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Crop Science 43:1448-1456 (2003)
© 2003 Crop Science Society of America

CELL BIOLOGY & MOLECULAR GENETICS

Performance of Backcrosses Involving Transgressant Doubled Haploid Lines in Rice under Contrasting Moisture Regimes

Yield Components and Marker Heterozygosity

Mahmoud Toorchia, H. E. Shashidhar*,b, T. M. Gireeshab and Shailaja Hittalmanib

a Marker–Assisted Selection Lab, Department of Genetics and Plant Breeding, College of Agriculture, University of Agricultural Sciences, GKVK, Bangalore, 560 065, India, and Department of Plant Breeding, Faculty of Agriculture, Tabriz University, Tabriz, Iran
b Marker–Assisted Selection Lab, Department of Genetics and Plant Breeding, College of Agriculture, University of Agricultural Sciences, GKVK, Bangalore, 560 065, India

* Corresponding author (heshashidhar{at}rediffmail.com)


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Improvement of grain yield in the rainfed lowland rice (Oryza sativa L.) ecosystem is important because that ecosystem covers a considerable area. The objectives of this study were first to find relationship(s) among grain yield and its components, second to assess the influence of root morphological characters on grain yield, and finally to find the relationship of molecular marker heterozygosity with heterosis–performance of yield related characters in rainfed lowland rice. Nine backcrosses involving transgressants for maximum root length in rice from a doubled haploid mapping population of IR64/Azucena along with parents were evaluated simultaneously for grain yield and related characters as well as root traits under contrasting moisture regimes. Grain yield showed maximum reduction under severe moisture stress conditions. Significant negative correlation between drought tolerance index and grain yield was observed in the well-watered conditions only. Multiple linear regression analysis revealed total dry weight (root + shoot dry weight) as the most significant variable under well-watered conditions because it could explain as much as 30% of variability in grain yield, followed by root number. Under severe stress conditions, root dry weight explained 20% of variability in grain yield. The relationships between yield and root related characters were determined. General heterozygosity, based on molecular marker data, was found to be correlated with hybrid performance and heterosis of yield related characters. Among the nine backcrosses, P331 x IR64 and P124 x IR64 were selected to map quantitative trait loci for root morphological characters and grain yield.

Abbreviations: Ch, chaffiness • cM, centimorgan • DAS, days after sowing • DF, days to 50% flowering • DH, doubled haploid • DTI, drought tolerance index • GY, grain yield • HI, harvest index • HIF, heterogeneous inbred families • MRL, maximum root length • NIL, near isogenic line • PCR, polymerase chain reaction • PH, plant height • PL, panicle length • PN, panicle number • QTL, quantitative trait loci • RAPD, random amplified polymorphic DNA • RDW, root dry weight • RFLP, restriction fragment length polymorphism • RN, root number • RT, root thickness • RV, root volume • SDW, shoot dry weight • SL, shoot length • SS, severe stress • SW, 200-seed weight • TDW, total dry weight (SDW+RDW) • TN, tiller number • WW, well watered


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
ENHANCEMENT OF GRAIN YIELD remains the principal objective of most breeding programs. The interaction of primary traits poses a formidable challenge while dealing with grain yield. The varied performance of plant genotypes vis-à-vis a range of agroclimatic conditions (like stress and nonstress, different growing seasons etc.) has further intrigued plant breeders. The variable performance of genotypes is ascribed mostly to governing of yield traits by different sets of genes in different environmental conditions (Atlin and Frey, 1990) as well as significant genotype x environment (GxE) interaction (Westcott, 1986; Zobel et al., 1989; Falconer and Mackay, 1996). In addition, simple, customary statistical analyses are found to be inadequate in discerning the effects of different yield components on yield (Zobel et al., 1989). Hence, studying grain yield under varying moisture conditions and employing more reliable statistical analyses may lead to better understanding of the influence of various traits on grain yield.

