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Quantitative Trait Loci Analysis of Allelopathy in Rice

^a Univ. of Aarhus, Dep. of Genetics and Biotechnology, DK-4200 Slagelse, Denmark
^b CIRAD-AMIS, UMR PIA, TA40/03, Avenue Agropolis 34398 Montpellier Cedex 5, France
^c Øresund Food Network, Arne Jacobsens Allé 15-17,1, DK-2300 København S, Denmark

^* Corresponding author (LouiseB.Jensen{at}agrsci.dk).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The allelopathic potential of rice (Oryza sativa L.) against Echinochloa crus-galli (L.) Beauv. was investigated under both laboratory and greenhouse conditions. A population of 150 recombinant inbred lines (RILs) was derived through single-seed descent from a cross between the indica cultivar AC1423, known to have a strong allelopathic potential, and the aus cultivar Aus196, with low allelopathic potential. The RILs together with the parents were first phenotyped using the laboratory assay known as the relay seeding technique. The screening showed that with regards to allelopathic potential the population phenotype was normally distributed. Two quantitative trait loci (QTLs) were located on chromosomes 4 and 7, explaining 20% of the phenotypic variation. A second relay seeding experiment was set up, this time including charcoal in the perlite. This screening showed that the allelopathic rice varieties did not have any effect on the weed species when grown with charcoal, the charcoal reversing the effect of any potential allelochemicals exuded from the rice roots. The second phenotypic experiment was conducted under greenhouse conditions in pots. Thirteen QTLs were detected for four different allelopathic measurements, located on seven chromosomes, and individually explaining between 5 and 10% of the phenotypic variation. Some QTLs from both experiments located to the same genomic regions. The results show that breeding to increase rice allelopathic potential is possible.

Abbreviations: GHWRB, greenhouse weed root biomass • GHWRL, greenhouse weed root length • GHWSB, greenhouse weed shoot biomass • GHWSL, greenhouse weed shoot length • LOD, logarithm of odds • PCR, polymerase chain reaction • QTL, quantitative trait locus • RIL, recombinant inbred line • RLSWRL, relay seeding weed root length • SSR, simple sequence repeat

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
THE TERM allelopathy was coined by Molisch (1937) to refer to biochemical interactions between all types of plants, including microorganisms. Rice (1984) defined allelopathy as "direct or indirect (harmful or beneficial) effects of a plant, including microbes, on another plant through the release of compounds that escape into the environment." Allelopathy must be distinguished from competition: Whereas the allelopathic effect depends on chemical compounds being added to the environment, competition involves the removal or reduction of an environmental factor that is required by some other plant or microorganism sharing the habitat (Rice 1984; Rizvi and Rizvi 1992). Plants can release allelopathic compounds into the environment through root exudation, leaching by dews and rains, volatilization, or decaying plant tissue (Rice 1984).

Allelopathy has been considered a potential environmental friendly approach for weed control in rice (Oryza sativa L.) production (Olofsdotter et al., 2002). However, very little effort has been made to understand the genetic control of the trait. Since allelopathy cannot be distinguished from competition under field conditions, selection under field conditions for cultivars with enhanced allelopathic expression is almost impossible. Therefore, locating the genes responsible for allelopathy is essential for including the trait in a breeding program.

In the poor uplands of South and Southeast Asia, weed infestation is the major problem in rice crops and may reduce yields between 30 and 100% (Pandey, 1996). In these rice production systems, the only affordable solution to the weed problem is hand weeding. In more favorable ecosystems, herbicide dependency is very large, often including preemergent herbicides as an insurance against later upcoming problems caused by weeds. Weed control methods that reduce herbicide dependency and offer the opportunity to spray when necessary would be of great importance to most rice growing areas. Superior competitive ability from the rice crop itself could be a way of reducing herbicide and labor dependency. However, so far, no cultivar of any crop has been released with superior competing ability as a marketing argument (Olofsdotter, 2001). Consequently, breeders are left with a challenging task. The upland rice breeding program at the International Rice Research Institute has set weed competitiveness as a major breeding objective (IRRI, 2001). Breeding for enhanced weed competitiveness, including improved allelopathic expression, could give superior varieties and thereby reduce the burden of hand weeding, which can represent up to 30% of the labor devoted to upland rice in traditional cropping systems, or herbicide dependency in improved cropping systems.

