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a Center for Plant Molecular Biology, Tamil Nadu Agrl. University, Coimbatore-641 003, India
b Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA
c Agricultural Research Station, Paramakudi, India
d International Rice Research Institute, Chatuchak, Bangkok 10900, Thailand
e University of Western Australia, 35, Stirling Highway, Crawley, WA 6009
f Department of Agronomy, University of Missouri, Columbia, MO 65211, USA
* Corresponding author (chandrarc{at}hotmail.com)
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: AFLP, amplified fragment length polymorphism • BRT, basal root thickness • DAS, days after sowing • DH, doubled haploid • MAS, marker-assisted selection • OA, osmotic adjustment • QTLs, quantitative trait loci • RFLP, restriction fragment length polymorphism • RPF, root pulling force • RPI, root penetration index • RWC, relative water content • SSR, simple sequence repeat
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Several putative traits contributing to drought resistance in rice have been suggested (Fukai and Cooper, 1995). Root characteristics such as thickness, depth of rooting, root length density, root pulling force (RPF), and root penetration ability have been associated with drought avoidance in rice (Nguyen et al., 1997). Osmotic adjustment (OA) capacity is an important, shoot-related component of drought tolerance in crop plants. OA, defined as the active accumulation of solutes during the development of water stress in plants (Blum, 1988), allows maintenance of higher turgor potential at a given leaf water potential. OA delays leaf rolling, tissue death, and leaf senescence under water stress in rice (Hsiao et al., 1984) and has been shown to enhance grain yield under water limited conditions in several other crops (Zhang et al., 1999a). However, a yield benefit due to OA is yet to be demonstrated in rice. Despite our increased understanding of the role of putative traits in drought resistance, these traits are rarely selected for in crop improvement programs because phenotypic selection for most root traits and OA is difficult and labor intensive. Considering these limitations to efficient selection, molecular marker technology is a powerful tool for selecting such traits. QTLs have been detected for several root-related traits and OA in rice (Champoux et al., 1995; Lilley et al., 1996; Ray et al., 1996; Price and Tomos, 1997; Yadav et al., 1997; Ali et al., 2000; Price et al., 2000; Zheng et al., 2000; Zhang et al., 2001). A significant proportion of the phenotypic variability of several of these putative drought resistance traits is explained by the segregation of relatively few genetic loci, thus leading to the possibility of indirect selection of these complex traits by means of marker-assisted selection (MAS) strategy.
Although previous analysis indicated the map positions of QTLs associated with drought resistance traits, the effects of those traits on plant production under drought has not yet been established. Thus there is a need to determine whether the QTLs linked to drought resistance traits also affect yield under stress. By comparing the coincidence of QTLs for specific traits and QTLs for plant production under drought, it is possible to test whether a particular constitutive or adaptive response to drought stress is of significance in improving field level drought resistance (Lebreton et al., 1995). Such associations would also improve the efficacy of MAS in breeding for drought tolerance in rice. Thus, DH lines developed from two rice lines, differing in root traits and OA, were used in this study to identify the QTLs linked to rice performance under drought and to genetically dissect the nature of association between drought resistance traits and yield under drought in the field. The specific objectives of the present study were (i) to identify genomic regions linked to plant water stress indicators, phenology, and production traits under drought stress in the field; (ii) to establish the nature of phenotypic and genetic association between various physio-morphological traits and rice performance under drought; and (iii) to identify useful QTLs for improving drought resistance in rice.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Field Trials
Three separate field trials were conducted under upland conditions in experimental fields of Tamil Nadu Agricultural University, India, at two different locations: Trial 1 at Coimbatore during 1999 wet season (July–December), Trial 2 at Paramakudi during 1999-2000 wet season (September–February), and Trial 3 at Coimbatore during 2000 dry season (February–June). The main soil and drought stress characteristics of the trials are summarized in Table 1. In all the trials, the DH lines and their parents were evaluated under two water regimes: fully irrigated (nonstress) control and water stress under a randomized complete block design. Both the treatments were replicated three times in Trial 1, whereas in Trials 2 and 3 the irrigated control had two replications and the stress treatments had three replications. Experimental plots were 2 m x 0.60 m in Trials 1 and 3, and 2 x 0.4 m in Trial 2. There were 20-and 10-cm spacing between and within rows, respectively. A buffer channel 1.0 m wide and 0.75 m deep along the length of the experimental plot divided the control and stress plots in trial 1 and 3. In trial 2, the control and stress plots were separated by 3.5 m. Seeds were hand-dibbled into dry soil at 100 kg ha-1. NPK fertilizers were applied at a rate of 120:40:40 kg ha-1. While P and K were applied in full at the time of sowing, N was applied in four splits as top dressing. Plants were thinned to 50 hills m-2 soon after emergence. Insect and weed control measures were applied periodically as required. In Trials 1 and 3, all plots were surface irrigated to field capacity once a week, except when water stress was imposed by withholding irrigation to stress plots from 63 and 83 d after sowing (DAS), respectively. In Trial 2, the control plots were surface irrigated, while the stress plots were rainfed from sowing to harvest.