Ample genetic variability for root morphological traits and other components (primary traits) of drought resistance has been documented over the past few decades. These studies have been conducted on specific primary trait(s) of interest and its (their) contribution to drought resistance (Ludlow and Muchow, 1990; Acevedo and Fereres, 1993; Sadiq et al., 1994; Hemamalini et al., 2000). Even after several well designed experiments across crops, consensus on selecting for either grain yield (potential or actual) and/or drought resistance traits eludes plant breeders. This could be because of the differences in intensity of drought (timing and severity), differences in the intrinsic potential of the crop to tolerate stress [like sorghum, Sorghum bicolor (L.) Moench, and rice), and the constraints of the study itself as standardized protocols for assessing drought resistance are conspicuously absent (Blum, 1999).

Studies designed to assess the influence of components associated with drought resistance on grain yield have produced contrasting results. Several studies have documented very small or no influence of the primary or secondary components of drought resistance on grain yield under well-watered condition and very small effects under moisture stress condition (Blum et al., 1999; Zhang et al., 1999). Contradictory reports on the relationship between root growth and grain yield are also evident in wheat (Triticum aestivum L.), with reports revealing positive correlation in some (Barraclough and Leigh, 1984) and no correlation in few others (Narayan and Misra, 1989). Most experiments aimed at studying the impact of moisture stress on yield levels have not included root study. Such exclusions are generally ascribed to difficulties associated with root studies particularly in large-scale experiments (Barraclough and Leigh, 1984; Robertson et al., 1985; O'Toole and Bland, 1987; Mian et al., 1994). In this context, inclusion of root studies will enhance the validity of conclusions derived from such experiments.

Recent advances in genome research, particularly in the field of molecular-marker technology, has generated considerable interest in predicting hybrid performance by means of molecular markers in crop breeding programs (Zhang et al., 1996; Zhao et al., 1999). Assuming a strong linear correlation between heterozygosity and heterosis is an essential prerequisite for prediction of hybrid performance (Zhang et al., 1996). Several studies have been conducted in rice as well as maize (Zea mays L.) to reveal the relationship between marker heterozygosity and hybrid performance–heterosis. Among these studies, some have revealed some significant correlations between marker heterozygosity and hybrid performance (Lee et al., 1989; Smith et al., 1990; Zhang et al., 1994, 1995; Xiao et al., 1996; Saghai Maroof et al., 1997), while some others revealed nonsignificant correlations (Godshalk et al., 1990; Dudley et al., 1991). Consideration of specific heterozygosity (using only those markers which are tightly linked to the trait under consideration for calculating percentage heterozygosity) is one of the reasons for getting significant correlations in the above-mentioned studies.

The goals of our study were (i) to discern relationships among grain yield and its attributes, (ii) to assess the influence of root morphological characters on grain yield, and (iii) to find the relationship between molecular marker heterozygosity and heterosis–performance with respect to yield related traits in rainfed lowland rice.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
Plant Material
Five deep rooted (P107, P192, P210, P331, and P333) and four shallow rooted (P124, P163, P442, and P467) transgressants for maximum root length were chosen from rice doubled haploid (DH) mapping population of IR64/Azucena. Each transgressant doubled haploid line was backcrossed to IR64. The nine BC1F1 populations, their parents (nine DH lines as female and IR64 as male parent) and standard checks: Azucena, Moroberekan, IR20, CO39, and Jaya, constituted the genetic materials for this study.

Identification of BC1F1 Plants Using RAPD Markers
Because some of the samplings were done at early vegetative stage it was difficult to identify true backcrossed plants on the basis of morphology. Hence RAPD marker assay was employed to identify true backcrossed plants. DNA from the parents and all the BC1F1 plants was extracted using miniprep protocol (Zheng et al., 1995). Several decamer primers were used to identify the crossed plants by polymerase chain reaction (PCR) (MJ Research, Waltham, MA USA). The PCR temperature profile was 1 cycle at 94°C for 2 min; 45 cycles at 94°C for 1 min, 40°C for 2 min, 72°C for 1 min, and finally 1 cycle at 72°C for 2 min. The amplified products were resolved in 1.4% (w/v) agarose gels, stained with ethidium bromide, and visualized with a UV transilluminator.

