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First published online April 30, 2004; 10.1104/pp.103.035527 Plant Physiology 135:384-399 (2004) © 2004 American Society of Plant Biologists Quantitative Trait Loci Associated with Drought Tolerance at Reproductive Stage in Rice1Rice Gene Discovery Unit, BIOTEC (J.C.L.) and Rice Gene Discovery Unit, BIOTEC, National Center for Genetic Engineering and Biotechnology (T.T.), Kasetsart University, Kamphangsaen Campus, Nakhon Pathom, Thailand, 73140; Ubon Rice Research Center, Rice Research Institute, Ubon Rachatani, Thailand (G.P.); and Phrae Rice Research Center, Rice Research Institute, Muang, Phrae, Thailand (B.J.)
Drought is a major constraint to rice (Oryza sativa) yield and its stability in rainfed and poorly irrigated environments. Identifying genomic regions influencing the response of yield and its components to water deficits will aid in our understanding of the genetics of drought tolerance and development of more drought tolerant cultivars. Quantitative trait loci (QTL) for grain yield and its components and other agronomic traits were identified using a subset of 154 doubled haploid lines derived from a cross between two rice cultivars, CT9993-510 to 1-M and IR62266-42 to 6-2. Drought stress treatments were managed by use of a line source sprinkler irrigation system, which provided a linearly decreasing level of irrigation coinciding with the sensitive reproductive growth stages. The research was conducted at the Ubon Rice Research Center, Ubon, Thailand. A total of 77 QTL were identified for grain yield and its components under varying levels of water stress. Out of the total of 77 QTL, the number of QTL per trait were: 7-grain yield (GY); 8-biological yield (BY); 6-harvest index (HI); 5-d to flowering after initiation of irrigation gradient (DFAIG); 10-total spikelet number (TSN); 7-percent spikelet sterility (PSS); 23-panicle number (PN); and 11-plant height (PH). The phenotypic variation explained by individual QTL ranged from 7.5% to 55.7%. Under well-watered conditions, we observed a high genetic association for BY, HI, DFAIG, PSS, TSN, PH, and GY. However, only BY and HI were found to be significantly associated with GY under drought treatments. QTL flanked by markers RG104 to RM231, EMP2_2 to RM127, and G2132 to RZ598 on chromosomes 3, 4, and 8 were associated with GY, HI, DFAIG, BY, PSS, and PN under drought treatments. The aggregate effects of these QTL on chromosomes 3, 4, and 8 resulted in higher grain yield. These QTL will be useful for rainfed rice improvement, and will also contribute to our understanding of the genetic control of GY under drought conditions at the sensitive reproductive stage. Close linkage or pleiotropy may be responsible for the coincidence of QTL detected in this experiment. Digenic interactions between QTL main effects for GY, BY, HI, and PSS were observed under irrigation treatments. Most (but not all) DH lines have the same response in measure of productivity when the intensity of water deficit was increased, but no QTL by irrigation treatment interaction was detected. The identification of genomic regions associated with GY and its components under drought stress will be useful for marker-based approaches to improve GY and its stability for farmers in drought-prone rice environments.
In rainfed lowland areas, one of the major abiotic constraints depressing rice (Oryza sativa) production is water stress, including deficit (drought) or excess water (flood; O'Toole and Chang, 1979  ; Herdt, 1991; Lin and Shen, 1993). Drought stress is a serious limiting factor to rice production and yield stability in rainfed rice areas (Dey and Upadhyaya, 1996). The occurrence of drought can be assessed by following variables such as weather conditions, soil moisture, and crop condition over a particular growing season. In rice, the effect of drought varies with the variety, degree, and duration of stress and its coincidence with different growth stages. Rice's susceptibility to water stress is more pronounced at the reproductive stage and causes the greatest reduction in grain yield when stress coincides with the irreversible reproductive processes (Matsushima, 1966; Cruz and O'Toole, 1984).
