Abstract
Salinity is a common and increasing problem in many coastal rice producing areas around the world. Salinity tolerance at the reproductive stage in rice is crucial as it determines grain yield. An F2 mapping population was developed from two modern rice cultivars contrasting in tolerance: NSIC Rc222 (a high-yielding salt-sensitive variety released in the Philippines) and BRRI dhan 47 (a salt-tolerant variety released in Bangaldesh). The performance of the F2 population showed transgressive segregation in the yield components under salinity stress of EC 10 dS/m under salinized field conditions. Ninety-six single nucleotide polymorphism (SNP) markers using 96-plex FluidigmTM genotyping were used to construct a linkage map of 1306.2 cM (Kosambi), with an average interval size of 13.6 cM. Seven putative quantitative trait loci (QTLs) for reproductive stage salinity tolerance traits having LOD values ranging from 2.9 to 4.1 were identified on chromosomes 1, 2, 5 and 11, explaining 13.4 to 18.4% of the phenotypic variation. Results of this mapping study identified a genomic region on chromosome 2 that confers salinity tolerance at the reproductive stage as measured by the number of filled spikelets, percent filled spikelets and yield. This study reports the molecular mapping of QTLs controlling reproductive-stage salinity tolerance-related traits, which will be useful in marker-assisted selection and breeding population development in rice.
-
Key words: Rice,
Oryza sativa
, Salinity tolerance, Reproductive stage, SNP marker, Quantitative trait locus (QTL)
INTRODUCTION
Rice is a diploid (2n = 2x = 24) glycophyte, of tropical origin, and is considered a model crop species for genetic studies. About 90% of the rice in the world is grown in Asia and 85% is devoted for human consumption. To meet the projected demand for rice, it has been estimated that global annual rice production needs to double by 2050; however, current rates of annual yield increase in rice are insufficient to meet this demand (
Ray et al. 2013). Various abiotic stresses greatly affect rice yield, and among the abiotic stresses, salinity is the second most prevalent soil problem in rice growing countries of the world and is a serious threat to increasing rice production worldwide (
Gregorio 1997).
Salinity tolerance in rice is a quantitative trait that is polygenic in nature. Rice is very sensitive at the seedling and reproductive stages; but the reproductive stage is crucial as it ultimately determines grain yield. However, the importance of the seedling stage cannot be ignored as it affects crop establishment. Hence, pyramiding of contributing traits at both stages is needed to develop resilient salt-tolerant cultivars for stress-prone environments (
Moradi et al. 2003). Salinity at the reproductive stage depresses grain yield much more than the vegetative stage. Conventional breeding has been used to develop stress-tolerant high-yielding varieties of rice, but plant selection for salt tolerance using conventional methods is not easy due to the large environmental effects and low heritability of salt tolerance (Gregorio and Senadhira 1993;
Gregorio et al. 2002).
DNA-based molecular markers have been extensively used to assess the genetic diversity across most crop species and to map quantitative trait loci (QTLs) across a large number of traits. Molecular markers can measure the contribution to phenotype for a set of QTLs, as well as enabling the selection of favorable alleles at these loci in a marker-assisted selection (MAS) scheme that aims to accelerate genetic advancement in rice. Thus, identifying molecular markers that are linked to genes controlling salinity tolerance can assist selection for salinity tolerance having low heritability and expressivity (
Singh et al. 2007). Moreover, single nucleotide polymorphism (SNP) markers have great potential to further increase the efficiency and number of available markers for high-throughput genotyping techniques and whole genome marker scans; consequently, molecular breeding is becoming more efficient (
Thomson 2014). High-throughput SNP techniques have been applied to investigate QTLs in this study.
