Molecualr Characterization And Varietal Identication For Multiple Abiotic Stress Tolerance In Rice (Oryza sativa L.)

Coexistence of two or more abiotic stresses is common in most of the rainfed lowland and upland rice growing areas of India and worldwide. Rice production under these conditions is not sustainable. Identication and development of multiple abiotic stress tolerant rice varieties are to be addressed. Here we tried to identify multiple abiotic stress tolerant varieties from a collection of earlier identied varieties for single stress and validated the known SSR markers for stress tolerance. Twenty rice genotypes were evaluated for individual abiotic stress such as drought, salinity and temperature initially and the tolerant three genotypes in each case were further evaluated for combination of stresses various physio-morphological and biochemical parameters were recorded . Among the genotypes evaluated for combination of stresses, PTB-7 was found to be tolerant for drought and salinity, Nagina-22 was tolerant against temperature and salinity. However, the seeds did not germinate in the presence of all three stresses simultaneously.. Twenty rice varieties viz ., Chomala, MO-16, PTB-35, PTB-60, PTB-39, PTB-55, PTB-30, PTB-7, CRdhan307, Apo, Vyttila-3, Vyttila-4, Vyttila-5, Vyttila-6, Vyttila-7, Vyttila-8, Vyttila-9, Vyttila-10, Nagina-22, and NL-44 were further investigated using microsatellite markers to conrm the genotypic level of tolerance to combination of abiotic stresses. Rice genotypes were screened using 30 reported simple sequence repeat (SSR) markers that are linked to drought, salinity and temperature. Molecular marker analysis of rice genotypes also conrmed that RM8904 and RM1287 were associated with salinity tolerance, RM2612, RM6100 and RM5749 were linked to high temperature tolerant trait. Population analysis also revealed that there is ve subpopulation among rice genotypes.


Introduction
Rice (Oryza sativa L.), a semi aquatic plant with an evolutionary advantage of growing under diverse water regimes (De Datta and Mikkelsen, 1985) starting from ooded conditions to dry land. Globally, the crop is cultivated in about 160.8 million hectares with annual production of more than 725.5 million tons of paddy (USDA, 2019). The food production needs to be increased even from the drought-prone areas with an increase of 40% from the di cult ecosystem to meet the targeted food production by 2025 (Pennisi, 2008). The irrigated and the rainfed rice ecosystem, which forms the major mainstay of food security in Asia has been highly sustainable with the environment having few adverse impacts. Increase in rice production should come from highly vulnerable, less productive drought-prone rainfed lowland and upland rice areas because there is limited scope for expanding irrigated rice area (Khush, 2007). These areas neglected during green revolution period and concentrated on input responsive high yielding varieties. These areas are affected by different types of abiotic stresses like heat, drought, cold, salinity and induce severe cellular damage in plant species, including rice plants. There is a cross talk between abiotic stresses either individually or in combination, they cause morphological, physiological, biochemical, and molecular changes that adversely affect plant growth and productivity, and ultimately yield. (Bita and Geras, 2013). Abiotic stress like drought, salinity, and low temperature manifest their effect through an osmotic stress, ultimately leading loss in turgor, disorganized membranes and denatured proteins triggering a cascade of events (Krasensky and Jonak, 2012). In addition, increased salinity of arable land is expected to have devastating global effects, resulting in up to 50% land loss by the middle of twenty-rst century.
In rice two reproductive development stages, anthesis and microsporogenesis are very sensitive to high (>33°C) and low (15°C) temperatures (Jagadish et al., 2015). Extreme temperatures affect anther dehiscence, pollination, and pollen germination, leading to spikelet sterility. Water de cit or drought stress has similar effects and exacerbates the problem by reducing transpirational cooling and thereby increasing canopy and tissue temperature (Sheshshayee et al., 2011). Excess salt in soil adversely affects plant growth, development, and productivity when osmotic stress reduces water uptake by roots (Munns and Tester 2008).
Adaptation mechanisms to these environmental cues in plants by appropriate physiological, developmental and biochemical changes through elaborate mechanisms of signal perception, transduction manifest in adaptive responses (Alcazar et al., 2003). However, tolerance or susceptibility to the abiotic stresses is a very complex phenomenon, in part because stress may occur at multiple stages of plant development and often multiple stresses simultaneously affect the plants during their development. Abiotic stress tolerance is quantitative in nature and governed by multiple loci by many genes; therefore, adapting to variable environmental stress is a highly intricate trait (Shinozaki and Shinozaki). Plants survival under environmental stresses depends on the severity of stress and the genetic background of the plant, even though plants constantly adapting these conditions by changing the physiology and metabolism (Pastori and Foyer, 2002). In addition, osmotic adjustment mechanism and redox metabolism help the plants to alter metabolism in such a way that protect plants from severity of abiotic stresses (Bartels and Sunkar, 2005;Valliyodan and Nguyen, 2006;Munns and Tester, 2008). At the molecular level, gene expression is modi ed upon stress (Chinnusamy et al., 2007) and epigenetic regulation plays an important role in the regulation of gene expression in response to environmental stress (Hauser et al., 2011;Khraiwesh et al., 2011).
