Abstract

Flash flooding of young rice plants is a common problem for rice farmers in south and south‐east Asia. It severely reduces grain yield and increases the unpredictability of cropping. The inheritance and expression of traits associated with submergence stress tolerance at the seedling stage are physiologically and genetically complex. We exploited naturally occurring differences between certain rice lines in their tolerance to submergence and used quantitative trait loci (QTL) mapping to improve understanding of the genetic and physiological basis of submergence tolerance. Three rice populations, each derived from a single cross between two cultivars differing in their response to submergence, were used to identify QTL associated with plant survival and various linked traits. These included total shoot elongation under water, the extent of stimulation of shoot elongation caused by submergence, a visual submergence tolerance score, and leaf senescence under different field conditions, locations and years. Several major QTL determining plant survival, plant height, stimulation of shoot elongation, visual tolerance score and leaf senescence each mapped to the same locus on chromosome 9. These QTL were detected consistently in experiments across all years and in the genetic backgrounds of all three mapping populations. Secondary QTL influencing tolerance were also identified and located on chromosomes 1, 2, 5, 7, 10 and 11. These QTL were specific to particular traits, environments, or genetic backgrounds. All identified QTL contributed to increased submergence tolerance through their effects on decreased underwater shoot elongation or increased maintenance of chlorophyll levels, or on both. These findings establish the foundations of a marker‐assisted scheme for introducing submergence tolerance into agriculturally desirable cultivars of rice.

Received: 6 August 2001; Returned for revision: 27 September 2001; Accepted: 8 January 2002

INTRODUCTION

Flooding is a serious, naturally occurring problem for rice production in the rain‐fed lowlands of south and south‐east Asia during the monsoon season. Fifty per cent of the rice‐growing area in this ecosystem is affected by flash flooding at various stages of growth (Dey and Upadhyaya, 1996). In addition, rice farming in the intensively farmed irrigated areas has become increasingly vulnerable to flooding because of the popularity of semi‐dwarf cultivars. As a consequence, there is also a need for irrigated rice cultivars that can survive a medium depth of water (75 cm). For these reasons, a high priority has long been set for breeding submergence‐tolerant rice in the tropics utilizing the inherent variability in tolerance known to be present in the available landraces (Mackill et al., 1993).

Submergence tolerance is defined as the ability of a rice plant to survive and continue growing after being completely submerged in water for several days. Rice plants are less tolerant of submergence at the early growth stages (Adkin et al., 1990). Submergence tolerance is a rare, genetically determined trait with relatively high heritability that is controlled by one or a few genes with major effects, and minor modifiers (Suprihatno and Coffman, 1981; Mohanty and Khush, 1985; Sinha and Saran, 1988; Haque et al., 1989). Several environmental factors such as duration of submergence, water level, temperature, water turbidity, photon flux density and soil conditions affect the tolerance of rice plants to complete submergence (Adkin et al., 1990; Jackson and Ram, 2003). Varietal differences in the degree of submergence tolerance have been noted many times (Palada and Vergara, 1972; Supapoj et al., 1982) and this genetic resource has been used in several conventional breeding programmes (Mohanty and Khush, 1985). The physiology of submergence tolerance has been studied extensively (reviewed in Jackson and Ram, 2003). Carbohydrate depletion and reduced photosynthesis are the main effects of submergence on plant tissues (Setter et al., 1989). Under submergence, gas exchange between soil and atmosphere is prevented. This leads to a decrease in the supply of oxygen and externally sourced carbon dioxide, and increases the internal concentration of ethylene in submerged rice plants (Jackson et al., 1987; Greenway and Setter, 1996). Flooding effects morphological and anatomical changes, such as leaf senescence, inhibition of growth in dry mass, faster underwater leaf elongation, partial injury, and even death of the entire plant. Chlorosis of tissues and leaf elongation are promoted by accumulation of ethylene within the shoot (Jackson et al., 1987). Phenotypic association of elongation growth, leaf senescence and the level of injury or mortality has been observed in several experiments (Mazaredo and Vergara, 1982).

To date, the physiological and molecular basis of submergence tolerance has not been completely resolved. Understanding the responses and state of the plants before, during and after submergence is a prerequisite for solving physiological questions. Visual observation of symptoms is useful in attempts to identify genes or quantitative trait loci (QTL) associated with tolerance because many lines can be screened in a reasonably short time. Using this approach, QTL for submergence tolerance have been reported on chromosomes 6, 7, 9, 11 and 12 (Xu and Mackill, 1996; Nandi et al., 1997). Of these, the major genes/QTL affecting submergence tolerance have been shown to reside on chromosome 9.

In recent years, more than 1000 DNA markers have been mapped onto the rice genome based on restriction fragment length polymorphisms (RFLPs), simple sequence length polymorphisms (SSLPs) and amplified fragment length polymorphisms (AFLPs) (McCouch et al., 1988; Kurata et al., 1994). These have played an essential part in locating genes and QTL. Using this approach to uncover the genetic basis of submergence tolerance and traits responsive to flash flooding will lead to a better understanding of the physiology of tolerance. The QTL information will also increase the efficiency with which submergence‐tolerant cultivars can be developed through breeding and selection processes.

In the present paper we report our attempts to create a genetic map of the traits associated with submergence tolerance and indicate how the molecular markers used in the mapping can assist in a molecular‐based breeding programme. We also identify QTL for plant traits associated with submergence stress tolerance at the seedling stage in three mapping populations, and explore the association between morphological and physiological traits and their genetic determinants.

MATERIALS AND METHODS

Plant material and linkage map construction

Plant material for mapping.

Three mapping populations were used for submergence‐tolerance studies. The first was the F1‐derived doubled haploid lines (DHLs) from a cross between IR49830‐7‐1‐2‐2 [IR49830; a submergence‐tolerant breeding line from the International Rice Research Institute (IRRI)] and CT6241‐17‐1‐5‐1 [CT6241; a submergence intolerant line from Centro International de Agriculura Tropical (CIAT)]. This population consists of 65 DHLs and was developed using an anther culture method (Lentini et al., 1995). The second population, derived from the cross between FR13A (an Indian land race cultivar and one of the most submergence‐tolerant lines) and CT6241, consists of 172 recombinant inbred lines (RILs). This population was developed via single seed descent (SSD) IRRI. The third population consists of 188 F2 plants developed from a cross made at Kasetsart University, Thailand, between Jao Hom Nin (a black rice with moderate tolerance to submergence) and Khaow Dawk Mali 105 (KDML105; an aromatic traditional cultivar from Thailand that is commercially important but that is intolerant of submergence).

