Mol. Cells, Vol. 24, No. 1, pp. 83-94
Molecules and Cells ©KSMCB 2007
Quantitative Trait Loci Associated with Functional Stay-Green SNU-SG1 in Rice Soo-Cheul Yoo†, Sung-Hwan Cho†, Haitao Zhang, Hyo-Chung Paik, Chung-Hee Lee, Jinjie Li, Jeong-Hoon Yoo, Byun-Woo Lee, Hee-Jong Koh, Hak Soo Seo, and Nam-Chon Paek* Department of Plant Science and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-921, Korea. (Received February 4, 2007; Accepted April 9, 2007)
During monocarpic senescence in higher plants, functional stay-green delays leaf yellowing, maintaining photosynthetic competence, whereas nonfunctional stay-green retains leaf greenness without sustaining photosynthetic activity. Thus, functional stay-green is considered a beneficial trait that can increase grain yield in cereal crops. A stay-green japonica rice ‘SNU-SG1’ had a good seedsetting rate and grain yield, indicating the presence of a functional stay-green genotype. SNU-SG1 was crossed with two regular cultivars to determine the inheritance mode and identify major QTLs conferring stay-green in SNU-SG1. For QTL analysis, linkage maps with 100 and 116 DNA marker loci were constructed using selective genotyping with F2 and RIL (recombinant inbred line) populations, respectively. Molecular marker-based QTL analyses with both populations revealed that the functional stay-green phenotype of SNU-SG1 is regulated by several major QTLs accounting for a large portion of the genetic variation. Three main-effect QTLs located on chromosomes 7 and 9 were detected in both populations and a number of epistatic-effect QTLs were also found. The amount of variation explained by several digenic interactions was larger than that explained by main-effect QTLs. Two main-effect QTLs on chromosome 9 can be considered the target loci that most influence the functional staygreen in SNU-SG1. The functional stay-green QTLs may help develop low-input high-yielding rice cultivars by QTL-marker-assisted breeding with SNU-SG1. Keywords: Epistasis; F2 Population; Functional StayGreen; Recombinant Inbred Lines; QTL Mapping; Rice; Selective Genotyping; SNU-SG1. †
These authors contributed equally to this work.
* To whom correspondence should be addressed. Tel: 82-2-880-4543; Fax: 82-2-873-2056 E-mail:
[email protected]
Introduction Leaf greenness depends on the concentration of chlorophyll, the most important green pigment absorbing sunlight energy for photosynthesis. Leaf yellowing generally results from progressive breakdown of chlorophyll during senescence. Plants assimilate carbohydrates and nitrogen in vegetative organs (source) and remobilize them to newly developing tissues during development, or to reproductive organs (sink) during senescence. To increase grain yield in cereal crops, source strength must be increased so that sink organs can be filled via efficient translocation. Photosynthates generated after heading are responsible for 60-90% of the total carbon accumulated in rice panicles at harvest, while 70−90% of total panicle nitrogen uptake occurs before heading and is subsequently remobilized from leaf to grain during monocarpic senescence (Mae, 1997; Yue et al., 2006). Both persistence of high photosynthetic capacity and efficient nitrogen remobilization during grain filling, therefore, have been considered key factors in increasing grain yield (Abdelkhalik et al., 2005; Yamaya et al., 2002). Stay-greenness (or delayed senescence) during the final stage of leaf development is an important trait in increasing source strength in grain production, and its physiological and genetic bases have been studied in several plants. Thomas and Howarth (2000) classified five staygreen phenotypes according to their senescing behaviors. Stay-green also can be generally divided into two groups, functional and nonfunctional. Functional stay-green is defined as retaining both leaf greenness and photosynthetic competence much longer during senescence than Abbreviations: cM, centiMorgan; DH lines, doubled haploid lines; LOD, logarithm of odds; PCR, polymerase chain reaction; PSII, photosystem II; QTL, quantitative trait loci; RFLP, restriction fragment length polymorphism; RIL, recombinant inbred line; SSD, single seed decent; SSR, simple sequence repeat.