Apart from rice, several studies have showed that the needed genetic variation for allelopathic expression is present in the germplasm for several plant species: Medicago sativa L. (Xuan and Tsuzuki, 2002), Capsicum annuum L. (González et al., 1997), Triticum speltoides (Quader et al., 2001), Triticum aestivum L. (Wu et al., 2000), and Cucumis sativus L. (Putnam and Duke, 1974). In rice, several studies have been performed to evaluate the genetic variation for allelopathic expression. During 1988–1990 a large number of rice accessions were studied for their allelopathic potential under field conditions at the USDA-ARS Dale Bumpers National Rice Research Center. Approximately 12,000 rice accessions were screened for their allelopathic potential against ducksalad [Heteranthera limosa (Sw.) Willd], and 5000 accessions against redstem (Purple ammannia) and barnyardgrass [Echinochloa crus-galli (L.) Beauv.]. In the field evaluation, allelopathic activity was defined as accessions with a weed-free area around the basis of the rice plant having a radius larger than 10 cm. The setup resulted in a total of 412 rice accessions being identified as allelopathic against ducksalad and 145 accessions as allelopathic against redstem. A total of 94 rice accessions showed allelopathic activity toward barnyardgrass (Dilday et al., 1998, 2000).

Screening for allelopathic rice cultivars has also been performed in the laboratory (Fujii, 1994; Navarez and Olofsdotter, 1996; Hassan et al., 1998; Ebana et al., 2001b). Most of the screenings mentioned are performed in vitro and are difficult to correlate with actual field conditions. This has been a general criticism formulated against allelopathy research. Therefore, Olofsdotter et al. (1999) correlated screening results from the laboratory with weed suppression in the field and found a correlation coefficient of 0.34, suggesting that allelopathy could explain 34% of the reduction in total weed dry weight 8 wk after seeding. This is equivalent to the importance of plant height for competitive ability of a crop.

Most studies of rice allelopathy have concentrated on revealing genetic variation in existing plant material. Only two studies have actually tried to locate the genes responsible for allelopathic expression. The first study used the relay seeding technique (Navarez and Olofsdotter, 1996) to measure allelopathic expression. Quantitative trait locus (QTL) mapping was performed using an already-mapped population of 142 recombinant inbred lines (RILs) derived from a cross between a japonica cultivar IAC165 (allelopathic parent) and an indica cultivar CO39 (nonallelopathic parent). Four main-effect QTLs located on three chromosomes (2, 3, and 8) were identified and collectively explained 35% of the total phenotypic variation of the allelopathic activity in the population (Jensen et al., 2001).

The second study used water-soluble extracts from rice seedlings and measured the growth inhibition on lettuce seedlings in a bioassay setup in the laboratory (Ebana et al., 2001a). Quantitative trait locus analysis was performed using an F₂ population of 192 plants derived from a cross between an indica cultivar line PI312777 (highly inhibitory) and a japonica cultivar Rexmont (less inhibitory). Seven QTLs were identified on chromosomes 1, 3, 5, 6, 7, 11, and 12. The QTL on chromosome six had the largest effect and explained 16.1% of the phenotypic variation. The remaining six QTLs explained from 9.4 to 15.1%.

To confirm our previously published results on the IAC165 x CO39 population (Jensen et al., 2001), we initiated the present study on a population specifically developed to map QTLs responsible for allelopathic expression, the parents being strongly contrasted. Besides testing the population using the relay seeding technique, our study included a greenhouse trial to simulate field conditions while still eliminating competition factors, since what ultimately matters is the trait expression under field conditions.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Plant Material
The mapping population consisted of 150 F₆ RILs, produced by single-seed descent from a cross between the indica cultivar AC1423 from India and the aus cultivar Aus196 from Bangladesh. AC1423 showed a strong and consistent allelopathic activity against Echinochloa crus-galli, whereas Aus196 was weakly allelopathic. The two parents were chosen among 50 different varieties because of their contrasted behavior in preliminary screenings using the relay seeding technique (Jensen et al., 2000).

DNA Extraction and Molecular Marker Analysis
Five grams of leaf samples was collected from each of the 150 RILs and the two parents, AC1423 and Aus196. DNA was extracted and purified following a modified CTAB procedure (Saghai Maroof et al., 1984). The genomic DNA was diluted to a concentration of 50 ng/µL for use in polymerase chain reaction (PCR) reactions. Each PCR solution consisted of 8.5 µL sterilized H₂O, 1.5 µL PCR buffer (10x), 1.5 µL MgCl₂ (10 mM), 0.3 µL dNTPs (5 mM), 0.3 µL of 10 µM forward and reverse primers, and 0.6 µL Taq polymerase. Simple sequence repeat (SSR) primers were run at optimal annealing temperature. The PCR profile was initialization at 94°C for 1 min; followed by 35 cycles of denaturation at 94°C for 1 min; primer annealing at specific temperature (T_a) for 1 min; and elongation at 72°C for 2 min, with a final extension of 72°C for 7 min.