Genetic mapping.

Genetic maps of DHLs, RILs and F2 populations were constructed using JoinMap software version 2·0 (Stam and Van Ooijen, 1995) and Mapmaker software (Lander and Boststein, 1989). Final maps were calculated using a maximum recombination frequency (rmax) of 0·50 and a LOD score of 6·0. The genetic distances (cM) were calculated from recombination values using the Kosambi function. A 1235 cM‐linkage map of the DHLs was constructed in 1997 using 105 markers including 72 RFLPs, 13 SSLPs, two randon amplified polymorphic DNAs (RAPDs) and 18 AFLPs. The average two‐locus interval was 11·8 cM. The RIL linkage map was constructed in 1999 using 183 markers including 46 RFLPs, 128 SSLPs, four single stranded conformation polymorphisms (SSCPs), one AFLP, one sequence tag site (STS), two bacterial artificial chromosome (BAC) ends (180D1R and 126G1R), and one yeast artificial chromosome (YAC) end (30L; Kamolsukyunyong et al., 2001), providing a total map distance of 1310 cM. The F2 linkage map consisted of 111 markers including 99 SSLPs, two SSCPs, three STSs and seven resistance gene analogue polymorphisms (RGAPs). To date, the linkage map covers 1279 cM, with an average distance of 11·5 cM between markers.

Integrated map.

An integrated genetic map was obtained using JoinMap software version 2·0 (Stam and Van Ooijen, 1995). Information from the three mapping populations was merged based on the recombination frequencies and LOD values. Kosambi’s mapping function was adopted for map distance calculations (Kosambi, 1944). Fixed order option was used to define ordering of doubtful markers. The final integrated map included 298 markers: 96 RFLPs, 166 SSLPs, seven SSCPs, 16 AFLPs, one STS, seven RGAs, two RAPDs, two BAC ends and one YAC end, with an average distance of 4·9 cM between markers. The ordering of markers corresponds well with published genetic maps (Chen et al., 1997).

Trait evaluation and data recording

The DHLs and five check cultivars were tested for submergence tolerance under field conditions at Huntra, Thailand, in 1994. The RILs, F3 families derived from the 188 single F2 plants, and the same check lines were tested for traits responsive to submergence stress under Thai field conditions at Huntra and at Ayuthaya in 1998, and at Kampangsaen and Nakorn Pathom in 2000 and 2001. Two ponds were used as replicate blocks in both 1994 and 1995 for the DHL population. A single replicate was used in 1998 and two replications were used in 2000–2001 for the RILs and F2 populations. FR13A and IR49830 were used as submergence‐tolerant checks. CT6241, KDML105 and IR42 (intolerant irrigated rice varieties) were used as the submergence‐intolerant checks. The DHLs, RILs or F2s, with five checks, were direct‐seeded in a plot on a puddled seed bed constructed in the base of a 60 m2 pond. Each plot included two rows, each 75 cm in length and with 25 cm between rows. Checks were seeded every ten plots. Four weeks after seedling establishment, the plants were completely submerged. The water level was kept 30 cm above the tallest entries to prevent their leaf tips emerging into the atmosphere. The average light intensity was 650–850 µmol m–2 s–1. Light transmissions measured at 0900 and 1330 h after 3 d of submergence were 15–25 and 7–15 % at the crop canopy and soil surface, respectively. Dissolved oxygen concentrations measured at the same times after 3 d of submergence were 5·0–6·3 and 4·8–5·5 ppm at the crop canopy and soil surface, respectively. Water temperature was 23–24 °C throughout the experiments. Plants were desubmerged when most of the intolerant checks had almost completely degenerated.

Submergence tolerance is a complex trait; it cannot be attributed to just one or a few physiological and morphological characteristics (Xu and Mackill, 1996). Five traits responsive to submergence were measured in the experiments in 1994, 1995 and 1998–2001 and are described below.

Per cent plant survival (PPS).

PPS was calculated as the total number of surviving seedlings counted 10 d after the water was drained from the submerging ponds, divided by the total number of seedlings counted before submergence. This value was then multiplied by 100.

Total shoot elongation under water (TSE).

TSE was used as a measure of the increment in shoot height during submergence and was calculated as the average difference in shoot height (in practice, shoot length) before and after desubmergence. In ten randomly selected plants per genotype, shoot height was taken as the distance from the soil surface to the tip of the longest leaf. TSE was recorded in the DHL experiments of 1994 and 1995, and in the RIL experiment in 1998.

Relative shoot elongation under submergence (RSE).

Carbohydrate depletion is one probable cause of injury following submergence. Less elongation growth under flash flooding could minimize carbohydrate depletion thus enhancing tolerance (Karin et al., 1982; Emes et al., 1988). To compare the impact of submergence on shoot elongation, the extension in height in each individual line was set to be 100 % under non‐submerged conditions and elongation growth under water was compared with this value. Thus relative shoot elongation under submergence (RSE) = (elongation growth under submergence × 100)/elongation growth under non‐submerged conditions. Values of RSE less than 100 indicate suppression of growth under submergence, whereas values over 100 indicate faster growth under water.

Tolerance score (TS).

TS summarizes the ability of plants to survive flash flooding. The standard evaluation system (SES) associating single‐digit scores for submergence tolerance with PPS was used to compute the TS (IRRI, 1988). The DHLs, RILs, F2s and checks of each experiments were scored for tolerance immediately after desubmergence using a scale of 1 to 5 (1, all plants survive; 5, all plants completely dead) for the DHL experiments, and of 1 to 9 (1, all plants survive; 9, all plants completely dead) for the RIL and F2 experiments.