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QTLs for Functional Stay-Green Rice
the wild-type, while nonfunctional stay-green is defined as maintaining only leaf greenness. Park and Lee (2003) found a stay-green variant in a japonica rice collection designated ‘SNU-SG1’. It was classified as a functional Type B stay-green japonica rice in which leaf senescence initiates on schedule but leaf photosynthetic rate and chlorophyll content decrease much more slowly during senescence than those of high-yielding cultivars. Because of the potential contribution of the stay-green trait to increased crop production, it has been intensively studied in many crops such as sorghum (Walulu, 1994), soybean (Pierce et al., 1984), maize (Gentinetta et al., 1986), Phaseolus vulgaris (Fang et al., 1998), durum wheat (Spano et al., 2003) and potato (Schittenhelm et al., 2004). Some reports suggest that functional stay-green arise from delays in the initiation or rate of senescence. Functional stay-green genotypes have been reported in durum wheat, which maintains longer photosynthetic activity, has higher seed weights, and yields more grain than the parental genotype (Spano et al., 2003). One of the staygreen lines, Trinakria (designated 504), was confirmed as a functional stay-green by analyzing the differential expression profile of photosynthetic parameters between staygreen and normal parental lines (Rampino et al., 2006). Schittenhelm et al. (2004) suggested that a transgenic potato plant, Dara-5, overexpressing phytochrome B has delayed onset of senescence and then shows normal declines in leaf chlorophyll and protein concentrations, leaf carbon exchange rate, and Rubisco activity. Thus, Dara-5 was classified as a functional Type A stay-green. Nonfunctional stay-green mutants have been also studied intensively in many plants. The sid (senescence-induced-deficiency) mutant of Festuca pratensis is the most intensively-studied nonfunctional stay-green (Thomas, 1987; 1997; Thomas and Stoddart, 1975). Cha et al. (2002) mapped a single recessive mutant gene, sgr, specifying nonfunctional staygreen to the long arm of chromosome 9 in rice. Armstead et al. (2006; 2007) recently reported that the stay-green sid locus in F. pratensis is syntenically equivalent to the sgr locus on rice chromosome 9, and genetic mapping of the green cotyledon color in peas demonstrated co-segregation of the pea Sgr locus with the yellow/green cotyledon polymorphism (I/i) reported by Gregor Mendel in 1866. The functional stay-green trait is generally regulated by complex factors: senescence-related genes and environmental factors. The identification of quantitative trait loci (QTLs) is, therefore, a useful approach to elucidating the molecular basis of functional stay-green. There have been a number of reports of QTLs affecting stay-green-related traits in plants. In sorghum, a stay-green genotype is considered resistant to post-flowering drought stress (Rosenow et al., 1983), and QTL mapping studies using RILs and NILs revealed both main-effect and epistatic QTLs (Harris et al., 2007; Sanchez et al., 2002; Xu et al., 2000). In Arabidopsis, four QTLs for post-bolting longevity were
found on chromosomes 1, 3, 4 and 5 using 155 RILs derived from a cross of Cape Verde Islands (Cvi)/Landsberg erecta (Ler) (Luquez et al., 2006). In rice, six QTLs for chlorophyll content were detected on five chromosomes using a backcross line (Ishimaru et al., 2001) and three QTLs for chlorophyll content on three chromosomes using a double haploid population derived from an indica and japonica hybrid [Teng et al. (2004)]. Jiang et al. (2004) analyzed the genetic basis of stay-green using doubled haploid (DH) lines derived from an indica/japonica cross and detected 46 main-effect QTLs in 25 chromosomal regions and 50 digenic interactions involving 66 loci on 12 chromosomes. Yue et al. (2006) also reported that six QTLs for degree of greenness and fourteen QTLs for stay-greenrelated traits during monocarpic senescence were resolved using RIL populations. The relationship between the stay-green trait and crop yield has been analyzed in some plants. Although the contribution of the stay-green genotype to stable yield production under drought stress has been studied in sorghum (Borrell et al., 2000), a meaningful correlation between leaf stay-greenness and grain yield increase has not yet been reported in rice. On the contrary, a negative relationship was reported (Jiang et al., 2004; Yue et al., 2006). In this study, we determined the genetic basis of SNU-SG1 and identified main- and epistatic-effect QTLs conferring a functional stay-green using F2 and RIL populations generated from a cross of the stay-green japonica rice ‘SNUSG1’ and the high-yielding tongil-type cultivar ‘Milyang23’. Genetic correlations between the stay-green trait and yield and yield components were also analyzed. Here we describe three QTLs detected in both F2 and RIL populations and novel QTLs for the functional stay-green trait in rice. In particular the QTLs on chromosome 9 representing most of the stay-green traits with large phenotypic variation should be useful for developing a MAS technique for a functional stay-green molecular breeding program.
Materials and Methods Plant materials and field conditions The functional stay-green rice, SNU-SG1, which has high chlorophyll content and delayed leaf senescence was first discovered in a field performance test with japonica rice collections introduced from China, at the Seoul National University Experimental Farm in 2001 (Park and Lee, 2003). In order to study its inheritance, SNU-SG1 was crossed with a regular japonica cultivar, Ilpumbyeo, that has a similar heading date. The two parents, F1s, and 252 F2 individuals from the cross were used to analyze the inheritance and the correlation between the stay-green trait and the yield and yield components. To identify the QTLs conferring functional staygreen, SNU-SG1 was crossed with a high-yielding tongil-type cultivar, Milyang23 (M23), which is derived from an indica/