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Trial 2. Leaf rolling score was recorded at midday, 12 d after cessation of rain. Plants were harvested at maturity. Days to heading, plant height, biomass, grain yield, and spikelet fertility were recorded in stress (rainfed) and irrigated control treatments.
Trial 3. Leaf rolling and drying scores were taken at midday 16 d after withholding irrigation. Leaf RWC and canopy temperature were determined midday, 17 and 19 d after withholding irrigation, respectively, in 40 DH lines at random and in parents. Canopy temperature was measured using a hand-held infrared thermometer (Model AG-42, Telatemp Corporation, Inc., Fullerton, CA, USA) as described by Garrity and O'Toole (1995). Stress was relieved 33 d after withholding irrigation by 14 mm rain. Plants were harvested 130 DAS and total above ground biomass was recorded. The plants did not reach maturity, since few DH lines flowered by 130 DAS and most panicles that exserted under stress were sterile. Days to heading was recorded for only the DH lines which flowered by 130 DAS.
Relative yield and relative biomass were calculated as yield and biomass under drought as a percentage of yield and biomass in control, respectively, in all the three trials.
Statistical Analysis
Analyses of variance (ANOVA) were performed to check the genetic variance among the DH lines for all traits. The broad sense heritabilities (H) were then computed from the estimates of genetic (
2G) and residual (2e) variances derived from the expected mean squares of the analysis of variances as H = (2G/
, where k was the number of replications. Phenotypic correlations among the traits within a trial were computed using the genotypic means.
Linkage Map and QTL Analysis
A genetic linkage map revised from a previous map (Zhang et al., 2001) consisting of 280 marker loci including 134 restriction fragment length polymorphisms (RFLPs), 131 amplified fragment length polymorphisms (AFLPs), and 15 simple sequence repeats (SSRs) was constructed on the basis of the 154 DH lines by means of MAPMAKER/Exp version 3.0. R1G1 was a cDNA fragment cloned via a differential display procedure homologous to water stress induced mRNA in rice. TGMSP2 was the DNA marker tightly linked to the thermo-sensitive genic male-sterile gene in rice. The map covered 1602 centimorgans (cM) in length on the basis of the Kosambi function with an average distance of 5.7 cM between adjacent markers. Using the genetic linkage map, we identified the QTLs linked to traits such as plant water relations, phenology, biomass, and yield using QTLMapper version 1.0 software (Wang et al., 1999a; 1999b). The threshold LOD score used to declare the presence of QTLs was 2.85, which was derived on the basis of the total map distance and average distance between markers according to Lander and Botstein (1989). Tests for independence of QTLs were conducted when two or more QTLs of the same trait within a trial were located on the same chromosome as described by Paterson et al. (1988).
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Water stress was very severe in Trial 3 with a continuous stress period of 33 d from 83 to 116 DAS. During the first 22 d after withholding irrigation, 70% of available soil moisture below 20-cm soil depth was depleted from a full profile and the soil strength increased from 0.27 to 3.78 MPa. Mean leaf rolling and drying scores were 4.6 and 3.7, respectively across the DH lines. There was 59% reduction in biomass under stress. Mean RWC was 55% and that of canopy temperature was 33.8°C across 40 DH lines. CT9993 had higher RWC, lower drought scores and cooler canopy temperature compared to IR62266. Heading was delayed by 5 d in CT9993 under stress. However, IR62266 did not flower until the time of sampling (130 DAS) under stress compared to 110 d to heading in the control. The broad-sense heritabilities were relatively high for most traits in Trial 3 (Table 2).
In summary, there was a significant genotypic effect for most traits except for percent spikelet fertility under stress in Trial 2 and days to heading under stress in Trial 3. Significant differences for plant phenology and production traits under control and water stress conditions and for indicators of plant water stress have been reported among a subset of 100 of these DH lines (Blum et al., 1999).