Leaf senescence (LS).

LS is characterized by dramatic yellowing resulting from chloroplast degeneration. Submergence‐tolerant plants are able to retain green leaves for longer than intolerant lines when under water. In 1995–1999, LS was assessed immediately after submergence on a plot basis using a visual scale of 1 to 5. This visual score was based on the proportion of leaves that were yellow: 1 = all leaves green; 5 = all leaves completely yellow or degenerate. In 2000–2001, a SPAD‐502 chlorophyll meter (Minolta Camera Co. Ltd, Japan) was used to measure leaf senescence based on the amount of chlorophyll or ‘greenness’ of leaves. Ten of the youngest leaves, selected at random, were used for each individual line. Measurements were made at the base, middle and tip of each leaf, and the average LS‐SPAD values of 30 readings were used.

Statistical and QTL analysis

QTL analysis was performed with the software package MQTL (Tinker and Mather, 1995). Both simple interval mapping (SIM) and simplified composite interval mapping (sCIM) techniques were used. Each data set was analysed with 1000 permutations, a 5 cM walking speed, and a pre‐defined genome‐wide type I error rate of 5 %. The significant threshold (an LOD score of 2·4 or above) was used to declare the presence of a QTL. In DHLs, RILs and F2 populations, 24 background markers were specified for use as cofactors in an sCIM procedure. A single‐marker analysis, using regression‐based software, STAT GRAPHICS 2·1 and ANOVA, was then used to detect two locus interactions of QTL with the traits responsive to submergence stress.

RESULTS

Expression of traits responsive to submergence and their correlation with each other

Highly significant correlations among the five response traits assessed were observed in most of the experiments (Tables 1–3), indicating that these traits were not expressed independently of one another. In all experiments, plants having less LS under submergence mostly showed high PPS and a low TS after submergence. In addition, lines showing less TSE under water exhibited higher levels of PPS and a lower TS than those having high TSE. The phenotypic distribution of PPS, TSE, RSE, TS and LS after desubmergence did not show discrete classes when data were subjected to Mendelian analysis in each of the three populations evaluated (data not shown). This lends some support to the concept of one major controlling gene with others modifying the effect. It is also possible that the results are the outcome of quantitative modes of inheritance. It is relevant that intolerant transgressive segregants were observed in both DHLs and RILs. Broad sense heritability (h2) of all the traits we investigated was relatively high. The hof PPS, TSE, RSE, TS and LS ranged from 0·86 to 0·92, 0·40 to 0·88, 0·20 to 0·63, 0·92 to 0·94 and 0·62 to 0·92, respectively.

QTL detection

QTL for traits responsive to submergence in all three mapping populations mapped to rice chromosomes 1, 2, 5, 7, 9, 10 and 11 (Table 4; Fig. 1). In general, the analysis indicated that genetic control of submergence tolerance in rice is more complex than reported earlier by Xu and Mackill (1996). Major QTL controlling traits associated with PPS, TSE, RSE, TS and LS all mapped to the same region of chromosome 9 in each of the three mapping populations and were consistently identified in most of, if not all, the experiments. This indicates that one QTL on chromosome 9 (QTLch9) is probably the major genetic contributor to submergence tolerance performance in these three rice populations. The QTLch9 was detected with both SIM and sCIM procedures of MQTL. Significant QTL that exceeded the significance threshold are shown in Fig. 1. They mapped to the RM41RG553 interval on the short arm of chromosome 9. This QTL accounted for 48, 63, 74 and 72 % of phenotypic variation explanation (PVE) for PPS, TS, TSE and LS, respectively, in the DHL (Table 4). In the RIL, the QTLch9 accounted for 41–77, 38–63, 10–52, 10–12 and 11–53 % of PVE in expression of PPS, TS, TSE, RSE and LS, respectively. Alleles from IR49830 and FR13A resulted in slower growth under water compared with those from CT6241. The QTLch9 detected in F2 populations was only significantly associated with TSE and LS. Its presence accounted for 5 and 20 % of PVE, respectively.

Secondary QTL located on chromosomes 1, 2, 5, 7, 10 and 11 were inconsistently detected for many traits in the three mapping populations. The QTLch1 was mapped to the A330402–RM104 interval. This QTL was detected by the SIM procedure for PPS in the doubled haploid population and by simple regression for LS‐SPAD (SPAD readings) in the F2 population. The effect of this QTL accounted for 15 and 1·3 % of PVE for those traits, respectively. QTLch2 mapped to the G45‐RM250 interval. This was only detected by the SIM procedures for TSE in the DHL population in 1994. This QTL accounted for 24 % of PVE. In the RIL population, the QTLch2 mapped to this same interval and was detected by the sCIM procedure for TS and LS and accounted for less than 3 % of PVE. This QTL showed a significant interaction with the QTLch9. In the RIL population, the FR13A allele on chromosome 2 promoted more tolerance and less senescence than did the same allele from CT6241, while in the DHL population the allele of tolerant IR49830 promoted slower elongation under water than the same allele in intolerant CT6241. Simple regression showed a significant association between the QTLch2 and RSE and LS‐SPAD in the F2 population. The Jao Hom Nin allele of QTLch2 expressed higher SPAD and lower RSE under water than the equivalent KDML105 allele. However, the map location of this QTL differed in the integrated map compared with the QTLch2 detected in the RIL and DHL populations.

QTLch5 was mainly detected by the SIM procedure in the DH population for all traits. QTLch5 was mapped to the R2289–C1018 interval and accounted for 34, 25, 28 and 30 % of PVE for PPS, TSE, TS and LS, respectively. The allele from tolerant IR49830 promoted higher PPS, less TSE, lower TS and higher LS‐SPAD under submergence stress than the allele from intolerant CT6241. In the RIL population, QTLch5 was detected for TS in 1998 and for LS‐SPAD in 2001, accounting for 8 and 11 % of PVE, respectively. Plants possessing the equivalent CT6241 allele had a lower TS and a lower LS‐SPAD after experiencing submergence. No QTL on chromosome 5 were detected in the F2 population.

