Separation of Drought Tolerance From Drought

Copyright Ó 2006 by the Genetics Society of America DOI: 10.1534/genetics.105.045062

Genetic Basis of Drought Resistance at Reproductive Stage in Rice: Separation of Drought Tolerance From Drought Avoidance Bing Yue,* Weiya Xue,* Lizhong Xiong,* Xinqiao Yu,† Lijun Luo,† Kehui Cui,* Deming Jin,* Yongzhong Xing* and Qifa Zhang*,1 *National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China and †Shanghai Agrobiological Gene Center, Shanghai 201106, China Manuscript received May 1, 2005 Accepted for publication October 19, 2005 ABSTRACT Drought tolerance (DT) and drought avoidance (DA) are two major mechanisms in drought resistance of higher plants. In this study, the genetic bases of DTand DA at reproductive stage in rice were analyzed using a recombinant inbred line population from a cross between an indica lowland and a tropical japonica upland cultivar. The plants were grown individually in PVC pipes and two cycles of drought stress were applied to individual plants with unstressed plants as the control. A total of 21 traits measuring fitness, yield, and the root system were investigated. Little correlation of relative yield traits with potential yield, plant size, and root traits was detected, suggesting that DTand DA were well separated in the experiment. A genetic linkage map consisting of 245 SSR markers was constructed for mapping QTL for these traits. A total of 27 QTL were resolved for 7 traits of relative performance of fitness and yield, 36 QTL for 5 root traits under control, and 38 for 7 root traits under drought stress conditions, suggesting the complexity of the genetic bases of both DT and DA. Only a small portion of QTL for fitness- and yield-related traits overlapped with QTL for root traits, indicating that DT and DA had distinct genetic bases.

D

ROUGHT is one of the major abiotic stresses limiting plant production. The worldwide water shortage and uneven distribution of rainfall makes the improvement of drought resistance especially important (Luo and Zhang 2001). Fulfillment of this goal would be enhanced by an understanding of the genetic and molecular basis of drought resistance. However, little progress has been made in characterizing the genetic determinants of drought resistance, because it is a complex phenomenon comprising a number of physio-biochemical processes at both cellular and organismic levels at different stages of plant development (Tripathy et al. 2000). Drought resistance includes drought escape (DE) via a short life cycle or developmental plasticity, drought avoidance (DA) via enhanced water uptake and reduced water loss, drought tolerance (DT) via osmotic adjustment (OA), antioxidant capacity, and desiccation tolerance. The recent development of high-density linkage maps has provided the tools for dissecting the genetic basis underlying complex traits, such as drought resistance, into individual components. Quantitative trait locus (QTL) mapping has been carried out in an attempt to determine the genetic basis of several traits that may be related to drought resistance, including OA (Lilley et al. 1996; Zhang et al. 1999, 2001; Robin et al. 2003), cell-

1 Corresponding author: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Hongshang District, Wuhan 430070, China. E-mail: [email protected]

Genetics 172: 1213–1228 (February 2006)

membrane stability (Tripathy et al. 2000), abscisic acid (ABA) content (Quarrie et al. 1994, 1997), stomatal regulation (Price et al. 1997), leaf water status, and root morphology (Champoux et al. 1995; Ray et al. 1996; Price and Tomos 1997; Yadav et al. 1997; Ali et al. 2000; Courtois et al. 2000; Zheng et al. 2000; Zhang et al. 2001; Kamoshita et al. 2002; Price et al. 2002). However, it is not clear how these attributes are related to the performance of the genotypes at the whole-plant level, and how they function to reduce the drought damage to fitness- and productivity-related traits. Plants are most susceptible to water stress at the reproductive stage. Dramatic reduction of grain yield occurs when stress coincides with the irreversible reproductive processes, making the genetic analysis of drought resistance at the reproductive stage crucially important (Cruz and O’Toole 1984; Price and Courtois 1999; Boonjung and Fukai 2000; Pantuwan et al. 2002). However, variation of flowering time in segregating populations often made the phenotyping of drought resistance rather inaccurate. Staggering the seed-sowing time has been suggested to synchronize the flowering time of a population in QTL mapping (Price and Courtois 1999). Lanceras et al. (2004) also reported QTL mapping of yield and yield components under different water regimes in the field by synchronizing flowering time of the mapping population. However, the success has been limited because of the difficulty in achieving a real synchronization of the flowering time in a segregating population. In addition

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to flowering time, segregation for plant size and root volumes also confounds the accuracy of QTL mapping. It is almost impossible to distinguish the genetic basis of DT from other contributing factors (such as DA and DE) in drought resistance under field conditions in which drought stress is applied to and withdrawn from all plants simultaneously. In this study, we adopted a protocol for drought treatment by planting and stressing rice plants of a recombinant inbred line (RIL) population in individual polyvinyl chloride (PVC) pipes in which the various genotypes were stressed to the same extent at the same developmental stage. We showed that such an experimental design cleanly separated DT from DA, thus allowing relatively independent analyses of the genetic bases of DT and DA.

MATERIALS AND METHODS Plant materials and drought stress treatment: A population consisting of 180 RILs at F9/F10 generation was developed from a cross between the lowland rice cultivar Zhenshan 97 (Oryza sativa L. ssp. indica) and the upland rice cultivar IRAT109 (O. sativa L. ssp. japonica). Zhenshan 97 is the maintainer line for a number of elite hybrids widely cultivated in China, and IRAT109 was developed in Cote d’Ivoire. For phenotyping, rice plants were grown in PVC pipes, one plant per pipe, under a rain-out shelter with movable roofs. The pipe was 20 cm in diameter and 1 m in length with holes on two sides at 25, 50, and 75 cm from the top. Each pipe was loaded with a plastic bag filled with 38 kg of thoroughly mixed soil composed of two parts of clay and one part of river sand, to which 25 g of fertilizers (including 4 g each of N, P2O5, and K2O) was added. Sowing time was staggered among the lines to synchronize flowering on the basis of the heading dates of the lines observed in 2002. Three to five germinated seeds were directly sown in each pipe and only one healthy plant was kept at 30 days after sowing. At the beginning of the tillering stage, 1 g of urea (dissolved in water) was applied to each pipe. The plants were fully irrigated by watering every day until the drought treatment. Drought stress was individually applied to each plant at the booting stage. To apply drought stress, water was added to the full capacity of the pipe, the plugs on the pipe were then removed, and small holes were punched on the plastic bag to drain the water slowly. Rain was kept off by closing the roof during periods of rain. When all leaves of a stressed plant became fully rolled, as visualized at noon—a point corresponding to the relative water content in the range of 72–75%, as checked in this study—watering was applied to the full capacity of the pipe. With the full water level maintained for 1 day, the second cycle of drought stress was applied to the plant until all leaves became fully rolled again. After the second round of stress, watering was resumed for the rest of the life cycle. The pipes were laid out in six blocks following a randomized complete block design. Drought stress was applied to three of the blocks with the other three blocks used as control. In 2003, 150 RILs and the parents were phenotyped with two pipes per block for each genotype. In 2004, 75 RILs and the parents were tested to represent the resistant and susceptible lines on the basis of relative yield in 2003, with only one pipe per block for each genotype.