Drought tolerance has been considered as a valid breeding target to partially compensate for the loss in yield. Phenotypic traits associated directly with drought tolerance are unclear; however, several investigations noted that deep rooting (Ekanayake et al., 1985 Molecular tools facilitate the identification and genomic locations of genes controlling traits related to drought tolerance using quantitative trait loci (QTL) analysis. This paper aims to improve understanding of genetic responses of agronomically important traits such as grain yield, biological yield, harvest index, number of panicles per hill, total spikelet number per panicle, days to flowering after initiation of the irrigation gradient, percent spikelet sterility, and plant height to drought stress of different intensities coinciding with the flowering stage of rice. Grain yield and its components are important traits for breeders since the ultimate goal in breeding programs is to obtain high and stable yield. After identifying the components contributing to grain yield, the relationship of yield and its components to morphophysiological traits will help to assess and understand the mechanisms of drought tolerance. Thus, morphophysiological traits act as secondary traits in relation to yield and its components. The line source sprinkler irrigation system was employed to conduct drought phenotyping experiments under field conditions to create a linear gradient of drought intensity. The number, genomic locations, and effects of QTL were determined. Epistasis and QTL x environment (Q x E) interactions were also investigated. This information will help us understand the genetic control of yield and its components under various intensities of drought stress. The results obtained may also be directly applicable in improving drought tolerance in rice. Correlating genetic information with physiological and morphological traits related to drought tolerance will allow the development of rice varieties tolerant to drought stress in a particular rice ecosystem.
Manipulation of Flowering Date
GY is generally considered to be the most important trait for rice farmers in rainfed lowland areas where drought developed late in the wet season (Cooper, 1999
Effects of Irrigation Treatments Standing water was drained out of the experimental field when most of the DH lines reached panicle initiation and then line source sprinkler (LSS) was applied until the plants reached maturity. This established a water gradient resulting in varying levels of drought stress. Although different levels of stress were created by LSS irrigation, wind was a factor making the water application not uniform. Because this problem was paramount to the experimental treatments, sprinkler irrigation was carried out in the early morning hours when wind speed was low. Before the end of water application, the amount of irrigation distributed to the field was known by determining the amount of water collected in catch cans versus the distance of the sprinkler line that showed a slightly bell-shaped relationship and the yield-to-water relationship averaged over all genotypes was remarkably linear (data not shown). ANOVA analysis indicated a statistically significant correlation of irrigation treatments for most of the measured traits using the average values of DH lines (Table III) indicating that the application of water gradients using the LSS method was successfully established.
Genotype by environment (G x E) interaction was a major contributor to the phenotypic variation for most agronomically important traits in rainfed lowland rice (IRRI, 1993 ; Cooper and Somrith, 1997; Wade et al., 1999). Different combinations of soil type, temperature, amount of water availability, and other agrohydrology with plant phenology were reported to influence the G x E interaction (Cooper and Somrith, 1997; Fukai et al., 1999). LSS and synchronization successfully minimized the variation contributed by the soil type and plant phenology, although other environmental factors could not be controlled. In this experiment, G x E interaction accounted for a small portion of the total sum of squares compared to the genotype and irrigation treatment, and showed that all traits measured were nonstatistically significant at P < 0.05 – P < 0.01 (Table III). This further verifies that the level and intensity of drought stress were uniformly developed from floral initiation until harvesting and water supply is the major discriminating factor among treatments.
Table III displays the grand means, ranges, and broad-sense heritabilities of measured traits for CT9993, IR62266, and the DH population. The parents showed statistically significant differences at P < 0.05 and P < 0.01 for the traits GY, BY, HI, TSN, PN, and PH but not for DFAIG and PSS (Table III). The phenotypic distributions in the DH lines for the traits mentioned did not show discrete classes but approximately fitted a normal distribution, indicating that all measured traits were quantitatively inherited in nature. Transgressive segregation in both directions was observed for most traits (Table III) under every drought intensity (W0 to W4), indicating that both parents transmitted favorable alleles for each trait. Broad-sense heritabilities (H2) computed across five water regimes were relatively moderate (Table III). PN and PH had the highest H2 at 0.61 and 0.73, respectively, while GY and BY had the lowest H2 at 0.32 and 0.31, respectively. This indicates that traits such as GY and BY are more prone and easily affected by drought stress than traits such as PN and PH. It can be noted that the trend of trait heritability increased until W2, and declined at W3 and W4.