The QTLch7 was also detected by the SIM procedure for PPS, TS and LS in the RIL population and for TSE in the F2 population in 2000. This QTL mapped to the RM214–RM234 interval and accounted for 11, 11 and 14 % of PVE for PPS, TS and LS, respectively, in the RIL and for 3 % of PVE for TSE in the F2 population. In the RIL population, the FR13A allele at QTLch7 promoted a higher rate of PPS, a lower TS and less LS than the same allele from CT6241. In the F2 population, the allele from intolerant KDML105 promoted higher TSE under submergence.

The QTLch10–1 mapped to the RM222–RM216 interval was consistently detected by the SIM procedure for PPS, TSE, TS and LS in the RIL population. This QTL accounted for 15, 10, 7–18 and 10 % of PVE for PPS, TSE, TS and LS, respectively. The FR13A allele of this QTL promoted a higher PPS, less TSE, lower TS and less LS than did the CT6241 allele. An additional two QTL on chromosome 10 were detected only in the F2 population for PPS and RSE in the experiment carried out in 2001. These QTL mapped close to RM271 and G127, respectively. The QTL for PPS accounted for 15 % of PVE, while the QTL for RSE accounted for 2 % of PVE. The submergence‐susceptible KDML105 alleles promoted a lower rate of PPS and higher RSE under water. A minor QTL on chromosome 11 was identified only for RSE in the F2 population in 2001. This QTL accounted for only 4 % of PVE. The JHN allele contributed to higher RSE than KDML105.

QTL × QTL interaction

All QTL detected by SIM or sCIM procedures applied together or separately are shown in Table 4. They were re‐analysed to characterize the mode of gene action by choosing the significant best‐fit model (P < 0·01) when tested with ANOVA and multiple regressions. Testing was based on genotypes with the closest marker locus of each QTL. The QTLch5 × QTLch9 interactions were significant when tested with ANOVA and multiple regression for all responsive traits in the DHL. IR49830 tolerant parents contributed favourable alleles for resistance to submergence injury to QTLch9 and QTLch5. The observed mean of responsive traits for CQTLch5 × IQTLch9 (CI) genotype was equivalent to IQTLch5 × IQTLch9 (II) genotype (Table 5). The IQTLch5 × CQTLch9 (IC) genotype showed intermediate tolerance while the CQTLch5 × CQTLch9 (CC) genotype was the most intolerant in the DHL population. The means of four allelic compositions at these QTL for TS (Fig. 2) illustrate that the QTLch9 was the major QTL for response to submergence stress. The allelic compositions of QTL on chromosomes 5 and 9 are also important for the expression of responsive traits to submergence stress. Figure 3 shows that the QTLch5 was one of the loci associated with plant elongation before submergence, whereas QTLch9 was strongly associated with TSE during submergence.

In the RIL population, analysis showed that only three common loci were frequently detected. Although the interaction among three QTL was not significant, two‐locus interactions could be seen. FR13A contributed alleles to QTLch9 that increased resilience to submergence in respect of all the responsive traits. The observed means of traits for CQTLch2 × FQTLch9 and FQTLch7 × FQTLch9 genotypes showed intermediate tolerance while FQTLch2 × FQTLch9 and FQTLch7 × FQTLch9 showed the most tolerance. The FQTLch2 × CQTLch9 and FQTLch7 × CQTLch9 genotypes were shown to be the most susceptible to submergence stress. The mean of four allelic compositions at these QTL illustrated that QTLch9 was the major QTL responsible for submergence tolerance (Tables 6 and 7).

DISCUSSION

Linking phenotypic responses to submergence tolerance

Strong phenotypic correlations among traits responsive to submergence stress that were observed in the DHLs and RILs indicated common mechanisms for submergence tolerance (Tables 13). Similar, but weaker, phenotypic correlations were also observed in the F2 population (data not shown). We hypothesize that there were probably other mechanisms supporting submergence tolerance in this population besides those discussed in this article. We provide further evidence that leaf senescence and the tolerance score are indeed genetically associated with total shoot elongation in submerged rice plants. This is supported by the observations that intolerant plants elongated rapidly under water. Mazaredo and Vergara (1982) and Setter and Laureles (1996) proposed that intolerant plants must consume more energy in elongation during flooding, possibly by adopting higher respiration rates or diverting energy supplies from maintenance processes (Jackson and Ram, 2003). In support of this link, plants that remained short under water suffered lower mortality and recovered more rapidly after desubmergence. This could be due to an increased amount of respirable reserves retained in short plants (Karin et al., 1982; Emes et al., 1988; Setter et al., 1989). The negative relationships between plant survival (%) and total shoot elongation, and the tolerance score and TSE, also support this hypothesis. The correlation between rapid plant elongation and more leaf senescence is paralleled by the responses given to exogenous ethylene (Jackson et al., 1987) and, in nature, may be the consequence of the action of endogenous ethylene that accumulates within submerged rice. Inhibition of plant elongation by paclo butrazol, a gibberellin‐biosynthesis inhibitor, increased tolerance of 14‐d‐old seedlings by 98 % (Setter and Laureles, 1996; Setter et al., 1997). All of these data suggest that rapid plant elongation when submerged and the associated leaf senescence are direct physiological causes of plant injury resulting from submergence.

A major QTL for submergence tolerance is located on chromosome 9

The expression of submergence tolerance is known to be environmentally dependent and genetically complex (Suprihatno and Coffman, 1981; Mohanty and Khush, 1985; Sinha and Saran, 1988; Haque et al., 1989; Adkin et al., 1990; Setter et al., 1997). In different years and seasons and with different mapping populations, the QTL controlling traits related to submergence tolerance were mapped on many genomic regions. However, the consistently detected QTLch9 indicated a common genetic factor controlling submergence tolerance in rice. Therefore, QTLch9 is the most important submergence tolerance QTL in the three populations. The QTLch9 was mapped near the Sub1 locus previously identified by Xu and Mackill (1996) and Nandi et al. (1997). The coincidence of the map locations of all traits on chromosome 9 may be due either to linkage or to pleiotropy. At the level of mapping resolution afforded by three populations, linkage and pleiotropy cannot be distinguished.