Traits and measurements: A total of 21 traits were scored in this study; 9 of them were traits collected from the aboveground part of the plants and the other 12 were root traits (Table 1). The traits collected from above-ground parts were related to fitness and productivity, including yield and yield component traits, biomass, and fertility. Yield and yield-related traits were examined for all plants under stress and the control conditions, including grain yield per plant (in grams), number of spikelets per panicle, 1000-grain weight (in grams), fertile panicle rate (%), spikelet fertility (%), biomass (in grams) and harvest index (%). Fertile panicle rate was the proportion of the number of fertile panicles (with 5 grains or more on each panicle) in all the panicles of a plant. Spikelet fertility was measured as the number of grains divided by the total number of spikelets of a plant. Harvest index was scored as grain yield divided by the total dry matter of the above-ground part. The relative performance of the phenotypes for each trait was measured simply as the ratios of the measurements taken under drought stress and control conditions. In addition, two traits related to the water status of the plants, leaf-drying score and number of days to leaf rolling, were also recorded. Leaf-drying score was recorded on the basis of the degrees of leaf drying immediately after rewatering as 0 (no leaf drying) to 4 (.20% of the leaf area was drying). Number of days to leaf rolling of each plant was recorded as the number of days from the application of drought stress to the day when all leaves became rolled at noon. The root traits were scored at seed maturity of the plants. To measure these traits, the plastic bag containing the soil and roots was pulled out from the PVC pipe and laid out on a 2-mm sieve screen frame. The lowest visible root in the soil after removing the plastic bag was scored as the maximum root depth (in centimeters). The body of soil and roots was cut into two parts at 30 cm from the basal node of the plant and the soil was washed away carefully to collect roots. The volumes (in milliliters) of roots from the two parts were measured in a cylinder using the water-replacing method (Price and Tomos 1997). The root mass below 30 cm was considered to be deep root, from which a number of measurements were derived. Root growth rate in depth and root growth rate in volume were calculated by dividing the maximum root depth and the total root volume, respectively, by the root growth period (number of days from sowing to heading of the plant). Droughtinduced root growth was evaluated by two traits: droughtinduced root growth in depth and drought-induced deep-root rate in volume, which were calculated as the differences of maximum root depth and deep-root rate in volume between the measurements obtained under drought stress and control conditions. The abbreviations for and descriptions of these traits are listed in Table 1 and used hereafter. DNA markers, map construction, and QTL analysis: A total of 245 nuclear simple sequence repeat (SSR) markers were used for constructing the linkage map. The SSR primers and marker assays essentially followed Temnykh et al. (2000, 2001) and McCouch et al. (2002). The program of Mapmaker/EXP 3.0 (Lincoln et al. 1992) was used to construct the genetic linkage map. The means of the traits were used to identify QTL by Windows QTL Cartographer 2.0 (Zeng 1994). The LOD thresholds were determined by 500 random permutations, which resolved that, at a false positive rate of ,0.05 for each trait, the LOD thresholds ranged from 1.9 to 2.4 for 20 of the 21 traits. The only exception was relative fertile panicles (RFP), in which the LOD threshold was 2.6 for the data of 2003 and 4.1 for 2004. For ease of presentation, a uniform threshold of 2.4 was adopted for the 20 traits, and 2.6 and 4.1 were used for RFP for the 2 years, respectively. The results of

Genetic Basis of Drought Resistance in Rice

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TABLE 1 Abbreviations, full names, and descriptions of the traits investigated in this study Abbreviation

Trait

RY

Relative yield per plant (%)

RSF

Relative spikelet fertility (%)

RBM

Relative biomass (%)

RFP

Relative rate of fertile panicles (%)

RHI

Relative harvest index (grain yield/biomass) (%)

RGW

Relative grain weight (%)

RSN

Relative number of spikelets per panicle (%)

LDS

Leaf-drying score

DLR

No. of days to leaf rolling

MRDC

Maximum root depth under control (cm)

MRDD

Maximum root depth under drought (cm)

DIRD

Drought-induced root growth in depth (cm)

RGDC

RVC

Root growth rate in depth under control conditions (cm/day) Root growth rate in depth under drought conditions (cm/day) Root volume under control conditions (ml)

RVD

Root volume under drought conditions (ml)

DRVC

Deep root rate in volume under control conditions (%) Deep root rate in volume under drought conditions (%) Root growth rate in volume under control conditions (ml/day) Root growth rate in volume under drought conditions (ml/day) Deep root rate in volume induced by drought conditions (%)

RGDD

DRVD RGVC RGVD DIDRV

both years were presented for QTL with a LOD score .2.4 in 1 year but in the range of 2.0–2.4 in the other year for the 20 traits.

RESULTS

Phenotypic variation of the parents and RILs: The phenotypic differences between parents as well as the variation in the RIL population are summarized in Table 2. Transgressive segregation was observed in the RIL population for all the traits investigated. ANOVA of the data collected in 2003 indicated that variation due to genotype differences was highly significant for all

Description Yield per plant under drought stress/Yield per plant under control conditions Spikelet fertility under drought/Spikelet fertility under control conditions Biomass per plant under drought/Biomass per plant under control conditions Rate of fertile panicles (with five seeds or more) per plant under drought/Rate of fertile panicles per plant under control conditions Harvest index under drought/Harvest index under control conditions Weight of 1000 seeds under drought/Weight of 1000 seeds under control conditions No. of spikelets per panicle under drought/no. of spikelets per panicle under control conditions Degrees of leaf drying immediately after rewatering, scored 1 (no drying) to 5 (.20% area dried) No. of days to leaf rolling starting from day of drought treatment The lowest visible root at the soil surface after removing the plastic bag under control conditions The lowest visible root at the soil surface after removing the plastic bag under drought conditions The difference of maximum root depth under drought and control conditions Maximum root depth divided by root growth period under control conditions Maximum root depth divided by root growth period under drought conditions The volume of roots under control conditions measured using the water-replacing method The volume of roots under drought conditions measured using the water-replacing method Percentage of root volume ,30 cm in the total root volume under control conditions Percentage of root volume ,30 cm in the total root volume under drought conditions Total root volume divided by root growth period under control conditions Total root volume divided by root growth period under drought conditions The difference in deep-root rate in volume under drought and control conditions

the traits, although the relative proportions of variance varied from one trait to another (Table 3). IRAT109 showed more drought resistance than Zhenshan 97 in both years by having higher values in relative performance of the traits related to fitness and productivity (Table 2). The differences between the two parents for relative yield, relative biomass, relative spikelet fertility, and relative grain weight were significant at the 0.01 probability level in 2003. Thus Zhenshan 97 suffered much more drought damage than IRAT109. The reverse performance was observed between the parents for the two traits related to water status (Table 2). The leaf-drying score of IRAT109 was significantly

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B. Yue et al. TABLE 2 The measurements of the traits in the RIL population and the parents in 2003 and 2004

Trait RY RSF RBM RFP RHI RGW RSN LDS DLR MRDC MRDD DIRD RGDC RGDD RVC RVD DRVC DRVD RGVC RGVD DIDRV

Zhenshan 97

IRAT109

Mean of RILs

Range of RILs

43.9/65.7*** 54.2/69.1 79.0/81.8 88.3/92.5**** 52.1/66.9 73.5/76.2 89.6/98.3**** 3.0*/2.67* 18.5**/22.0*,*** 53.6/53.3 76.7/82.7 23.1*/29.4 0.8/0.8 1.2/1.3 84.0***/51.0 73.0***/45.2 8.7/8.9 17.6/16.4 1.3***/0.8 1.1/0.7 8.9/7.5

