Abstract

Media summary

Keywords

NUE, SSR markers, QTL

Introduction

Global production of rice, the world’s most important staple crop, has risen three fold over the past three decades. This increased rice production achieved during the second half of the 20^th century required high input of fertilizers, pesticides and water. However, the yields are fast approaching a theoretical limit set by the crop’s efficiency in harnessing applied inputs. The varieties were not improved to maximize nutrient absorption, they often take up only half of the applied nutrients. Hence, rice cultivars with improved nitrogen use efficiency are becoming a prerequisite for lowering production costs. Such cultivars protect the environment and improves rice yield with a guarantee for sustainability in agriculture while maintaining soil and ground water quality. Eventhough significant genotypic differences in nitrogen use efficiency (NUE) have been reported in rice, genetic selection to improve the rice crop’s NUE has not yet been done (Singh et al., 1998). This may be because of the complexity involved in the overall phenotype and its evaluation and non availability of genetic tools to use. The advent of molecular marker technology, construction of genetic maps and quantitative trait loci (QTL) mapping strategies in mid eighties made situation easy to study many of the complex traits such as drought, salinity, disease and insect resistance in crops plants. In this context, we attempted to map the QTL associated with nitrogen use efficiency in rice using DNA markers.

Materials and Methods

The F₂ segregating population of the cross between Basmati370 (selection from Punjab local Basmati) and ASD16 (cross derivative of ADT31/CO39) was genotyped for SSR and ISSR marker alleles. Basmati 370 is a semi dwarf, photosensitive variety with long slender and scented grains. ASD 16 is a semi dwarf, photo-insensitive variety with short bold grains. Genomic DNA was isolated from frozen leaf tissue collected from each F₂ lines. A set of 290 SSR primer pairs was used for PCR amplification of the parental DNA to identify polymorphic primers. The polymorphic primers were surveyed on the individuals of F₂ population. In addition 10 primers were tested for ISSR (Inter Simple Sequence Repeats) amplification in parents and F₂ individuals. Linkage analysis was performed using Mapmaker/Exp 3.0. Multipoint analysis was carried out to confirm the order and recombination fraction by keeping threshold LOD value of 3.0. The CentiMorgan distances (cM) between markers were worked out based on Kosambi mapping function.

Seeds collected from 190 individual F2 plants were used to raise F3 families (30 individuals per family) for phenotypic evaluation. The experimental soil was clay loam in texture coming under the group of Typic Haplustalf with ammoniacal N concentration (NH4+ - N) of 40.10ppm and available N (KMnO4 – N) content of 289kg/ha. No nitrogen was applied in the field in the form of fertilizer. Phosphorus and potassium were applied at the rate of 60kg/ha. At maturity, the plants were cut just above the ground level. Grain yield (GY)

was recorded at 14 per cent moisture level. The dry weight of filled grains, unfilled grains, straw and rachis were determined after ovendrying to a constant weight at 70oC. The oven-dried weight of unfilled grains, straw, and rachis were pooled and expressed as total dry matter production (DMP). The straw and grains were ground separately in a cyclone mill to obtain fine powdered sample. A composite straw and grain samples were prepared by mixing equal amount of samples from individual plants of each F3 families. A sub sample of 0.5g was used to determine the N content in grain (GN) and straw (SN) separately by employing microkjeldhal N distillation method (Humphries, 1956) and the following parameters were derived: Grain N uptake (GNUP) = Grain dry weight × [GN/100]; Straw N uptake (SNUP) = Straw dry weight × [SN/100]; Total N uptake = GNUP + SNUP; N use efficiency (NUE) = GY / TNUP; N translocation efficiency (NTE) = (GNUP/TNUP) × 100. To identify markers associated with phenotypic parameters, simple regression analysis was performed according to the method described by Haley and Knott (1992) using regression coefficient as a function of unknown QTL parameters. Then, stepwise regression was performed to identify the most probable marker combinations associated with the trait of interest with their contributions towards total phenotypic variance. Each quantitative trait was treated as dependent variable and various marker genotypes (scored as 1, 2 and 3 for SSR and 1 and 3 for ISSR) as independent variables.

Results and discussion

The survey of the parents with 264 informative SSR primer pairs resulted in an accepted level of polymorphism (52%) between two parents indicating the possibilities of constructing a linkage map of Basmati370/ASD16. Though a total of 138 polymorphic SSR markers was identified, we surveyed only 79 primer pairs on 190 F₂ individuals. Of the 79 markers, only 60 showed clear scorable banding pattern. The two-point analysis carried out to establish the linkage between these segregating markers resulted in linked marker pairs specific to each chromosome and the strength of the linkage between marker pairs ranged from 6.78 cM to 49.30 cM. Scoring individual marker’s segregation pattern revealed skewness for most of the markers (51 out of 60). Among the 51 SSR markers that showed skewed segregation, 43 were towards ASD16 and 8 were towards Basmati370. Out of 10 ISSR markers, 7 skewed towards ASD16 and 3 towards Basmati370. This kind of distorted segregation of genetic markers was observed in cross combination wherein distantly related parents were involved. Wang et al. (1994) observed segregation distortion for almost all the 127 RFLP markers used in the recombinant inbred population of CO39, an indica variety and Morberekan, a japonica variety. The same kind of segregation distortion for many of the RFLP markers was observed in rice mapping populations (McCouch, 1990). Marker loci associated with skewed allele frequencies were distributed on all 12 chromosomes. Since Basmati370 falls in the fifth group of rice varieties (more towards japonica sub species) as per the Glazmann (1987) grouping, the observed segregation distortion could be possible.