The role of secondary QTL

The contributions to submergence tolerance of secondary QTL on chromosomes 2, 5, 7 and 10 have not been reported previously. Although the effects of these QTL were small, they were often detected by the SIM procedure for several traits across mapping populations, years and environments. However, the best‐fit multi‐locus model in the presence of QTLch9 excluded QTLch5 and QTLch7. The reason why QTLch5 and QTLch7 were not expressed in the presence of QTLch9 is not clear from the current study. It is possible that both QTL were part of the submergence‐tolerance pathways which play certain roles that are not essential in the presence of the QTLch9. Comparing four group means of the digenic interaction in the DHL population also supports this idea. This suggests that the QTLch5 acted passively in the presence of the QTLch9. Without the dominant QTLch9, it is possible that the presence of the QTLch5 played an intermediate role in promoting plant survival by inhibiting plant elongation and maintaining leaf greenness. The analysis of digenic interactions in the RIL population revealed a similar picture. In the RIL population, the effects of QTLch2 and QTLch7 were obviously much less important than those of QTLch9. As a result, progenies carrying the same FQTLch9 allele but retaining the FQTLch2 and FQTLch7 alleles were superior to those carrying CQTLch2 and CQTLch7 alleles. We note that the population sizes in our experiments were small (<200 individuals); therefore, it is difficult to detect specific interactions due to statistical limitations. However, the mean of allelic compositions of digenic interactions in both populations illustrated that QTLch9 was the major QTL responsible for submergence tolerance. To understand the role of secondary QTL, development of near isogenic lines will be necessary. Such a population would also provide useful information about epistatic interactions and clarify the true nature of epistatic interaction among the various QTL.

The relationship of the QTL to genes known to be responsive to anoxia

It is worthwhile considering the association between the identified QTL controlling submergence tolerance and genes known to be regulated by anoxia. Genes for alcohol dehydrogenase (ADH), pyruvate decarboxylase (PDC) and some enzymes in the glycolysis pathway have been considered as candidates that could play important roles in genetically modified rice having improved submergence tolerance (Setter et al., 1997). An ADH1 gene has been mapped on chromosome 11 and an ADH2 gene on chromosome 9 (Ranjhan et al., 1991). The map location of ADH2 differed from that of the QTLch9, but the QTLch11 detected in the F2 population was located near the ADH1 gene. The relationship between the QTLch11 and ADH1 deserves closer investigation. The PDC1 gene is located on chromosome 5 between BCD454A and RZ67. PDC2 is located on chromosome 3 and PDC3 maps onto chromosome 7 in a distal position to RG351(Huq et al., 1999). PDC1 maps close to QTLch5. No other genes known to respond to anoxia were located on chromosomes 2 and 10 (Kurata et al., 1994) where QTL for submergence tolerance were located. Therefore, we have some genetic evidence that some genes responsive to anoxia may be involved in some minor pathway of submergence tolerance.

Breeding for submergence tolerance

Understanding the roles of secondary QTL and QTLch9 will open the way to selecting plants with improved submergence tolerance. PCR‐based markers flanking the peak of each QTL are already being used in our breeding programmes for submergence tolerance. There are PCR‐based markers near the peak of each QTL. Availability of several markers tightly linked to submergence tolerance, especially to the QTLch9, will permit marker‐assisted selection in breeding programmes and positional cloning. Althought FR13A has long been known as the lowland rice that is most tolerant to flash flooding, its direct utilization as a tolerant donor is limited by its many poor agronomic traits. Such traits include a long awn; short, thick grains; photoperiod sensitivity; and low productivity. This has meant that conventional breeding strategies have been unable to generate agronomically useful cultivars using FR13A as the tolerance donor. Therefore, fine mapping around these regions using larger sets of the RIL progenies is needed not only for map‐based cloning of these genes, but also for developing even more tightly linked markers to be used for marker‐assisted selection in breeding programmes.

CONCLUSIONS

The coincidence of QTL for traits important for submergence tolerance provides significant genetic evidence linking physiological responses to submergence tolerance in rice. Multiple‐season QTL analysis in three mapping populations has enabled us to identify a major QTL and several minor QTL that are of potential value for the improvement of submergence tolerance in rice. Availability of cloned genes present at these loci will be important stepping stones towards a fuller understanding of the regulation of submergence tolerance. Map‐based cloning of the major QTL for submergence tolerance on chromosome 9 holds the key to improving our understanding of the physiological basis of submergence tolerance in rice and developing dependable marker‐aided selection schemes for breeding submergence‐tolerant cultivars with desirable agronomic features.

ACKNOWLEDGEMENTS

This research was supported by the Rockefeller Foundation’s International Rice Biotechnology Program, the EU‐INCO, Rice‐for‐Life project (IC18‐CT96‐00078), and the Thai National Center for Genetic Engineering and Biotechnology (BIOTEC).

Fig. 1. Summary of QTL for traits responsive to submergence in three mapping populations of rice (65 DHLs from IR49830 × CT6241, 172 RILs from FR13A × CT6241 and 188 F2 from Joa Hom Nin × KDML105). Results are from submergence tests in outdoor ponds and analyses in the years from 1994 to 2001 inclusive. The chromosome map is an integrated map from all three populations.

Fig. 1. Summary of QTL for traits responsive to submergence in three mapping populations of rice (65 DHLs from IR49830 × CT6241, 172 RILs from FR13A × CT6241 and 188 F2 from Joa Hom Nin × KDML105). Results are from submergence tests in outdoor ponds and analyses in the years from 1994 to 2001 inclusive. The chromosome map is an integrated map from all three populations.

Fig. 2. Two locus interactions between QTL on chromosomes 5 and 9 based on visual examination of damage (tolerance score) made 0, 3, 5 and 8 d after desubmerging rice plants after 8 d of complete submergence. CC, IC, CI and II refer to allelic composition of DH lines at QTL on chromosomes 5 and 9, respectively. C, Allele from submergence‐intolerant parent CT 6241; I, allele from submergence‐tolerant parent IR49830.