80.6**/81.9 74.3**/88.6 94.9**/89.6 93.5/100.0**** 65.6/74.8 88.0**/97.8*,**** 91.9/94.8*** 1.7/1.3 10.3/16.7**** 61.1**/67.0* 79.5/92.3*** 18.4/25.3*** 0.8/0.9 1.0/1.3*** 84.3***/70.0* 102.5**,***/75.7 22.4**,***/12.8* 25.6/33.0*,*** 1.1/0.8 1.3/1.1 3.2/20.2**,****

58.2/52.6 63.9/63.7 90.4/81.0 80.0/94.0 59.2/58.6 87.6/82.0 84.8/94.3 2.4/1.8 12.1/19.4 61.8/57.9 81.9/87.1 20.1/29.2 0.8/0.9 1.0/1.1 112.3/82.6 107.8/89.7 13.3/9.2 19.0/24.8 1.4/1.0 1.3/1.1 5.7/15.6

(19.6–90.8)/(17.9–90.5) (24.2–94.5)/(22.4–95.6) (70.3–100.0)/(57.1–99.2) (28.1–100.0)/(68.6–100.0) (20.3–100.0)/(18.3–96.9) (58.0–104.1)/(63.2–104.1) (52.1–100.5)/(68.6–100.2) (1.0–3.8)/(0.3–3.3) (7.0–17.5)/(8.0–26.7) (47.2–79.8)/(39.0–75.5) (64.8–94.5)/(69.0–95.7) (7.0–33.8)/(14.7–48.0) (0.6–1.0)/(0.5–1.0) (0.7–1.4)/(0.8–1.6) (46.3–231.4)/(43.9–146.9) (43.0–234.6)/(29.8–175.1) (2.5–28.8)/(0.8–22.4) (3.7–36.3)/(10.6–44.1) (0.8–2.3)/(0.7–1.7) (0.6–2.3)/(0.4–1.8) (ÿ4.2–18.9)/(1.6–29.1)

The number at the left of the ‘‘/’’ is the result of 2003, and the number at the right is the result of 2004. *,**Significantly higher than the other parent at the 0.05 and 0.01 probability levels based on t-test. ***,****Significantly higher than the other year of the same parent at the 0.05 and 0.01 probability levels based on t-test.

less than that of Zhenshan 97 in both years, while Zhenshan 97 could sustain longer time than IRAT109 before leaf rolling as reflected by the DLR scores. For most of the root traits (Table 2), IRAT109 had higher values than Zhenshan 97 under both control and drought stress conditions in both years. In at least one year, the differences between parents for maximum root depth under control, root volume and deep-root rate under both drought stress and control conditions, and drought-induced deep-root rate in volume were significant. Zhenshan 97, however, showed more droughtinduced root growth in depth than IRAT109 did, and the difference was significant in 2003. Again, transgressive segregation was observed in all the root traits. When the data collected from the 2 years were compared, DLR was substantially higher in 2004 than in 2003 for both parents (Table 2), indicating that the stress developed more slowly in 2004 due to the milder weather conditions during drought stress (the temperature and evaporation was higher in 2003). Consequently, a number of other traits also showed significant differences between the 2 years in one or both parents, including relative yield, relative number of fertile panicles, relative grain weight, and relative spikelet number. Significant differences between the 2 years were also observed in several root traits in one or both parents. Correlations of the traits: The traits related to fitness and productivity, e.g., relative yield, relative spikelet fertility, relative rate of fertile panicle, relative biomass, relative

grain weight, and relative harvest index were highly correlated with each other (Table 4). This suggested that the yield loss and harvest index reduction under drought stress in late season were associated with the reduction of spikelet fertility, fertile panicle rate, biomass and grain weight. In particular, a very high correlation (0.85–0.95) was observed between relative yield, relative spikelet fertility, and relative harvest index in both years. Figure 1 illustrates the relationships of relative yield and relative biomass with yield and biomass under control conditions. It was clear from Figure 1 that relative yield was not correlated with yield under control conditions, and thus genotypes with high and low yield potential were equally stressed. Similarly, there was little correlation between relative biomass and biomass under control conditions, and thus genotypes with large and small plant sizes were equally stressed. Moreover, relative yield was not significantly correlated with biomass under control, and neither was relative biomass significantly correlated with yield under control. There was no correlation between the two traits related to water status of the plants (Table 4). There were no consistent correlations between these two traits with the relative performance of the traits related to fitness and productivity in 2 years, except the negative correlation detected in both years between relative biomass and number of days to leaf rolling. The root traits investigated were also highly correlated with each other (Table 5). In general, constitutive

Genetic Basis of Drought Resistance in Rice TABLE 3

TABLE 3

ANOVA of the traits based on the data of 2003

(Continued)

Trait

Variation

d.f.

MS

F

P

RY

Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error Genotype Block Error

151 2 302 150 2 300 150 2 300 150 2 300 149 2 298 150 2 300 150 2 300 149 2 298 151 2 302 151 2 302 150 2 300 149 2 298 151 2 302 150 2 300 151 2 302 151 2 302 151 2 302 151 2 302 151 2 302

1262.89 1550.86 174.56 1222.68 946.45 319.06 289.24 623.25 209.63 589.01 1738.76 203.45 1560.11 978.40 497.14 138.38 28.76 49.93 323.34 31.27 114.38 2.32 5.89 0.48 16.00 6.67 2.26 109.84 1397.86 29.48 126.42 3330.98 46.84 123.42 875.99 61.53 0.02 0.22 0.01 0.06 0.17 0.01 4398.12 411.89 424.96 5195.99 1578.62 404.31 0.02 0.07 0.004 0.04 0.04 0.01 0.42

7.23 8.88

RSF

RBM

RFP

RHI

RGW

RSN

LDS

DLR

MRDC

MRDD

DIRD

RGDC

RGDD

RVC

RVD

DRVC

DRVD

RGVC

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Trait

Variation

d.f.

MS

F

P

0.0000 0.0002

RGVD

DIDRV

0.43 0.74 0.06 209.78 204.93 76.72

0.0000 0.0000

0.0000 0.0521

151 2 302 151 2 302

6.9 11.92

3.83 2.97

Genotype Block Error Genotype Block Error

2.73 2.67

0.0000 0.0702

1.38 2.97

0.0120 0.0518

2.90 8.54

0.0000 0.0003

3.14 1.97

0.0000 0.1391

2.77 0.58

0.0000 0.5683

2.83 0.27

0.0000 0.7655

4.83 12.27

0.0000 0.0000

7.07 2.95

0.0000 0.0525

3.73 47.42

0.0000 0.0000

2.70 71.11

0.0000 0.0000

2.01 14.24

0.0000 0.0000

2.59 25.52

0.0000 0.0000

5.07 15.4

0.0000 0.0000

10.35 0.97

0.0000 0.3824

12.85 3.90

0.0000 0.0211

5.45 19.09

0.0000 0.0000

4.32 4.87

0.0000 0.0083

5.85

0.0000

0.07 (continued )

MS, mean square; F, F-statistic.