Soo-Cheul Yoo et al. japonica cross and is similar to indica in its genetic make-up (Lee et al., 2006). A ‘selective genotyping’ method was used in the F2 analysis to test the suitability of the mapping population for detecting stay-green QTLs and identify major QTLs in that population. Among 235 F2 individuals from the SNU-SG1/M23 cross, we selected for genotyping 46 that had extreme phenotypes (23 lines with extremely delayed senescence and 23 lines exhibiting early yellowing during grain filling) but similar heading dates. The two F2 populations, F1s, and parental lines were planted in the Seoul National University Experimental Farm in 2002. The RIL population was developed via the single seed descent (SSD) method through the F6 generation. A population of 425 F6 RILs was developed and 92 individuals with extreme phenotypes (46 with extremely delayed senescence and 46 with early yellowing during grain filling), with little variation of heading date, were used to identify consistent main-effect QTLs. The parents, F1s, and RILs were planted in different blocks within the Experimental Farm. Field management followed normal rice practice in Korea. The rice field was regularly irrigated to avoid drought stress to the late-maturing F2 and RIL lines. Measurement of stay-green traits The chlorophyll contents of flag and second leaves of each F2 line were measured at heading and thereafter four times at 10-d intervals using a Minolta Chlorophyll Meter SPAD-502 (Minolta, Japan), an indirect indicator of chlorophyll content. To ensure that the measurements were taken on the correct day for the right tiller, tillers were tagged on the heading date. SPAD readings were taken by measuring three panicles per plant and at least three parts of each leaf. Chlorophyll contents of flag and second leaves measured on the heading date were designated as DCF and DCS, respectively, and DCFS was based on the average of DCF and DCS to complement the functional stay-green trait. Cumulative chlorophyll contents of flag (CCF) and second leaves (CCS) were calculated by summing the first to fourth SPAD values. SPAD readings of flag and second leaves were only measured in the RIL population on the heading day using the same method as in F2 population. Measurement of chlorophyll fluorescence, and yield and yield components Photosynthetic activities were measured with a portable PAM2000 chlorophyll fluorometer (Heinz Walz, Germany) as described by Fukushima et al. (2001). Minimum fluorescence (F0) and maximum fluorescence (Fm) were measured in the dark-adapted leaves, using a two-second light pulse (3000 µmol photons−2 s−1 in the range of 350 to 700 nm) to saturate all photosystem II (PSII) reaction centers. The photochemical efficiency of PSII was calculated as the ratio of variable fluorescence (Fv = Fm – Fo) to maximum fluorescence (Fm) to determine the potential activity of PSII (Fv/Fm) as previously described (Genty et al., 1989; Kooten and Snel, 1990). Yield and yield components were examined by measuring: grain yield per plant as the total grain weight (g) per plant, the number of reproductive tillers per plant, the number of grains per panicle, 1000-grain weight (g), and seed-setting rate. Seed-setting rate (or fertility) was scored as the number of grains divided by total
85
number of spikelets from the reproductive tillers of a plant, with three replications. Trait measurements averaged for the three replications were used in the analyses. Molecular makers Leaf tissue was harvested from each line at the maximum tillering stage. Genomic DNA was extracted using the CTAB method described by Murray and Thompson (1980). SSR and STS markers were used for map construction. The markers of the RM-series were designed according to Temnykh et al. (2000; 2001), and those of the S-series were based on the sequence differences between japonica and indica rice, using information available from the Crop Molecular Breeding Lab, Seoul National University (unpublished). The markers showing polymorphism between the parents and having a good coverage of 12 chromosomes were used to assay both populations. The DNA amplification protocol comprised 5 min at 94°C, followed by 35 cycles of 1 min at 94°C, 1 min at 55 or 60°C, 1 min at 72°C and a final extension for 5 min at 72°C in a thermocycler (MJ Research, USA). PCR was performed with 50 ng of genomic DNA, 0.2 μM of each primer and 1 unit of Taq DNA polymerase in a 20 μl reaction volume. PCR products were resolved on 2.5% agarose gels. Linkage map construction and data analysis A linkage map was constructed using Mapmaker 3.0 (Lander et al., 1987; Lincoln et al., 1993) and MapChart 2.0. Distances between markers are given in centiMorgans (cMs) using the Kosambi map function (Kosambi, 1944), and the order of markers was established by three-point analysis. The chromosomal location of maineffect QTLs and epistatic interactions were determined by interval mapping using a mixed linear model and a QTL Mapper version 2.0 software (Gao et al., 2004). To determine the empirical significance threshold for declaring a QTL, 5000 permutations were performed to calculate LOD thresholds for each trait at p = 0.05 and p = 0.01 using the Qgene 3.06 software for Macintosh (Nelson, 1997). The proportion of phenotypic variation explained by each QTL was calculated as the R2 value, and the degree of dominance of a QTL was estimated as the ratio of dominance effect to additive effect (D/A).
Results Characterization of the stay-green trait in SNU-SG1 SNU-SG1 exhibited delayed senescence during grain filling compared to the two regular domestic cultivars, Ilpumbyeo and Milyang23 (M23), used in this study (Fig. 1). Temporal changes in chlorophyll content showed that SNU-SG1 maintained chlorophyll content much longer during monocarpic senescence than the two parental varieties (Fig. 2A). To evaluate photosynthetic competence, we measured the Fv/Fm ratio representing the efficiency of PSII, because photosynthesis depends on the function of light-harvesting and electron transport systems within the chloroplasts. SNU-SG1 maintained values of Fv/Fm close
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QTLs for Functional Stay-Green Rice
Table 1. The status of the populations used in this study. F2 population Crossesa SNU-SG1/M23 SNU-SG1/Ilpum
RIL population
No. of progenyb
Selected progenyc
No. of progeny
Selected progeny
Purpose
Cross Typed
235 252
46 -
425 -
92 -
Mapping Phenotyping
japonica/tongil japonica/japonica
a
M23, Milyang23; Ilpum, Ilpumbyeo. No. of progeny: total number of progeny in the population. c Selected progeny, the number of progeny used for map construction. d tongil, a hybrid rice cultivar of japonica and indica.