The parents as well as four RILs from the population were used to screen the SSR markers for polymorphism and PCR product size within the mapping population. The PCR products were initially separated on a 3% agarose gel and stained with ethidium bromide. Polymorphic primer pairs were selected, and PCR was subsequently performed on the entire mapping population. The PCR products were separated on a 5% polyacrylamide gel, and marker bands were revealed using silver staining as described in the technical manual from Promega (Promega, 1996). Multiple loading of markers was performed to increase the efficiency of the mapping process. In detail, three separate amplification products from one primer pair were loaded in the same gel lane, with a 20-min time difference between the three loadings. No size standard were used, since sizing had already been estimated on agarose gels.

A total of 313 SSR markers were screened for polymorphism in the parents AC1423 and Aus196. The SSR markers were chosen to ensure an even coverage of the genome. Out of these 313 SSR markers, 119 were polymorphic and 108 were subsequently mapped. Eleven markers were found to be unlinked during linkage map construction. The primer sequences and chromosome position of the SSR markers (see Fig. 4 for names of mapped primers) were taken from the Gramene database (http://www.gramene.org, version 15, December 2005).

View larger version (36K): [in this window] [in a new window]   Figure 4. Location of the quantitative trait loci controlling allelopathy against barnyardgrass in the AC1423 x Aus196 recombinant inbred line population. Confidence intervals of the map position were indicated by a two–logarithmic odds score support interval. RLSWRL, relay seeding weed root length; GHWRL, greenhouse weed root length; GHWRB, greenhouse weed root mass; GHWSL, greenhouse weed shoot length; GHWSB, greenhouse weed shoot mass.

Relay Seeding Technique
The first phenotyping was done using the relay seeding technique (Navarez and Olofsdotter, 1996), with minor modifications (Jensen et al., 2001). Specifically, 30 sterilized rice seeds (soaked in 2.5% sodium hypochlorite for 10 min and washed three times in distilled water) were sown in two parallel rows (3.0 cm apart, 15 seeds in each row) inside a Petri dish. The seeds were covered with 7.0 g of perlite to restrain arching of the rice roots. The Petri dishes were placed inside a germination box, and 30 mL of distilled water was added to each germination box outside the Petri dishes. The water moved into the Petri dishes through a bridge of filter paper, leaving the perlite inside the Petri dish aerobic. No nutrient was added at any time during the experiment. The germination box was covered with a thin transparent plastic sheet held in place by a rubber band. Four days after sowing, another 20 mL of distilled water was added to each germination box. On Day 7, 20 sterilized Echinochloa crus-galli (barnyardgrass) seeds (soaked in 2.5% sodium hypochlorite for 10 min and washed three times in distilled water) were sown in one row in between the two rows of rice seedlings on top of the perlite. One box treated the same way but without rice seedlings represented the no-rice check. At the same time, 30 mL of distilled water was added to all the germination boxes. On Day 14, two-leaf rice seedlings were trimmed to prevent competition for light between rice and weed seedlings. The setup was placed on wire shelves covered with white Styrofoam plates. The light intensities on the shelves ranged from 1300 to 3000 lux. The photoperiod was 12 h. The temperature inside the room was kept between 29 and 33°C. Thus, the relay seeding technique ensured that there was no competition for light, water, or nutrients. Preliminary experiments showed that pH in the water stayed stable in the time course of the experiment. On Day 17, the perlite was washed off the roots of E. crus-galli, and the root length of 10 weed seedlings from each germination box was measured. Assuming that allelopathic chemicals released from the rice plants would inhibit the growth of weed roots, the average root length of the ten seedlings was used as an indicator of the allelopathic potential of the rice plants in question.

To test if there was any correlation between the root length of E. crus-galli (i.e., the rice line's allelopathic potential) and rice root mass, the roots of each rice accession were washed and dried for 72 h at 70°C. The root mass was then recorded for each rice line. The screenings were performed as a completely randomized design with five sets of independent experiments, each replicated twice, separated in time because of space constraints. Each replication included the 150 RILs and the two parents, with the Petri dish inside the germination box as the experimental unit.