Earlier studies mainly focused on the effect of individual stress on morpho-physiological, biochemical, molecular and yield parameters in rice Silvas et al., 2015;Naresh et al., 2018;Beena et al., 2018a, Beena et al., 2018c. When abiotic stresses happen concomitantly in eld condition, tolerant varieties identi ed of single stress need not necessarily tolerate the combined effect of stresses (Atkinson et al. 2013). Hence identi cation of multiple stress tolerant rice varieties is needed for climatic extremities with increasing population (Khush, 2005). Molecular markers, especially DNA-based markers, have been used extensively for the study of genetic diversity, unambiguous identi cation of germplasm and their protection. Exploitation of rice germplasm and identi cation of speci c molecular markers linked to different abiotic stresses is highly critical to ensuring sustainable rice production and global food security in the ever-changing climatic conditions. Molecular marker technology is helpful in unraveling the genetic basis of complex traits within rice germplasm to identify major genes/QTLs for use in rice breeding (Beena, 2005;Beena, 2011;Swamy and Kumar, 2013;Swamy et al. 2014). With this background this study was undertaken to identify multiple abiotic stress tolerant rice varieties and to validate the reported SSR markers for abiotic stress tolerance.
Three independent lab studies were conducted for evaluating drought, salinity and high temperature tolerance. Seeds of each varieties were surface sterilized with 70% ethanol solution for 5 minutes. The seeds were then washed three times with sterilized distilled water. Germination assays were performed by paper towel method using 10 seeds per each towel. Each set of paper towel was moistened with 20 ml distilled water or uniform amounts of desired osmotic solutions to mimic drought stress and salinity.

Tolerance evaluation against individual stresses:
Screening of rice genotypes for individual stress tolerance using paper towel method: In this part of study, drought stress was arti cially induced by desired strengths of polyethylene glycol 6000 (PEG-6000; Sigma Chemicals). Polyethylene glycol has been used to simulate water stress effects in plants (Swapna and Shylaraj, 2017). The experiment was laid out in a complete randomized design (CRD) with four levels of drought stress and control with three replications. Distilled water was used as a control (0 MPa) and osmotic potentials -1.0, -3.0, -5.0 and -7.0 bars were created by adding PEG-6000 @ 5, 10, 15 and 20 g per 100 ml distilled water. Four replicates of 50 seeds of each osmotic potential were used to assess the germination percentage. This experiment was carried out in growth chamber at 25±0.5°C and 80%±1 of relative humidity.
Salinity stress was arti cially induced by desired strengths of NaCl (High Media). The experiment was laid out in a complete randomized design (CRD) with four levels of salinity stress and control with three replications 50 seeds each. Distilled water was used as a control (0 mM NaCl) and osmotic potentials of 100mM NaCl, 150mM NaCl, 200mM NaCl, 250mM NaCl were created by adding @ 5, 10, 15 and 20 g per 100 ml distilled water. This experiment was carried out in growth chamber at 25±0.5°C and 80%±1 of relative humidity.
High temperature was induced by keeping the moistened seeds in petri plates covered with two layers of moistened lter papers and kept in incubators at various temperatures 35 0 C, 40 0 C, 45 0 C, 50 0 C and 80%±1 of relative humidity. The experiment was laid out in a complete randomized design (CRD) with four levels of high temperature stress and control with three replications of 50 seeds each. From these three independent experiments, the number of germinated seed was recorded at 24 hours interval. The seedling height and seedling dry weights were measured on the 14th day. Seeds were considered germinated when both plumule and radicle extended to more than 2 mm from the seeds. Seedling vigour index was calculated from germination percentage and seedling height (root length + shoot length). Physiological traits like proline content as per the protocol by Bates et al. (1973) for drought, Na + K + ratio for salinity) by Zasoski and Buraum (1977), cell membrane stability trait by Blum and Ebercon (1981) for high temperature were studied on 14 th day of germination.
Further experiment was carried out by selecting the genotypes that are capable of germinating under individual stress at possible highest extent. These selected nine genotypes were evaluated for combination of stresses.