Phenotyping for Root Morphological Traits
The experiment was performed at Main Research Station, University of Agricultural Sciences, Bangalore, India, between February and July 1999 (Summer season). Twenty-four genotypes were grown in light-gray polyvinylchloride cylinders (100 cm long and 18-cm diameter) filled with clayey soil and farmyard manure, in a random complete block design with 10 replications. One healthy plant was maintained in each cylinder. The cylinders were clumped with the spacing almost resembling the natural paddy field conditions. Two moisture regimes, well watered (WW) and severe stress (SS), were imposed. In WW conditions, all the entries were watered daily throughout the cropping period. In the SS treatment, moisture stress was imposed from 65 d after sowing (DAS) to 80 DAS by withholding irrigation and preventing rainwater using a rainout shelter. Sampling in both WW and SS treatments was done at three stages: (i) 65 DAS (before imposition of stress) for two randomly selected replications, (ii) 80 DAS (when stress was relieved) for four randomly selected replications, and (iii) At maturity for the remaining four replications.

Cylinders with soil and plants were thoroughly soaked in water overnight and sampled the next day. Sampling was done as described by Shashidhar et al. (1999) with care to retain roots, root hairs, and branches. Observations in all the three samplings were recorded on maximum root length (MRL) in centimeters, number of roots (RN), root dry weight (RDW) in grams, shoot dry weight (SDW) in grams, root to shoot dry weight ratio (RDW/SDW) and total dry weight (RDW+SDW; TDW) in grams. Further, root volume (RV) in cubic centimeters, root thickness (RT) in millimeters, and root:shoot length ratio (MRL/SL) were computed at the third sampling. Shoot length (plant height) was measured from the soil surface (in pipes) to the tip of the shoot. Root thickness was measured with a standardized ocular micrometer at 100x magnification through a microscope. Roots about 3 cm below the crown region were used to study thickness.

At harvest, we observed the following traits and recorded or computed them in the following units: grain yield per plant (GY) in grams; panicle length (PL) in centimeters; panicle number (PN), tiller number (TN), and 200-seed weight (SW) in grams; plant height (PH) in centimeters; days to 50% flowering (DF); chaffiness (Ch) in percent; and harvest index (HI) in percent. In addition, drought tolerance index (DTI) was estimated according to Fischer and Maurer (1978): DTI = GY (SS)/GY (WW).

Statistical Analysis
Data were subjected to individual and combined ANOVA for two environments by PROC GLM in SAS program (SAS Institute Inc., 1989). The environments were considered as fixed effects as they had been created artificially for this experiment. Genotypic correlation coefficients, variance components, heritability in broad sense (h2), genetic advance (GA), phenotypic (PCV), and genotypic (GCV) coefficient of variability and heterosis were estimated by employing standard methods (Falconer and Mackay, 1996). Stepwise multiple linear regression using PROC REG command in SAS was employed for better understanding of relationships between grain yield and root related characters. This procedure first fits a simple linear regression for each of the root characters as independent variable (X) and grain yield as dependent variable (Y). The X variable with F-value exceeding a predetermined significance level is the candidate for first addition. After entering the first variable, stepwise procedure fits all regression models with two X variables, where, the previous variable is one among the pair. The X variable with a significant partial F-value is the candidate for addition at the second stage. Next, the procedure examines whether any of the other X variables already in the model should be dropped, and so on until no further X variable can either be added or deleted. At this point the search terminates.