Rice is a transplanted crop and it is sometimes possible to use early maturing genotypes to avoid terminal salinity that occurs under coastal saline conditions (Singh and Flowers 2010); however, in most salt-prone areas, it is generally not possible to avoid salinity stress at the reproductive stage. Therefore, studies investigating the genetic components of salinity tolerance at the reproductive stage will be essential in developing high yielding rice varieties with salt tolerance. Mapping of QTLs for salinity tolerance at seedling and reproductive stages of rice can aid in the identification of the genetic control of salinity tolerance leading to development of varieties with improved tolerance by precisely transferring QTLs into adapted varieties (
Thomson et al. 2010a). Considerable efforts have been diverted towards the development of salinity tolerance at the seedling stage of rice, such as with the characterization of the
Saltol QTL (
Thomson et al. 2010b); however relatively few attempts have been made to identify QTLs associated with the reproductive stage salinity tolerance in rice (
Zeng et al. 2002;
Mohammadi et al. 2013;
Bimpong et al. 2014;
Hossain et al. 2015). Therefore, the main objective of this study was to identify QTLs for reproductive-stage salinity tolerance from the cross between modern salt-tolerant (BRRI dhan 47) and salt-sensitive (NSIC Rc222) varieties using a F
2 mapping population and genotyping with SNP markers.
MATERIALS AND METHODS
Reproductive stage salinity tolerance phenotyping
The salinity tolerance phenotyping was conducted in an artificially salinized field of the International Rice Research Institute (IRRI) experimental station in Los Baños, Laguna, Philippines, which was similar to naturally saline field conditions (minus the soil heterogeneity). A modern salt-tolerant IRRI-bred variety released in Bangladesh and Philippines, BRRI dhan 47 (IRRI line IR63307-4B-4-3), and a modern high yielding salt-sensitive IRRI-bred variety released in the Philippines, NSIC Rc222 (IRRI line IR78581-12-3-2-2), were used in the experiment as parents to develop the mapping population. The tolerant donor parent (IR63307-4B-4-3) is a medium-maturing, advanced breeding line in an indica background that was bred for salt tolerance at IRRI, and is a cross between a somaclonal variant of Pokkali (TCCP 266-2-49-B-B-3) and the breeding line IR51511-B-B-34-B, which itself was derived from a cross between the breeding line IR8909 and the cultivar IR34 (
Gregorio et al. 2002;
Reddy et al. 2014; Angeles-
Shim et al. 2019). The cross was conducted by the Plant Breeding Division crossing program and F
1 seeds were generated and selfed to produce the F
2 population. The 92 F
2 progenies were chosen randomly and used for the mapping population. The initial salinity level of EC 6 to 8 dS/m was imposed beginning in the early vegetative stage, i.e. two weeks after transplanting, and it was increased to EC 10 dS/m at the panicle initiation stage. A few days before harvesting, salinity scoring was performed to identify the tolerant and sensitive plants based on visual symptoms by using the Modified Standard Evaluation System (SES) of IRRI (
Table 1). Data on yield, yield components and important agronomic parameters such as plant height, number of productive, unproductive and total tillers, percentage of productive and unproductive tillers, fresh weight, number of filled, unfilled and total spikelets, percentage of filled and unfilled spikelets and grain yield were collected from the population.
The ninty-two F2 individuals were genotyped with SNP markers at the Genotyping Services Laboratory (GSL) of IRRI. Leaf samples were collected from the ninety-two F2 plants and their parents for DNA extraction using a modified CTAB protocol. After extracting DNA, samples were run in agarose for DNA quantification and quality control. Then a 96-plex FluidigmTM high-throughput SNP genotyping protocol was used for SNP analysis with 96 SNP markers, with allele calling performed by the GSL using the clusters from the Fluidigm SNP genotyping software.
QTL mapping analysis
QTL analysis using the yield components and other agronomic traits was performed by composite interval mapping (CIM) with the Java-based QGENE software version 4.0 (Joehanes and Nelson 2008). CIM analysis was performed to precisely identify the location of the identified QTLs for salt tolerance. A default LOD threshold score of 2.9 was used and interval map distances based on the result of linkage map analysis were used to determine the association between markers and QTL.
