the extremely high temperature (above 38℃ for 1 week) at reproductive stage in 2003 resulted in higher degree of spikelet fertility (data not shown); and the correlations between spikelet fertility and the physiological traits were negative in 2003 but slightly posi-
YUE Bing et al.: QTL Analysis for Flag Leaf Characteristics and Their Relationships with Yield and Yield Traits in Rice
construction near isogenic lines, and the contribution of QTLs and domains to grain yield necessitates to be assessed in field conditions.
831
related physiological traits in rice (Oryza sativa L.). Euphytica, 2004, 135 : 1-7. [9] Li Z, Pinson S R M, Stansel J W, Paterson A H. Genetic dissection of the source-sink relationship affecting fecun-
References: [1] Ashraf M, Akbar M, Salim M. Genetic improvement in physiological traits of rice yield. In: Slafer G A, eds. Genetic Improvement of Field Crops. Marcel Dekker Incorporates, New York, 1994, 413-455. [2] Chen Wen-Fu, Xu Zheng-Jin, Zhang Long-Bu. Physiological bases of super high yield breeding in rice. Shenyang: Liaoning Science and Technology Publishing Company, 1995(in Chinese). [3] Sicher R C. Assimilate partitioning within leaves of small grain cereals. In: Yash P A, Prasanna M, Govindjee D, eds. Photosynthesis Photoreactions to Plant Productivity. Kluwer Academic Publishers, Dordrecht, Netherlands, 1993, 351-360. [4] Foyer C H. The basis for source-sink interaction in leaves. Plant Physiol Biochem, 1987, 25 : 649-657. [5] Gladun I V, Karpov E A. Distribution of assimilates from the flag leaf of rice during the reproductive period of development. Russ J Plant Physiol, 1993, 40 : 215-219. [6] Hirota O, Oka M, Takeda T. Sink activity estimation by sink size and dry matter increase during the ripening stage of barley (Hordeum vulgare) and rice (Oryza sativa). Ann Bot, 1990, 65 : 349-354. [7] Kholupenco I P, Burundukova O L, Zhemchugova V P, Voronkova N M, Chernoded G K. Source-sink relations in Far-Eastern rice cultivars as related to their productivity. Russ J Plant Physiol, 1996, 43 : 141-148. [8] Teng S, Qian Q, Zeng D, Kunihiro Y, Fujimoto K, Huang D, Zhu L. QTL analysis of leaf photosynthetic rate and
dity and yield in rice (Oryza sativa L.). Mol Breed, 1998, 4 : 419-426. [10] Cui K H, Peng S B, Xing Y Z, Yu S B, Xu C G, Zhang Q. Molecular dissection of the genetic relationships of source, sink and transport tissue with yield traits in rice. Theor Appl Genet, 2003, 106 : 649-658. [11] Rosenow D T, Quisenberry J E, Wendt C W, Clark L E. Drought-tolerant sorghum and cotton germplasm. Agric Water Manage, 1983, 7 : 207-222. [12] Cha K W, Koh H J, Lee Y J, Lee B M, Nam Y W, Paek N C. Isolation, characterization, and mapping of the stay-green mutant in rice. Theor Appl Genet, 2002, 104 : 526-532. [13] Jiang G H, He Y Q, Xu C G, Li X H, Zhang Q. The genetic basis of stay-green in rice analyzed in a population of doubled haploid lines derived from an indica by japonica cross. Theor Appl Genet, 2004, 108 : 688-698. [14] Yue B, Xiong L Z, Xue W Y, Xing Y Z, Luo L J, Xu C G. Genetic analysis for drought resistance of rice at reproductive stage in field with different types of soil. Theor Appl Genet, 2005, 111 : 1127-1136. [15] Zeng Z B. Precision mapping of quantitative trait loci. Genetics, 1994, 136 : 1457-1468. [16] Bolanos J, Edmeades G O. The importance of the anthesis-silking interval in breeding for drought tolerance in tropical maize. Field Crop Res, 1996, 48 : 65-80. [17] Abdelkhalik A F, Shishido R, Nomura K, Ikehashi H. QTL-based analysis of leaf senescence in an indica/japonica hybrid in rice (Oryza sativa L.). Theor Appl Genet, 2005, 110 : 1226-1235.
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水稻剑叶部分形态生理特性 QTL 分析以及它们与产量、产量 性状的关系 岳 兵 1，薛为亚 1，罗利军 2，邢永忠 1 1. 华中农业大学作物遗传改良国家重点实验室，武汉 430070； 2. 上海市农业基因中心，上海 201106 摘 要：光合产物是水稻产量的主要来源，因此对水稻后期功能叶片尤其是剑叶形态生理性状的遗传分析对水稻高产育种 很重要。利用来源于籼/粳交后代的重组自交系群体为材料对水稻剑叶形态（叶片长、宽、面积）和生理性状（叶绿度、持 绿性）进行了 QTL 定位，并对这些性状与产量、产量性状的相关性进行了分析。两年分别定位了 17、6 和 14 个与剑叶形 态性状、叶绿度和持绿性有关的 QTL，其中 10 个 QTL 在两年中共同检测到。相关分析表明，较大的剑叶可以增加穗粒数 并显著增加产量，然而叶绿度和持绿性与产量、产量性状无关或呈显著负相关。叶绿度与剑叶大小呈显著负相关以及籼/ 粳交群体后代半不育是叶绿度和持绿性与产量、产量性状无关或呈显著负相关的可能原因。染色体 4 上的 RM255-RM349 区域同时控制 3 个剑叶形态性状并且解释的变异也较大，该区域可用于遗传改良以提高水稻产量。染色体 3 上的 RM422-RM565 区域重叠了 3 个与持绿性有关的 QTL，它们对产量的贡献有待于通过构建近等基因系进行深入研究。 关键词：水稻；QTL 定位；剑叶大小；持绿性；产量 作者简介：岳兵，男（1972-），博士，研究方向：水稻分子遗传，现工作单位：湖北省农业科学研究院粮食作物研究所。 E-mail: [email protected]
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