Apart from exploiting the simplicity and sensitivity of SSR marker analysis, we tried to use the multi-locus marker system viz., ISSR to generate polymorphic markers in less time with minimum PCR runs. ISSR analysis was chosen over the AFLP analysis considering its simplicity. Among the 10 ISSR primers used, 2 were monomorphic and one produced bands, which could not be scored. Seven ISSR primers amplified 87 bands, of which 44 were polymorphic between parents indicating 51 per cent polymorphism comparable to SSR. Of these 10 primers, 3 produced a maximum of 9 polymorphic markers. But all the three primers amplified similar fragments as the sequences of the primers were essentially same except for one selective nucleotide. Some of the ISSR primers produced monomorphic bands or bands not scorable. Only three ISSR primers were used for the segregation analysis on the F₂ individuals. These three primers produced 17 markers. When two-point analysis was done involving 60 SSR markers and 17 ISSR markers, 11 ISSR markers remained unlinked. Only two markers (ISSR1-5 and ISSR1-9) showed linkage with SSR markers. ISSR1-5 was linked with RM305 on chromosome 5 whereas ISSR1-9 was linked with RM221 on chromosome 2, but these two ISSR markers were linked themselves making the situation complex to place the ISSR markers on specific chromosome. The cause for not having accurate linkage could be due to the skewness in the population for marker allele distribution. Smaller number of recombinants in a population due to random assortment or non-linkage might be major limiting factors to accurately assemble linkage groups (Wang et al., 1994). Hence, the problem of assembling ISSR markers to specific chromosome could be overcome, if more number of chromosome specific markers is surveyed on this population. At this stage comparing the SSR and ISSR analyses reveals the fact that ISSR analysis produced more markers with less PCR runs. Hence, the ISSR analysis could remain as an alternative option to speed up the process of linkage map construction in conjunction with highly informative SSR markers as anchors.

Though it was possible to have a genetic map of Basmati370 / ASD16 with 60 SSR markers, the map was very sparse and incomplete. The attempt to add more markers by adopting ISSR analysis did not yield the expected results. Considering the situation, the QTL analysis was carried out involving single marker analysis through regression approach. Based on the phenotypic values of the parents (Table 1), it was evident that ASD16 was more efficient in N uptake and use compared to Basmati370. Wider variation with transgressive segregation for different traits was observed among the F_2:3 families indicating the quantitative nature of the traits.

Table1. Phenotypic performance of parents and F₃ families

The SSR markers associated with each phenotypic trait based on regression analysis are given in Table 2. The results of stepwise regression analysis showed that a group of markers viz., RM2, RM282, RM38 and RM131 had significant association with grain yield, which put together explained 27.04 per cent of phenotypic variation. The markers RM282, RM2 and RM38 accounted for 22.9 per cent of phenotypic variation for grain N uptake. For nitrogen use efficiency, the significant marker combination was RM225, RM202 and RM246 with the phenotypic contribution of 17.2 per cent. A comparative analysis on the QTL mapping for NUE in rice was mot possible since there was no other work except the report of Senthilvel (1999), who detected QTL for NUE on chromosomes 3 (RZ574-RZ284) and 11 (RG247-RG103). In the present study, QTL for NUE were detected on chromosomes 1 (RM246), 6 (RM225) and 11 (RM202). The QTL on chromosome 6 also showed its association with grain yield. From both the studies, it appeared that genomic regions on chromosomes 3, 6 and 11 might harbour genes for grain yield and NUE.

Table 2. SSR markers putatively associated with phenotypic traits identified through regression analysis

*Chromosome number of the SSR markers is given in the parenthesis

Though this study has some constraints with regard to mapping population, the number of genetic markers employed and the screening method, it has generated valuable information to proceed further to track down the network of genes associated with NUE in rice, a complex trait by nature. For a complex trait like NUE, it is very difficult to discern out all possible loci unless the efficiency of phenotypic screening is improved or new screens are established. The improvements in QTL mapping methodologies will pave way to move from individual QTL to the actual gene as was demonstrated for single fruit size QTL in tomato (Frary et al., 2000). By “candidate gene” approach, a gene known to be involved in a specific pathway or having a predicted function can be related to QTL detected for specific phenotypes, thus making the QTL, biologically meaningful entities. For these known genes, the genetic information from the model organisms such as Arabidopsis can be exploited. Arabidopsis genome project has uncovered more than 600 genes involved in the uptake, metabolism and allocation of nitrogen. Colocalization of these genes with QTL associated with N use efficiency in rice will give biological meaning to the QTL detected so far. Amalgamating the information on biochemical and regulatory aspects of inorganic N nutrition available in Arabidopsis with genetic bases of NUE established in recombinant inbred population of Basmati 370/ASD16 mapped with SSR markers, will be helpful in rice improvement in coming years.

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