Charcoal Experiment
To validate the results from the relay seeding experiment and to assess whether rice roots effectively released allelochemicals, a small experiment including activated charcoal was initiated. Activated charcoal is known to adsorb some types of organic chemicals. The experiment was performed as a relay seeding experiment, as described above, except for the addition of activated charcoal to the perlite. Specifically 0, 1, 2, and 5% activated charcoal was mixed into the perlite. The parents of the population plus varieties previously identified as different with respect to their ability to suppress weed root length using the relay seeding technique (Jensen et al., 2000) were selected. Eleven different rice cultivars plus a no-rice check were included with four replications of each treatment per variety (Table 1 ). Weed root length and rice root biomass were measured.

Data Analysis and QTL Mapping
Analysis of variance was performed to partition the different sources of variation for the traits measured using SAS Proc GLM and SAS Proc Mixed (SAS Institute, 1999). The heritability for the trait was estimated from the pooled data from the experiments using the formula h² = ²_G/(²_G + ²_e/n), where ²_G and ²_e were the estimates of genetic and residual variances derived from the mean square expectations of the ANOVA, and n was the number of replications. Least-square means for all traits were calculated using the Proc Mixed analysis in SAS. The analysis of the charcoal experiment was performed using the statistical procedures in the Excel program (Microsoft Corp., 2000). The significance of the correlation coefficients was tested using the Student's t-test.

The least-square mean values of weed root length, weed root biomass, weed shoot length, and weed shoot biomass from both the relay seeding technique and the greenhouse experiment were used to map QTLs associated with allelopathy, using interval mapping with the software MAPQTL 4.0 (Van Ooijen et al., 2002). The likelihood of odds (logarithmic odds score [LOD]) threshold for the significance test was estimated from 1000 permutations of the quantitative trait data. The software estimated additive effects and percentage of variation explained by individual QTLs. Confidence intervals of the map position were indicated by a two-LOD support interval.

	RESULTS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Statistical Parameters of the Population
The phenotypic values of the parents and of the RILs are presented in Table 2 . The two parents were different, with AC1423, the allelopathic parent, reducing weed growth. This was the case in both the relay seeding experiment and the greenhouse experiment. Comparing the nonallelopathic parent Aus196 with the no-rice check also showed significant differences. Weeds grown together with Aus196 had smaller root and shoot length and smaller root and shoot biomass than when grown with the no-rice check.

The average frequency histograms (Fig. 1 ) were monomodal and normally distributed. No transgressive segregation was observed for any traits, which can be seen from the large standard deviations (Table 2).

View larger version (24K): [in this window] [in a new window]   Figure 1. Frequency of weed root and shoot measurements when grown together with the recombinant inbred lines in both the relay seeding experiment and the greenhouse experiment. Aus196, nonallelopathic parent; AC1423, strongly allelopathic parent.

View larger version (26K): [in this window] [in a new window]   Figure 2. Measurements of rice root mass in the relay seeding technique with and without addition of activated charcoal to the perlite. 5%, 5% activated charcoal; normal, 0% activated charcoal.

View larger version (29K): [in this window] [in a new window]   Figure 3. Measurements of weed root length in the relay seeding technique, with and without addition of activated charcoal to the perlite. 5%, 5% activated charcoal; normal, 0% activated charcoal.

For weed root length, significant differences were seen between the two treatments for weeds grown together with AC1423, Daelipbyeo, IAC165, Musashikogane, and Nongan, with weeds having significantly longer root length when grown in 5% charcoal perlite. AC1423, IAC165, and Musashikogane have all been tested using the relay seeding experiment before and found to be allelopathic (Olofsdotter et al., 2002), whereas the allelopathic potential of Daelipbyeo and Nongan was unknown. The weed root lengths measured in the presence of charcoal for these rice varieties were not significantly different from those of the treatments in the no-rice check. The increase in weed root length ranged from 30 to 50 mm for weeds grown in 5% charcoal, compared to the weeds grown in normal perlite. The five varieties belonged to different varietal groups and originated from both Asia and South America (Table 1). No significant difference between treatments was seen for the remaining six varieties including the no-rice check.

Identification of QTLs Associated with Allelopathic Potential
The results of interval mapping are presented in Table 5 and Fig. 4 . In total for all five weed traits measured in both the relay seeding experiment and in the greenhouse experiment, 15 main-effect QTLs involved in allelopathy were found located on eight different chromosomes.