Correlation between measured traits and GY at different water levels was evaluated at P < 0.05 and P < 0.01 for each irrigation treatment as shown in Table IV. Highly significant positive correlation between GY and BY ranging from 0.73 at W1 to 0.54 at W4 was found, indicating that genetic improvement in the GY would likely be accompanied by improvement of BY. Positive correlation between GY and HI was also highly significant (Table IV). As drought stress increased, the correlation between GY and HI increased dramatically, indicating that HI is also a primary determinant of GY under stress. Therefore, genetic improvement of HI would also improve GY (Fukai et al., 1999
In this study, BY, HI, DFAIG, TSN, PSS, and PH were the traits determining GY under well-watered condition (Table V). Path coefficient analysis permits the separation of the correlation coefficient into components of direct and indirect effect on GY. GY under well-watered condition was important in determining GY under water-limiting conditions. Better performance of cultivars with high potential yield under rainfed lowland regions was proposed (Pantuwan et al., 1996 ; Fukai et al., 1999) and demonstrated by Rajatasereekul et al. (1997). Although yield potential has no direct genetic relationship to drought tolerance, under drought stress it contributed to higher grain yield. On the other hand, GY under very severe stress was mainly related to BY and HI. In a study conducted for pearl millet under drought stress condition, HI and BY were also found to increase yield under drought stress (Yadav et al., 2002).
QTL Detection The number, genomic locations, and effects of QTL associated with GY and its components were summarized in Table VI.
Grain Yield
IR62266 produced higher GY than CT9993 under fully irrigated conditions (W0). The GY of IR62266 decreased dramatically as drought stress became more severe while the GY of CT9993 declined slower than IR62266. The extended effects of yield potential (Blum, 1988
Seven QTL located on chromosomes 3, 4, 6, and 10 were identified for GY. The IR62266 alleles of QTL on chromosomes 4, 6, and 10 increased GY in all water levels (W0, W1, W3, and W4), while the CT9993 alleles of QTL on chromosome 3 increased GY only under severe drought stress (W3 and W4). The QTL on chromosome 4 designated as qgy4.1, qgy4.2, and qgy4.3 flanked by markers ME10_11 and RZ565 were identified under well-watered condition (W0), mild stress (W1), and severe stress (W3), respectively. The QTL on chromosome 6 was identified only under W1. Considering the coincidence of QTL on chromosome 4 and allelic contribution by IR62266 at both QTL locations, these QTL might contribute to the increased yield potential derived from IR62266. The QTL on chromosome 3 (qgy3.1 and qgy3.2) flanked by markers EM11_9 to RM231 were identified only in the severe drought stress in which, as mentioned earlier, W3 and W4 were the appropriate stress intensities to monitor drought tolerance. CT9993 contributed the allele that increases grain yield for the two QTL (qgy3.1 and qgy3.2) on chromosome 3. These QTL might contribute to the maintenance of GY through the control of complex biochemical and physiological processes associated with drought tolerance. The QTL on chromosome 10 designated as qgy10.1 was detected only under W4. The detected QTL, qgy3.2 and qgy10.1, supported the evidence of transgressive segregation for GY under severe drought stress. These data suggest that unique configurations of multiple alleles may be required for high levels of drought tolerance while maintaining GY. No QTL was detected for W2, although the segregation for GY ranged from 0.01 to 3.47 t/ha in the DH lines. Previous studies by Xing et al. (2002)
Fourteen QTL for GY were reported by Zhang et al. (1999a)
IR62266 produced higher BY than CT9993 under all irrigation treatments, but the difference was only significant under W0 and W3. The BY of IR62266 was reduced by approximately 50% under severe drought stress. That of CT9993 was reduced by only 25%, but suggesting IR62266 still out-yielded CT9993. Evidence of transgressive segregation was also observed. This may indicate that both parents contribute alleles to increase BY. Eight QTL were detected for BY. These QTL were localized in different chromosomal locations for each irrigation treatment except for the QTL on chromosome 11 designated as qby11.1, qby11.2, and qby11.3 that were found in the same region flanked by markers ME4_14 to EM17_16 under W1, W2, and W3, respectively. A single QTL was identified under W0, W1, and W2. Two QTL, qby9.1 and qby11.3, located on chromosomes 9 and 11 were detected under W3. Three QTL located on chromosomes 8, 9, and 10, designated as qby8.1, qby9.2, and qby10.1, were identified under W4. IR62266 alleles contributed to higher BY for all identified QTL. Phenotypic variation explained by QTL ranged from 7.9% to 31%. The QTL for canopy temperature were also located in the same regions of chromosomes 9 and 10 (data not shown). The radiation load on the leaf canopy affects the leaf temperature and transpiration. This in turn has a relation to the water uptake and nutrient assimilation necessary for grain production. Stem reserves are an important source of carbohydrates and nitrogen for grain filling, especially at times when transient photosynthesis is inhibited by drought and other factors like heat or leaf disease that occurred during grain filling. The efficiency of stem reserves in overcoming the effect of drought during grain filling is also dependent on the amount of reserves in the stem before flowering. It can be seen in the result of this study that GY under W4 is highly correlated with BY under the same water condition.