Fig. 2. Two locus interactions between QTL on chromosomes 5 and 9 based on visual examination of damage (tolerance score) made 0, 3, 5 and 8 d after desubmerging rice plants after 8 d of complete submergence. CC, IC, CI and II refer to allelic composition of DH lines at QTL on chromosomes 5 and 9, respectively. C, Allele from submergence‐intolerant parent CT 6241; I, allele from submergence‐tolerant parent IR49830.

Fig. 3. Two locus interactions between QTL on chromosomes 5 and 9 based on plant height before 8 d submergence and plant height at the time of desubmergence. CC, IC, CI and II refer to allelic composition of DH lines at QTL on chromosomes 5 and 9, respectively. C, Allele from submergence‐intolerant parent CT 6241; I, allele from submergence‐tolerant parent IR49830.

Fig. 3. Two locus interactions between QTL on chromosomes 5 and 9 based on plant height before 8 d submergence and plant height at the time of desubmergence. CC, IC, CI and II refer to allelic composition of DH lines at QTL on chromosomes 5 and 9, respectively. C, Allele from submergence‐intolerant parent CT 6241; I, allele from submergence‐tolerant parent IR49830.

Table 1.

R2 values for correlations between submergence response traits in rice obtained in a doubled haploid mapping population derived from a cross between submergence‐tolerant IR49830 and submergence‐intolerant CT 6241

Traits TSE TS LS 
PPS –0·57** –0·81** –0·71** 
TSE  0·74** 0·77** 
TS   0·93** 
Traits TSE TS LS 
PPS –0·57** –0·81** –0·71** 
TSE  0·74** 0·77** 
TS   0·93** 

Plants were submerged for 8 d in an outdoor pond in Thailand in 1994.

PPS, Per cent plant survival; TSE, total stem elongation; TS, tolerance score; LS, leaf senescence.

**P < 0·01.

Table 2.

R2 values for correlations between submergence response traits in rice obtained in a recombinant inbred line mapping population derived from a cross between submergence‐tolerant FR13A and submergence‐intolerant CT 6241

 TSE2000 TSE2001 RSE2000 RSE2001 TS1998 TS2000 LS1998 LS‐SPAD2001 
PPS2000 –0·64**  0·25   –0·91**   
PPS2001  –0·34**  –0·30*    0·59** 
TSE1998     0·24*  0·24*  
TSE2000   –0·27*   0·64**   
TSE2001    0·77**    –0·20 
RSE2000      –0·38**   
RSE2001        –0·12 
TS1998       0·89**  
 TSE2000 TSE2001 RSE2000 RSE2001 TS1998 TS2000 LS1998 LS‐SPAD2001 
PPS2000 –0·64**  0·25   –0·91**   
PPS2001  –0·34**  –0·30*    0·59** 
TSE1998     0·24*  0·24*  
TSE2000   –0·27*   0·64**   
TSE2001    0·77**    –0·20 
RSE2000      –0·38**   
RSE2001        –0·12 
TS1998       0·89**  

Plants were submerged for 8–10 d in an outdoor pond in Thailand in 1998, 2000 and 2001.

PPS, Per cent plant survival; TSE, total shoot elongation; TS, tolerance score; RSE, relative shoot elongation; LS, leaf senescence; LS‐SPAD, leaf senescence using SPAD‐502 chlorophyll meter.

*, **P < 0·05 and 0·01, respectively.

Table 3.

R2 values for correlations between submergence response traits in rice obtained in an F2 population derived from a cross between moderately submergence‐tolerant Jao Hom Nin and submergence‐intolerant Khaow Dawk Mali 105

 TSE2001 RSE2000 RSE2001 LS‐SPAD2000 LS‐SPAD2001 
PPS2001 0·18*  –0·05  0·18* 
TSE2000  0·004  –0·15*  
TSE2001   –0·02  0·20** 
RSE2000    –0·006  
RSE2001     –0·05 
 TSE2001 RSE2000 RSE2001 LS‐SPAD2000 LS‐SPAD2001 
PPS2001 0·18*  –0·05  0·18* 
TSE2000  0·004  –0·15*  
TSE2001   –0·02  0·20** 
RSE2000    –0·006  
RSE2001     –0·05 

Plants were submerged for 8 d in an outdoor pond in Thailand in 2000–2001.

PPS, Per cent plant survival; TSE, total shoot elongation; RSE, relative shoot elongation; LS‐SPAD, leaf senescence using SPAD‐502 chloro phyll meter.

*, ** P < 0·05 and 0·01, respectively.

Table 4.

A compilation of QTL that influenced five submergence‐responsive traits studied in three mapping populations of rice in 5 years of testing from 1994