For the traits GHWRL and GHWRB, six QTLs were found on six different chromosomes, explaining between 5.0 and 9.6% of the total phenotypic variation. For GHWRL, weed-suppressing alleles from both parents were found: two QTLs on chromosomes 4 and 9 with the suppressing allele from the allelopathic parent AC1423 and a combined additive effect of 26.07 mm; another QTL with a suppressing allele from the nonallelopathic parent Aus196 on chromosome 10, with an additive effect of 13.84 mm. The suppressive allele from the nonallelopathic parent Aus196 explains why this variety is still seen as different from the no-rice check. For GHWRB three QTLs were found on chromosomes 3, 6, and 12, explaining 17.7% of the total phenotypic variation. All three QTLs had weed-suppressing alleles from the allelopathic parent AC1423 and would in combination decrease weed root biomass by 170 mg.

Lastly, seven QTLs were found on three chromosomes for GHWSL and GHWSB. For GHWSL, the QTLs were found on chromosomes 3, 8, and 9, explaining 19.8% of the total phenotypic variation. All three QTLs had weed-suppressing alleles from the allelopathic parent AC1423 and would collectively decrease weed shoot length by 39.33 mm. For GHWSB, four QTLs on chromosome 3, 8, and 9 were found, which combined explained 23.8% of the total phenotypic variation.

In total three QTLs for GHWRB, GHWSL, and GHWSB were found on chromosome 3, with the SSR markers RM168 and RM293 flanking the region of 27 cM. On chromosome 4, two QTLs were found for RLSWRL and GHWRL, with SSR markers RM131 and OSR15 flanking the region of 15 cM. Furthermore, two QTLs for GHWSL and GHWSB were found on chromosome 8, spanning over five markers, with the flanking markers being RM223 and RM256, covering 7 cM. Lastly, four QTLs on chromosome 9 were found in two regions. The first region covering 11 cM with the flanking markers RM321 and RM215 had QTLs for GHWSL and GHWSB. The second region, at the very end of chromosome 9, covered 7 cM with the flanking markers RM245 and RM205 and had QTLs for GHWRL and GHWSB.

	DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
With an ultimate goal to develop rice varieties with enhanced allelopathic potential, the results reported here may mark an initial step. We have confirmed previous findings (Jensen et al., 2001) that allelopathic potential in rice is a quantitative inherited character by showing that a RIL population specifically developed for the mapping of allelopathic potential had a continuous distribution of phenotypes. Furthermore, QTL mapping of genes involved in allelopathy in the cross has been performed using two different assays. Finally, the results from one of the bioassays have been validated using activated charcoal. These results will be further discussed.

The distributions of the RILs, which cover a broad range of values, confirm that the population in question was well chosen for our objective (Fig. 1). Weeds grown together with AC1423 consistently had smaller root and shoot length as well as root and shoot biomass when compared to those grown with Aus196. Still, weeds grown together with the nonallelopathic parent Aus196 were consistently smaller when compared to the ones grown with the no-rice check. Two explanations for this effect are possible. One likely explanation is that Aus196 is not strictly nonallelopathic and therefore also inhibits weed root and shoot growth, although less than does AC1423. The fact that varieties known as nonallelopathic show a certain degree of allelopathic activity when compared to the no-rice check has been previously noticed (Jensen et al., 2001). The second explanation could be that the weeds grown alone in the no-rice check experiment had more space and for this reason were more developed than in the cases where weeds were grown together with rice. Thus, the effect on weed growth may at least partly be assigned to space competition. This explanation is supported by the fact that there is a highly significant correlation between weed root length and rice root biomass in the relay seeding technique. Also, as regards the greenhouse experiment, several rice parameters are significantly correlated with the measured weed parameters (Table 4). However, in a different RIL population Jensen et al. (2001) found that there was no correlation between root morphology characteristics and allelopathic potential.

Courtois and Olofsdotter (1998) listed the qualities required from a screening technique suitable for carrying out a successful breeding program. A high heritability, which measures the confidence one may have in the phenotype as regards its ability to represent the genotype, was one of them. Ten replications were used in the present relay seeding experiment, and the heritability of 0.71 in most quantitative genetic studies would indicate good genetic determination of the trait. In a different rice population, Jensen et al. (2001) previously found a similarly high broad-sense heritability of 0.68, also using the relay seeding technique. Heritabilities of the same traits measured in the greenhouse experiment ranged from 0.43 to 0.73. Heritabilities like the ones obtained here for various measurements of allelopathy indicate the segregation in the population of major QTLs for the traits.