root growth (maximum root depth and root volume under control) had high and consistent correlations with other root traits. For example, maximum root depth was highly significantly correlated in both years with all the root traits, except drought-induced root growth in volume. A similar situation was also obvious for root volume under control that was also highly correlated with most root traits. The highest correlation (.0.90) detected was between root volume and root growth rate under both control and drought conditions. Correlations between traits in different groups are shown in Table 6. In general, there was not much correlation between the relative performance of fitnessand productivity-related traits and the root traits, with exceptions of only a few marginal cases in 2004, all of which suggested root growth under drought had small negative effects on yield and biomass. Thus, variation in root traits contributed very little toward reducing the drought stress of the upground parts in this experiment. In addition, relative yield, relative biomass, and relative fertility were not significantly correlated with flowering time (data not shown), as expected on the basis of the experimental design. All this demonstrated that the pipe planting effectively minimized the effects of DA or DE on relative yield and yield-related traits. Therefore, the relative yield, relative spikelet fertility, and relative biomass examined in this study were indeed regulated almost exclusively by DT mechanisms under the experimental conditions and thus can be viewed as DT traits although the underlying mechanisms remain to be investigated. Table 6 also showed no correlation between leafdrying score and the root traits. Number of days to leaf rolling was negatively correlated with a number of traits measuring root volumes under both drought stress and control conditions; thus leaf rolling occurred sooner in plants with larger root volumes. However, there was one highly significant positive correlation between number of days to leaf rolling and root growth in depth under drought, indicating drought-induced root growth in depth may have a positive effect on delaying leaf rolling. The linkage map: A total of 410 SSR markers were surveyed and 245 (59.8%) of them showed polymorphism between the two parents. A linkage map was

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B. Yue et al. TABLE 4 Coefficients of pairwise correlations of the above-ground traits investigated in 2003 and 2004

RSF RBM RFP RHI RGW RSN LDS DLR

RY

RSF

RBM

RFP

RHI

RGW

RSN

LDS

0.88/0.85 0.35/0.40 0.58/0.46 0.95/0.85 0.44/0.61 0.37/0.03 ÿ0.31/0.03 ÿ0.36/ÿ0.21

0.15/0.03 0.64/0.51 0.89/0.94 0.36/0.47 0.21/ÿ0.07 ÿ0.26/0.05 ÿ0.23/ÿ0.11

0.26/0.14 0.15/ÿ0.07 0.10/0.27 0.23/ÿ0.04 ÿ0.23/0.13 ÿ0.29/ÿ0.37

0.46/0.44 0.30/0.38 0.27/0.01 ÿ0.34/0.14 ÿ0.12/0.00

0.44/0.48 0.33/0.08 ÿ0.24/0.05 ÿ0.33/ÿ0.03

0.32/0.04 ÿ0.15/0.04 ÿ0.39/0.05

ÿ0.21/0.09 ÿ0.27/0.12

0.09/ÿ0.21

Critical values at the 0.01 probability level are 0.21 and 0.30 for 2003 and 2004, respectively. The number at the left of the ‘‘/’’ is the result of 2003, and the number at the right is the result of 2004.

constructed using Mapmaker analysis based on data from the 245 SSR markers assayed on the 180 RILs (Figure 2). The map covered a total length of 1530 cM with an average interval of 6.2 cM between adjacent markers. QTL for relative performance of the traits related to fitness and productivity: QTL detected for relative performance of seven traits related to fitness and productivity are listed in Table 7(see also Figure 2). A total of 27 QTL were resolved for the seven traits, including 8 QTL detected in both years and 19 QTL observed in only 1 year. The detection is quite consistent, considering the large scale of the experiment, the nature of the traits, and the secondary statistics of ratios as input data. All the QTL that were detected in both years appeared to have larger effects in 2004 than in 2003, as indicated by the LOD scores and the amounts of variation explained. This is expected since the lines planted in 2004 were selected on the basis of the extreme phenotypes from the previous year. Alleles from IRAT109 at 14 of the QTL had positive effects on the relative performance of these traits, while alleles from Zhenshan 97 at the other 13 loci contributed positively to the relative performance (Table 7). Of the 8 QTL that were consistently detected in both years, the IRAT109 alleles at 7 QTL had positive effects on the relative performance of these traits. Interestingly, one region on chromosome 9, RM316–RM219, was particularly active by exhibiting significant effects simultaneously on relative yield (QRy9), relative spikelet fertility (QRsf9), relative biomass (QRbm9), and relative harvest index (QRhi9). Another region on chromosome 8, RM284–RM556, was detected to have a significant effect on relative yield (QRy8), relative spikelet fertility (QRsf8), and relative number of fertile panicles (QRfp8). It is also worth noting that almost all the QTL detected in both years had large effects on the traits as reflected by the large proportions of the phenotypic variation explained (10% or more). QTL for the two plant water status traits: Six QTL were resolved for leaf-drying score and four QTL for number of days to leaf rolling (Table 8; Figure 2). In

both cases, one QTL was detected in both years and the others were detected in only 1 year. As in the traits for relative performance described above, the region RM219–RM296 on chromosome 9 showed a large effect on number of days to leaf rolling (QDlr9). Also a QTL for leaf-drying score (QLds3b) had a large effect on the trait in both years. QTL for root traits under control conditions: A total of 36 QTL were resolved for the five root traits under control conditions (Table 9; Figure 2), of which 7 were detected in both years and the remaining 29 in only 1 year. Again, the effects observed in 2004 were larger than those in 2003 for all the QTL detected in both years, except for one QTL, QRgvc3, for root growth rate in volume under control conditions. While the IRAT109 alleles at 22 of the 36 QTL contributed positively to the root traits, alleles from Zhenshan 97 at 5 of the 7 QTL that were observed in both years had positive effects on the root traits. Of the 19 QTL each explaining .10% of phenotypic variation, the IRAT109 alleles at 12 QTL contributed to the increase of the trait measurements. Again, there were a number of regions where QTL for two or more traits were detected, including the intervals RM472–RM104 on chromosome 1, RM231–RM489 on chromosome 3, both RM471–RM142 and RM349– RM131 on chromosome 4, both RM125–MRG4499 and RM429–RM248 on chromosome 7, RM316-RM219 on chromosome 9, and RM287–RM229 on chromosome 11. In all the QTL having effects on multiple traits, except one, alleles from the same parents contributed in the same direction to different traits, suggesting the likelihood that different QTL are due to the effects of the same genes. QTL for root traits under drought stress: A total of 38 QTL were observed for the seven root traits under drought stress conditions (Table 10; Figure 2), including 6 detected in both years and 32 detected in only 1 year. Unlike other traits described above, the effects of QTL detected in 2004 were not necessarily larger than those resolved in 2003 for the QTL detected simultaneously in both years. Alleles from IRAT109 at 23 of the 38 QTL


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Figure 1.—Scatter plots of relative performance of yield and biomass against yield and biomass under control conditions in 2003 (left) and 2004 (right). (A) Relative yield against yield under control; (B) relative biomass against biomass under control; (C) relative yield against biomass under control; (D) relative biomass against yield under control.

contributed to the increase of the trait measurements, whereas at the other 15 QTL, alleles from Zhenshan 97 were in the direction of increasing the trait measurements. Of the 22 QTL each explaining .10% of phe-

notypic variation, alleles from IRAT109 at 17 loci had positive effects on these root traits. The QTL were distributed very unevenly among the chromosomes, with 11 QTL on chromosome 4, 5 QTL

0.81/0.73 ÿ0.21/0.06 0.26/0.33 0.21/0.51 0.58/0.80 0.62/0.75 0.50/0.46 0.42/0.50 ÿ0.26/0.19 0.46/0.51 0.19/0.45 0.80/0.77 0.96/0.94 ÿ0.23/0.21 0.89/0.87 0.53/0.47 0.22/0.32 0.95/0.93 0.83/0.73 ÿ0.27/0.04 ÿ0.49/ÿ0.65 ÿ0.52/ÿ0.62 ÿ0.10/ÿ0.19 0.28/0.03 ÿ0.29/ÿ0.43 ÿ0.34/ÿ0.38 0.46/0.21 See Table 4 legend for explanations.

