2006 genetic basis of drought resistance at reproductive stage in rice
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  • 1. Copyright Ó 2006 by the Genetics Society of AmericaDOI: 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 watershortage and uneven distribution of rainfall makes the 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 rootimprovement of drought resistance especially impor- morphology (Champoux et al. 1995; Ray et al. 1996;tant (Luo and Zhang 2001). Fulfillment of this goal Price and Tomos 1997; Yadav et al. 1997; Ali et al. 2000;would be enhanced by an understanding of the genetic Courtois et al. 2000; Zheng et al. 2000; Zhang et al.and molecular basis of drought resistance. 2001; Kamoshita et al. 2002; Price et al. 2002). However, However, little progress has been made in character- it is not clear how these attributes are related to theizing the genetic determinants of drought resistance, performance of the genotypes at the whole-plant level,because it is a complex phenomenon comprising a num- and how they function to reduce the drought damage tober of physio-biochemical processes at both cellular fitness- and productivity-related traits.and organismic levels at different stages of plant de- Plants are most susceptible to water stress at thevelopment (Tripathy et al. 2000). Drought resistance reproductive stage. Dramatic reduction of grain yieldincludes drought escape (DE) via a short life cycle or occurs when stress coincides with the irreversible re-developmental plasticity, drought avoidance (DA) via productive processes, making the genetic analysis ofenhanced water uptake and reduced water loss, drought drought resistance at the reproductive stage crucially im-tolerance (DT) via osmotic adjustment (OA), antioxi- portant (Cruz and O’Toole 1984; Price and Courtoisdant capacity, and desiccation tolerance. The recent 1999; Boonjung and Fukai 2000; Pantuwan et al.development of high-density linkage maps has provided 2002). However, variation of flowering time in segre-the tools for dissecting the genetic basis underlying gating populations often made the phenotyping ofcomplex traits, such as drought resistance, into individ- drought resistance rather inaccurate. Staggering theual components. Quantitative trait locus (QTL) map- seed-sowing time has been suggested to synchronize theping has been carried out in an attempt to determine flowering time of a population in QTL mapping (Pricethe genetic basis of several traits that may be related to and Courtois 1999). Lanceras et al. (2004) also re-drought resistance, including OA (Lilley et al. 1996; ported QTL mapping of yield and yield componentsZhang et al. 1999, 2001; Robin et al. 2003), cell- under different water regimes in the field by synchro- nizing flowering time of the mapping population. How- 1 ever, the success has been limited because of the Corresponding author: National Key Laboratory of Crop GeneticImprovement, Huazhong Agricultural University, Hongshang District, difficulty in achieving a real synchronization of theWuhan 430070, China. E-mail: qifazh@mail.hzau.edu.cn flowering time in a segregating population. In additionGenetics 172: 1213–1228 (February 2006)
  • 2. 1214 B. Yue et al.to flowering time, segregation for plant size and root Traits and measurements: A total of 21 traits were scored involumes also confounds the accuracy of QTL mapping. this study; 9 of them were traits collected from the above- ground part of the plants and the other 12 were root traitsIt is almost impossible to distinguish the genetic basis of (Table 1).DT from other contributing factors (such as DA and The traits collected from above-ground parts were related toDE) in drought resistance under field conditions in fitness and productivity, including yield and yield componentwhich drought stress is applied to and withdrawn from traits, biomass, and fertility. Yield and yield-related traits wereall plants simultaneously. examined for all plants under stress and the control con- In this study, we adopted a protocol for drought ditions, including grain yield per plant (in grams), number of spikelets per panicle, 1000-grain weight (in grams), fertiletreatment by planting and stressing rice plants of a panicle rate (%), spikelet fertility (%), biomass (in grams) andrecombinant inbred line (RIL) population in individual harvest index (%). Fertile panicle rate was the proportion ofpolyvinyl chloride (PVC) pipes in which the various the number of fertile panicles (with 5 grains or more on eachgenotypes were stressed to the same extent at the same panicle) in all the panicles of a plant. Spikelet fertility wasdevelopmental stage. We showed that such an experi- measured as the number of grains divided by the total number of spikelets of a plant. Harvest index was scored as grain yieldmental design cleanly separated DT from DA, thus divided by the total dry matter of the above-ground part. Theallowing relatively independent analyses of the genetic relative performance of the phenotypes for each trait was mea-bases of DT and DA. sured 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, MATERIALS AND METHODS were also recorded. Leaf-drying score was recorded on the basis of the degrees of leaf drying immediately after rewatering Plant materials and drought stress treatment: A population as 0 (no leaf drying) to 4 (.20% of the leaf area was drying).consisting of 180 RILs at F9/F10 generation was developed Number of days to leaf rolling of each plant was recorded asfrom a cross between the lowland rice cultivar Zhenshan 97 the number of days from the application of drought stress to(Oryza sativa L. ssp. indica) and the upland rice cultivar the day when all leaves became rolled at noon.IRAT109 (O. sativa L. ssp. japonica). Zhenshan 97 is the main- The root traits were scored at seed maturity of the plants. Totainer line for a number of elite hybrids widely cultivated in measure these traits, the plastic bag containing the soil andChina, and IRAT109 was developed in Cote d’Ivoire. roots was pulled