RESULTS
Physio-morphological variation of F2 progenies at the reproductive stage
The phenotypes of the NSIC Rc222 (sensitive parent) and BRRI dhan 47 (tolerant parent) and their F
2 progenies salinized at EC 10 dS/m showed significantly different responses at the reproductive stage. Most of the traits, including plant height, number of productive and unproductive tillers, and total tillers, exhibited mean values of their progenies which were similar to the mid parental value, suggesting that alleles from the two parents contributed equally to the expression of those traits (
Table 2). Although the mean values of the F
2 progenies were higher than the mid parental values for fresh weight and percent unfilled spikelet, the F
2 progenies mean values were lower than the mid parental values for percent filled spikelet and grain yield (
Table 2).
Correlation analysis was performed in order to determine the relationship between grain yield and other yield components under salt stress conditions. Positive and significant correlations were observed with grain yield to number of filled spikelets (0.979), percent filled spikelet (0.731), number of unfilled spikelets (0.697), total spikelets (0.846), number of productive tillers (0.669), percent productive tiller (0.589), and total tillers (0.336) (
Table 3). On the other hand, and as expected, percent unproductive filled spikelet, percent unproductive tiller, and the number of unproductive tillers showed a negative and significant correlation with grain yield under salinity stress (
Table 3).
QTLs related with yield and yield components and several important agronomic components under salinity stress were identified through CIM using QGENE software. The total length of the distribution of the 96 SNP markers was 1306.2 cM, with an average interval size of 13.6 cM. The proportion of the total phenotypic variation explained by each QTL was calculated as R
2 value (R
2 = PVE, phenotypic variation explained by the QTL). A total of seven QTLs identified in six traits: plant height (PLH), number of total tillers (TT), number of filled spikelets (FS), total spikelets (TS), percent filled spikelet (PFS) and yield, were detected on chromosomes 1, 2, 5 and 11, as described below (
Table 4,
Fig. 1). The phenotypic variation of the identified QTLs individually accounted for 13.4 to 18.4% phenotypic variation. The chromosomal location, nearest marker, peak LOD, phenotypic variation explained by QTL (R
2) and direction of the phenotypic effect (additive effect) for each QTL are presented in
Table 4.
One QTL (
qPLH1.1) was identified for plant height on chromosome 1 (
Table 4). The QTL was mapped at 98.1 cM near the marker id1013920 on the long arm of chromosome 1, and phenotypic variation was 14.9% with a 3.2 LOD value. BRRI dhan 47 (tolerant parent) was responsible for contributing towards taller plant height.
Two QTLs for total tillers were detected, one on chromosome 5 (
qTT5.1) and another on chromosome 11 (
qTT11.1), both of which had a 2.9 LOD value (
Table 4). The positions of these QTLs were 53.8 cM and 10.9 cM on the genetic map of chromosomes 5 and 11, respectively; with 13.4% phenotypic variation explained for both QTLs. BRRI dhan 47 was responsible for contributing towards a higher number of total tillers. The QTL
qTT5.1 was mapped near the marker id5005551 on the long arm of chromosome 5, and
qTT11.1 was mapped near the marker id11000858 on the short arm of chromosome 11.
The number of filled spikelets is an important yield component of the salinity tolerance at the reproductive stage. In the present study, one QTL was identified for this trait on the short arm of chromosome 2. The LOD value of this QTL was 3.4 and phenotypic variation was 15.3% (
Table 4,
Fig. 2). BRRI dhan 47 (tolerant parent) was responsible for contributing towards a larger number of filled spikelets.
The total spikelets is also a main component of the grain yield. At panicle initiation, salinity stress reduced the total spikelets of the plant, especially the more sensitive plants. One QTL on chromosome 11 (
qTS11.1) near SNP marker id11000858 was detected for total spikelets with a 3.5 LOD value. This QTL
qTS11.1 explained 15.8% of the phenotypic variation (
Table 4,
Fig. 3).