Screening of rice genotypes for combination of stresses using paper towel method: In this experiment , seeds were exposed to combinations of above mentioned stresses i.e., D h x S h , D h x T h , T h x S h and D h x S h x T h . Rice seeds failed to germinate when a combination of all the stress (D h x S h x T h ) and combination of water and high temperature stress (D h x S h ) was given.
Validation of rice genotypes using microsatellite (SSR) markers: In this experiment, all the twenty rice genotypes were screened with reported SSR markers for various traits. The documented SSR pro les were carefully examined for the polymorphism in banding pattern between the genotypes. All the 20 genotypes were analyzed for the identi cation of reported markers linked to stress tolerance such as drought, salinity and temperature. Reported microsatellite markers linked to drought, salinity and temperature were used to screen 20 rice varieties in order to analyze the presence of QTLs linked to stress tolerance in varieties which were selected as multiple abiotic stress tolerant based on phenotypic parameters. The sequence of RM primers used for screening is presented in Table 9.
SSR genotyping: Total genomic DNA of 20 rice genotypes was extracted from young leaves at 15 days old plants. The leaves were excised from each genotype, washed with distilled water to remove dust and any other foreign particles. The leaf samples were placed in small plastic bags individually and kept in container having cool environment, and carried immediately to the laboratory. The leaf sample then stored at -80•C until used for DNA extraction. Total genomic DNA was extracted using the method suggested by Dellaporta et al, (1983). The quantity and quality of DNA of each sample was determined by reading the absorbance at 260nm and 280nm in a spectrophotometer (ELICO, SL 21 UV-Vis spectrophotometer). The ratio between the readings at 260nm and 280nm (A 260/A 280) was used as an estimate of the purity of the DNA samples. Pure preparations of DNA have A260 nm/ A280 nm ratio between 1.7 and 1.8 (Sambrook and Russell, 2000). Quality was assessed by using gel electrophoresis with 5µl of crude DNA sample on agarose gel (0.8%). stained with ethidium bromide 0.5 µg/ml DNA. A total of 30 SSR markers were used, out of which nine markers were found to be polymorphic.
The PCR conditions (annealing temperature etc.) for SSRs were optimized through gradient PCR. A total of 20 μl nal voloume of PCR reaction was made. The DNA concentration for each reaction was 25ng/µl DNA, 10mM dNTPs, 1 µl forward and reverse primer each, 1 unit of TaqDNA polymerase with 10X reaction buffer. The thermal pro le starts with initial denaturation at 94°C for 3 min continued to 35 cycles of denaturation at 94°C for 1 minute, primer annealing 55°C for 1 min, extension 72°C for 1 min and nal extension at 72°C for 5 min. After completion of ampli cation, PCR products were kept in a -20°C deep freeze. An aliquot of 10 μl PCR ampli cation products were loaded in an agarose gel of 3.5% containing 0.08 μg/ml of ethidium bromide for electrophoresis. The electrophoresis was carried out at 80 volts (2.5 V/cm) in 1X TBE (pH 8.0). Sizes of amplicons were determined by using 100bp DNA ladder. The gel was visualized under UV (312nm) transilluminator and documented in gel documentation system (Syngene G-box documentation system). The documented SSR pro les were carefully examined for the polymorphism in banding pattern between the genotypes.
Population Structure: Population structure of 20 rice genotypes was estimated using a STRUCTURE software V2.3.4 based on Bayesian clustering algorithm (Pritchard et al, 2000). In order to conclude the optimum number of subpopulations, values for K= 2 to K = 8 were given using a burn length of 50,000 and run length of 50,000. Five independent runs yielded the reproducible results. The results were imported to STRUCTUREHARVESTER software to calculate exact value of 1K (Earl and Vonholdt, 2012).

Statistical analysis:
The overall effects of treatment and rice genotypes and their interaction were analysed by means of twoway ANOVA with different stress levels and genotypes were taken as xed factors. Genotypes were treated as xed factors because we were interested in the response of the speci c genotypes used in this experiment. Design of experiment was CRD with treatment levels . Twenty rice genotypes were analysed with three replications each for the treatment levels. The statistical analysis were done using OPSTAT software (Two factorial CRD). Means were separated by least signi cant difference (LSD).

Results
Screening of rice genotypes for individual stress tolerance using paper towel method: Evaluation of rice genotypes for seedling vigour index and proline content under varied drought stress condition: There was signi cant difference for seedling vigour index among treatments and genotypes under diverse drought induction (Table 1). Among the treatments, control recorded the highest seedling viogur index of 2447. This treatment was followed by the treatment -1bar PEG6000 with an average seedling vigour index of 1746. None of the genotypes were germinated under -7bar PEG6000. Thus it was identi ed that -5barPEG6000 was the highest tolerating level of drought stress.