Population Period of submergence (d) Year Linked markers/marker interval Method Chromosome QTL effect Parent allele Effect LOD score R2 Multilocus R2 
Per cent plant survival 
 DHL 1994 A330402 0·8 IR49830 High survival 5·4 15·8  
   R1553 0·8 IR49830 High survival 11·2 34·1 64·0 
   RZ698 a, b 2·6 IR49830 High survival 17·3 48·3  
 RIL 2000 RM11–OSR22 25·6 FR13A High survival 5·4 11·0  
   180D1R–126G1R a, b 60·2 FR13A High survival 65·8 77·0 78·0 
   RM222 10 27·8 FR13A High survival 8·5 15·0  
 2001 180D1R a, b 17·8 FR13A High survival 36·4 41·0  
 F2 2000 – – – – – – – – 
 2001 RM271 10 2·6 Jao Hom Nin High survival – 6·7  
   RM206 11 2·3 Jao Hom Nin High survival – 2·7 27·4 
   RM209 11 4·6 KDML105 High survival – 15·4  
Total shoot elongation 
 DHL 1994 RG157 5·8 CT6241 Fast elongation 7·3 24·0  
   RZ70 1·6 CT6241 Fast elongation 6·2 25·0 78·8 
   RZ698 a, b 14·8 CT6241 Fast elongation 27·3 74·0  
 RIL 1998 RM219 a, b 7·3 CT6241 Fast elongation 3·9 9·7 9·7 
 2000 R1164 a, b 13·4 CT6241 Fast elongation 28·1 52·0 54·0 
   RM222 10 7·1 CT6241 Fast elongation 5·1 10·0  
 2001 30L a, b 3·8 CT6241 Fast elongation 7·1 21·0 21·0 
 F2 2000 RM234 1·2 KDML105 Fast elongation – 3·1 4·6 
   RM219 1·1 KDML105 Fast elongation – 2·1  
Relative shoot elongation 
 RIL 2000 S10709 a, b 18·3 FR13A Fast elongation 16·2 9·7 – 
 2001 30L a, b 23·0 CT6242 Fast elongation 3·8 12·0 – 
 F2 2000 RM264 22·3 Jao Hom Nin Fast elongation – 5·3 7·2 
   RM206 11 16·6 Jao Hom Nin Fast elongation – 2·7  
 2001 G393 39·8 Jao Hom Nin Fast elongation – 2·6  
   S_AS2D 10 33·7 Jao Hom Nin Fast elongation – 2·1 8·7 
   RM270 12 37·8 KDML105 Fast elongation – 2·3  
Tolerance score 
 DHL 1994 R1553 a, b 1·3 IR49830 High tolerance 12·3 28·3 76·9 
   R1164 a, b 3·9 IR49830 High tolerance 49·1 72·2  
 RIL 10 1998 RM164 0·3 CT6241 High tolerance 3·2 8·2  
   C451 1·1 FR13A High tolerance 4·4 11·7 72·1 
   180D1R a, b 4·2 FR13A High tolerance 38·3 63·3  
   RM222 10 0·4 FR13A High tolerance 3·7 9·4  
 10 1999 180D1R a, b 1·3 FR13A High tolerance 26·6 38·3  
   RM222 10 0·2 FR13A High tolerance 4·6 7·0  
 10 2000 180D1R a, b 2·3 FR13A High tolerance 52·6 61·0  
   OSR22 1·0 FR13A High tolerance 5·6 10·0 65·0 
   RM222 10 1·3 FR13A High tolerance 9·5 18·0  
Leaf senescence 
 DHL 1994 RZ70 0·5 IR49830 Leaves stay green 5·5 30·2 73·8 
   RZ698 a, b 1·9 IR49830 Leaves stay green  18·0 72·0  
 RIL 10 1998 C451 0·6 FR13A Leaves stay green 5·6 14·3 61·0 
   180D1R a, b 1·8 FR13A Leaves stay green  29·3 53·3  
   RM222 10 0·3 FR13A Leaves stay green  4·0 10·1  
 2001 R1553 4·4 FR13A High chlorophyll – 11·1 – 
 F2 2000 RM212 0·9 Jao Hom Nin High chlorophyll – 1·7  
   RM213 1·0 Jao Hom Nin High chlorophyll – 5·5 27·1 
   RM317 1·1 KDML105 High chlorophyll – 4·2  
   RM219 2·3 Jao Hom Nin High chlorophyll – 18·8  
 2001 RM104 1·6 Jao Hom Nin High chlorophyll – 7·6  
   RM341 1·0 Jao Hom Nin High chlorophyll – 2·5 13·3 
Population Period of submergence (d) Year Linked markers/marker interval Method Chromosome QTL effect Parent allele Effect LOD score R2 Multilocus R2 
Per cent plant survival 
 DHL 1994 A330402 0·8 IR49830 High survival 5·4 15·8  
   R1553 0·8 IR49830 High survival 11·2 34·1 64·0 
   RZ698 a, b 2·6 IR49830 High survival 17·3 48·3  
 RIL 2000 RM11–OSR22 25·6 FR13A High survival 5·4 11·0  
   180D1R–126G1R a, b 60·2 FR13A High survival 65·8 77·0 78·0 
   RM222 10 27·8 FR13A High survival 8·5 15·0  
 2001 180D1R a, b 17·8 FR13A High survival 36·4 41·0  
 F2 2000 – – – – – – – – 
 2001 RM271 10 2·6 Jao Hom Nin High survival – 6·7  
   RM206 11 2·3 Jao Hom Nin High survival – 2·7 27·4 
   RM209 11 4·6 KDML105 High survival – 15·4  
Total shoot elongation 
 DHL 1994 RG157 5·8 CT6241 Fast elongation 7·3 24·0  
   RZ70 1·6 CT6241 Fast elongation 6·2 25·0 78·8 
   RZ698 a, b 14·8 CT6241 Fast elongation 27·3 74·0  
 RIL 1998 RM219 a, b 7·3 CT6241 Fast elongation 3·9 9·7 9·7 
 2000 R1164 a, b 13·4 CT6241 Fast elongation 28·1 52·0 54·0 
   RM222 10 7·1 CT6241 Fast elongation 5·1 10·0  
 2001 30L a, b 3·8 CT6241 Fast elongation 7·1 21·0 21·0 
 F2 2000 RM234 1·2 KDML105 Fast elongation – 3·1 4·6 
   RM219 1·1 KDML105 Fast elongation – 2·1  
Relative shoot elongation 
 RIL 2000 S10709 a, b 18·3 FR13A Fast elongation 16·2 9·7 – 
 2001 30L a, b 23·0 CT6242 Fast elongation 3·8 12·0 – 
 F2 2000 RM264 22·3 Jao Hom Nin Fast elongation – 5·3 7·2 
   RM206 11 16·6 Jao Hom Nin Fast elongation – 2·7  
 2001 G393 39·8 Jao Hom Nin Fast elongation – 2·6  
   S_AS2D 10 33·7 Jao Hom Nin Fast elongation – 2·1 8·7 
   RM270 12 37·8 KDML105 Fast elongation – 2·3  
Tolerance score 
 DHL 1994 R1553 a, b 1·3 IR49830 High tolerance 12·3 28·3 76·9 
   R1164 a, b 3·9 IR49830 High tolerance 49·1 72·2  
 RIL 10 1998 RM164 0·3 CT6241 High tolerance 3·2 8·2  
   C451 1·1 FR13A High tolerance 4·4 11·7 72·1 
   180D1R a, b 4·2 FR13A High tolerance 38·3 63·3  
   RM222 10 0·4 FR13A High tolerance 3·7 9·4  
 10 1999 180D1R a, b 1·3 FR13A High tolerance 26·6 38·3  
   RM222 10 0·2 FR13A High tolerance 4·6 7·0  
 10 2000 180D1R a, b 2·3 FR13A High tolerance 52·6 61·0  
   OSR22 1·0 FR13A High tolerance 5·6 10·0 65·0 
   RM222 10 1·3 FR13A High tolerance 9·5 18·0  
Leaf senescence 
 DHL 1994 RZ70 0·5 IR49830 Leaves stay green 5·5 30·2 73·8 
   RZ698 a, b 1·9 IR49830 Leaves stay green  18·0 72·0  
 RIL 10 1998 C451 0·6 FR13A Leaves stay green 5·6 14·3 61·0 
   180D1R a, b 1·8 FR13A Leaves stay green  29·3 53·3  
   RM222 10 0·3 FR13A Leaves stay green  4·0 10·1  
 2001 R1553 4·4 FR13A High chlorophyll – 11·1 – 
 F2 2000 RM212 0·9 Jao Hom Nin High chlorophyll – 1·7  
   RM213 1·0 Jao Hom Nin High chlorophyll – 5·5 27·1 
   RM317 1·1 KDML105 High chlorophyll – 4·2  
   RM219 2·3 Jao Hom Nin High chlorophyll – 18·8  
 2001 RM104 1·6 Jao Hom Nin High chlorophyll – 7·6  
   RM341 1·0 Jao Hom Nin High chlorophyll – 2·5 13·3 