HI is the ratio of GY to BY. HI indicates the efficiency of translocation of food assimilates from the vegetative tissue to the reproductive tissue. Thus for breeders, it serves as a means to predict grain growth and yield in many crops. The HI of IR62266 and CT9993 was not significantly different under well-watered to mild stresses. Their values were in a range of 0.30 to 0.42 (Table III). The HI of IR62266, however, decreased dramatically to 0.12 and 0.03, as drought became more severe (W3 and W4) while the HI of CT9993 decreased slowly to 0.33 and 0.15 for the same water stress levels, respectively. Phenotypic correlations between HI and GY were high under all conditions (Table V). The strength of correlation between HI and GY were directly proportional to the severity of the stress, increasing from 0.68 (P < 0.01) in very mild stress (W1) to 0.91 (P < 0.001) in severe stress (W4). The increasing value of the correlation coefficient of HI and GY as drought intensity increase further confirms the importance of HI in determining GY. Better maintenance of HI may have contributed to high GY under drought stress. In pearl millet, QTL that contribute to increased drought tolerance through an ability to maintain HI and BY was also reported (Yadav et al., 2002
Interestingly, the QTL specific to better yield performance under drought stress, qgy3.1 and qgy3.2, were clearly located in the same marker interval. This result showed that close linkage or pleiotropy might be responsible for the high correlation of GY with HI and the coincidence of the QTL for both traits. As it was mentioned, the favorable QTL for GY were located in this region was contributed by CT9993. The same region was reported to harbor QTL for days to 50% flowering under control and stress conditions by Zhang et al. (2001)
Ontogenetic characters, especially appropriate flowering time, play an important role in drought avoidance of rainfed lowland rice (Fukai et al., 1999
The QTL for DFAIG detected in this experiment were located in the same position as the QTL for days to heading mapped by Zhang et al. (2001)
Total spikelet number per panicle was not significantly different between the two parents and was not affected by drought stresses (Table III). It indicates that the onset of drought stress was probably beyond the floral initiation stage. Ten QTL for TSN were identified on chromosomes 3, 4, 5, and 9. The QTL on chromosomes 4 and 9 were consistently identified in different irrigation treatments. The QTL on chromosome 4 were located between EMP3_10 and RG214 markers. Same genomic locations were identified by Hittalmani et al. (2003)
Sheoran and Saini (1996)
Zhang et al. (2001)
IR62266 produced a higher number of panicles than CT9993 under all irrigation treatments except W4, in which both produced the same number (Table III). Average values of PN of the DH lines were not significantly different across the water regimes. This indicates the developmental stage at which water deficit was encountered was beyond panicle initiation. PN was determined before the onset of drought stress. Phenotypic correlation between PN and GY was low (r2 = 0.1707 – 0.3357). The correlation was lower when drought stress was more severe. It is suggested that PN was not a major factor in the loss in GY by drought stress. A total of 23 QTL for PN were identified on chromosomes 1, 2, 4, 7, 8, and 10 (Table IV), collectively explaining 47.2% to 55.3% of phenotypic variance. Out of the total, the number of QTL per chromosome were: 5-chromosome 1; 1-chromosome 2; 10-chromosome 4; 2-chromosome 7; 4-chromosome 8; and 1-chromosome 10. IR62266 alleles of all QTL contributed high PN except for the QTL on chromosome 2. The QTL on chromosomes 1, 4, and 8 were identified in all irrigation treatments except that the QTL on chromosome 8 was not detected under W4. The QTL on chromosome 1 designated qpn1.1, qpn1.2, qpn1.3, qpn1.4, and qpn1.5 were mapped to the ME4_18-CDO345 interval. The QTL on chromosome 8 designated as qpn8.1, qpn8.2, qpn8.3, and qpn8.4 were mapped to the G187 -EM18_5 interval. The QTL on chromosome 4 were found significantly linked to ME6_10-EMP2_2 and ME4_9-RG214 intervals. Two QTL on chromosome 7 designated as qpn7.1 and qpn7.2 were detected only as water stress became severe (W3 and W4). These QTL were located between EAAM17_5 and ME4_3 markers.