The activated charcoal experiment was included to verify that the effect seen on weeds when applying the relay seeding technique indeed was due to the rice roots' addition of allelochemicals to the environment. Activated charcoal has successfully been used in other studies to assess the importance of allelopathy in plant-to-plant interactions (Ridenour and Callaway, 2001). The measurements of rice root biomass revealed that there were significant differences only between the normal and the 5% charcoal experiment for one variety, where the addition of activated charcoal to the perlite resulted in a slight increase in rice root biomass. On the basis of these results we can conclude that addition of activated charcoal does not promote root growth in itself. This means that any differences seen on the roots of the weeds can be ascribed to the inactivation of chemicals normally present in the system. It was found that the weeds grown together with 6 of the 11 rice varieties tested had significantly larger weed root length when grown in 5% charcoal than when grown in normal perlite. Furthermore, for these six rice varieties, the weed root length measured in the presence of charcoal was not significantly different from the weed root length measured in the no-rice check. This suggests that the weed roots reached their normal length when grown in 5% charcoal. The fact that no significant differences in weed root length between the treatments were seen between the no-rice check and the remaining five varieties, which are known to be the least allelopathic, may be explained by the limited precision in the experiment, which left only strong effects detectable. In fact, even if there were no significant differences between the treatments, weed root length was always longer in the 5% charcoal treatment compared to the normal treatment. The significant differences actually seen between the control treatment with no charcoal and the 5% charcoal treatment lead us to the conclusion that decrease in weed root length measured using the relay seeding technique for the six rice varieties in question can indeed be reversed by the addition of activated charcoal to the perlite. Thus, the weed-inhibiting effect may partly be ascribed to the addition by the rice plants of allelochemicals to the perlite, which leads to stunted weed root growth.

Several QTLs found in both the relay seeding technique and the greenhouse experiment were located at the same chromosomal intervals. Interestingly, the three QTLs on chromosome 3 in this study are located in the same chromosomal interval as the two QTLs previously found in a different rice RIL population (Jensen et al., 2001). On chromosome 4, QTLs for RLSWRL and GHWRL were found in the exact same interval, confirming that a major gene for weed root length growth may be located in this region. Another two QTLs for GHWSB and GHWSL, located on chromosome 8 in this study, coincide with a QTL previously found in the rice IAC165 x CO39 RIL population (Jensen et al., 2001). Finally, four QTLs for GHWSL, GHWSB, and GHWRL were located on chromosome 9. Nearly all weed-suppressing alleles came from the allelopathic parent AC1423, but one weed-suppressing allele for GHWRL derived from nonallelopathic Aus196. The most significant contribution came from GHWRL, where two large QTLs located on chromosome 4 and 10 had an additive effect of 14 and 13 mm, respectively, with the decrease of weed root length on chromosome 4 from the allelopathic parent AC1423 and on chromosome 10 from the nonallelopathic parent Aus196. The weed-suppressing allele from the nonallelopathic parent Aus196 confirms previous findings that this parent is not strictly nonallelopathic when compared to the no-rice check (Table 2). In the article by Ebana et al. (2001a), QTLs associated with allelopathic effect using water-soluble extracts were found on chromosome 1, 3, 5, 6, 7, 11, and 12. Whether these QTLs are at the same location as the QTLs found in this or the previous (Jensen et al., 2001) article will need further study, since no markers in the chromosomal area are common for the two studies.

The level of explained phenotypic variation for the individual QTLs ranges from 5.0 to 11.1%. This is comparable to what has been found in earlier work using different methods to identify QTLs for allelopathy (Ebana et al., 2001a; Jensen et al., 2001). The relatively low phenotypic variation for the individual QTLs is explained by the difficulty in measuring the allelopathic trait at the individual genotype level.

The next steps may include the development of near-isogenic lines for the major allelopathic loci through marker-assisted selection. This may enable analysis of the relationship between allelopathic potential and other important agronomic traits. For identification of chemical compounds responsible for the allelopathic effect in rice (Mattice et al., 1998; Rimando et al., 2001) cosegregation studies between concentration of such compounds and mapped QTL for allelopathy may support identification of candidate genes.

	ACKNOWLEDGMENTS

 
The authors thank DANIDA for financial support in the conduct of the study.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Received for publication September 27, 2007.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 

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