out from the PVC pipe and laid out on a 2-mm For phenotyping, rice plants were grown in PVC pipes, one sieve screen frame. The lowest visible root in the soil afterplant per pipe, under a rain-out shelter with movable roofs. removing the plastic bag was scored as the maximum rootThe pipe was 20 cm in diameter and 1 m in length with holes depth (in centimeters). The body of soil and roots was cut intoon two sides at 25, 50, and 75 cm from the top. Each pipe was two parts at 30 cm from the basal node of the plant and the soilloaded with a plastic bag filled with 38 kg of thoroughly mixed was washed away carefully to collect roots. The volumes (insoil composed of two parts of clay and one part of river sand, to milliliters) of roots from the two parts were measured in awhich 25 g of fertilizers (including 4 g each of N, P2O5, and cylinder using the water-replacing method (Price and TomosK2O) was added. 1997). The root mass below 30 cm was considered to be deep Sowing time was staggered among the lines to synchronize root, from which a number of measurements were derived.flowering on the basis of the heading dates of the lines Root growth rate in depth and root growth rate in volume wereobserved in 2002. Three to five germinated seeds were directly calculated by dividing the maximum root depth and the totalsown in each pipe and only one healthy plant was kept at root volume, respectively, by the root growth period (number30 days after sowing. At the beginning of the tillering stage, 1 g of days from sowing to heading of the plant). Drought-of urea (dissolved in water) was applied to each pipe. The induced root growth was evaluated by two traits: drought-plants were fully irrigated by watering every day until the induced root growth in depth and drought-induced deep-rootdrought treatment. Drought stress was individually applied to rate in volume, which were calculated as the differences ofeach plant at the booting stage. To apply drought stress, water maximum root depth and deep-root rate in volume betweenwas added to the full capacity of the pipe, the plugs on the pipe the measurements obtained under drought stress and controlwere then removed, and small holes were punched on the conditions.plastic bag to drain the water slowly. Rain was kept off by The abbreviations for and descriptions of these traits areclosing the roof during periods of rain. When all leaves of a listed in Table 1 and used hereafter.stressed plant became fully rolled, as visualized at noon—a DNA markers, map construction, and QTL analysis: A totalpoint corresponding to the relative water content in the range of 245 nuclear simple sequence repeat (SSR) markers wereof 72–75%, as checked in this study—watering was applied to used for constructing the linkage map. The SSR primers andthe full capacity of the pipe. With the full water level main- marker assays essentially followed Temnykh et al. (2000, 2001)tained for 1 day, the second cycle of drought stress was applied and McCouch et al. (2002). The program of Mapmaker/EXPto the plant until all leaves became fully rolled again. After the 3.0 (Lincoln et al. 1992) was used to construct the geneticsecond round of stress, watering was resumed for the rest of linkage map. The means of the traits were used to identify QTLthe life cycle. by Windows QTL Cartographer 2.0 (Zeng 1994). The LOD The pipes were laid out in six blocks following a randomized thresholds were determined by 500 random permutations,complete block design. Drought stress was applied to three of which resolved that, at a false positive rate of ,0.05 for eachthe blocks with the other three blocks used as control. In 2003, trait, the LOD thresholds ranged from 1.9 to 2.4 for 20 of150 RILs and the parents were phenotyped with two pipes per the 21 traits. The only exception was relative fertile paniclesblock for each genotype. In 2004, 75 RILs and the parents were (RFP), in which the LOD threshold was 2.6 for the data oftested to represent the resistant and susceptible lines on the 2003 and 4.1 for 2004. For ease of presentation, a uniformbasis of relative yield in 2003, with only one pipe per block for threshold of 2.4 was adopted for the 20 traits, and 2.6 and 4.1each genotype. were used for RFP for the 2 years, respectively. The results of
  • 3. Genetic Basis of Drought Resistance in Rice 1215 TABLE 1 Abbreviations, full names, and descriptions of the traits investigated in this studyAbbreviation Trait DescriptionRY Relative yield per plant (%) Yield per plant under drought stress/Yield per plant under control conditionsRSF Relative spikelet fertility (%) Spikelet fertility under drought/Spikelet fertility under control conditionsRBM Relative biomass (%) Biomass per plant under drought/Biomass per plant under control conditionsRFP Relative rate of fertile panicles (%) Rate of fertile panicles (with five seeds or more) per plant under drought/Rate of fertile panicles per plant under control conditionsRHI Relative harvest index (grain yield/biomass) (%) Harvest index under drought/Harvest index under control conditionsRGW Relative grain weight (%) Weight of 1000 seeds under drought/Weight of 1000 seeds under control conditionsRSN Relative number of spikelets per panicle (%) No. of spikelets per panicle under drought/no. of spikelets per panicle under control conditionsLDS Leaf-drying score Degrees of leaf drying immediately after rewatering, scored 1 (no drying) to 5 (.20% area dried)DLR No. of days to leaf rolling No. of days to leaf rolling starting from day of drought treatmentMRDC Maximum root depth under control (cm) The lowest visible root at the soil surface after removing the plastic bag under control conditionsMRDD Maximum root depth under drought (cm) The lowest visible root at the soil surface after removing the plastic bag under drought conditionsDIRD Drought-induced root growth in depth (cm) The difference of maximum root depth under drought and control conditionsRGDC Root growth rate in depth under control Maximum root depth divided by root growth period conditions (cm/day) under control conditionsRGDD Root growth rate in depth under drought Maximum root depth divided by root growth period conditions (cm/day) under drought conditionsRVC Root volume under control conditions (ml) The volume of roots under control conditions measured using the water-replacing methodRVD Root volume under drought conditions (ml) The volume of roots under drought conditions measured using the water-replacing methodDRVC Deep root rate in volume under control Percentage of root volume ,30 cm in the total root conditions (%) volume under control conditionsDRVD Deep root rate in volume under drought Percentage of root volume ,30 cm in the total root conditions (%) volume under drought conditionsRGVC Root growth rate in volume under control Total root volume divided by root growth period under conditions (ml/day) control conditionsRGVD Root growth rate in volume under drought Total root volume divided by root growth period under conditions (ml/day) drought conditionsDIDRV Deep root rate in volume induced by drought The difference in deep-root rate in volume under conditions (%) drought and control conditionsboth years were presented for QTL with a LOD score .2.4 in the traits, although the relative proportions of variance1 year but in the range of 2.0–2.4 in the other year for the 20 varied from one trait to another (Table 3).traits. 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 RESULTS productivity (Table 2). The differences between the two Phenotypic variation of the parents and RILs: The parents for relative yield, relative biomass, relative spike-phenotypic differences between parents as well as the let fertility, and relative grain weight were significant atvariation in the RIL population are summarized in the 0.01 probability level in 2003. Thus Zhenshan 97Table 2. Transgressive segregation was observed in the suffered much more drought damage than IRAT109.RIL population for all the traits investigated. ANOVA of The reverse performance was observed between thethe data collected in 2003 indicated that variation due parents for the two traits related to water status (Tableto genotype differences was highly significant for all 2). The leaf-drying score of IRAT109 was significantly
  • 4. 1216 B. Yue et al. TABLE 2 The measurements of the traits in the RIL population and the parents in 2003 and 2004Trait Zhenshan 97 IRAT109 Mean of RILs Range of RILsRY 43.9/65.7*** 80.6**/81.9 58.2/52.6 (19.6–90.8)/(17.9–90.5)RSF 54.2/69.1 74.3**/88.6 63.9/63.7 (24.2–94.5)/(22.4–95.6)RBM 79.0/81.8 94.9**/89.6 90.4/81.0 (70.3–100.0)/(57.1–99.2)RFP 88.3/92.5**** 93.5/100.0**** 80.0/94.0 (28.1–100.0)/(68.6–100.0)RHI 52.1/66.9 65.6/74.8 59.2/58.6 (20.3–100.0)/(18.3–96.9)RGW 73.5/76.2 88.0**/97.8*,**** 87.6/82.0 (58.0–104.1)/(63.2–104.1)RSN 89.6/98.3**** 91.9/94.8*** 84.8/94.3 (52.1–100.5)/(68.6–100.2)LDS 3.0*/2.67* 1.7/1.3 2.4/1.8 (1.0–3.8)/(0.3–3.3)DLR 18.5**/22.0*,*** 10.3/16.7**** 12.1/19.4 (7.0–17.5)/(8.0–26.7)MRDC 53.6/53.3 61.1**/67.0* 61.8/57.9 (47.2–79.8)/(39.0–75.5)MRDD 76.7/82.7 79.5/92.3*** 81.9/87.1 (64.8–94.5)/(69.0–95.7)DIRD 23.1*/29.4 18.4/25.3*** 20.1/29.2 (7.0–33.8)/(14.7–48.0)RGDC 0.8/0.8 0.8/0.9 0.8/0.9 (0.6–1.0)/(0.5–1.0)RGDD 1.2/1.3 1.0/1.3*** 1.0/1.1 (0.7–1.4)/(0.8–1.6)RVC 84.0***/51.0 84.3***/70.0* 112.3/82.6 (46.3–231.4)/(43.9–146.9)RVD 73.0***/45.2 102.5**,***/75.7 107.8/89.7 (43.0–234.6)/(29.8–175.1)DRVC 8.7/8.9 22.4**,***/12.8* 13.3/9.2 (2.5–28.8)/(0.8–22.4)DRVD 17.6/16.4 25.6/33.0*,*** 19.0/24.8 (3.7–36.3)/(10.6–44.1)RGVC 1.3***/0.8 1.1/0.8 1.4/1.0 (0.8–2.3)/(0.7–1.7)RGVD 1.1/0.7 1.3/1.1 1.3/1.1 (0.6–2.3)/(0.4–1.8)DIDRV 8.9/7.5 3.2/20.2**,**** 5.7/15.6 (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 higherthan the other parent at the 0.05 and 0.01 probability levels based on t-test. ***,****Significantly higher than the other year of thesame parent at the 0.05 and 0.01 probability levels based on t-test.less than that of Zhenshan 97 in both years, while grain weight, and relative harvest index were highlyZhenshan 97 could sustain longer time than IRAT109 correlated with each other (Table 4). This suggested thatbefore leaf rolling as reflected by the DLR scores. the yield loss and harvest index reduction under drought For most of the root traits (Table 2), IRAT109 had stress in late season were associated with the reduction ofhigher values than Zhenshan 97 under both control and spikelet fertility, fertile panicle rate, biomass and graindrought stress conditions in both years. In at least one weight. In particular, a very high correlation (0.85–0.95)year, the differences between parents for maximum root was observed between relative yield, relative spikeletdepth under control, root volume and deep-root rate fertility, and relative harvest index in both years.under both drought stress and control conditions, and Figure 1 illustrates the relationships of relative yielddrought-induced deep-root rate in volume were signif- and relative biomass with yield and biomass under con-icant. Zhenshan 97, however, showed more drought- trol conditions. It was clear from Figure 1 that relativeinduced root growth in depth than IRAT109 did, and yield was not correlated with yield under control con-the difference was significant in 2003. Again, trans- ditions, and thus genotypes with high and low yieldgressive segregation was observed in all the root traits. potential were equally stressed. Similarly, there was little When the data collected from the 2 years were correlation between relative biomass and biomass un-compared, DLR was substantially higher in 2004 than der control conditions, and thus genotypes with largein 2003 for both parents (Table 2), indicating that the and small plant sizes were equally stressed. Moreover,stress developed more slowly in 2004 due to the milder relative yield was not significantly correlated with bio-weather conditions during drought stress (the tempera- mass under control, and neither was relative biomassture and evaporation was higher in 2003). Consequently, a significantly correlated with yield under control.number