DHL, Doubled haploid line (IR49830 × CT6241); RIL, Recombinant inbred line (FR13A × CT6241); F2, F2 line (Jao Hom Nin × KDML105); a, SIM main effect; b, sCIM main effect; r, single marker analysis (regression).

Table 5.

Mean values of QTLch5 × QTLch9 interactions between phenotypic traits for submergence responses of rice to 8 d complete submergence in a population of doubled haploid lines derived from a cross between submergence‐tolerant IR49830 (I) and submergence‐intolerant CT6241 (C)

Mean of allelic compositions of QTLs 
Traits Unit CC CI IC II 
PPS Score 4·07a 1·77c 3·03b 1·99c 
TS Score 7·14a 2·37c 5·14b 2·28c 
LS Score 4·01a 1·74c 3·14b 1·58c 
TSE cm 32·60a 14·29c 22·95b 15·32c 
Mean of allelic compositions of QTLs 
Traits Unit CC CI IC II 
PPS Score 4·07a 1·77c 3·03b 1·99c 
TS Score 7·14a 2·37c 5·14b 2·28c 
LS Score 4·01a 1·74c 3·14b 1·58c 
TSE cm 32·60a 14·29c 22·95b 15·32c 

CC, CI, IC and II refer to the allelic composition of doubled haploid lines at QTL on chromosomes 5 and 9, respectively.

Values in the same row with the same superscript do not differ statistically at the 95 % level (Fisher’s least significant difference test).

Table 6.

Mean values of QTLch2 × QTLch9 interactions between phenotypic traits for submergence responses of rice to complete submergence for 8 d in a population of RI lines derived from a cross between submergence‐tolerant FR13A (F) and submergence‐intolerant CT6241 (C) tested in 1998

 Allelic compositions of QTLs 
Traits CC FC CF FF 
Plant height before submergence (cm) 48·72a 47·8a 46·45a 47·0a 
Plant height after submergence (cm) 66·83a 66·33a 59·75b 57·56b 
TSE (cm) 18·11a 18·53a 13·3ab 10·56b 
LS (cm) 5·0a 4·93a 4·35b 4·02c 
TS (score) 8·63a 8·15a 5·38b 3·38c 
 Allelic compositions of QTLs 
Traits CC FC CF FF 
Plant height before submergence (cm) 48·72a 47·8a 46·45a 47·0a 
Plant height after submergence (cm) 66·83a 66·33a 59·75b 57·56b 
TSE (cm) 18·11a 18·53a 13·3ab 10·56b 
LS (cm) 5·0a 4·93a 4·35b 4·02c 
TS (score) 8·63a 8·15a 5·38b 3·38c 

CC, FC, CF and FF refer to the allelic composition of doubled haploid lines at QTL on chromosomes 2 and 9, respectively.

Values in the same row followed by the same letter do not differ statistically at the 95 % level (Fisher’s least significant difference test).

Table 7.

Mean values of QTLch7 × QTLch9 interactions between phenotypic traits for submergence responses of rice to complete submergence for 8 d in a population of RILs derived from a cross between submergence‐tolerant FR13A (F) and submergence‐intolerant CT6241 (C) tested in 1998

 Allelic compositions of QTLs 
Traits CC FC CF FF 
Plant height before submergence (cm) 45·89b 49·21a 48·6ab 47·27ab 
Plant height after submergence (cm) 64·76a 65·27a 68·39a 57·33b 
TSE (cm) 18·87a 19·19a 16·67a 10·06b 
LS (cm) 4·92a 4·96a 4·38b 4·0c 
TS (score) 8·63a 8·07a 5·44b 3·4c 
 Allelic compositions of QTLs 
Traits CC FC CF FF 
Plant height before submergence (cm) 45·89b 49·21a 48·6ab 47·27ab 
Plant height after submergence (cm) 64·76a 65·27a 68·39a 57·33b 
TSE (cm) 18·87a 19·19a 16·67a 10·06b 
LS (cm) 4·92a 4·96a 4·38b 4·0c 
TS (score) 8·63a 8·07a 5·44b 3·4c 

CC, FC, CF and FF refer to the allelic composition of RILs at QTL on chromosomes 7 and 9, respectively. Values in the same row followed by the same superscript do not differ statistically at the 95 % level (Fisher’s least significant difference test).

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Author notes

1BIOTEC, 2Rice Gene Discovery Unit, 3DNA Technology Laboratory and 4Department of Agronomy, National Center for Genetic Engineering and Biotechnology, Kasetsart University, Kampangsaen Campus, Nakorn Pathom, Thailand

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