ÿ0.13/ÿ0.42 0.60/0.44 ÿ0.26/ÿ0.42 ÿ0.26/ÿ0.35 ÿ0.33/ÿ0.53 0.09/ÿ0.13 ÿ0.20/ÿ0.39 ÿ0.20/ÿ0.27 0.46/0.28 MRDD DIRD RGDC RGDD RVC RVD DRVC DRVD RGVC RGVD DIDRV

0.48/0.42 ÿ0.49/ÿ0.80 0.38/0.48 ÿ0.20/ÿ0.30 0.62/0.58 0.56/0.50 0.79/0.76 0.57/0.48 0.57/0.53 0.51/0.43 ÿ0.13/0.01

0.54/0.19 0.24/0.15 0.41/0.19 0.33/0.31 0.29/0.29 0.43/0.46 0.64/0.59 0.35/0.29 0.29/0.31 0.34/0.46

0.67/0.55 ÿ0.21/ÿ0.25 ÿ0.29/ÿ0.27 0.32/0.36 0.43/0.22 ÿ0.02/ÿ0.04 ÿ0.15/ÿ0.10 0.19/ÿ0.01

RGVC DRVD DRVC RVD RVC RGDD RGDC DIRD MRDD MRDC

Coefficients of pairwise correlations of the root traits investigated in this study in 2003 and 2004

TABLE 5

ÿ0.18/0.30

B. Yue et al. RGVD

1220

on chromosome 7, 4 QTL on each of chromosomes 2 and 3, 3 QTL on each of chromosomes 1, 8, 9, and 11, 1 QTL on each of chromosomes 6 and 10, but none on chromosomes 5 and 12. There were also obvious hotspots where QTL for two or more of the root traits under drought stress were detected, including regions mostly on chromosome 4, as well as chromosomes 3, 7, 9, and 11 (Figure 2). Comparison of chromosomal locations of QTL for different types of traits: Of the 21 chromosomal regions resolved with QTL for relative performance of fitness- and productivity-related traits, 9 overlapped with the QTL intervals for root traits (Figure 2). One region on chromosome 9, RM316–RM219, in which multiple QTL were detected, showed relatively large effects on both root traits and relative performance of fitness and productivity; the other 9 regions had only 1 QTL, each with relatively small effects on the respective traits (Figure 2; Tables 7, 9, and 10). In addition, positive alleles for the two types of traits were contributed by different parents in 4 of the 9 overlapping regions, including the region RM316–RM219 on chromosome 9. The distinct chromosomal locations between QTL for fitness- and productivity-related traits and root traits, and the different directions of the allelic contributions for most overlapping QTL, were in good agreement with the results of correlation analysis, further suggesting that root traits and relative performance of the fitness and productivity traits had different genetic determinants. Number of days to leaf-rolling and leaf-drying score are two traits reflecting plant water status. All four QTL for number of days to leaf rolling overlapped with one or more QTL for root traits, but none of them overlapped with QTL for the relative performance of fitnessand productivity-related traits (Figure 2). Of the six QTL for leaf-drying score, only one with small effect overlapped with a QTL for relative spikelet number that also seemed to have impact on deep-root rate in volume induced by drought. Again, these results agreed well with the correlation results above, in which number of days to leaf rolling was significantly correlated with some of the root traits, while the leaf-drying score had little correlation with either root traits or above-ground traits (Tables 4 and 6).

DISCUSSION

The PVC pipe protocol successfully separated drought tolerance and drought avoidance: A major difficulty in genetic analysis of drought resistance by applying and relieving drought treatment at the same time for all plants, as adopted by many previous studies, is the inability to resolve the whole-plant resistance into individual components, such as DE, DA, and DT. Previous studies showed that the drought resistance index (relative yield) was often negatively correlated with

ÿ0.12/ÿ0.24 0.20/ÿ0.04 0.32/0.24 0.21/0.22 0.36/0.45 ÿ0.34/ÿ0.46 ÿ0.36/ÿ0.55 ÿ0.10/ÿ0.34 0.30/ÿ0.19 ÿ0.31/ÿ0.40 ÿ0.37/ÿ0.51 0.46/0.04 ÿ0.02/0.12 ÿ0.11/ÿ0.12 ÿ0.09/ÿ0.20 ÿ0.10/0.11 ÿ0.12/ÿ0.07 0.04/0.01 0.07/0.07 0.05/0.12 ÿ0.05/0.09 ÿ0.02/0.04 0.00/0.09 ÿ0.11/ÿ0.02 0.09/ÿ0.30 0.09/ÿ0.12 0.01/0.25 ÿ0.13/ÿ0.11 ÿ0.13/0.13 0.13/ÿ0.18 0.16/ÿ0.18 0.04/ÿ0.25 0.01/ÿ0.13 0.08/ÿ0.15 0.13/ÿ0.14 ÿ0.05/0.04 0.09/ÿ0.01 ÿ0.05/ÿ0.15 ÿ0.14/ÿ0.08 ÿ0.05/0.11 ÿ0.14/0.06 0.08/ÿ0.15 ÿ0.04/ÿ0.40 0.12/ÿ0.10 ÿ0.03/ÿ0.31 0.04/ÿ0.12 ÿ0.10/ÿ0.45 ÿ0.16/ÿ0.36 See Table 4 legend for explanations.

MRDC MRDD DIRD RGDC RGDD RVC RVD DRVC DRVD RGVC RGVD DIDRV

0.01/ÿ0.05 ÿ0.07/ÿ0.14 ÿ0.07/ÿ0.02 ÿ0.08/0.05 ÿ0.10/0.07 0.04/ÿ0.22 ÿ0.03/ÿ0.29 0.06/ÿ0.16 ÿ0.10/ÿ0.29 0.01/ÿ0.23 ÿ0.07/ÿ0.29 ÿ0.18/ÿ0.27

0.05/0.02 ÿ0.02/ÿ0.15 ÿ0.06/ÿ0.12 ÿ0.08/0.08 ÿ0.11/0.01 ÿ0.04/ÿ0.11 ÿ0.08/ÿ0.31 0.04/ÿ0.04 ÿ0.03/ÿ0.24 ÿ0.09/ÿ0.11 ÿ0.14/ÿ0.38 ÿ0.08/ÿ0.32

0.01/ÿ0.16 0.11/ÿ0.17 0.10/0.06 ÿ0.07/ÿ0.10 ÿ0.02/ÿ0.02 0.05/ÿ0.21 0.11/0.04 0.00/ÿ0.15 ÿ0.03/ÿ0.11 0.04/ÿ0.27 0.13/0.09 ÿ0.03/ÿ0.02

ÿ0.03/ÿ0.16 0.03/0.01 0.05/0.18 ÿ0.09/0.01 ÿ0.03/0.17 ÿ0.09/ÿ0.1 ÿ0.07/ÿ0.18 ÿ0.06/ÿ0.07 0.05/0.01 ÿ0.18/ÿ0.06 ÿ0.11/ÿ0.15 0.13/0.08