The molecular map of this population with 135 DH lines was developed by Huang et al. (1994). It encompasses 175 markers (146 restriction fragment length polymorphism, RFLP; 3 isozymes, 14 random amplified polymorphic DNAs, RAPD; and 12 cloned genes) with coverage of 2005 centimorgans (cM) with an average density of 11.5 cM. Marker heterozygosity percentage for each backcross was estimated as proportion of heterozygous marker loci by looking at the marker genotype of the respective parental DH line because, parents of DH lines are IR64 and Azucena. In the backcrosses, IR64 being the recurrent parent, if a marker locus in the parental DH lines was homozygous for IR64 type of marker allele, the locus remained homozygous in the backcrosses. However, it became heterozygous if that locus was homozygous for Azucena type of marker allele in the parental DH lines. Hence, proportion of Azucena type marker alleles in the parental DH lines gives the proportion of heterozygous marker loci (Zhang et al., 1996; Zhao et al., 1999). Percentage marker-allelic distribution of parental DH lines is given in Table 1; percentage heterozygosity of each backcross is calculated.


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  		Table 1. Percentage marker-allelic distribution of the selected doubled haploid lines of IR64/Azucena mapping population, used as parents in the backcrosses.

 

   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
True BC1F1 plants were identified on the basis of the presence of male (IR64)-specific band following amplification with RAPD primers. Of the 24 RAPD primers used, seven (AA-03, AF-06, AY-05, AY-11, BB-05, BH-16, and C-04) showed polymorphism between IR64 and individual DH transgressant used as female parent (results not shown).

Individual and combined ANOVA revealed significant genotypic differences for grain yield and related traits under WW and SS conditions. Combined ANOVA also showed significant GxE interaction for all the characters studied. The mean values of GY, PN, SW, PH, and HI were reduced significantly under SS condition. Not surprisingly, DF and Ch increased significantly under SS condition, contributing to yield reduction. Grain yield showed maximum reduction (29.4%) under SS signifying cumulative influence of other traits on yield (Table 2). Ribaut et al. (1997), in their studies on maize, observed similar marked reductions in the F3 family mean for yield related traits from WW to SS conditions with maximum reduction in GY (60%). Veldboom and Lee (1996) too observed similar reductions in maize. The results of contrast between long rooted backcrosses and shallow rooted backcrosses showed that in WW condition, long rooted backcrosses performed significantly better than the shallow rooted backcrosses with respect to PN and TN, whereas, under SS condition, the long rooted backcrosses outperformed the shallow rooted backcrosses for GY, TN, and HI (Table 2). These results project root length as one of the potential traits particularly under severe water stress conditions.


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  		Table 2. Mean values of yield and related characters of deep and shallow rooted backcrosses and parents in rice under well-watered and severe stress conditions.

 
Linear regressions were drawn to find relationship between DTI and GY under WW as well as SS conditions. Significant negative association was found between DTI and GY under WW conditions (r = 0.61, Fig. 1a) but, not under SS conditions (Fig. 1b). This implies that, genotypes performing well under WW conditions were affected most in terms of yield reduction under SS conditions. However, marked negative association was observed between DTI and GY, obtained in WW as well as SS conditions, in maize (Ribaut et al., 1997). These results imply that genotypes performing well under WW conditions may not perform well under water stress conditions. For some genotypes, DTI exceeded 1.00, indicating their stable performance under SS as well as WW conditions. Frova et al. (1999) too identified some genotypes in maize showing similar kind of stable performance.



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  		Fig. 1. Relationship between drought tolerance index and grain yield in rice under (a) well-watered and (b) severe stress conditions