of other traits also showed significant differences There was no correlation between the two traits re-between the 2 years in one or both parents, including lated to water status of the plants (Table 4). There wererelative yield, relative number of fertile panicles, relative no consistent correlations between these two traits withgrain weight, and relative spikelet number. Significant the relative performance of the traits related to fitnessdifferences between the 2 years were also observed in and productivity in 2 years, except the negative corre-several root traits in one or both parents. lation detected in both years between relative biomass Correlations of the traits: The traits related to fitness and number of days to leaf rolling.and productivity, e.g., relative yield, relative spikelet fertil- The root traits investigated were also highly corre-ity, relative rate of fertile panicle, relative biomass, relative lated with each other (Table 5). In general, constitutive
  • 5. Genetic Basis of Drought Resistance in Rice 1217 TABLE 3 TABLE 3 ANOVA of the traits based on the data of 2003 (Continued)Trait Variation d.f. MS F P Trait Variation d.f. MS F PRY Genotype 151 1262.89 7.23 0.0000 RGVD Genotype 151 0.43 6.9 0.0000 Block 2 1550.86 8.88 0.0002 Block 2 0.74 11.92 0.0000 Error 302 174.56 Error 302 0.06RSF Genotype 150 1222.68 3.83 0.0000 DIDRV Genotype 151 209.78 2.73 0.0000 Block 2 946.45 2.97 0.0521 Block 2 204.93 2.67 0.0702 Error 300 319.06 Error 302 76.72RBM Genotype 150 289.24 1.38 0.0120 Block 2 623.25 2.97 0.0518 MS, mean square; F, F-statistic. Error 300 209.63RFP Genotype 150 589.01 2.90 0.0000 Block 2 1738.76 8.54 0.0003 root growth (maximum root depth and root volume Error 300 203.45 under control) had high and consistent correlationsRHI Genotype 149 1560.11 3.14 0.0000 with other root traits. For example, maximum root Block 2 978.40 1.97 0.1391 depth was highly significantly correlated in both years Error 298 497.14 with all the root traits, except drought-induced rootRGW Genotype 150 138.38 2.77 0.0000 growth in volume. A similar situation was also obvious Block 2 28.76 0.58 0.5683 for root volume under control that was also highly cor- Error 300 49.93RSN Genotype 150 323.34 2.83 0.0000 related with most root traits. The highest correlation Block 2 31.27 0.27 0.7655 (.0.90) detected was between root volume and root Error 300 114.38 growth rate under both control and drought conditions.LDS Genotype 149 2.32 4.83 0.0000 Correlations between traits in different groups are Block 2 5.89 12.27 0.0000 shown in Table 6. In general, there was not much cor- Error 298 0.48 relation between the relative performance of fitness-DLR Genotype 151 16.00 7.07 0.0000 and productivity-related traits and the root traits, with Block 2 6.67 2.95 0.0525 Error 302 2.26 exceptions of only a few marginal cases in 2004, all ofMRDC Genotype 151 109.84 3.73 0.0000 which suggested root growth under drought had small Block 2 1397.86 47.42 0.0000 negative effects on yield and biomass. Thus, variation in Error 302 29.48 root traits contributed very little toward reducing theMRDD Genotype 150 126.42 2.70 0.0000 drought stress of the upground parts in this experiment. Block 2 3330.98 71.11 0.0000 In addition, relative yield, relative biomass, and relative Error 300 46.84 fertility were not significantly correlated with floweringDIRD Genotype 149 123.42 2.01 0.0000 Block 2 875.99 14.24 0.0000 time (data not shown), as expected on the basis of the Error 298 61.53 experimental design. All this demonstrated that theRGDC Genotype 151 0.02 2.59 0.0000 pipe planting effectively minimized the effects of DA or Block 2 0.22 25.52 0.0000 DE on relative yield and yield-related traits. Therefore, Error 302 0.01 the relative yield, relative spikelet fertility, and relativeRGDD Genotype 150 0.06 5.07 0.0000 biomass examined in this study were indeed regulated Block 2 0.17 15.4 0.0000 almost exclusively by DT mechanisms under the ex- Error 300 0.01RVC Genotype 151 4398.12 10.35 0.0000 perimental conditions and thus can be viewed as DT Block 2 411.89 0.97 0.3824 traits although the underlying mechanisms remain to Error 302 424.96 be investigated.RVD Genotype 151 5195.99 12.85 0.0000 Table 6 also showed no correlation between leaf- Block 2 1578.62 3.90 0.0211 drying score and the root traits. Number of days to leaf Error 302 404.31 rolling was negatively correlated with a number of traitsDRVC Genotype 151 0.02 5.45 0.0000 measuring root volumes under both drought stress and Block 2 0.07 19.09 0.0000 Error 302 0.004 control conditions; thus leaf rolling occurred sooner inDRVD Genotype 151 0.04 4.32 0.0000 plants with larger root volumes. However, there was one Block 2 0.04 4.87 0.0083 highly significant positive correlation between number Error 302 0.01 of days to leaf rolling and root growth in depth underRGVC Genotype 151 0.42 5.85 0.0000 drought, indicating drought-induced root growth in Block 2 depth may have a positive effect on delaying leaf rolling. Error 302 0.07 The linkage map: A total of 410 SSR markers were (continued ) surveyed and 245 (59.8%) of them showed polymor- phism between the two parents. A linkage map was