b
Table 2. Descriptive statistics for the stay-green traits in the parents and F1s of the two crosses. Traita DCF
SNU-SG1/M23b SNU-SG1
M23
046.3 ± 1.3
39.2 ± 2.0
SNU-SG1/Ilpumbyeo F1 38.6 ± 3.1
SNU-SG1
Ilpumbyeo
F1
046.1 ± 1.6
039.0 ± 3.3
041.9 ± 2.0
DCS
046.7 ± 1.5
38.6 ± 1.9
37.8 ± 2.8
047.1 ± 1.4
039.7 ± 2.0
043.6 ± 1.8
DCFS
046.5 ± 1.3
38.9 ± 1.9
38.2 ± 2.9
046.6 ± 1.3
039.2 ± 2.6
042.8 ± 1.9
CCF
132.8 ± 3.4
94.8 ± 5.4
99.4 ± 5.0
131.6 ± 3.2
101.2 ± 8.1
115.5 ± 9.9
CCS
133.0 ± 5.2
91.3 ± 6.9
96.3 ± 3.0
131.9 ± 4.3
104.1 ± 5.7
115.2 ± 6.4
CCFS
132.9 ± 4.3
93.0 ± 6.1
97.9 ± 4.0
131.8 ± 3.8
102.7 ± 6.9
115.4 ± 8.2
a
DCF, degree of chlorophyll content of flag leaf at the heading date; DCS, degree of chlorophyll content of the second leaf at the heading date; DCFS, degree of mean chlorophyll content of the flag and second leaves; CCF, cumulative chlorophyll content of the flag leaf; CCS, cumulative chlorophyll content of the second leaf; CCFS, mean of cumulative chlorophyll contents of the flag and second leaves. b Mean ± standards deviation of SPAD readings for the parent and F1 plants. Each pair of parents showed statistically significant differences at the 0.01 probability level.
A
B
Fig. 1. Temporal changes in leaf color during grain filling in Milyang23 (M23), SNU-SG1 and Ilpumbyeo (Ilpum). Fieldgrown plants were transferred to pots and photographed at heading (left panel), and 50 DAH (right panel). The plant color differences between Milyang23, Ilpumbyeo and SNU-SG1 became significant at 50 DAH. The heading date of SNU-SG1 was 4 d earlier than Ilpumbyeo and Milyang23. DAH, days after heading.
to 0.80, which is the typical potential efficiency of PSII in non-stressed plants (Larcher, 2003), for 42 d after heading (DAH), while the values in the other two varieties decreased rapidly after 35 DAH (Fig. 2B). Table 2 shows
the descriptive statistics of the functional stay-green traits for two parental pairs and their corresponding F1s. Important differences were found for all traits between the parents. The phenotypic value of the F1s from the SNUSG1/Ilpumbyeo cross was near the mid-parent score, while that of the F1s from the SNU-SG1/M23 cross was close to that of the mapping parent M23. However, this recessiveprone pattern of the stay-green trait in the SNU-SG1/M23 cross is probably caused by environmental factors, not genetic factors since the heading dates of the F1 plants in the field were delayed by about 2 weeks compared to their parents, as often observed in indica/japonica hybrids. The phenotypic distributions of all traits in both the F2 and RIL populations (Table 3) demonstrate that both populations exhibited an almost normal distribution for the stay-green trait, indicating a quantitative mode of inheritance of functional stay-green in SNU-SG1. Relationship between stay-green trait and the yield and yield components The stay-green trait in SNU-SG1 was positively correlated with seed-setting rate and grain yield, but there was no significant correlation with other yield components including tillers per plant, grains per panicle, or grain weight. This indicates that the functional stay-
Soo-Cheul Yoo et al.
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Table 3. Phenotypic values of parents, F1 plants, F2 populations, and RIL population for the stay-green traits studied. Population F2
RIL
Traita
SNU-SG1/M23 Range
b
Mean
Kurtosis
SNU-SG1/Ilpumbyeo Skewness
DCF
29.0−49.5
039.0
−0.63
00.04
DCS
28.8−53.0
039.4
−0.50
−0.02
DCFS
29.2−50.8
039.2
−0.61
00.01
CCF
70.1−146.7
108.4
−0.54
−0.01
CCS
58.8−142.1
105.0
−0.37
−0.18
CCFS
65.7−143.8
106.7
−0.49
−0.09
DCF
21.8−62.3
038.3
−0.12
00.22
DCS
20.6−60.0
038.2
00.10
00.09
DCFS
21.2−61.2
038.3
−0.03
00.15
Range
Mean
Kurtosis
Skewness
30.8−51.7
038.9
00.46
0.28
83.9−149.4
113.40
−0.07
0.08
a
See footnote to Table 2. b The numbers in each of the cells indicate the range of SPAD readings.
A
B
Fig. 2. Temporal changes in chlorophyll content and photosynthetic competence of the parents and F1s from the two crosses, SNU-SG1/Ilpumbyeo and SNU-SG1/M23. A. Changes in chlorophyll content of the senescing leaves. Chlorophyll content was measured with a SPAD-502 chlorophyll meter. M23: Milyang 23. B. Efficiency of PSII shown as a ratio of the variable to maximal chlorophyll a fluorescence at ambient temperature in dark-adapted leaves.