PH was also affected by drought (Table III) but was not correlated with GY in all irrigation treatments (Table IV). The PH of IR62266 and CT9993 was not significantly different (P < 0.05) under well watered (W0) and very mild stress (W1). The difference, however, was observed under mild and severe drought stresses. Drought had little effect on the PH of the CT9993 but did affect IR62266. The reduction in PH was 21.2 cm for IR62266 and 2 cm for CT9993 under W4. Eleven QTL for PH were identified on chromosomes 1, 8, and 10 under different irrigation treatments (Table VI). The QTL on chromosome 1 designated as qph1.1, qph1.2, qph1.3, qph1.4, and qph1.5 were located in the C813-RZ909 interval, where the semi-dwarfing locus, sd-1, was reported (Price et al., 2000
The genetic improvement of adaptation to rainfed lowland environments has been focused on improving higher and more stable yields (Mackill et al., 1999
In most cases, the phenotypic variance explained is largely from the genetic variance ( For example, the qgy4.2 and qgy6.1 identified at W1 showed an additive by additive interaction (Fig. 2A) where higher GY was contributed by IR62266 alleles at both loci. DH lines possessing the IR62266 allele at these two loci produced an average GY of 2.25 t/ha, while those possessing the CT9993 allele produced an average yield of 1.65 t/ha under W1. On the other hand, DH lines having one CT9993 and one IR62266 allele each from either of the two loci produced an average GY of 1.91 to 1.93 t/ha. As water stress became more severe at W3 and W4, the additive by additive interaction was also identified between significant main-effect QTL. At W3, the presence of CT9993 allele on the qgy3.1 and IR62266 allele on the qgy4.3 in the population was found to be significant in maintaining yield. The DH lines having this allelic combination produced an average yield of 1.58 t/ha under the W3 (Fig. 2A). The DH lines possessing CT9993 alleles at both loci produced higher yield (1.15 t/ha) than those having IR62266 allele in both loci, which produced an average GY of 0.68 t/ha under W3. The additive by additive interaction between significant main-effect QTL (qgy3.2 x qgy10.1 interaction) was repeatedly identified at W4. The average GY of DH lines with the CT9993 allele at the qgy3.2 and IR62266 allele at the qgy10.1 was 0.95 t/ha. Those with the CT9993 allele at both loci had average GY of 0.64 t/ha and those with the IR62266 allele at both loci had an average GY of 0.31 t/ha.
Additive by additive interactions of main effect QTL were also identified for HI at W4. Two QTL (qhi1.1 and qhi3.5) were detected in which qhi3.5 was contributed by CT9993 and qhi1.1 was contributed by IR62266. The presence of the IR62266 allele at the qhi1.1 and CT9993 allele at the qhi3.5 resulted in comparatively high HI of 0.2 at W4. DH lines possessing an opposite allelic profile of these loci have a low HI value of 0.05 at the same stress condition (Fig. 2B). Main-effect QTL for PSS and BY also showed additive by additive interactions. Low PSS (Fig. 2C) was contributed by the IR62266 alleles at all detected QTL loci at W0. IR62266 alleles contributed higher BY that also showed additive by additive interactions (Fig. 2D).