  • 6. 1218 B. Yue et al. TABLE 4 Coefficients of pairwise correlations of the above-ground traits investigated in 2003 and 2004 RY RSF RBM RFP RHI RGW RSN LDSRSF 0.88/0.85RBM 0.35/0.40 0.15/0.03RFP 0.58/0.46 0.64/0.51 0.26/0.14RHI 0.95/0.85 0.89/0.94 0.15/ÿ0.07 0.46/0.44RGW 0.44/0.61 0.36/0.47 0.10/0.27 0.30/0.38 0.44/0.48RSN 0.37/0.03 0.21/ÿ0.07 0.23/ÿ0.04 0.27/0.01 0.33/0.08 0.32/0.04LDS ÿ0.31/0.03 ÿ0.26/0.05 ÿ0.23/0.13 ÿ0.34/0.14 ÿ0.24/0.05 ÿ0.15/0.04 ÿ0.21/0.09DLR ÿ0.36/ÿ0.21 ÿ0.23/ÿ0.11 ÿ0.29/ÿ0.37 ÿ0.12/0.00 ÿ0.33/ÿ0.03 ÿ0.39/0.05 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 ‘‘/’’ isthe result of 2003, and the number at the right is the result of 2004.constructed using Mapmaker analysis based on data from both cases, one QTL was detected in both years andthe 245 SSR markers assayed on the 180 RILs (Figure 2). the others were detected in only 1 year. As in the traitsThe map covered a total length of 1530 cM with an av- for relative performance described above, the regionerage interval of 6.2 cM between adjacent markers. RM219–RM296 on chromosome 9 showed a large effect QTL for relative performance of the traits related on number of days to leaf rolling (QDlr9). Also a QTLto fitness and productivity: QTL detected for relative for leaf-drying score (QLds3b) had a large effect on theperformance of seven traits related to fitness and pro- trait in both years.ductivity are listed in Table 7(see also Figure 2). A total QTL for root traits under control conditions: A totalof 27 QTL were resolved for the seven traits, including of 36 QTL were resolved for the five root traits under8 QTL detected in both years and 19 QTL observed in control conditions (Table 9; Figure 2), of which 7 were de-only 1 year. The detection is quite consistent, consider- tected in both years and the remaining 29 in only 1 year.ing the large scale of the experiment, the nature of the Again, the effects observed in 2004 were larger thantraits, and the secondary statistics of ratios as input data. those in 2003 for all the QTL detected in both years,All the QTL that were detected in both years appeared except for one QTL, QRgvc3, for root growth rate into have larger effects in 2004 than in 2003, as indicated volume under control conditions. While the IRAT109by the LOD scores and the amounts of variation ex- alleles at 22 of the 36 QTL contributed positively to theplained. This is expected since the lines planted in 2004 root traits, alleles from Zhenshan 97 at 5 of the 7 QTLwere selected on the basis of the extreme phenotypes that were observed in both years had positive effects onfrom the previous year. the root traits. Of the 19 QTL each explaining .10% of Alleles from IRAT109 at 14 of the QTL had positive phenotypic variation, the IRAT109 alleles at 12 QTLeffects on the relative performance of these traits, while contributed to the increase of the trait measurements.alleles from Zhenshan 97 at the other 13 loci contrib- Again, there were a number of regions where QTL foruted positively to the relative performance (Table 7). Of two or more traits were detected, including the intervalsthe 8 QTL that were consistently detected in both years, RM472–RM104 on chromosome 1, RM231–RM489the IRAT109 alleles at 7 QTL had positive effects on the on chromosome 3, both RM471–RM142 and RM349–relative performance of these traits. Interestingly, one RM131 on chromosome 4, both RM125–MRG4499 andregion on chromosome 9, RM316–RM219, was partic- RM429–RM248 on chromosome 7, RM316-RM219 onularly active by exhibiting significant effects simulta- chromosome 9, and RM287–RM229 on chromosome 11.neously on relative yield (QRy9), relative spikelet fertility In all the QTL having effects on multiple traits, except(QRsf9), relative biomass (QRbm9), and relative harvest one, alleles from the same parents contributed in theindex (QRhi9). Another region on chromosome 8, same direction to different traits, suggesting the likeli-RM284–RM556, was detected to have a significant ef- hood that different QTL are due to the effects of thefect on relative yield (QRy8), relative spikelet fertility same genes.(QRsf8), and relative number of fertile panicles (QRfp8). QTL for root traits under drought stress: A total ofIt is also worth noting that almost all the QTL detected 38 QTL were observed for the seven root traits underin both years had large effects on the traits as reflected drought stress conditions (Table 10; Figure 2), includingby the large proportions of the phenotypic variation 6 detected in both years and 32 detected in only 1 year.explained (10% or more). Unlike other traits described above, the effects of QTL QTL for the two plant water status traits: Six QTL detected in 2004 were not necessarily larger than thosewere resolved for leaf-drying score and four QTL for resolved in 2003 for the QTL detected simultaneously innumber of days to leaf rolling (Table 8; Figure 2). In both years. Alleles from IRAT109 at 23 of the 38 QTL
  • 7. Genetic Basis of Drought Resistance in Rice 1219 Figure 1.—Scatter plots of relative performance of yield and biomass against yield and biomass under control conditions in2003 (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, notypic variation, alleles from IRAT109 at 17 loci hadwhereas at the other 15 QTL, alleles from Zhenshan 97 positive effects on these root traits.were in the direction of increasing the trait measure- The QTL were distributed very unevenly among thements. Of the 22 QTL each explaining .10% of phe- chromosomes, with 11 QTL on chromosome 4, 5 QTL
  • 8. 1220 B. Yue et al. on chromosome 7, 4 QTL on each of chromosomes 2 ÿ0.18/0.30 RGVD 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 0.81/0.73 ÿ0.21/0.06 RGVC 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 re- 0.26/0.33 0.21/0.51 0.58/0.80 gions resolved with QTL for relative performance of DRVD fitness- and productivity-related traits, 9 overlapped with the QTL intervals for root traits (Figure 2). One region Coefficients of pairwise correlations of the root traits investigated in this study in 2003 and 2004 0.62/0.75 on chromosome 9, RM316–RM219, in which multiple 0.50/0.46 0.42/0.50 ÿ0.26/0.19 QTL were detected, showed relatively large effects on DRVC 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 0.46/0.51 0.19/0.45 0.80/0.77 0.96/0.94 ÿ0.23/0.21 the two types of traits were contributed by different RVD 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 dif- 0.89/0.87 0.53/0.47 0.22/0.32 0.95/0.93 0.83/0.73 ÿ0.27/0.04 ferent directions of the allelic contributions for most RVC overlapping QTL, were in good agreement with the results of correlation analysis, further suggesting that TABLE 5 root traits and relative performance of the fitness and productivity traits had different genetic determinants. ÿ0.49/ÿ0.65 ÿ0.52/ÿ0.62 ÿ0.10/ÿ0.19 ÿ0.29/ÿ0.43 ÿ0.34/ÿ0.38 0.28/0.03 0.46/0.21 Number of days to leaf-rolling and leaf-drying score RGDD 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 over- lapped with QTL for the relative performance of fitness- ÿ0.21/ÿ0.25 ÿ0.29/ÿ0.27 ÿ0.02/ÿ0.04 ÿ0.15/ÿ0.10 0.19/ÿ0.01 and productivity-related traits (Figure 2). Of the six 0.67/0.55 0.32/0.36 0.43/0.22 RGDC 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 ÿ0.13/ÿ0.42 ÿ0.26/ÿ0.42 ÿ0.26/ÿ0.35 ÿ0.33/ÿ0.53 0.09/ÿ0.13 ÿ0.20/ÿ0.39 ÿ0.20/ÿ0.27 0.60/0.44 0.46/0.28 days to leaf rolling was significantly correlated with some DIRD of the root traits, while the leaf-drying score had little correlation with either root traits or above-ground traits (Tables 4 and 6). See Table 4 legend for explanations. 