green trait in SNU-SG1 contributes to increased grain yield by enhancing seed-setting rates (Table 4). Linkage maps of F2 and RIL populations A total of 145 polymorphic SSR primer sets, out of 250 sets applied, were polymorphic between SNU-SG1 and M23, and 100 and 116 SSR loci in the F2 and RIL populations, respectively, with a good coverage of all 12 chromosomes, were selected to assay the entire population. A linkage map of 100 SSR markers in the F2 population in 12 linkage groups was constructed using Mapmaker 3.0 (data not shown). The map covered 1301.9 cM with an average distance of 13 cM between markers, which is less than the minimum required level, 20 cM, for QTL mapping (Lander and Botstein, 1989). In the RIL population, a linkage map of 116 SSR markers was constructed in 12 linkage groups, which spanned 976.3 cM with an average interval of 8.4 cM between adjacent markers (data not shown). Comparison between the resulting linkage maps and the previous maps (Temnykh et al., 2000) revealed that almost all of the markers were located in the expected order on the twelve chromosomes. Degree of chlorophyll content at the heading date in the F2 population Interval mapping identified a total of eight main-effect QTLs over the LOD thresholds that were raised through permutation test p = 0.05 (Table 5 and Fig. 3) for the traits that were associated with chlorophyll content at the heading date across the 12 chromosomes. Two QTLs, dcf3 and dcf9, were detected for DCF on chromosomes 3 and 9, respectively, and only one QTL was detected for DCS on chromosome 3. For DCFS, the trait derived from the mean of DCF and DCS, five QTLs were independently resolved on five chromosomes. At all of the QTLs, the alleles from SNU-SG1 genotype had a positive effect on the three traits (DCF, DCS and DCFS),
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QTLs for Functional Stay-Green Rice
Table 4. Correlations of the stay-green traits with yield and yield-component traits analyzed in the SNU-SG1/Ilpumbyeo and SNUSG1/M23 F2 populations. Traits CCF Yield Tillers/plant Grains/panicle Seed setting (%) 1000 Weight
DCF 00.90** (0.91**) 00.23** 00.01 00.01 00.29** −0.09
CCF
Yield
Tillers/plant
Grains/panicle
Seed setting (%)
00.13* −0.04 −0.05 00.24** −0.18**
0.63** 0.34** 0.56** 0.33**
00.11 00.20** −0.01
0.21** 0.08
0.29**
A total of 235 F2 individual lines was used to analyze the traits. * and ** mean significant at P < 0.05 and P < 0.001 levels, respectively. The figure in parenthesis gives the correlation derived from the SNU-SG1/M23 cross.
Table 5. Main-effect QTLs for the traits related to stay-green, resolved using QTL Mapper 2.0 in the F2 population derived from the SNU-SG1/M23 cross with the LOD thresholds raised through permutation tests p = 0.05 and 0.01. D/A
R2d (%)
−1.56 −0.12
−0.29 −0.02
13.42 11.24
05.16
−1.10
−0.21
24.66 14.20
04.70 08.77 04.70 03.85 09.59
02.41 04.58 −3.13 03.67 05.20
−1.75 −1.16 00.71 −0.68 00.10
−0.73 −0.25 −0.23 −0.19 00.02
14.20 03.56 10.52 04.88 06.65 13.14
RM3867-S03136 RM16-RM282
06.59 12.34
14.33 23.72
−5.66 −2.23
−0.39 −0.09
38.75 06.26 15.99
Total 3 6 9
RM282-RM251 RM253-RM587 RM257-RM566
09.04 04.60 05.61
15.08 −9.76 12.54
−1.10 −2.22 −0.82
−0.07 00.23 −0.07
22.25 13.92 05.96 09.62
Total 3 9
RM16-RM282 RM257-RM566
12.23 04.85
22.59 11.53
−2.09 00.27
−0.09 00.02
29.50 36.45 09.45
Intervalb
LOD
Ac
3 9
RM282-RM251 RM257-RM566
10.36 07.58
05.33 04.98
dcs3
Total 3
RM16-RM282
07.74
DCFS
dcfs1 dcfs3 dcfs6 dcfs7 dcfs9
Total 1 3 6 7 9
RM24-RM9 RM282-RM251 RM253-RM587 RM455-RM10 RM257-RM566
CCF
ccf3a ccf3b
Total 3 3
CCS
ccs3 ccs6 ccs9
CCFS
ccfs3 ccfs9
Trait
QTL
DCF
dcf3 dcf9
DCS
Chra
Total
Dc
Permutatione 95%
99%
3.67
4.57
3.73
4.56
3.64
4.61
3.70
4.38
3.70
4.55
3.66
4.55
45.90
a,b
Chromosome number and marker intervals. c A and D are additive and dominant effects, and the positive values indicate the alleles from SNU-SG1 that increase the trait score. d Phenotypic variation rate explained by the detected QTLs for each trait. Bold letters indicate the QTLs detected in both populations. e The numbers in each of the cells indicate the LOD thresholds that are raised through permutation tests p = 0.05 and 0.01.
except for dcfs6, at which the Milyang23 genotype contributed to increased DCFS. The additive effect of the QTLs ranged from 2.41 to 5.33 SPAD units for the three traits. Taken together, these QTLs explained 24.7, 14.2 and 38.8% of the phenotypic variation for DCF, DCS and
DCFS, respectively. A total of seven digenic interactions were also detected for the three traits (Table 6). Six of the seven pairs involved loci with at least one significant QTL main-effect. Overall, the epistatic effects accounted for 68.9, 40.9 and 81.1% of the total phenotypic variation
Soo-Cheul Yoo et al.
89
Fig. 3. Locations of the main-effect QTLs on the molecular linkage map as detected by QTL Mapper 2.0. This linkage maps show only the regions where QTLs were detected. The number at the top indicates the chromosome number together with the population type.