 
Genetic Parameters
Genetic parameters estimated from individual analysis of variance for two conditions showed higher estimates of GCV and PCV for GY and HI under both WW and SS conditions (Table 3). GCV and PCV estimates were more in SS compared to WW owing to reduced mean under stress condition. However, Blum (1988) has reported reduction in genetic variance under SS. PCV was slightly higher than GCV values for all the traits in both the environments (WW and SS) indicating prominent role of genotype in creating variability. Higher estimates of heritability were found for PN, TN, PH, GY, SW, and DF under both WW and SS conditions. Heritabilities of all traits except PL, DF, and Ch, decreased from WW to SS conditions as a result of increased environmental variance. Blum (1988) has revealed similar pattern of heritability decrease. Estimates of heritability based on combined ANOVA were considerably reduced compared to individual analyses. This reduction is, mostly due to the effect of G x E interaction that was significant for all the characters but for PL (Table 4). Similar trends were also observed for other genetic parameters for most of the traits. GA as a percentage of the mean was also high for GY under both WW (74.4) and SS (82.49) conditions. In spite of higher environmental variance, it is interesting to note that GA estimates were higher under stress condition forecasting better response for selection. Gomathinayagam et al. (1990) too observed higher GA as percentage of the mean for grain yield under stress in their variability studies on drought tolerant genotypes in rice.


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  		Table 3. Genetic parameters for yield and related characters{dagger}in rice estimated using combined ANOVA.

 

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  		Table 4. Combined analysis of variance for grain yield and its related characters{dagger} over both well-watered and low moisture stress conditions.*

 
Correlations among Grain Yield and Related Characters
Grain yield was found to be significantly negatively correlated with DF and Ch but positively correlated with HI under both conditions (Table 5). Many rice workers (Saurez et al., 1989; Rao, 1990; Mirza et al., 1992) have reported similar results. Significant negative association between GY and DF was observed under both WW and SS conditions. It seems that rising temperature during grain filling period disfavored the entries for grain formation and growth. Although there was a good influence of TN on GY, the effect was not statistically significant. There are evidences that correlation between GY and panicle density, which is highly correlated with TN, is inconsistent. Some researchers (Miller et al., 1991; Gravois and Helms, 1992) found that panicle number per square meter was the most important factor influencing variation in grain yield, while others (Wells and Faw, 1978; Jones and Synder, 1987) found that final panicle population measurements were poorly correlated to grain yield. Some pairs of traits showed significant correlations only under WW conditions and some other showed only under SS conditions (Table 5). This could be due to effect of differential sensitivity of traits to environmental perturbations (Aastveit and Aastveit, 1993; Veldboom and Lee, 1996). Such correlations will influence selection strategy.


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  		Table 5. Correlation coefficients among yield and related characters{dagger} under well-watered (above the diagonal) and severe stress (below the diagonal) conditions.

 
Correlations between Grain Yield and Root Morphological Characters
MRL was significantly and positively correlated with PL and PH, and negatively correlated with PN and TN under both WW and SS conditions (Table 6, Table 7). Traits related to increase in length (PH, PL and MRL) seem to be correlated positively to one another suggesting common control of cell division and elongation events. In a related study, where we were trying to identify markers linked to root length, a locus on chromosome 10 was found to be associated with cell length mutant (OSDIM) and maximum root length (Shashidhar, unpublished data). The negative association between MRL and TN is well established. RN was significantly, positively correlated with TN (only under WW), but negatively correlated with SW (under both WW and SS). Grain yield was found to be associated significantly with RN (r = 0.40*), RDW (r =-0.49**), SDW (r =-0.45*), and TDW (r =-0.55**) only under WW conditions. One of the reasons for not getting significant association between GY and root traits under SS conditions may be due to differential sensitivity of these traits to change in moisture regime (Aastveit and Aastveit, 1993; Veldboom and Lee, 1996). Negative association between grain yield and dry weights suggests that scope exists for improvement of HI. DF and HI showed similar but contrasting associations with different components of dry weights under both conditions. This may be due to the effect of environment as explained earlier. In toto, associations between root morphological traits and yield or related components were variable. Under these circumstances, multivariate techniques would be a more reliable way of dealing with such associations.


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  		Table 6. Correlation coefficients of grain yield components{dagger} with root morphological characters in rice under well watered condition.

 

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  		Table 7. Correlation coefficients of grain yield components{dagger} with root morphological characters{ddagger} under severe stress condition.