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 MRDD DISCUSSION The PVC pipe protocol successfully separated ÿ0.49/ÿ0.80 ÿ0.20/ÿ0.30 drought tolerance and drought avoidance: A major dif- 0.48/0.42 0.38/0.48 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 MRDC ficulty in genetic analysis of drought resistance by apply- ing 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. Pre- DIDRV MRDD RGDD RGDC DRVD RGVD DRVC RGVC DIRD vious studies showed that the drought resistance in- RVD RVC dex (relative yield) was often negatively correlated with
  • 9. Genetic Basis of Drought Resistance in Rice 1221 potential yield and was also dependent on the actual ÿ0.12/ÿ0.24 0.20/ÿ0.04 ÿ0.34/ÿ0.46 ÿ0.36/ÿ0.55 ÿ0.10/ÿ0.34 0.30/ÿ0.19 ÿ0.31/ÿ0.40 ÿ0.37/ÿ0.51 0.32/0.24 0.21/0.22 0.36/0.45 0.46/0.04 developmental stage of the plants when stress treatment DLR 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 stag- gering the sowing date, installing a drip irrigation sys- ÿ0.11/ÿ0.12 ÿ0.09/ÿ0.20 ÿ0.12/ÿ0.07 ÿ0.11/ÿ0.02 ÿ0.02/0.12 ÿ0.10/0.11 0.04/0.01 0.07/0.07 0.05/0.12 ÿ0.05/0.09 ÿ0.02/0.04 0.00/0.09 tem in the plots, normalizing the data by statistical LDS Coefficients of pairwise correlations between above-ground traits and root traits investigated in this study in 2003 and 2004 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 0.09/ÿ0.30 0.09/ÿ0.12 ÿ0.13/ÿ0.11 0.13/ÿ0.18 0.16/ÿ0.18 0.04/ÿ0.25 0.01/ÿ0.13 0.08/ÿ0.15 0.13/ÿ0.14 0.01/0.25 ÿ0.13/0.13 ÿ0.05/0.04 to overall drought resistance at the whole-plant level. RSN In this study, the effect of DE was completely elim- inated 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 ÿ0.02/ÿ0.25 ÿ0.10/ÿ0.02 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 ÿ0.08/0.26 ÿ0.20/0.12 ÿ0.18/0.34 of stress treatment such that genotypes with a deep-root RGW 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 0.09/ÿ0.01 ÿ0.05/ÿ0.15 ÿ0.14/ÿ0.08 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 ÿ0.05/0.11 ÿ0.14/0.06 and biomass under the control conditions. All this indi- RHI cates that the effects of DA and DT were well separatedTABLE 6 in this experimental design. Thus, the relative perfor- mance of fitness- and yield-related traits under drought stress and control conditions unambiguously provided measurements for DT. The root traits, however, pro- ÿ0.03/ÿ0.16 ÿ0.07/ÿ0.18 ÿ0.06/ÿ0.07 ÿ0.18/ÿ0.06 ÿ0.11/ÿ0.15 ÿ0.09/ÿ0.1 0.03/0.01 0.05/0.18 ÿ0.09/0.01 ÿ0.03/0.17 0.05/0.01 0.13/0.08 vided the measurements for DA, although the contri- RFP bution 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 0.01/ÿ0.16 0.11/ÿ0.17 ÿ0.07/ÿ0.10 ÿ0.02/ÿ0.02 0.05/ÿ0.21 0.00/ÿ0.15 ÿ0.03/ÿ0.11 0.04/ÿ0.27 ÿ0.03/ÿ0.02 0.10/0.06 0.11/0.04 0.13/0.09 genetic bases of DT and DA were rarely addressed RBM 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 be- tween QTL for root traits and OA, with alleles for ÿ0.02/ÿ0.15 ÿ0.06/ÿ0.12 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 0.05/0.02 ÿ0.08/0.08 ÿ0.11/0.01 increasing OA and root traits derived from different See Table 4 legend for explanations. RSF 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 DT- 0.01/ÿ0.05 ÿ0.07/ÿ0.14 ÿ0.07/ÿ0.02 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 ÿ0.08/0.05 ÿ0.10/0.07 and DA-related traits were clustered, nearly a half of the RY 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 contrib- DIDRV MRDD MRDC RGDD RGDC DRVD RGVD DRVC RGVC DIRD uted positively to relative yield, relative spikelet fertility, RVD RVC relative biomass, and relative harvest index. The distinct
  • 10. 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 andright sides of the chromosomes, respectively. QTL detected in both years are shown in boldface type. QTL in italics indicate thatthe 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 to the flanking markers. Comparisons with previouscontributions of the parents in QTL in overlapping results indicated that 4 of the 36 QTL regions had posi-regions for DT- and DA-related traits suggested that DT tional correspondence with QTL for root or other DA-and DA had different genetic bases. This also explained related traits reported in previous studies of rice. Forthe lack of correlation between these two sets of traits example, in the region RM472–RM104 on chromosomeunder the experimental conditions. 1 where QTL for root volume, root growth in volume, The genetic complexity of the root traits: Rapid and number of days to leaf rolling were detected in thisdevelopment of a deep-root system is considered a DA study, QTL were also identified for root thickness andstrategy for plants as it enables absorption of water in root weight (Zheng et al. 2003), as well as for relativedeep soil layers (Fukai and Cooper 1995; Price and water content, leaf rolling, and leaf-drying score (BabuCourtois 1999). Although the putative contribution of et al. 2003). The region RM160–RM215 on chromosomeroot traits to DA or drought resistance could not be 9, contributing to maximum root depth under both con-estimated in this experimental design, the QTL map- trol and drought stress in this study, was also identifiedping of various root traits under control and drought as harboring QTL for upland seminal root length andstress conditions in this study provided a comprehensive relative seminal root length in a previous study (Zhengscenario of the genetic controls of root morphology et al. 2003). The region RM470–RM303 on chromosomeunder normal conditions and root restructuring under 4, controlling deep-root rate, maximum root length,drought stress. and root volume under drought stress in this study, A total of 74 QTL were resolved for the 12 root traits corresponded to a region controlling root thickness,that could be assigned to 36 genomic regions according root penetration index, and penetrated root dry weight