of DCF, DCS and DCFS, respectively. Cumulative chlorophyll content to 30 DAH in the F2 population Seven main-effect QTLs across 12 chromosomes were identified for three cumulative chlorophyll content-related traits (CCF, CCS and CCFS) (Table 5 and Fig. 3). Two QTLs, ccf3a and ccf3b, were detected for CCF on chromosome 3 and explained 22.3% of the phenotypic variation. Three QTLs were detected for CCS on chromosomes 3, 6, and 9 and explained 29.5% of phenotypic variation. For CCFS, two QTLs were resolved on chromosomes 3 and 9, explaining 45.9% of phenotypic variation. Alleles from SNU-SG1 at all of the QTLs were responsible for a positive effect on the three traits. The additive effect of the QTLs ranged from 9.76 to 23.72
cumulative SPAD units. A total of nine pairs of loci were also detected as they showed significant epistatic interactions for the three traits (Table 6). Eight of the nine pairs involved loci that had at least one significant QTL maineffect. Altogether, the epistatic effects accounted for 45.7, 94.2 and 67.0% of total phenotypic variation for CCF, CCS and CCFS, respectively. Degree of chlorophyll content at the heading date in the RIL population We found strong correlations (more than 90%) between DCF and CCF in both F2 populations (Table 4) and the genetic loci of all the QTLs for cumulative chlorophyll content overlapped the QTL regions for chlorophyll content at the heading date, except for one QTL region (Table 5). In the light of the results from
90
QTLs for Functional Stay-Green Rice
Table 6. Digenic epistatic effects for stay-green analyzed with QTL Mapper 2.0 in the F2 population derived from the SNU-SG1/M23 cross. Traits Chr DCF
DCS
Interval i
Chr
Interval j
LOD
Aia
Dia
Aja
Dja
AAijb ADijb DAijb DDijb
2 RM207-RM250 2 RM207-RM250 3 RM282-RM251 Total
03 RM175-RM231 09 RM257-RM566 05 RM421-RM305
08.09 09.59 12.41
03.6** 004.4* 5.2** 7.1*** 0−4.8* 0−3.7*
−5.7* 05.5*
3 RM16-RM282 Total
09 RM257-RM566
10.06
4.2*
06.2*
DCFS 2 6 9 Total CCF 3 3 Total
RM207-RM250 RM253-RM587 RM257-RM566
03 RM282-RM251 09 RM257-RM566 11 RM202-RM167
11.69 10.99 10.91
RM3867-S03136 RM16-RM282
04 RM273-RM5979 08 RM284-RM339
08.45 16.8*** 11.99 17.1***
CCS
RM16-RM282 RM16-RM282 RM282-RM251 RM282-RM251
06 09 06 09
12.09 13.31 12.16 13.53
RM424-RM8 RM16-RM282 RM257-RM566
06 RM527-RM276 09 RM566-S09040 11 RM21-RM229
3 3 3 3 Total
CCFS 2 3 9
RM253-RM587 RM257-RM566 RM253-RM587 RM257-RM566
0−5.1* −3.3*
7.0*** 5.5**
15.1** 17.1** 15.0*** 14.7**
03.5*
40.9 40.9
14.7 32.5 14.4 32.7 94.2
15.0** 15.8**
Total
07.5 25.3 36.2 68.9
23.7 15.1 42.3 81.1 20.6 25.1 45.7
−7.8*
07.11 15.33 24.2*** −18.4* 6.1 07.31 12.7***
R2c
19.0** −14.8*
30.0**
26.3 34.7 06.0 67.0
a
Ai and Aj are additive effects, and Di and Dj are dominant effects of loci i and j, respectively. A positive value indicates that the SNU-SG1 genotype has a positive effect on the trait. b AAij, ADij, DAij and DDij are the effects of additive by additive, additive by dominant, dominant by additive, and dominant by dominant interactions between loci i and j, respectively. c 2 R is the proportion of the total phenotypic variation explained. *, **, and *** mean significant at P < 0.05, P < 0.001, and P < 0.0001, respectively. Bold letters indicate similar positions to the loci detected by main-effect QTLs.
these analyses, we performed the QTL analysis by measuring the stay-green traits at only the heading date in the RIL population. The analysis identified 12 main-effect QTLs for the three traits related to chlorophyll content (Table 7 and Fig. 3). Four QTLs were detected for DCF across four chromosomes. The ‘SNU-SG1’ alleles at two of the QTLs (dcf7 and dcf9) increased DCF, while at the other two, the QTLs alleles from ‘M23’ contributed to chlorophyll content. Five QTLs were independently detected for DCS on five chromosomes. At two (dcs7 and dcs9) of these, the SNU-SG1 alleles had positive effects on DCS. For DCFS, four QTLs were resolved on chromosomes 5, 7, 9 and 12, and the alleles from SNU-SG1 at the QTLs on chromosomes 7 and 9 were responsible for increasing chlorophyll content. The additive effect of the QTLs ranged from 2.08 to 5.25 SPAD units for DCF, 2.0 to 6.29 for DCS and 3.14 to 5.07 for DCFS. Together, these QTLs explained 35.7, 65.0 and 64.9% of the total
phenotypic variation for DCF, DCS and DCFS, respectively. Epistatic effects were detected for 16 pairs of the loci, which together explained 86.8, 74.3 and 77.5% of phenotypic variation for DCF, DCS and DCFS, respectively (Table 8). Among the 19 pairs of epistatic-effect QTLs, 14 pairs involved at least one significant maineffect QTL. The estimated effects of epistasis showed that the recombinant two-locus genotypes for all five pairs of the loci had positive effects on the degree of cumulative chlorophyll content.
Discussion Preparation of the F2 and RIL populations In some cases there are serious problems in investigating physiological traits without changing inherent features of the F2 population; this is presumably due to digenic factors such
Soo-Cheul Yoo et al.