Epistasis, in the form of additive by additive interactions, played a role in controlling the expression of GY and its components in this experiment. Yu et al. (1997)
The complexity of rainfed lowland environments and the incidence of large genotype-by-environment (G x E) interactions were documented and led to the implementation of multi-environmental trials to determine the adaptation of rice in rainfed conditions (Cooper, 1999
Different irrigation treatments had a significant effect on GY and its components, except for the traits TSN and PN. There was no QTL by environment interaction detected when all irrigation treatments were combined for QTL analysis (Table III). Comparing the number and locations of QTL detected in different environments will provide genetic information on the possible QTL that may perform differently under different environmental conditions. The comparison could not provide direct estimates for QTL by environment interactions. Direct estimation of QTL by environment interaction cannot be done with QTL information alone but it is possible to infer from the QTL data that crossover interactions might occur at the level of the genotype of a particular line. Different combinations of individual genes may give rise to the behavior of multi-gene genotypes, thus may allow the observation of crossover G x E interaction (Cooper, 1999
QTL for GY overlapped or were linked with several QTL for yield components. For example, the region on chromosome 3 flanked by the markers RG104 and RM231 contained the qdfaig3.1, qdfaig3.2, qdfaig3.3, qdfaig3.4, qdfaig3.5, qgy3.1, qgy3.2, qhi3.1, qhi3.2, qhi3.3, qhi3.4, and qhi3.5 (Fig. 3). This result was supported by high correlation between traits. Under severe stress, the correlation coefficient between GY and HI was 0.91, and –0.76 between GY and DFIAG (Table IV). This result implies that molecular mechanisms of drought tolerance to overcome the drastic yield reduction under severe drought stress involved traits such as maintaining high harvest index and less delay in flowering time. The physiological processes involved in this regard are still unknown. The large sink size and efficient transport of assimilates from leaves and stems into developing spikelets were reported to determine grain yield in rice (Cui et al., 2003
The RZ69-RZ565 interval on chromosome 4 is another example of coincidence of QTL locations. This region contained the qgy4.1, qgy4.2, qgy4.3, qpn4.1, qpn4.2, qpn4.3, qpn4.4, qpn4.6, qpn4.7, qpn4.9, and qpn4.10 for GY and PN. The QTL for GY and PN were mapped near the QTL for PSS and TSN (Fig. 3). CT9993 contributed the favorable alleles for all QTL in this region except PSS.
The region on chromosome 1 flanked by the markers C813 and RZ909 markers contained qhi1.1, qpn1.1, qpn1.2, qpn1.3, qpn1.4, and qpn1.5. The QTL for PH was also located in this region. In this experiment, the onset of stress developed after panicle initiation. The colocation of qhi1.1, qpn1.5, and qph1.5 therefore suggested that high PN (IR62266 contributed favorable allele) contributed to a high HI, and consequently high yield under stress. This finding was supported by the significant positive correlation (r2 = 0.1789) between GY and PN at W4. QTL identified for trait-related drought tolerance such as total root dry weight (Zhang et al., 2001
Coincidence of QTL for BY, PSS, PN, and PH was observed on chromosome 8 in the G187-RG997 interval (Fig. 3). Most favorable alleles at QTL were contributed by IR62266, except for PH. QTL for osmotic adjustment (Zhang et al., 2001
The drought experiment conducted at Ubon Ratchatani Thailand in the wet season of 2000 allows the identification of QTL for grain yield and its components under various drought intensities. The genetic variation of measured traits is mainly contributed by main effect QTL. However, digenic interactions between main effect QTLs were observed for GY, BY, HI, and PSS. Phenotypic variance explained by QTL ranged from 7.8% to 55%. In this experiment, manipulation of flowering time by staggering planting date and managed drought intensities by using LSS minimized the variance contributed by the G x E interaction. Thus, G x E interaction for all measured traits was not observed in this experiment. Coincidence of QTL for GY and its components were identified in many regions especially on chromosomes 3, 4, and 8 suggesting a tight linkage or pleiotropy. These QTL coincided with QTL for root system and osmotic adjustment detected in the same mapping population. The aggregate effects of these QTL resulted in better management of GY under drought stress. These QTL could therefore be of interest for rice breeders to use as targets to improve GY under stress through the selection of QTL by molecular markers. This information will be useful for rice improvement by marker-assisted selection.