Percent filled spikelet is one of the most important yield components. Generally, due to salinity stress, the percentage of filled spikelets will be reduced. But if the percentage is moderate to high, the population is considered as tolerant to moderately tolerant. For that reason it is a good indicator for determining salinity tolerance of the population. Re sults showed that one QTL (
qPFS2.1) was identified on the long arm of chromosome 2 with LOD value of 4.1 and showed the highest phenotypic variation (18.4%) explained (
Table 4,
Fig. 2). The alleles of this QTL came from the tolerant parent, BRRI dhan 47, and the position of the QTL was 122.1 cM.
Yield is the most important and ultimate factor for determining salinity tolerance at reproductive stage. The findings of the current study detected one QTL (qYLD2.1) on chromosome 2 with LOD value of 3.1 and accounting for 14.6% of the phenotypic variation. The position of this QTL was 40.1 cM and near id2004774 SNP marker (
Table 4,
Fig. 2). BRRI dhan 47 was the source of the increased grain yield at this QTL.
DISCUSSION
The study was carried out to identify the QTLs that are associated with salinity tolerance at the reproductive stage of rice using F
2 individuals of a NSIC Rc222 and BRRI dhan 47 cross. Both are modern rice varieties released and commercially produced; however, NSIC Rc222 is sensitive to salinity stress, while BRRI dhan 47 (IRRI line IR63307-4B-4-3) is a salt-tolerant variety derived from a cross between a somaclonal variant of Pokkali (TCCP 266-2-49-B-B-3) and the breeding line IR51511-B-B-34-B. Somaclonal variation is variation that occurs in plants that have been produced by plant tissue culture, which leads to the creation of additional genetic variability in the plant (Larkin and Scowcroft 1981). As a result of novel mutations during
in vitro culture, a number of trait characteristics can be selected, including tolerance to high salt concentration, mineral toxicity and tolerance to environmental or chemical stress (Krisna
et al. 2016). The highly salt-tolerant variety Pokkali underwent cell culture to produce somaclonal variation with improved agronomic traits, resulting in a variant, TCCP 266-2-49-B-B-3, with desirable agronomic characteristics and retained salinity tolerance similar to Pokkali (
Gregorio et al. 2002). It is vigorous in growth, semi-dwarf and lodging resistant, with white pericarp and improved cooking quality compared to Pokkali. Although these characteristics make the somaclonal variant superior to Pokkali as an improved genetic donor for crop improvement programs, it should have retained the natural genetic variation for salt tolerance from the original Pokkali parent.
Salinity stress injury scoring (rated as a SES score) was performed to evaluate the performance of each F2 individual in the population, but was not considered as a reliable parameter to represent the tolerance level of the population, since the correlation between grain yield and SES score was positive but not significant. This suggests that using the SES score alone may not always be a reliable parameter to measure salinity tolerance at the reproductive stage because the amount of visual salinity stress damage (represented by the SES score) cannot consider the overall effect of salinity at reproductive stage, unlike at seedling stage. Yield is the ultimate stress indicator, as well as several important yield components such as the percent filled spikelets, number of filled spikelets, total spikelets, number and percentage of productive tillers and total tillers, all of which were significantly correlated with yield under stress. Moreover, the percent unfilled spikelet was highly correlated with grain yield under salinity stress as well. This was unexpected, but a possible reason behind this may be the ability of the tolerant genotype BRRI dhan 47 to produce enough spikelets at panicle initation stage since its not affected by saline stress but at the later reproductive stage it produced a higher percent of unfilled spikelets, but still had more filled spikelets overall than the sensitive genotypes.
No QTLs were identified for the number of productive and unproductive tillers per plant, percent of productive and unproductive tillers, fresh weight and percent unfilled spikelet, possibly due to similar underlying genetic control of these traits between the two parents and fact that the salinity stress was introduced at the vegetative stage where the plants are almost at their maximum tillering stage. There is also the possibility that constraints in the study design (92 individuals × 96 SNPs markers) could have limited the number of detected QTLs, especially on chromosomes 7, 9 and 12 which had several gaps in the chromosome coverage. However, a total of seven QTLs were successfully detected in this study across multiple traits, several of which were located in regions previously identified as having QTLs under salinity stress. For example, one QTL was identified for plant height (
qPLH1.1) on chromosome 1. Several plant height QTLs under stress had been previously observed on chromosome 1:
Hossain et al. (2015) identifed QTLs on chromosome 1, 4 and 7 for plant height and
Mohammadi et al. (2013) identified QTLs on chromosomes 1, 3, 4 and 7. Two QTLs were identified for total tiller number (
qTT5.1 and
qTT11.1). QTLs for total tillers were identified by
Hossain et al. (2015) on chromosomes 7 and 8, but in this experiment, the QTLs were identified on chromosome 5 and 11, indicating that these may be novel QTLs.