ÿ0.02/ÿ0.25 ÿ0.10/ÿ0.02 ÿ0.08/0.26 ÿ0.20/0.12 ÿ0.18/0.34 0.10/ÿ0.34 0.10/ÿ0.40 0.06/ÿ0.32 ÿ0.11/ÿ0.29 0.04/ÿ0.24 0.08/ÿ0.30 ÿ0.20/ÿ0.13

LDS RSN RGW RHI RFP RBM RSF RY

Coefficients of pairwise correlations between above-ground traits and root traits investigated in this study in 2003 and 2004

TABLE 6

DLR


1221

potential yield and was also dependent on the actual developmental stage of the plants when stress treatment was applied (Price and Courtois 1999; Venuprasad et al. 2002; Toorchi et al. 2003). In an attempt to separate the components in the field experiments, several approaches have been adopted, including staggering the sowing date, installing a drip irrigation system in the plots, normalizing the data by statistical method, and utilizing advanced backcross lines (Blum 1988; Price and Courtois 1999; Robin et al. 2003). Although such measures were useful for improving the accuracy of QTL mapping, it is nonetheless impossible to assess the relative contributions of DE, DT, and DA to overall drought resistance at the whole-plant level. In this study, the effect of DE was completely eliminated because stress treatment was individually applied to pipes on the basis of the developmental stage of the plants. A plant-wise drought treatment protocol was used to ensure that all the plants received a similar level of stress treatment such that genotypes with a deep-root system or small size did not have an advantage in avoiding drought damage. This was confirmed by the very low correlation of the relative performance of the fitness- and yield-related traits with the root traits, as well as with potential yield and plant size as defined by yield and biomass under the control conditions. All this indicates that the effects of DA and DT were well separated in this experimental design. Thus, the relative performance of fitness- and yield-related traits under drought stress and control conditions unambiguously provided measurements for DT. The root traits, however, provided the measurements for DA, although the contribution of this component to drought resistance at the whole-plant level was eliminated by the experimental design. Therefore the genetic bases of DA and DT can be separately analyzed using this data set. The genetic bases of DT and DA are different: The genetic bases of DT and DA were rarely addressed separately in previous studies. Zhang et al. (2001) studied QTL for OA and root traits and found that no QTL for OA overlapped with any of the QTL for root traits. Lilley et al. (1996) reported tight linkage between QTL for root traits and OA, with alleles for increasing OA and root traits derived from different parents. In this study, a large number of QTL for DT and DA were detected. The results indicated that most of the QTL for putative DT-related traits did not overlap with QTL for DA-related traits. In regions where QTL for DTand DA-related traits were clustered, nearly a half of the positive alleles for DT- and DA-related traits were from different parents. For example, in the QTL hotspot of RM316–RM219 on chromosome 9, the positive alleles of QTL for deep-root traits (MRDC and DRVC) were from Zhenshan 97, while the IRAT109 alleles contributed positively to relative yield, relative spikelet fertility, relative biomass, and relative harvest index. The distinct

1222

B. Yue et al.

Figure 2.—The molecular marker linkage map based on the RIL population from a cross between Zhenshan 97 and IRAT109. Genetic distance is given in Kosambi centimorgans. The QTL for above-ground traits and root traits are placed on the left and right sides of the chromosomes, respectively. QTL detected in both years are shown in boldface type. QTL in italics indicate that the alleles for increasing trait values are from Zhenshan 97. The full names of the traits are listed in Table 1.

locations of the QTL and different directions of allelic contributions of the parents in QTL in overlapping regions for DT- and DA-related traits suggested that DT and DA had different genetic bases. This also explained the lack of correlation between these two sets of traits under the experimental conditions. The genetic complexity of the root traits: Rapid development of a deep-root system is considered a DA strategy for plants as it enables absorption of water in deep soil layers (Fukai and Cooper 1995; Price and Courtois 1999). Although the putative contribution of root traits to DA or drought resistance could not be estimated in this experimental design, the QTL mapping of various root traits under control and drought stress conditions in this study provided a comprehensive scenario of the genetic controls of root morphology under normal conditions and root restructuring under drought stress. A total of 74 QTL were resolved for the 12 root traits that could be assigned to 36 genomic regions according

to the flanking markers. Comparisons with previous results indicated that 4 of the 36 QTL regions had positional correspondence with QTL for root or other DArelated traits reported in previous studies of rice. For example, in the region RM472–RM104 on chromosome 1 where QTL for root volume, root growth in volume, and number of days to leaf rolling were detected in this study, QTL were also identified for root thickness and root weight (Zheng et al. 2003), as well as for relative water content, leaf rolling, and leaf-drying score (Babu et al. 2003). The region RM160–RM215 on chromosome 9, contributing to maximum root depth under both control and drought stress in this study, was also identified as harboring QTL for upland seminal root length and relative seminal root length in a previous study (Zheng et al. 2003). The region RM470–RM303 on chromosome 4, controlling deep-root rate, maximum root length, and root volume under drought stress in this study, corresponded to a region controlling root thickness, root penetration index, and penetrated root dry weight

QRsn1 QRsn2

8 12

1 2

2 5 7

2 9

RFP

RSN

RGW

RHI

QRhi2 QRhi9

QRgw2 QRgw5 QRgw7

QRbm2 QRbm9 QRbm10

RM221–RM573 RM316–RM219

RM145–RM324 RM509–RM430 RM125–MRG4449

RM237–RM403 RM324–RM29

RM284–RM556 RM235–MRG5454

RM573–RM318 RM316–RM219 RM596–RM271

RM421–RM274 RM284–RM556 RM316–RM219

RM240–RM166 RM284–RM556 RM316–RM219

Intervala

3.2 4.4

2.5 3.2 2.6

3.5 2.6

3.1 4.2

2.3 3.2 2.3

3.1 2.4 3.0

2.0 3.4 5.0

LOD

8.79 11.45

5.97 9.34 8.36

ÿ1.8 2.2 2.1 6.5 7.4

9.55 6.06

7.52 39.17

ÿ4.2 ÿ14.1 3.2 ÿ2.6

6.17 7.45 9.17

7.64 6.20 7.18

5.81 9.42 14.16

Phenotype variationc

1.5 1.6 1.8

5.3 ÿ4.8 5.2

4.9 ÿ6.2 7.7

Additive effectb

b

QRgw3 QRgw9

QRhi9 QRhi10

9 10

QRsn1 QRsn6 QRsn12

QRfp12

QRbm2 QRbm5 QRbm10

QRsf3 QRsf9

QRy2 QRy3 QRy9 QRy10

QTL

3 9

1 6 12

12

2 5 10

3 9

2 3 9 10

Chromosome

Underlined chromosome number and marker intervals indicate QTL detected in both years. The positive values indicate the alleles from IRAT109 with increasing effects. c Amount of phenotype variation (%) explained by the QTL.