Plant Materials
A doubled haploid (DH) population was derived from a cross between CT9993-510-1-M (Oryza sativa; abbreviated as CT9993, an upland japonica type) and IR62266-42-6-2 (Oryza sativa; abbreviated as IR62266, an indica type). These breeding lines show variations in potential yield, osmotic adjustment (OA), and root characters such as deep and thick rooting system. This population was developed at Centro Internacional de Agricultura Tropical, Columbia, and the International Rice Research Institute, Philippines. Several collaborating research institutes have used this population for the genetic study of traits associated with drought tolerance (Blum et al., 1999
The field experiment was conducted under a rainfed lowland environment at Ubon Ratchathani Rice Research Center (latitude 15° 19' 52.35'' N, longitude 104° 40' 55.15'' E, altitude 110 m), located in northeast Thailand, during the 2000 wet season. The 220 DH lines, and the parents CT9993 and IR62266, were first evaluated under irrigated conditions in the 1996 wet season. The DH lines showed a wide range of flowering dates. In order to synchronize the flowering date in succeeding experiments, the flowering dates of 220 DH lines were used to group the DH lines into four maturity groups. Four seeding dates were then staggered at 6 to 7 d-intervals i.e. the latest flowering lines were seeded early and the earliest flowering lines were seeded last (Fig. 1).
Seeds were sown by hand on a slightly acidic, infertile, sandy loam soil with low organic matter and total nitrogen, at a rate of 4 to 6 seeds/hill in rows 0.15 m apart and hills within each row spaced 0.20 m apart. Plots were replicated twice and each replication was arranged in randomized complete block design. Plot size was 0.90 x 14.0 m (Fig. 1; 6 rows of 70 hills) and arranged perpendicularly to the line source sprinkler system (LSS; Hanks et al., 1976 Surface irrigation was applied during the vegetative stage. When the majority of the lines reached the panicle initiation stage, standing water was drained from the field and LSS irrigation was applied thereafter and continued until maturity. LSS irrigation produced gradients of five moisture levels, from high to low. In order to minimize the effect of wind, irrigation by the LSS was applied everyday between 4 AM and 8 AM. Water treatments were assigned, i.e. W0 was a full irrigation condition (control) and W1 to W4 were the four levels of water deficit (from mild to severe) as described in Table I. Each water treatment was arranged perpendicularly to the LSS. A schematic diagram illustrating LSS with the water gradient creating different water stress levels and the total amount of water applied using catch cans are shown in Figure 1. Areas of W0, W1, W2, W3, and W4 received 9.4 mm, 5.4 mm, 2.9 mm, 0.9 mm, and 0.0 mm of average water applied per day, respectively, and the average pan evaporation was 4.14 ± 0.20 mm per day during the period of water stress treatment.
Determination of Yield, Yield Components, and Agronomic Traits DFAIG was determined starting from water drainage until 50% of the total panicles in each plot were fully exerted. When each line reached maturity, PN per hill and PH were determined from five randomly sampled plants per plot. At harvest, filled and unfilled spikelets were counted to determine TSN and PSS. PSS was calculated from the filled and unfilled grain numbers per panicle. The above-ground plant parts (panicles, stems, and leaves) of the center four rows in each plot (0.96 m2) were harvested. Samples were sun dried for 3 weeks and then weighed to determine BY. Grains were threshed from the samples, dried in a hot air oven at 70°C for 5 d, and then weighed to determine GY. HI was calculated as a ratio of grain yield to the total above ground biological yield.
ANOVA appropriate for the specified experimental and treatment design was performed on each measured trait listed in Table II using the multi-factor ANOVA procedure in the STATGRAPHICS 3.0 (Manugistics, 1997
QTL analysis was conducted based on the subset of 154 DH lines. The 154 DHL were originally used for the linkage map construction at Texas Tech University (Zhang et al., 2001
Simple interval mapping and simplified composite interval mapping procedures were performed to determine the single-locus QTL using the computer program MQTL (Tinker and Mather, 1995
The authors would like to thank Dr. Apichart Vanavichit and Dr. Somvong Tragoonrung for kindly providing the facilities of DNA analysis at Rice Gene Discovery Unit and DNA Technology Laboratory and also Dr. Peerasak Srinives for his comments. Thanks to Dr. J.C. O'Toole for helpful comments on the manuscript. Received October 30, 2003; returned for revision January 9, 2004; accepted March 2, 2004.
1 This work was supported by grants (2000FS057 and 2000FS058) from the Rockefeller Foundation of New York.  Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.103.035527. * Corresponding author; e-mail theerayut{at}dna.kps.ku.ac.th; fax 66–034–355196.
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ABSTRACT
RESULTS AND DISCUSSION
2g) or main-effect QTL in this experiment. The 
where r is the number of replications, e is the number of irrigation treatments, 