One QTL for number of filled spikelets per plant (
qNFS2.1) was identified on chromosome 2. QTLs were also detected for number of filled grains by previous studies on several chromosomes, including chromosome 2 (
Jubay 2012;
Mohammadi et al. 2013). The LOD value of the QTL
qFRSP2.1s was 3.3 with 6.3% phenotypic variation and the position of this QTL was around 16.8 cM to 39.4 cM. The current study was similar to the findings of
Mohammadi et al. (2013) as the QTL was also detected in a similar region on the short arm of chromosome 2 with a 3.4 LOD value. Likewise, the QTL identified for total spikelets per plant (
qTS11.1) was on a different chromosome than the ones detected by
Mohammadi et al. (2013) on chromosomes 4, 7 and 9. This indicates that the QTL for total spikelets per plant is potentially novel. However, the QTL identified in this study for percent filled spikelet (
qPFS2.1) is similar to the one found by
Mohammadi et al. (2013) on chromosome 2. In case of both studies, the QTL came from the salt tolerant variety.
Moreover, a single QTL was detected for yield per plant (
qYLD2.1), which was in a similar region on chromosome 2 as a previously published QTL with 6.9% phenotypic variation and 3.6 LOD value from the Sadri/FL478 population (
Mohammadi et al. 2013). Likewise,
Hossain et al. (2015) also identified a QTL for yield under stress on chromosome 2 explaining 12.2% of the phenotypic variation with 7.9 LOD value. These results suggest that the QTL on chromosome 2 for grain yield under salinity stress may be conserved across several salt-tolerant genetic donors and is being passed to the modern salt tolerant varieties.
Among the seven QTLs observed in this experiment, the QTL qPFS2.1 on chromosome 2 near SNP id2013434 showed the largest effect with 4.1 LOD value and 18.4% of the phenotypic variation explained. A separate region on the other end of chromosome 2 affects salinity tolerance at reproductive stage through alterations in the number of filled spikelets and grain yield, at the SNP marker id2004774. These loci present promising targets for marker-assisted selection activities aimed at improving reproductive-stage salininty tolerance. Another interesting region is on chromosome 11, where QTLs were identified for total tillers and total spikelets.
Although beneficial QTLs have been identified, more work is needed to make use of them in a molecular breeding program to improve reproductive-stage salinity tolerance in rice. Many studies using unimproved genetic donors (i.e. landraces such as Pokkali and Nona Bokra) require specialized genetic populations (such as Advanced Backcross or Backcross-Inbred Lines) or extensive backcrossing to transfer novel QTLs into elite genetic backgrounds. The current study employed a F2 population structure; however, one advantage of using two elite varieties as parents is that the F2 individuals can be directly used in a variety development program without negative linkage drag. The beneficial QTLs can then be transferred using marker-assisted selection with flanking SNP markers to select for the tolerant allele. The QTLs identified in this study for reproductive-stage salt tolerance can also be further confirmed in near-isogenic lines (NILs) and fine-mapped to demonstrate their usefulness for future studies and identify candidate genes underlying the QTLs. The identification of the genes constituting these major QTLs would help in dissecting the molecular mechanisms of salinity tolerance at the reproductive stage of rice and provide valuable tools for breeding salinity tolerant lines with high yield under stress.