  • 11. TABLE 7 QTL for relative performance of traits related to fitness and productivity resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109 2003 2004 Additive Phenotype Additive PhenotypeTraits Chromosome QTL Intervala LOD effectb variationc Chromosome QTL Intervala LOD effectb variationcRY 2 QRy2 RM240–RM166 2.0 4.9 5.81 2 QRy2 RM240–RM166 3.8 7.4 13.64 8 QRy8 RM284–RM556 3.4 ÿ6.2 9.42 3 QRy3 RM203–RM520 2.6 ÿ6.5 9.84 9 QRy9 RM316–RM219 5.0 7.7 14.16 9 QRy9 RM316–RM219 6.8 10.1 25.70 10 QRy10 RM496–RM228 2.5 ÿ5.8 8.29RSF 5 QRsf5 RM421–RM274 3.1 5.3 7.64 3 QRsf3 RM293–RM571 3.2 ÿ7.1 11.51 8 QRsf8 RM284–RM556 2.4 ÿ4.8 6.20 9 QRsf9 RM219–RM296 5.4 10.9 30.46 9 QRsf9 RM316–RM219 3.0 5.2 7.18RBM 2 QRbm2 RM573–RM318 2.3 1.5 6.17 2 QRbm2 RM573–RM318 3.3 5.1 14.56 9 QRbm9 RM316–RM219 3.2 1.6 7.45 5 QRbm5 RM507–RM13 3.7 5.4 18.79 10 QRbm10 RM596–RM271 2.3 1.8 9.17 10 QRbm10 RM596–RM271 5.6 8.5 28.60RFP 8 QRfp8 RM284–RM556 3.1 ÿ4.2 7.52 12 QRfp12 RM235–MRG5454 4.2 ÿ14.1 39.17 12 QRfp12 RM235–MRG5454 4.7 ÿ14.2 37.96RSN 1 QRsn1 RM237–RM403 3.5 3.2 9.55 1 QRsn1 RM237–RM403 2.7 2.8 9.98 2 QRsn2 RM324–RM29 2.6 ÿ2.6 6.06 6 QRsn6 RM454–MRG4371 3.4 ÿ3.3 14.04 12 QRsn12 RM19–RM453 5.3 ÿ4.0 20.86 Genetic Basis of Drought Resistance in RiceRGW 2 QRgw2 RM145–RM324 2.5 ÿ1.8 5.97 3 QRgw3 RM523–RM231 2.9 2.5 11.78 5 QRgw5 RM509–RM430 3.2 2.2 9.34 9 QRgw9 RM444–RM316 2.5 2.3 9.99 7 QRgw7 RM125–MRG4449 2.6 2.1 8.36RHI 2 QRhi2 RM221–RM573 3.2 6.5 8.79 9 QRhi9 RM316–RM219 3.4 7.4 13.57 9 QRhi9 RM316–RM219 4.4 7.4 11.45 10 QRhi10 RM496–RM228 3.5 ÿ7.5 13.90 a Underlined chromosome number and marker intervals indicate QTL detected in both years. b The positive values indicate the alleles from IRAT109 with increasing effects. c Amount of phenotype variation (%) explained by the QTL. 1223
  • 12. 1224 B. Yue et al. as reported previously (Zhang et al. 2001). The region Phenotype variationc 10.95 11.33 16.10 26.36 6.55 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 Additive 32 chromosomal regions, 7 regions harbored QTL for effectb 0.2 0.2 ÿ0.3 ÿ0.9 ÿ1.8 root traits detected under both control and drought stress conditions, 7 regions included root QTL repeat- QTL for plant water status traits resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109 edly detected in 2 years, and 15 were regions in which LOD multiple QTL were resolved. 2.9 2.9 4.2 2.4 7.7 At the 74 QTL for root traits resolved, alleles from the upland parent IRAT109 at 45 QTL had positive effects RM279–RM555 RM489–RM517 RM520–RM293 RM219–RM296 for increasing the trait values, while positive alleles were RM544–RM72 2004 Intervala 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 QLds3a QLds3b QLds2 QTL QDlr8 QDlr9 attributes of the root traits, although IRAT109 may contribute more to DA than Zhenshan 97. The likely mechanisms of DT in this population: The Chromosome strategy for establishment of DT involves OA and main- tenance of cell-membrane stability, as well as detoxifi- 2 3 3 8 9 cation (Tripathy et al. 2000; Chaves and Oliveira Underlined chromosome number and marker intervals indicate QTL detected in both years. 2004). Although we did not measure these physiological traits directly in this study, possible mechanisms of the TABLE 8 DT may be deduced on the basis of collocations of QTL Phenotype variationc detected in this and previous studies. 5.13 6.98 9.05 7.11 9.30 14.98 11.05 Three chromosomal regions with major QTL for The positive values indicate the alleles from IRAT109 with increasing effects. 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– Additive effectb ÿ0.2 ÿ0.3 ÿ0.2 0.2 ÿ0.7 ÿ0.6 ÿ0.7 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 Amount of phenotype variation (%) explained by the QTL. flanking markers RM284–RM210 was identified by Robin LOD 2.4 4.7 2.9 2.8 3.0 3.3 3.6 et al. (2003). In the same region, QTL for OA and cell- membrane stability were also detected in other studies (Tripathy et al. 2000; Zhang et al. 2001). Compara- RM237–RM403 RM520–RM293 RM502–RM264 RM434–RM257 RM472–RM104 RM335–RM307 RM219–RM296 2003 tive mapping indicated that this genomic region corre- Intervala sponded 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 chromo- some 9 where major QTL for relative yield, relative grain QLds3b QLds1 QLds8 QLds9 QTL QDlr1 QDlr4 QDlr9 fertility, relative biomass, and relative harvest index were identified in this study, a QTL for cell-membrane stabil- ity (marked by RZ698–RM219) was also reported pre- viously (Tripathy et al. 2000). In another study, a QTL Chromosome for OA was identified in this region, and it was also shown that this region corresponded to a region in 1 3 8 9 1 4 9 wheat where a QTL for ABA content was detected (Zhang et al. 2001). In the region RM240–RM166 on chromosome 2 Traits where a QTL for relative yield was detected in this study, DLR LDS a b c a QTL for relative yield (Babu et al. 2003) and two