91
Table 7. Main-effect QTLs for the traits related to stay-green, resolved using QTL Mapper 2.0 in the RIL population derived from the SNU-SG1/M23 cross with the LOD thresholds raised through a permutation test, p = 0.05 and 0.01. Trait
QTLa
Chr
Intervalb
LOD
Ac
DCF
dcf4 dcf5 dcf7 dcf9
4 5 7 9
RM255-RM303 RM440-RM430 RM505-RM455 RM566-S09040
03.3 08.8 08.09 15.39
−2.08 −3.8 04.39 05.25
DCS
dcs3 dcs5 dcs7 dcs9 dcs12
3 5 7 9 12
RM251-RM489 RM440-RM430 RM505-RM455 RM566-S09040 RM277-S12030
04.05 11.62 09.24 18.34 03.04
−2.2 −3.93 03.92 06.29 −1.98
DCFS
dcfs5 dcfs7 dcfs9 dcfs12
5 7 9 12
RM440-RM430 RM505-RM455 RM566-S09040 RM277-S12030
12.14 06.4 11.66 04.79
−4.91 03.85 05.07 −3.14
R2d (%) 02.35 07.85 10.47 14.98 35.65 03.97 12.68 12.62 32.49 03.22 64.98 21.0 12.91 22.39 08.59 64.89
Permutatione 95% 99% 2.82 3.63
2.77
3.51
2.87
3.65
a−e
See footnote to Table 5. The bold format denotes QTLs detected in both F2 and F6 RIL populations.
as hybrid sterility and/or hybrid breakdown that result from inter-subspecies crosses between indica and japonica rice (Li et al., 1997). To overcome this problem, we constructed two F2 populations: one for investigating the inheritance mode and the correlation between the staygreen trait and the yield and yield components from the japonica/japonica cross, and the other for QTL identification using the indica/japonica cross (Table 1). For QTL identification, RIL and DHL (double haploid line) populations are preferred over F2 population because the progenies of RIL and DHL are homozygous lines that make it possible to replicate the QTL analysis under different environmental conditions. QTL analysis of both F2 and RIL populations derived from a cross has been applied in many genetic studies, because it yields consistent QTL detection (Tan et al., 2000). In this study, we also performed a QTL analysis for the stay-green trait in SNUSG1 using both F2 and RIL populations. The experiments on the F2 population were designed to examine the suitability of the mapping population derived from the SNUSG1/M23 cross for detecting QTLs for the functional stay-green trait by observing the characteristics of the population, such as inheritance mode, skewness and kurtosis (Table 3), and then by conducting QTL analysis with a relatively small number of selected progeny. The result of QTL analysis from the F2 population was then compared to the QTLs obtained from the RIL population. To improve the efficiency of QTL mapping, selective genotyping, which uses only individuals exhibiting high and low
phenotypic extremes (Darvasi and Soller, 1992), was used for QTL mapping in both F2 and RIL populations. This selective genotyping method has been used in various species to reduce cost, time, and labor. It has been shown to be most appropriate for cases where only one trait is being analyzed (Darvasi, 1997). In our study, selective genotyping was an effective strategy for the functional stay-green analysis because we investigated only one trait (chlorophyll content of leaves) and selected progeny with similar heading dates to reduce the influence of environmental factor as well as the cost of genotyping. The QTL analysis The analyses resolved a total of 15 main-effect QTLs for all six traits investigated, and 16 epistatic effects involving a total of 15 loci located on eight chromosomes in the F2 population (Tables 5 and 6). In the RIL population, we detected a total of 13 maineffect QTLs for all three traits and 19 epistatic interactions involving a total of 25 loci located on eleven chromosomes (Tables 7 and 8). The amount of variation explained by epistasis for all stay-green traits analyzed was much greater than that explained by the main-effect QTLs in both populations. This result suggests that the phenotypic variation of the QTL on stay-green traits is explained by a combination of the main-effect and epistatic effects, and that selection for higher stay-green plants will be more effective when main-effect and epistatic-effect QTLs are considered simultaneously. In the present study, three main-effect QTLs were consistently discovered on
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QTLs for Functional Stay-Green Rice
Table 8. Digenic epistatic effects for stay-green analyzed using QTL Mapper 2.0 in the F6 population derived from the SNU-SG1/M23 cross. DCF
DCS
DCFS
a−c
Chr
Name
Chr
Name
LOD
1 1 1 1 5 5 6
RM246-RM306 RM306-RM9 RM306-RM9 RM259-RM1 RM440-RM430 RM440-RM430 RM275-RM162
5 4 5 5 9 12 7
S05054-S05030A RM255-RM303 S05054-S05030A RM480-RM421 RM201-S09073 S12109B-S12056 RM505-RM455
5.96 4.36 5.38 4.45 7.88 9.76 10.03
9 Total 1 3 3 5 7 7 Total
RM566-S09040
11
RM202-RM167
16.5
RM297-RM246 RM514-RM143 RM251-RM489 S05030A-RM437 RM505-RM455 RM10-RM11
5 9 7 8 8 9
RM440-RM430 S09040-S09026 RM505-RM455 RM80-RM339 RM339-RM44 RM566-S09040
10.78 4.88 14.07 4.99 8.69 18.11
RM85-RM514 RM143-RM3867 RM440-RM430 S05054-S05030A RM201-S09073
7 9 10 7 12
RM505-RM455 RM566-S09040 RM467-RM6142 RM505-RM455 RM277-S12030
6.36 11.34 10.95 8.32 5.08
3 3 5 5 9 Total
Aia
Aja
AAijb
R2c
−3.47***
−1.91** −3.53*** −3.98*** 5.54***
8.63 −2.54*** 4.67 −3.12*** 7.15 3.76 9.22 −2.04** 14.74 04.61*** 15.69 −1.26* −4.36*** −2.7***
−2.23** −1.47* 3.96***
04.63*** −2.2* 06.43***
1.48* −4.47*** −1.88*
03.91*** 04.54*** 04.19*** −3.39***
27.92 86.78 11.76 5.57 16.21 4.35 10.37 26.15 74.31 12.85 19.34 17.11 17.86 10.36 77.52
See footnotes to Table 6.