ACKNOWLEDGEMENTS
The assistance of the Genotyping Services Lab, led by Maria Ymber Reveche, for running the SNP genotyping is greatly appreciated. Also the assistance of the Salinity Tolerance breeding team of IRRI in the field preparation and salinity stress maintenance as well as data collection is greatly appreciated as well.
Fig. 1Genetic linkage map showing the location of QTLs for salinity tolerance at reproductive stage using 92 individuals (F2) of NSIC Rc222 × BRRI dhan47. Genetic distances are expressed in centi-Morgans (cM). QTLs on the right-hand side of the chromosomes indicate the negetive allele is from BRRI dhan 47 and QTLs on the left-hand side of the chromosomes indicate the positive allele is form NSIC Rc222.
Fig. 2Chromosome locations of QTLs for Number of filled Spikelets, Yield and Percent Filled Spikelet (Chr. 2) under salinity stress on 92 F2 individuals from the NSIC Rc222 × BRRI dhan 47 population based on a significance threshold of LOD = 3 using CIM and direction of phenotypic effect (NSI = NSIC Rc222, BR4 = BRRI dhan 47).
Fig. 3Chromosome locations of QTLs for Total Spikelets (Chr. 11) under salinity stress on 92 F2 individuals from the NSIC Rc222 × BRRI dhan 47 population based on a significance threshold of LOD = 3 using CIM and direction of phenotypic effect (NSI = NSIC Rc222, BR4 = BRRI dhan47).
Table 1Modified Standard Evaluation System (SES) for scoring of visual salt injury at seedling and reproductive stages in rice.
Table 1
|
Score |
Observation |
Rating |
|
1 |
Normal growth, no leaf symptoms |
Highly tolerant |
|
3 |
Nearly normal growth, but leaf tips or few leaves whitish and rolled |
Tolerant |
|
5 |
Growth severely retarded, most leaves are rolled; few elongating |
Moderately tolerant |
|
7 |
Complete cessation of growth; most leaves dry; some plants dying |
Sensitive |
|
9 |
Almost all plants dead or dying |
Highly sensitive |
Table 2Mean values of yield and agronomic characters of NSIC Rc222/BRRI dhan47 F2 progenies and their parents grown under salt stress of EC 10 dS/m at reproductive stage in the artificially salinized field condition.
Table 2
|
Traits |
NSIC Rc222 |
BRRI dhan47 |
Mid parent |
F2 progenies |
|
|
Range |
Mean |
Skewness |
|
Plant height (cm) |
74.00 |
98.00 |
86.00 |
66-106 |
92.00 |
‒0.71 |
|
No of productive tillers |
10.00 |
14.00 |
12.00 |
0-24 |
11.00 |
0.00 |
|
No of unproductive tillers |
9.00 |
7.00 |
8.00 |
0-19 |
6.39 |
0.84 |
|
Total tillers |
19.00 |
21.00 |
20.00 |
7-34 |
17.00 |
0.90 |
|
Percent productive tillers |
52.63 |
66.67 |
59.65 |
0-100 |
62.74 |
‒0.92 |
|
Percent unproductive tillers |
47.47 |
33.33 |
40.40 |
0-100 |
37.26 |
0.92 |
|
Fresh weight (g) |
42.55 |
71.71 |
57.13 |
5.2-178.3 |
64.83 |
0.98 |
|
Percent filled spikelets |
8.24 |
88.03 |
48.13 |
0-88.03 |
20.07 |
0.30 |
|
Percent unfilled spikelets |
91.76 |
11.97 |
51.87 |
0-100 |
75.59 |
‒2.05 |
|
Yield (g/plant) |
6.20 |
14.50 |
10.35 |
0-14.4 |
4.00 |
0.83 |
Table 3Correlation coefficients for grain yield under stress and yield components using the 92 individuals (F2) of NSIC Rc222 × BRRI dhan47 under salt stress of 10 dS/m.