a

QRfp8 QRfp12

2 9 10

RBM

QRsf5 QRsf8 QRsf9

5 8 9

RSF

QRy2 QRy8 QRy9

QTL

2 8 9

Chromosome

RY

Traits

2003

RM316–RM219 RM496–RM228

RM523–RM231 RM444–RM316

RM237–RM403 RM454–MRG4371 RM19–RM453

RM235–MRG5454

RM573–RM318 RM507–RM13 RM596–RM271

RM293–RM571 RM219–RM296

RM240–RM166 RM203–RM520 RM316–RM219 RM496–RM228

Intervala

2004

3.4 3.5

2.9 2.5

2.7 3.4 5.3

4.7

3.3 3.7 5.6

3.2 5.4

3.8 2.6 6.8 2.5

LOD

7.4 ÿ7.5

2.5 2.3

2.8 ÿ3.3 ÿ4.0

ÿ14.2

13.57 13.90

11.78 9.99

9.98 14.04 20.86

37.96

14.56 18.79 28.60

11.51 30.46

ÿ7.1 10.9

5.1 5.4 8.5

13.64 9.84 25.70 8.29

Phenotype variationc 7.4 ÿ6.5 10.1 ÿ5.8

Additive effectb

QTL for relative performance of traits related to fitness and productivity resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109

TABLE 7

Genetic Basis of Drought Resistance in Rice 1223

6.55 26.36 ÿ0.9 ÿ1.8 2.4 7.7 RM544–RM72 RM219–RM296 QDlr8 QDlr9 Underlined chromosome number and marker intervals indicate QTL detected in both years. The positive values indicate the alleles from IRAT109 with increasing effects. c Amount of phenotype variation (%) explained by the QTL. b

a

9.05 7.11 9.30 1 4 9 DLR

QDlr1 QDlr4 QDlr9

RM472–RM104 RM335–RM307 RM219–RM296

3.0 3.3 3.6

ÿ0.7 ÿ0.6 ÿ0.7

8 9

0.2 0.2 ÿ0.3 2.9 2.9 4.2 RM279–RM555 RM489–RM517 RM520–RM293 QLds2 QLds3a QLds3b 5.13 14.98 11.05 6.98 1 3 8 9 LDS

QLds1 QLds3b QLds8 QLds9


2.4 4.7 2.9 2.8

ÿ0.2 ÿ0.3 ÿ0.2 0.2

2 3 3

Additive effectb LOD Intervala Phenotype variationc Chromosome Traits

QTL

Intervala

LOD

Additive effectb

Chromosome

QTL

2004 2003

QTL for plant water status traits resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109

TABLE 8

10.95 11.33 16.10

B. Yue et al. Phenotype variationc

1224

as reported previously (Zhang et al. 2001). The region RM231–RM489 on chromosome 3 controlling root volume and root growth rate in volume in this study corresponded with the QTL for total root volume and root weight (Venuprasad et al. 2002). In the remaining 32 chromosomal regions, 7 regions harbored QTL for root traits detected under both control and drought stress conditions, 7 regions included root QTL repeatedly detected in 2 years, and 15 were regions in which multiple QTL were resolved. At the 74 QTL for root traits resolved, alleles from the upland parent IRAT109 at 45 QTL had positive effects for increasing the trait values, while positive alleles were contributed by the lowland parent Zhenshan 97 at the other 29 QTL. Among the 41 QTL with relatively large effects (explaining .10% variation), alleles from the upland parent at 29 loci had positive effects on these root traits. Thus, both upland and paddy rice could make positive contributions to DA, given the various attributes of the root traits, although IRAT109 may contribute more to DA than Zhenshan 97. The likely mechanisms of DT in this population: The strategy for establishment of DT involves OA and maintenance of cell-membrane stability, as well as detoxification (Tripathy et al. 2000; Chaves and Oliveira 2004). Although we did not measure these physiological traits directly in this study, possible mechanisms of the DT may be deduced on the basis of collocations of QTL detected in this and previous studies. Three chromosomal regions with major QTL for relative yield and yield-component traits in this study matched very well with DT-related physiological traits reported previously. For example, in the region RM284– RM556 on chromosome 8 harboring QTL for relative yield, relative grain fertility and relative fertile panicle rate resolved in this study, and a QTL for OA with flanking markers RM284–RM210 was identified by Robin et al. (2003). In the same region, QTL for OA and cellmembrane stability were also detected in other studies (Tripathy et al. 2000; Zhang et al. 2001). Comparative mapping indicated that this genomic region corresponded to a segment on wheat chromosome 7S where a locus associated with OA was identified (Tripathy et al. 2000). In the genomic region RM316–RM219 on chromosome 9 where major QTL for relative yield, relative grain fertility, relative biomass, and relative harvest index were identified in this study, a QTL for cell-membrane stability (marked by RZ698–RM219) was also reported previously (Tripathy et al. 2000). In another study, a QTL for OA was identified in this region, and it was also shown that this region corresponded to a region in wheat where a QTL for ABA content was detected (Zhang et al. 2001). In the region RM240–RM166 on chromosome 2 where a QTL for relative yield was detected in this study, a QTL for relative yield (Babu et al. 2003) and two

2 4 5 9 11

7 11 12

1 2 4 4 7 9 11

1 3 4 7 8

1 3 4 7 8 11

MRDC

RGDC

DRVC

RVC

RGVC

QRgvc1 QRgvc3 QRgvc4 QRgvc7a QRgvc8 QRgvc11

QRvc1 QRvc3 QRvc4 QRvc7 QRvc8

QDrvc1b QDrvc2a QDrvc4a QDrvc4b QDrvc7 QDrvc9 QDrvc11b

QRgdc7 QRgdc11b QRgdc12

QMrdc2 QMrdc4a QMrdc5 QMrdc9a QMrdc11

QTL

RM472–RM104 RM231–RM489 RM349–RM131 RM125–MRG4449 RM72–RM331 RM202–RM287

RM472–RM104 RM231–RM489 RM349–RM131 RM125–MRG4449 RM404–RM339

RM23–RM493 RM526–RM221 RM471–RM142 RM470–RM317 RM134–RM248 RM316–RM219 RM287–RM229

RM125–MRG4449 RM287–RM229 RM511–MRG4341

MRG2762–RM526 RM307–RM471 RM421–RM274 RM316–RM219 RM287–RM229

Intervala

2.5 6.6 5.9 3.9 3.6 2.1

2.6 8.9 6.4 5.6 4.9

5.6 6.0 2.5 4.8 2.8 3.2 2.4

7.5 3.6 2.8

3.0 4.7 3.0 4.5 5.6

LOD

10.60 20.29 5.43 8.56 4.93 5.52 4.60

ÿ1.80 2.51 ÿ1.30 1.63 1.23 ÿ1.30 ÿ1.20

0.09 ÿ0.14 0.16 0.12 0.10 ÿ0.08

5.99 14.13 17.17 10.64 7.07 4.87

5.30 19.83 17.70 13.31 9.75

17.49 8.63 6.24

ÿ0.04 ÿ0.03 0.02

8.83 ÿ17.44 16.11 14.42 12.31

11.07 11.16 7.70 9.20 14.04


2.05 ÿ2.07 1.74 ÿ1.88 ÿ2.33

Additive effectb

b

QRvc3 QRvc4 QRvc6 QRvc7

QRgvc3 QRgvc7b QRgvc11

3 7 11

QDrvc1a QDrvc2b QDrvc4a QDrvc1a

QRgdc3 QRgdc4 QRgdc11a QRgdc11b

QMrdc4b QMrdc9b

QTL

3 4 6 7

1 2 4 11

3 4 11 11

4 9

Chromosome


a

Chromosome

Traits

2003

RM231–RM489 RM134–RM248 RM202–RM287

RM231–RM489 RM349–RM131 RM527–RM564 RM125–MRG4449

RM428–RM490 M262–MRG2762 RM471–RM142 RM286–RM332


RM255–RM349 RM160–RM215

Intervala

2004

2.7 2.9 3.5

8.6 5.0 2.7 4.0

2.5 2.7 4.5 2.8

2.9 2.4 3.7 3.3

2.8 2.6

LOD

16.35 12.13 18.19 9.29

31.47 22.34 9.07 15.03

9.70 10.53 13.71

ÿ18.28 14.96 9.72 12.26 ÿ0.09 0.09 ÿ0.10

14.80 9.70 16.85 16.40

12.94 10.96


1.74 1.54 ÿ1.88 1.34

ÿ0.04 ÿ0.04 0.05 ÿ0.05

2.84 2.53

Additive effectb

QTL for root traits under controlled conditions resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109