 
For effectively relating root morphological characters with grain yield, we employed multiple regression approach (Table 8). In WW condition, TDW was found to be the most significant variable as it could explain about 30% of variability in GY per se, followed by RN (17%), and MRL/SL (7%), and these three together explaining about 53% of variability in GY. A similar trend was observed under SS condition, where RDW, as one component of TDW, could explain 20% of variability in GY followed by RN (8%); together they explained about 28% of variability. Mugo et al. (1999) have suggested that no secondary trait appeared to confer a high level of drought tolerance on its own; a combination of multiple drought adaptive traits through a suitable index will be most effective in a breeding program.


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  		Table 8. Stepwise multiple regression with grain yield as dependent variable and all root morphological characters{dagger} as independent variables in rice.

 
Correlation of General Heterozygosity with Hybrid Performance and Heterosis
General heterozygosity indicates the distance between the two parents calculated on the basis of all the markers employed in the study (Zhang et al., 1994). We tried to correlate the general heterozygosity with hybrid performance and heterosis (Table 9). Though high correlation coefficient was observed for panicle length and harvest index under severe stress condition, it was not significant. Nevertheless, about 37% of variability in midparent heterosis of PL and HI can be explained on the basis of the total heterozygous marker loci of the backcrosses. Correlation of general heterozygosity was found to be maximal with panicle length (r = 0.54) in a study on a set of indica and japonica varieties (Zhang et al., 1996). Correlation with yield and its components were low in general. Zhang et al. (1994)(1996) and Zhao et al. (1999) observed lower magnitudes of correlations between general heterozygosity with hybrid performance and heterosis for most of yield related characters. The relationship between molecular marker heterozygosity and hybrid performance in rice seems to be complex and differs greatly from one trait/condition to another. Zhao et al. (1999), in their study on marker heterozygosity in rice, have made similar conclusions. Zhang et al. (1996) who studied the correlation between marker heterozygosity and hybrid performance in two different sets of rice varieties, have suggested to use informative markers or markers that are significantly associated with traits of interest, to obtain a better correlation.


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  		Table 9. Correlation of marker heterozygosity with trait expression and midparent heterosis of backcrosses (DH line/IR64) in rice under well-watered and severe stress conditions.

 
The mean value of grain yield and maximum root length were converted to z values, by which they can be compared with each other (Fig. 2). In WW condition (Fig. 2a), the four backcrosses involving DH lines P124, P331, P333, and P210 fell in positive coordinates and showed advantage of grain yield as well as maximum root length. But, only two of these, P331 x IR64 and P124 x IR64, were able to retain their advantage under severe stress condition (Fig. 2b). P331, a long rooted DH line, maintained its ability to tolerate drought even after crossing with IR64 to such an extent that its MRL is reduced least compared with other backcrosses and had grain yield almost equal to its grain yield under WW condition. However, the backcross P124 x IR64 was a stable cross under both the conditions. It seems that these two crosses may have a good combination of quantitative trait loci (QTLs) conferring drought tolerance and yielding ability. Therefore the BC1F2 populations of these two backcrosses have been increased to identify NILs (near isogenic lines) as well as HIFs (heterogenous inbred families) for fine-mapping of QTLs controlling root morphological characters and grain yield. Earlier, several workers mapped QTLs for root traits (Champoux et al., 1995; Yadav et al., 1997; Hemamalini et al., 2000) and some of the QTLs for MRL are found to be common across environments (Hemamalini et al., 2000). In further studies, these hot spots will be used for fine mapping QTLs.



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  		Fig. 2. The z value of grain yield and maximum root length of nine transgressant backcrosses in rice involving doubled haploid lines and IR64 under (a) well-watered and (b) severe stress conditions.

 

   ACKNOWLEDGMENTS
 
We acknowledge the funds from The Rockefeller Foundation, New York (RF98001#671). The financial assistance by Ministry of Science, research and technology of Islamic Republic of Iran for M. Toorchi to pursue his Ph.D. degree is thankfully acknowledged.

Received for publication March 29, 2001.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 




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