Table 3
|
|
DAM |
PLH |
PT |
UPT |
TT |
PPT |
PUPT |
FW |
FS |
UFS |
TS |
PFS |
PUFS |
YLD |
|
Salscr |
0.123 |
‒0.386**
|
‒0.16 |
‒0.193 |
‒0.318**
|
0.048 |
‒0.048 |
‒0.638**
|
0.132 |
0.136 |
0.145 |
‒0.016 |
0.157 |
0.109 |
|
DAM |
‒0.004 |
‒0.216*
|
0.196 |
‒0.041 |
‒0.232*
|
0.232*
|
0.083 |
‒0.121 |
‒0.166 |
‒0.164 |
‒0.155 |
0.13 |
‒0.126 |
|
PLH |
0.460**
|
‒0.123 |
0.388**
|
0.325**
|
‒0.325**
|
0.584**
|
0.396**
|
0.434**
|
0.455**
|
0.288**
|
‒0.066 |
0.420**
|
|
PT |
‒0.517**
|
0.611**
|
0.809**
|
‒0.809**
|
0.380**
|
0.662**
|
0.751**
|
0.782**
|
0.519**
|
0.015 |
0.669**
|
|
UPT |
0.357**
|
‒0.895**
|
0.895**
|
0.221*
|
‒0.445**
|
‒0.457**
|
‒0.489**
|
‒0.445**
|
‒0.188 |
‒0.425**
|
|
TT |
0.048 |
‒0.048 |
0.650**
|
0.316**
|
0.406**
|
0.408**
|
0.166 |
‒0.072 |
0.336**
|
|
PPT |
‒1 |
0.026 |
0.600**
|
0.672**
|
0.701**
|
0.557**
|
0.155 |
0.589**
|
|
PUPT |
‒0.026 |
‒0.600**
|
‒0.672**
|
‒0.701**
|
‒0.557**
|
‒0.155 |
‒0.589**
|
|
FW |
0.146 |
0.197 |
0.196 |
0.053 |
‒0.026 |
0.197 |
|
FS |
0.670**
|
0.832**
|
0.779**
|
‒0.252*
|
0.979**
|
|
UFS |
0.969**
|
0.298**
|
0.173 |
0.697**
|
|
TS |
0.481**
|
0.045 |
0.846**
|
|
PFS |
‒0.342**
|
0.731**
|
|
PUFS |
‒0.285**
|
|
YLD |
Table 4QTLs identified with composite interval mapping using QGENE for yield and agronomic components in rice F2 population of NSIC Rc222/BRRI dhan47 under salt stress of 10 dS/m.
Table 4
|
Traits |
Chromosome |
QTL |
Nearest marker |
Position |
LOD |
R2 (%) |
Additive effect |
Allelic effect |
|
Plant height |
1 |
qPLH1.1
|
id1013920 |
98.1 |
3.2 |
14.9 |
‒12.51 |
BRRI dhan47 |
|
Total tillers |
5 |
qTT5.1
|
id5005551 |
53.8 |
2.9 |
13.4 |
‒0.56 |
BRRI dhan47 |
|
11 |
qTT11.1
|
id11000858 |
10.9 |
2.9 |
13.4 |
‒0.76 |
BRRI dhan47 |
|
Filled spikelet (no) |
2 |
qNFS2.1
|
id2004774 |
40.1 |
3.4 |
15.3 |
‒75.00 |
BRRI dhan47 |
|
Total spikelet (no) |
11 |
qTS11.1
|
id11000858 |
14.0 |
3.5 |
15.8 |
‒19.9 |
BRRI dhan47 |
|
Filled spikelet (%) |
2 |
qPFS2.1
|
id2013434 |
122.1 |
4.1 |
18.4 |
‒5.4 |
BRRI dhan47 |
|
Yield |
2 |
qYLD2.1
|
id2004774 |
40.1 |
3.1 |
14.6 |
‒1.17 |
BRRI dhan47 |
References
Reference
- Angeles-Shim RB, Reyes VP, Valle MMD, Lapis RS, Shim J, Sunohara H, et al. 2019. Marker-assisted introgression of the quantitative resistance gene pi21 confers broad spectrum resistance of rice to blast. Rice Sci.. 26.