TABLE 9

Genetic Basis of Drought Resistance in Rice 1225

QDrvd2 QDrvd4a QDrvd4b QDrvd9 QDrvd10

QDird2 QDird4b

2 4 4 9 10

2 4

1 7 7

1 3 4 7 8

1 3 4 7 8 11

DRVD

DIRD

DIDRV

RVD

RGVD

QRgvd1 QRgvd3 QRgvd4b QRgvd7 QRgvd8 QRgvd11

QRvd1 QRvd3 QRvd4b QRvd7 QRvd8 RM472–RM104 RM231–RM489 RM349–RM131 RM125–RM4449 RM404–RM339 RM202–RM287

RM472–RM104 RM231–RM489 RM349–RM131 RM125–RM4449 RM404–RM339

RM237–RM403 RM125–RM4449 RM134–RM248

RM29–RM341 RM471–RM142

RM526–RM221 RM335–RM307 RM470–RM303 RM219–RM296 RM596–RM271

RM523–RM231 RM454–MRG4371 RM125–RM4449 RM339–RM342

RM335–RM307 RM470–RM303 RM286–RM332

Intervala

2.9 6.0 4.4 2.7 5.1 3.5

2.7 8.3 6.1 5.9 5.2

3.4 3.0 2.8

3.0 3.6

5.5 3.5 6.0 3.6 2.4

5.2 3.4 7.6 2.4

3.2 6.3 2.8

LOD

0.11 ÿ0.15 0.13 0.10 0.13 ÿ0.11

11.37 ÿ19.00 16.40 15.61 14.15

8.33 14.29 12.28 6.36 10.73 8.78

7.43 19.72 15.27 12.23 11.13

11.88 7.22 6.66

7.09 8.89

ÿ1.81 1.94 1.81 ÿ1.46 ÿ1.37

11.05 7.28 11.26 7.99 5.06

2.23 ÿ1.77 2.21 ÿ1.86 ÿ1.5

11.18 9.23 16.81 5.69

7.01 14.63 6.19

ÿ1.77 2.51 1.67 0.05 0.04 ÿ0.06 ÿ0.03


Additive effectb

b

QRgvd2 QRgvd4a QRgvd11

QRvd3 QRvd4a QRvd7

3 4 7

2 4 11

QDidrv2

QDird2 QDird4a

QDrvd3 QDrvd11

QRgdd3 QRgdd7 QRgdd9

QMrdd4c QMrdd9

QTL

2

2 4

3 11

3 7 9

4 9

Chromosome


a

QRgdd3 QRgdd6 QRgdd7 QRgdd8

3 6 7 8

RGDD

QDidrv1 QDidrv7a QDidrv7b

QMrdd4a QMrdd4b QMrdd11

4 4 11

MRDD

QTL

Chromosome

Traits

2003

RM145–RM324 RM470–RM303 RM202–RM287

RM231–RM489 RM470–RM303 RM125–RM4449

RM573–RM497

RM29–RM341 RM307–RM471

RM473–RM487 RM286–RM332

RM523–RM231 RM125–RM4449 RM219–RM296

RM349–RM131 RM160–RM215

Intervala

2004

4.1 4.4 2.3

4.1 3.5 3.8

2.9

3.5 3.0

3.6 3.2

2.4 2.8 2.4

2.6 4.4

LOD

12.53

15.23 12.94 15.22

14.09 15.97 9.89

2.30

ÿ15.21 13.28 15.93

ÿ0.13 0.14 ÿ0.11

17.25 11.92

19.77 13.18

ÿ3.15 2.53

ÿ0.21 0.16

8.52 12.17 9.18

11.86 19.59


0.05 ÿ0.06 ÿ0.05

1.62 4.04

Additive effectb

The main-effect QTL for root traits under drought stress and drought-induced root growth resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109

TABLE 10

1226 B. Yue et al.


OA-related candidate genes, BADH1 and BADH2, were located (Robin et al. 2003). These positional correspondences were suggestive of the possible mechanisms underlying the QTL for DTrelated traits identified in this study, including OA and/ or cell membrane stability, as well as ABA response. Such correspondence also enhanced the assertion that the relative performance of the fitness and productivity traits used in this study largely resulted from the effects of DT and provided further support for the plant-wise drought stress protocol for investigating the genetic basis of DT at the reproductive stage in rice with the effect of DA eliminated. Implications of the results in improvement of plant drought resistance programs: The results may have important implications in improvement of drought resistance in rice-breeding programs. The upland cultivar IRAT109 has higher values in all the important traits of relative performance such as relative yield, relative spikelet fertility, relative biomass, relative grain weight, and relative harvest index. The QTL analysis also showed that it contributed positively in most of the QTL that were consistently resolved for these traits. In addition, this cultivar also has high potential yield and biomass comparable with many high-yielding cultivars (data not shown). Thus, IRAT109 may be a very good germ plasm resource for drought resistance breeding in rice and also for discovering genes for drought resistance. In this study, more than a half of the QTL each explained .10% of phenotypic variation for the DT and DA traits, which should be potentially useful for genetic improvement of late season drought resistance in rice. In particular, QTL in the four regions related to DT and in the two regions related to DA that matched with QTL in previous reports may be exploited in breeding programs. Indicator traits for drought resistance can provide convenient selection criteria for breeding programs. In maize the silk-tassel interval was identified as a highly indicative secondary trait for drought-resistant breeding (Bolanos and Edmeades 1996). Spikelet fertility can be visually estimated under field conditions and has been used as an indirect index for drought screening in rice (Garrity and O’Toole 1994; Fukai et al. 1999). The results from this study also clearly showed that relative spikelet fertility or fertility under drought stress conditions is not only a highly informative indicator for severity of drought stress, but also the most important determinant of yield under drought stress conditions. In addition, a major QTL (QRsf9) governing this trait was identified in this study together with closely linked flanking markers on both sides. Thus, spikelet fertility under drought stress can be used as both an indicator and a target in the selection processes using molecularmarker-assisted selection. In summary, the results clearly indicated that the genetic dissection of complex traits like drought re-

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sistance could be achieved with special experimental designs. In particular, the plant-wise drought treatment protocol as adopted in this study may provide a generally useful method for independent evaluation of the individual components (such as DA, DE, and DT) contributing to drought resistance in rice as well as in other species. The genetic basis of each component could be characterized by further resolving the component into individual QTL that could be used either in breeding programs by marker-assisted selection or as the starting point for gene identification using various approaches. We thank Abraham Blum and John O’Toole for their technical advice at various stages of this work. This research was supported by grants from the National Program on the Development of Basic Research, the National Special Key Project on Functional Genomics and Biochips, the National Natural Science Foundation of China, and the Rockefeller Foundation.

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