Relationship between Water Stress Indices and Rice Production under Drought Stress
A major problem for direct selection under drought is the management of experimental conditions. There is high probability that a genotype performing well under control conditions will also perform well under drought, even if the relative yield reduction for this genotype is large, because of spillover effects of yield potential (Blum, 1988). Stress yield and control yields were significantly correlated across DH lines (r = 0.51** and 0.34** in Trial 1 and 2, respectively). Similar relation between potential yield in control and yield under water stress was reported in rice (Blum et al., 1999). Drought tolerance of the DH lines can be assessed by several parameters, namely yield (or biomass) under drought, yield under drought as a percentage of yield in control (relative yield), and drought susceptibility index (Fischer and Maurer, 1978). Relative yield (or biomass) is used as an index of drought tolerance in this study as also done by others (Ribaut et al., 1997; Blum et al., 1999). Relative yields under stress were negatively correlated with actual yields in the control in Trial 1 and 2 among the DH lines (data not shown). Thus, the DH lines performing best in control conditions had the most marked yield reduction under drought. On the other hand, relative yields under stress were very significantly positively correlated with actual yields under drought (r = 0.68** and 0.56**, respectively in Trial 1 and 2) among the DH lines. Therefore, absolute yield of the DH lines under stress represents quite well their relative drought tolerance, despite its association with potential yield in the control. Similar results were reported in rice (Blum et al., 1999). Similar correlations between relative yields under stress and actual yields under stress and in control conditions have been reported in maize (Ribaut et al., 1997).
The phenotypic correlations between traits showed that parameters of water stress indicators were significantly correlated with plant phenology and production traits under stress in all the three trials. The correlation coefficients (r) among various traits under drought stress were presented for Trial 1 (Table 3), since it was considered comprehensive in terms of data collection from this series of field trials. In addition, out of the two trials, Trial 1 and 2 wherein data on yield were recorded, Trial 1 had severe water stress with an average yield reduction of 67% under drought compared with control. A yield reduction of more than 50% under stress is considered critical for the expression of drought resistance mechanisms in rice (Pantuwan et al., 2002). Leaf RWC was negatively correlated with leaf rolling and days to heading under stress. Leaf drying scores had negative correlations with yield and harvest index under stress (Table 3). Grain yield in control was not correlated with leaf RWC, leaf rolling, and drying scores determined under water stress, as normally expected (data not shown). Biomass under stress was positively correlated with yield, percent spikelet fertility, number of grains per panicle, harvest index, and relative yield under stress (r = 0.74**, 0.35**, 0.45**, 0.40** and 0.41**, respectively). Percent spikelet fertility and harvest index under stress were positively correlated with relative yield under stress (r = 0.31** and 0.68**, respectively).
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In Trial 3, the severe and long (33 d) stress period coincided with flowering stage (days to heading ranged from 87 to 118 under control among the DH lines, Table 2), and heading either did not occur before 130 DAS or the panicles were sterile. Therefore, biomass was the only and best measure of plant production under stress in this trial. As in Trials 1 and 2, plant water stress indicators were correlated with biomass under stress in this trial (data not shown). Leaf RWC was negatively correlated with leaf rolling, leaf drying, canopy temperature, and days to heading under stress (r = -0.58**, -0.58**, -0.38*, and -0.44*, respectively). Canopy temperature had a positive correlation with leaf rolling and drying scores (r = 0.34* and 0.33*, respectively). Drought scores were positively correlated with days to heading under stress. Biomass under stress was negatively correlated with canopy temperature (r = -0.69**).
These correlations between plant water status indicators and plant phenology and production traits under stress in this study confirmed the earlier reports in rice (Blum et al., 1999).
QTLs Linked to Plant Water Stress Indicators, Phenology and Production Traits
A total of 47 QTLs, significant at a LOD score of ≥2.85, were identified for various plant water relations, phenology and production traits under control and stress conditions from the three field trials (Table 4). The number of QTLs identified for each trait within a trial varied from one to four with the proportion of explained phenotypic variation (R2) ranging from 5 to 59%. The QTL, rwc9.1 explained the highest proportion of the phenotypic variation (59%) for leaf RWC under stress in Trial 3. QTLs linked to various traits from the different trials were located throughout the genome except chromosome 5 (Fig. 2). QTLs for different traits were mapped to similar chromosomal locations within a trial. For example, phs4.1, gys4.1 and gpps4.1 were mapped to the RG939-RG476-RG214 region on chromosome 4 in Trial 1. Similarly, lr1.1, ld1.1, phc1.1, phs1.1, and gppc1.1 were mapped to the EM11_11-RG109-ME10_14 region on chromosome 1 in Trial 1, corresponding to the sd-1 semidwarfing locus (Price et al., 2000). QTLs, lr8.1 and his8.1 were mapped to the ME6_13-G187-ME2_11 region on chromosome 8 in Trial 1. Common QTLs across trials and water regimes were also detected for a given trait. For example, phs1.1 and phc1.1 were consistent across trials and water regimes. In all the trials, for traits related to higher plant water status and production under stress, the majority of the favorable alleles came from CT9993, the japonica parental line. The indica accession, IR62266 contributed most of the alleles for leaf rolling, leaf drying and delay in days to heading under stress. However, favorable alleles from IR62266 also contributed to plant production in terms of biomass, yield, harvest index, and relative yield under stress.