- Bimpong IK, Manneh B, Diop B, Ghislain K, Sow A, Amoah NKA, et al. 2014. New quantitative trait loci for enhancing adaptation to salinity in rice from Hasawi, a Saudi landrace into three African cultivars at the reproductive stage. Euphytica. 200: 45-60.
- Gregorio GB. 1997. Tagging salinity tolerance genes in rice using amplified fragment length polymorphism. University of the Philippines Los Baños (UPLB). Laguna, Philippines, 118. Ph.D. Dissertation.
- Gregorio GB, Senadhira D. 1993. Genetic analysis of salinity tolerance in rice (O. sativa L.). Theor. Appl. Genet.. 86: 333-338.
- Gregorio GB, Senadhira D, Mendoza D, Manigbas NL, Roxas JP, Guerta CQ. 2002. Progress in breeding for salinity tolerance and associated abiotic stresses in rice. Field Crops Res.. 76: 91-101.
- Hossain H, Rahman MA, Alam MS, Singh RK. 2015. Mapping of quantitative trait loci associated with reproductive‐ stage salt tolerance in rice. J. Agron. Crop Sci.. 201: 17-31.
- Joehanes R, Nelson JC. 2008. QGene 4.0, an extensible Java QTL-analysis platform. Bioinformatics. 24: 2788-2789.
- Jubay ML. 2012. Salinity tolerance QTL mapping at seedling and reproductive stages of rice (Oryza sativa L) using F8 recombinant inbred lines of IR29xPokkali.
- Krishna H, Alizadeh M, Singh D, Singh U, Chauhan N, Eftekhari M, et al. 2016. Somaclonal variations and their applications in horticultural crops. 3 Biotech.. 6: 54
- Larkin PJ, Scowcroft WR. 1981. Somaclonal variation-A novel source of variability from cell cultures for plant improvement. Theor. Appl. Genet.. 60: 197-214.
- Mohammadi R, Mendioro MS, Diaz GQ, Gregorio GB, Singh RK. 2013. Mapping quantitative trait loci associated with the yield and yield components under reproductive stage salinity stress in rice (Oryza sativa. L). J. Genet.. 92: 433
- Moradi F, Ismail AM, Gregorio GB, Egdane JA. 2003. Salinity tolerance of rice during reproductive development and association with tolerance at the seedling stage. Indian J. Plant Physiol.. 8: 105-116.
- Ray DK, Mueller ND, West PC, Foley JA. 2013. Yield trends are insufficient to double global crop production by 2050. PLoS One. 8: e66428
- Reddy MA, Francies RM, Rasool SK, Reddy VRP. 2014. Breeding for tolerance to stress triggered by salinity in rice. Int. J. Appl. Biol. Pharm.. 5: 167-176.
- Singh RK, Flowers TJ. Pessarakli M., 2010. The physiology and molecular biology of the effects of salinity on rice. editor. Handbook of Plant and Crop Stress. 3rd ed. Taylor and Francis. Florida, U.S.A.: pp. 910-942.
- Singh RK, Gregorio GB, Jain RK. 2007. QTL mapping for salinity tolerance in rice. Physiol. Mol. Biol. Plants. 13: 87-99.
- Thomson MJ. 2014. High-throughput SNP genotyping to accelerate crop improvement. Plant Breed. Biotech.. 2: 195-212.
- Thomson MJ, Ismail AM, Mccouch SR, Mackill DJ. Pareek A., SK. , H. , Govindjee , 2010a. Marker assisted breeding. editors. Abiotic Stress Adaptation in Plants: Physiological, molecular and genomic foundation. Springer Science. pp. 451-469.
- Thomson MJ, Ocampo DE, Egdane MJ, Rahman MA, Sajise AG, Adorada DL, et al. 2010b. Characterizing the Saltol quantitative trait locus for salinity tolerance in rice. Rice. 3: 148-160.
- Zeng L, Shannon MC, Grieve CM. 2002. Evaluation of salt tolerance in rice genotypes by multiple agronomic parameters. Euphytica. 127: 235-245.