two chromosomes in both the F2 and RIL populations (Fig. 3): dcfs7 was located near RM455 on the long arm of chromosome 7, and the other two QTLs, dcf9 and dcfs9, were located in an adjacent region of RM566 on chromosome 9. dcfs7 may correspond to rgaf7 that controls retention of the green area of the flag leaf, in the RM11-RM346 interval (Jiang et al., 2004). The other two consistent QTLs, dcf9 and dcfs9, could also be equivalent to rdgf9 affecting retention of greenness of flag leaf, flanking RM257 (Jiang et al., 2004), and QDg9 (RM434-RM257) affecting leaf greenness (Yue et al., 2006), respectively. The QTL regions flanking RM251 on the long arm of chromosome 3 were also found to contain main-effect QTLs in both populations but their additive effects were opposite, indicating that they are different QTLs located in an adjacent region. Furthermore, dcfs6 for the degree of mean chlorophyll content of flag and second leaves, detected in the interval of RM253-RM587, appears to correspond to the QTLs for relative retention of leaf greenness of the second leaf and reduced chlorophyll content discovered on the short arm of chromosome 6 by Jiang et al. (2004) and Abdelkhalik (2005), respectively. The ‘SNUSG1’ allele contributed to stay-green at all of the QTLs for stay-green-related traits in the F2 population, with the
exception of one QTL on chromosome 6. In the RIL population, dcf4 for the degree of chlorophyll content of flag leaf detected in the interval of RM255-RM303 on the chromosome 4 might be related to QDg4b in the interval of MRG4503-RM255 discovered by Yue et al. (2006), and dcfs12 for the degree of mean chlorophyll content of the flag and second leaves in the region of RM277S12030 on chromosome 12 could be related to rrgf12 for the relative retention of greenness of the flag leaf in the region of RM277-RM309 analyzed by Jiang et al. (2004). The ‘SNU-SG1’ allele contributed to stay-green at the QTLs detected on chromosomes 7 and 9 in the RIL population, but the ‘M23’ allele positively affected chlorophyll content at the other QTLs. Novel main-effect QTLs in two chromosomal regions were also observed. The QTLs for the stay-green traits in the regions of RM24-RM9 on chromosome 1 and RM440-RM430 on chromosome 5 were detected in the F2 and RIL populations, respectively. The QTLs detected on chromosome 5 in the RIL population showed strong main-effects for all three stay-green traits with a relatively high LOD score. The functional stay-green trait in rice Leaf stay-greenness with delaying senescence has been of great importance in
Soo-Cheul Yoo et al.
increasing crop yield production. However, it has been claimed that the stay-green traits are negatively correlated with yield and yield components in maize and rice (Bolanos et al., 1996; Jiang et al., 2004; Yue et al., 2006). Jiang et al. (2004) suggested that this effect was due to partial sterility of some of the individuals of the mapping population resulting from the inter-subspecies crosses used for constructing the mapping population; this would impair translocation of carbohydrates and nitrogen from leaves to the panicles and consequently cause a lower seed-setting rate accompanied by greener leaves. The effect could be due to the use of a digenic stay-green type that is affected by physiological factors such as partial sterility and hybrid breakdown resulting from the intersubspecies cross. Abdelkhalik et al. (2005) reported that the hybrid sterility problem, occurring frequently in the indica/japonica crosses and affecting both grain-filling process and senescence, was overcome by using NK2 (japonica), which has a wide compatibility gene (Yanagihara et al., 1995), as the stay-green parent, but the correlation between the stay-green traits and grain yield at harvest was not investigated. In this study, we used the intrasubspecies cross, japonica/japonica, to investigate the inheritance mode and the genetic correlation between the stay-green traits and the yield and yield components. The intra-subspecies cross helps reduce potential side-effects which might occur in the inter-subspecies cross, such as partial sterility. The stay-green traits of flag leaf, DCF and CCF, were positively correlated with grain yield and seedsetting rate. In particular, seed-setting rate was more highly correlated with the stay-green trait than yield, indicating that the stay-green trait contributes to improving seedsetting rate, followed by grain yield. Grain yield is most likely to be achieved by simultaneously increasing both source (photosynthetic rate) and sink (partitioning to grain) strengths. Park and Lee (2003) measured chlorophyll content and photosynthesis at light saturation (Pmax) in SNU-SG1 and two other rice varieties. The Pmax values of flag and second leaves in SNU-SG1 were over 20% higher than those of the other varieties during grain ripening. In this respect, it is reasonable to suppose that SNUSG1 contributes to grain yield by improving source strength through maintaining a high chlorophyll content and photosynthetic competence of flag and second leaves during grain filling as well as by increasing sink strength via the high seed-setting rate. In fact, SNU-SG1 had a high seed-setting rate (over 95%; data not shown). In spite of its potential importance, there has been no report that functional stay-green improves grain yield as well as increasing source and sink. Therefore we conclude that SNU-SG1 could be used as a desirable genetic source of functional stay-green which increases crop production by maintaining high chlorophyll content and photosynthetic competence for longer during monocarpic senescence and by contributing to seed-setting efficiency during the ter-
93
minal stage of seed maturation in rice. Acknowledgments This research was supported by a grant (CG3131) from the Crop Functional Genomics Center of the 21st century Frontier Research Program funded by the Ministry of Science and Technology (MOST) and Rural Development Administration (RDA) of the Republic of Korea.
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