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The QTLs rwc9.1 and dhs9.1 on chromosome 9 identified in the present study mapped to the same location as QTLs for grain yield, panicle number, plant height, and days to 50% flowering under rainfed conditions in an experiment conducted in Thailand by means of this DH population (Zhang et al., 1999b). This region was found to be closer to QTLs for penetrated root thickness, ME9_6-K985 in this population (Zhang et al., 2001) and Amy3ABC-RZ12 in IR64/Azucena DH lines (Zheng et al., 2000) under simulated soil hardpans. In this same region, QTLs (RZ12-RG570) for root thickness and root/shoot dry weight ratio were identified in CO39/Moroberekan RI lines (Champoux et al., 1995). QTLs for several root morphological traits and leaf rolling under drought stress in IR64/Azucena DH lines were also found to overlap at this region (Yadav et al., 1997). This QTL region was consistently linked to leaf rolling in two different populations of rice, viz., CO39/Moroberekan RI lines (Champoux et al., 1995) and IR64/Azucena DH lines (Courtois et al., 2000).
There are several such instances wherein some of the QTLs for rice plant performance under stress in this study mapped to the same location as QTLs for different root traits in this population. rwc1.1 on chromosome 1 identified in this study mapped to the same region linked to deep root mass, deep root ratio, and deep roots per tiller in this population (Kamoshita et al., 2002). On chromosome 2, ry2.1 overlapped with QTLs for shoot biomass, root depth, and root thickness in these DH lines (Kamoshita et al., 2002). Price et al. (2002) have reported poor colocation of drought avoidance and root trait QTLs in rice. The results of the present study on the other hand showed considerable amount of colocation of drought avoidance and root trait QTLs in these DH lines. More than 50% of the QTLs for field drought avoidance overlapped with QTLs for root morphology in Co39/Moroberekan RI population and all the alleles that had positive effect in either case were derived from Moroberekan, the japonica parent (McCouch and Doerge, 1995).
Comparison of QTLs for OA and Rice Performance under Drought Stress
The QTLs ld8.1 and dhc8.1 on chromosome 8 identified in the present study were located in the same genomic region as a QTL for OA (Zhang et al., 2001), canopy temperature, and days to heading under drought at Bet Dagan, Israel, and days to 50% flowering under rainfed conditions in Thailand (Zhang et al., 1999b) in this population. QTLs lr8.1 and his8.1 on chromosome 8 identified in the present study mapped to the same region as the OA QTL detected in a recombinant inbred (RI) line population of rice (Lilley et al., 1996). OA maintains positive turgor in water-stressed plants allowing better panicle exsertion which enables higher spikelet fertility, harvest index, and yield stability in crop plants (Ludlow and Muchow, 1990). However, no QTL for grain yield under stress was mapped to this chromosomal region in this study.
The QTL bms3.1 on chromosome 3 was detected for biomass under mild stress in Trial 2 of the present study. A major QTL for OA was earlier located at the same region in these DH lines (Zhang et al., 2001) and the positive alleles for both biomass and OA were contributed by the indica parent, IR62266. Two RFLP markers flanking this QTL are RZ313 and RG369. In this region, a QTL for stomatal behavior was detected in a rice F2 mapping population (Price et al., 1997). These results suggest that genes in this genomic region might have been conserved to respond to drought.