  • 13. TABLE 9 QTL for root traits under controlled conditions resolved using composite interval mapping in the RIL population of Zhenshan 97/IRAT109 2003 2004 Additive Phenotype Additive PhenotypeTraits Chromosome QTL Intervala LOD effectb variationc Chromosome QTL Intervala LOD effectb variationcMRDC 2 QMrdc2 MRG2762–RM526 3.0 2.05 11.07 4 QMrdc4b RM255–RM349 2.8 2.84 12.94 4 QMrdc4a RM307–RM471 4.7 ÿ2.07 11.16 9 QMrdc9b RM160–RM215 2.6 2.53 10.96 5 QMrdc5 RM421–RM274 3.0 1.74 7.70 9 QMrdc9a RM316–RM219 4.5 ÿ1.88 9.20 11 QMrdc11 RM287–RM229 5.6 ÿ2.33 14.04RGDC 7 QRgdc7 RM125–MRG4449 7.5 ÿ0.04 17.49 3 QRgdc3 RM473–RM487 2.9 ÿ0.04 14.80 11 QRgdc11b RM287–RM229 3.6 ÿ0.03 8.63 4 QRgdc4 RM471–RM142 2.4 ÿ0.04 9.70 12 QRgdc12 RM511–MRG4341 2.8 0.02 6.24 11 QRgdc11a RM332–RM167 3.7 0.05 16.85 11 QRgdc11b RM287–RM229 3.3 ÿ0.05 16.40DRVC 1 QDrvc1b RM23–RM493 5.6 ÿ1.80 10.60 1 QDrvc1a RM428–RM490 2.5 1.74 16.35 2 QDrvc2a RM526–RM221 6.0 2.51 20.29 2 QDrvc2b M262–MRG2762 2.7 1.54 12.13 4 QDrvc4a RM471–RM142 2.5 ÿ1.30 5.43 4 QDrvc4a RM471–RM142 4.5 ÿ1.88 18.19 4 QDrvc4b RM470–RM317 4.8 1.63 8.56 11 QDrvc1a RM286–RM332 2.8 1.34 9.29 7 QDrvc7 RM134–RM248 2.8 1.23 4.93 9 QDrvc9 RM316–RM219 3.2 ÿ1.30 5.52 11 QDrvc11b RM287–RM229 2.4 ÿ1.20 4.60RVC 1 QRvc1 RM472–RM104 2.6 8.83 5.30 3 QRvc3 RM231–RM489 8.6 ÿ18.28 31.47 3 QRvc3 RM231–RM489 8.9 ÿ17.44 19.83 4 QRvc4 RM349–RM131 5.0 14.96 22.34 Genetic Basis of Drought Resistance in Rice 4 QRvc4 RM349–RM131 6.4 16.11 17.70 6 QRvc6 RM527–RM564 2.7 9.72 9.07 7 QRvc7 RM125–MRG4449 5.6 14.42 13.31 7 QRvc7 RM125–MRG4449 4.0 12.26 15.03 8 QRvc8 RM404–RM339 4.9 12.31 9.75RGVC 1 QRgvc1 RM472–RM104 2.5 0.09 5.99 3 QRgvc3 RM231–RM489 2.7 ÿ0.09 9.70 3 QRgvc3 RM231–RM489 6.6 ÿ0.14 14.13 7 QRgvc7b RM134–RM248 2.9 0.09 10.53 4 QRgvc4 RM349–RM131 5.9 0.16 17.17 11 QRgvc11 RM202–RM287 3.5 ÿ0.10 13.71 7 QRgvc7a RM125–MRG4449 3.9 0.12 10.64 8 QRgvc8 RM72–RM331 3.6 0.10 7.07 11 QRgvc11 RM202–RM287 2.1 ÿ0.08 4.87 a Underlined chromosome number and marker intervals indicate QTL detected in both years. b The positive values indicate the alleles from IRAT109 with increasing effects. c Amount of phenotype variation (%) explained by the QTL. 1225
  • 14. 1226 TABLE 10 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 2003 2004 Additive Phenotype Additive PhenotypeTraits Chromosome QTL Intervala LOD effectb variationc Chromosome QTL Intervala LOD effectb variationcMRDD 4 QMrdd4a RM335–RM307 3.2 ÿ1.77 7.01 4 QMrdd4c RM349–RM131 2.6 1.62 11.86 4 QMrdd4b RM470–RM303 6.3 2.51 14.63 9 QMrdd9 RM160–RM215 4.4 4.04 19.59 11 QMrdd11 RM286–RM332 2.8 1.67 6.19RGDD 3 QRgdd3 RM523–RM231 5.2 0.05 11.18 3 QRgdd3 RM523–RM231 2.4 0.05 8.52 6 QRgdd6 RM454–MRG4371 3.4 0.04 9.23 7 QRgdd7 RM125–RM4449 2.8 ÿ0.06 12.17 7 QRgdd7 RM125–RM4449 7.6 ÿ0.06 16.81 9 QRgdd9 RM219–RM296 2.4 ÿ0.05 9.18 8 QRgdd8 RM339–RM342 2.4 ÿ0.03 5.69DRVD 2 QDrvd2 RM526–RM221 5.5 2.23 11.05 3 QDrvd3 RM473–RM487 3.6 ÿ3.15 19.77 4 QDrvd4a RM335–RM307 3.5 ÿ1.77 7.28 11 QDrvd11 RM286–RM332 3.2 2.53 13.18 4 QDrvd4b RM470–RM303 6.0 2.21 11.26 9 QDrvd9 RM219–RM296 3.6 ÿ1.86 7.99 10 QDrvd10 RM596–RM271 2.4 ÿ1.5 5.06DIRD 2 QDird2 RM29–RM341 3.0 ÿ1.81 7.09 2 QDird2 RM29–RM341 3.5 ÿ0.21 17.25 B. Yue et al. 4 QDird4b RM471–RM142 3.6 1.94 8.89 4 QDird4a RM307–RM471 3.0 0.16 11.92DIDRV 1 QDidrv1 RM237–RM403 3.4 1.81 11.88 2 QDidrv2 RM573–RM497 2.9 2.30 12.53 7 QDidrv7a RM125–RM4449 3.0 ÿ1.46 7.22 7 QDidrv7b RM134–RM248 2.8 ÿ1.37 6.66RVD 1 QRvd1 RM472–RM104 2.7 11.37 7.43 3 QRvd3 RM231–RM489 4.1 ÿ15.21 15.23 3 QRvd3 RM231–RM489 8.3 ÿ19.00 19.72 4 QRvd4a RM470–RM303 3.5 13.28 12.94 4 QRvd4b RM349–RM131 6.1 16.40 15.27 7 QRvd7 RM125–RM4449 3.8 15.93 15.22 7 QRvd7 RM125–RM4449 5.9 15.61 12.23 8 QRvd8 RM404–RM339 5.2 14.15 11.13RGVD 1 QRgvd1 RM472–RM104 2.9 0.11 8.33 2 QRgvd2 RM145–RM324 4.1 ÿ0.13 14.09 3 QRgvd3 RM231–RM489 6.0 ÿ0.15 14.29 4 QRgvd4a RM470–RM303 4.4 0.14 15.97 4 QRgvd4b RM349–RM131 4.4 0.13 12.28 11 QRgvd11 RM202–RM287 2.3 ÿ0.11 9.89 7 QRgvd7 RM125–RM4449 2.7 0.10 6.36 8 QRgvd8 RM404–RM339 5.1 0.13 10.73 11 QRgvd11 RM202–RM287 3.5 ÿ0.11 8.78 a Underlined chromosome number and marker intervals indicate QTL detected in both years. b The positive values indicate the alleles from IRAT109 with increasing effects. c Amount of phenotype variation (%) explained by the QTL.
  • 15. Genetic Basis of Drought Resistance in Rice 1227OA-related candidate genes, BADH1 and BADH2, were sistance could be achieved with special experimentallocated (Robin et al. 2003). designs. In particular, the plant-wise drought treatment These positional correspondences were suggestive of protocol as adopted in this study may provide a gen-the possible mechanisms underlying the QTL for DT- erally useful method for independent evaluation ofrelated traits identified in this study, including OA and/ the individual components (such as DA, DE, and DT)or cell membrane stability, as well as ABA response. Such contributing to drought resistance in rice as well as incorrespondence also enhanced the assertion that the other species. The genetic basis of each componentrelative performance of the fitness and productivity could be characterized by further resolving the compo-traits used in this study largely resulted from the effects nent into individual QTL that could be used either inof DT and provided further support for the plant-wise breeding programs by marker-assisted selection or asdrought stress protocol for investigating the genetic the starting point for gene identification using variousbasis of DT at the reproductive stage in rice with the approaches.effect of DA eliminated. We thank Abraham Blum and John O’Toole for their technical Implications of the results in improvement of plant advice at various stages of this work. This research was supported bydrought resistance programs: The results may have im- grants from the National Program on the Development of Basicportant implications in improvement of drought re- Research, the National Special Key Project on Functional Genomics and Biochips, the National Natural Science Foundation of China, andsistance in rice-breeding programs. The upland cultivar the Rockefeller Foundation.IRAT109 has higher values in all the important traitsof relative performance such as relative yield, relativespikelet fertility, relative biomass, relative grain weight,and relative harvest index. The QTL analysis also LITERATURE CITEDshowed that it contributed positively in most of the Ali, M. L., M. S. Pathan, J. Zhang, G. Bai, S. Sarkarung et al.,QTL that were consistently resolved for these traits. In 2000 Mapping QTLs for root traits in a recombinant inbredaddition, this cultivar also has high potential yield and population from two indica ecotypes in rice. Theor. Appl. 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