Phenotypic Correlations between Secondary Traits and Field Performance
Results thus indicated overlapping of some of the QTLs for plant water stress indicators and production traits under drought stress identified in this study and QTLs for several root traits reported earlier (Zhang et al., 1999b; Zhang et al., 2001; Kamoshita et al., 2002) in these 154 DH lines. Champoux et al. (1995) found that 12 out of 14 QTLs for drought avoidance in the field overlapped with QTLs for root morphology in rice. Courtois et al. (2000) observed that some of the QTLs for relative growth rate of rice under water stress in the field conditions were mapped to the same regions as QTLs for root morphology. The same location of QTLs for different traits should be associated with a correlation of the phenotypic data, if there is pleiotropy or genetic linkage among the traits (Paterson et al., 1991). Phenotypic correlations were estimated between plant water stress indicators, phenology, and production traits under drought stress from the three trials of this study and the drought resistance component traits (root traits and OA capacity) of these 154 DH lines (as determined by Zhang et al., 1999b, 2001; Kamoshita et al., 2002). Plant water stress indicators, phenology, and production traits under stress in this study were positively correlated with several root traits and the correlation coefficients for Trial 1 are presented (Table 5), as the water stress was more severe and also data for grain yield was available in this trial. While, grain yield in control was not correlated with most traits (data not shown), except BRT (r = 0.36**), grain yield under drought was positively correlated with several root traits. Plant biomass, grain yield and number of grains per panicle under stress in trial 1 were positively correlated with BRT (r = 0.32**, 0.34**, and 0.36**, respectively). Further, the correlations between several root traits with grain yield, biomass, and number of grains per panicle were higher under drought stress than in control (data not shown). While 1000-grain weight in control was not correlated to any of the root traits, 1000-grain weight under stress was positively correlated with deep root dry weight (r = 0.32**) and other root traits in this trial. Harvest index under stress was positively correlated with several of the root traits in these DH lines. Root morphology and rooting patterns directly affect the amount of water available to a crop, and increased width, depth and branching of root system have been shown to decrease plant water stress in rice (O'Toole and Soemartono, 1981). The ability of rice to penetrate compacted soil is linked with the capacity to develop thick and long root axes (Yu et al., 1995) and this has been shown to contribute to drought resistance. A root system that extends the root zone to more fully exploit available soil water has the potential to increase yield under drought (Mambani and Lal, 1983).
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In Trial 3, where stress was very severe, plant biomass under stress (the only measure of plant production in this trial) was positively correlated with BRT (r = 0.37**). Biomass under stress in turn had significant negative correlation with canopy temperature (r = -0.69**) measured in a subset of 40 DH lines in this trial. Canopy temperature on the other hand showed significant negative correlation with BRT (r = -0.44**). Differences in canopy temperature among rice cultivars are known to be related to drought avoidance based mainly on the potential to maintain transpiration under stress, and canopy temperature was shown to be negatively correlated with biomass and grain yield under water-deficit in rice (Garrity and O'Toole, 1995; Blum et al., 1999). Root architecture greatly affects water balance of the plant and therefore yield reduction under stress. Champoux et al. (1995) earlier reported good correlation between root thickness and field drought avoidance in rice. Plants with a deeper root system would maintain a cooler canopy temperature and ultimately higher yield under drought as seen in maize (Sanguineti et al., 1999). However, in a study by Pantuwan et al. (2002), root pulling resistance was not correlated with grain yield in rice genotypes under drought in rainfed lowland conditions. Deep root systems would be normally beneficial in upland conditions, as in this study, where drought stress is common in the continuously aerobic soils. Further, the importance of phenotyping environment and the prospects for selection of QTLs for deep root morphology and root thickness under anaerobic conditions to improve constitutive root system of rainfed lowland rice has been demonstrated (Kamoshita et al., 2002). Thus, the finding of the present study indicated the scope for drought resistance improvement in rice through selection for root traits by means of MAS.
Yield and plant production traits under stress were not correlated with the capacity for OA (as determined by Zhang et al., 2001) in these 154 DH lines in the three trials (data not shown). Thus, capacity for OA did not impact plant production under stress in the two sites of this study. Similar results were reported earlier in rice (Zhang et al., 1999b). One of the possible reasons for lack of correlation between OA capacity and field performance in the present experiments may be that the OA in these DH lines was determined in a separate experiment, where all the lines were subjected to the same leaf RWC by growing them in pots (Zhang et al., 2001). This was done because leaf water status affects the rate of OA. In the present field experiments, leaf water status was not the same in all the DH lines (because of their difference in root traits and also due to the unlimited vertical soil volume inherent to field conditions) and hence the differences in OA capacity among the lines could not be fully expressed.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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ACKNOWLEDGMENTS |
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Received for publication April 17, 2002.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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-Jan
i, A. Steed, S. Peki, and S.A. Quarrie. 1995. Identification of QTL for drought responses in maize and their use in testing causal relationships between traits. J. Exp. Bot. 46:853–865.This article has been cited by other articles:
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