There was signi cant difference for proline content among treatments and genotypes for diverse drought stress condition (Table 2). Among the treatments, -5barPEG 6000 recorded the highest average proline content of 37.87µg/g tissue. Lowest proline content was recorded in control condition with an average of 16.82 µg/g tissue.
Evaluation of rice genotypes for seedling vigour index and Na + /K + ratio under varied salinity stress condition: There was signi cant difference for seedling vigour index among treatments and genotypes under diverse salinity condition (Table 3). Among the treatments, control recorded the highest seedling vigour index of 3017. This treatment was followed by the treatment 100mM NaCl with an average seedling vigour index of 2502. There was a gradual reduction in seedling vigour from 100 mM NaCl (2502) to 250 mM NaCl (257.9).
At the highest tolerating level of salinity stress (250mMNaCl), highest seedling vigour index was recorded by . This genotype was on par with Vyttila 3(822) and Vyttila 9(790). Chomala and Vyttila 4 were not germinated under 250 mM NaCl (Table 3). At 200mMNaCl, highest seedling vigour index was recorded by Vyttila9 (1674).This genotype was followed by MO-16(1272). At 150 mMNaCl, Vyttila 9 recorded the highest seedling vigour index of 2276. This genotype was followed by MO-16(1737). At 100 mM NaCl, Vyttila 3 recorded the highest seedling vigour index of 3055. This genotype was followed by Nagina-22 (2846). There was no signi cant difference among the genotypes for seedling vigour index under control condition.
There was signi cant difference in Na + /K + ratio among the treatments and genotypes for diverse salinity stress condition (Table 4). Among the treatments, 250 mM NaCl recorded the highest average Na + /K + ratio of 5.53. Lowest Na + /K + ratio was recorded in control condition with an average of 0.588.
At the highest tolerating level of salinity stress (250mMNaCl), highest Na + /K + ratio was recorded by PTB-55 (9.5). Lowest Na + /K + ratio was recorded by Vyttila 6 (2.83). This was followed by Nagina-22 and NL-44. Under 200 mM NaCl level, lowest Na + /K + ratio was recorded by Nagina -22(1). This was followed by . This genotype was on par with Vyttila -8(1.25). At 150 mM NaCl, lowest Na + /K + ratio was recorded by Vyttila 10(0.87). There is no signi cant difference among the genotypes for Na + /K + ratio under both 100 mM NaCl and control condition.
Evaluation of rice genotypes for seedling vigour index and cell membrane stability index under high temperature condition: Seeds were not germinated at temperature 40°C, 45°C and 50°C. There was signi cant difference between the treatments. Highest seedling vigour index was recorded under control condition (1797) compared to the treatment-35°C (1049). There was signi cant difference among the genotypes for seedling vigour index under both treatment (35°C) and control condition. Under high temperature condition, Vyttila -3 and Vyttila-5 were not germinated (Table 5). Under both control and high temperature condition, Nagina-22 recorded the highest seedling vigour index of (2807), (2584) respectively. Under high temperature condition Nagina-22 recorded the highest cell membrane stability index of 99.02% (Table 6). This was followed by Vyttila-10, Vyttila-8 and Vyttila-6 and these genotypes were on par with each other.
Screening of rice genotypes for combination of stresses using paper towel method: Among 20 genotypes, nine genotypes were selected for combination of stresses, of which three are tolerant to high level of salinity stress (Uma, Vyttila-3 and Vyttila-9), three for highest level of drought stress (PTB-7, PTB-60 and PTB-35) and three from highest tolerated temperature stress (Nagina-22, NL-44 and Vytilla-6). The selected genotypes from previously mentioned highest tolerated level of individual stress were subjected to combination of stresses. At highest tolerated level of drought (D h ) and highest tolerated level of salinity stress (S h ) PTB-7 (1615) recorded the highest seedling vigour index, followed by Vyttila-9 (293.6) and the least seedling vigour index was observed in PTB-35 (209.2) ( Table 7). At highest tolerated level of Temperature (T h ) and highest tolerated level of salinity (S h ), highest seedling vigour index was recorded by . This is followed by NL-44 (1.067) and MO-16(1.00) which are on par with each other (Table 8).
Polymorphism information content: PIC of markers ranged from 0 to 0.69.The primer which showed the highest PIC was RM 26212 (0.69) followed by RM 5749 (0.67).
Population Structure Analysis: STRUCTURE software was used to analyze genetic structure of different populations and it was run for K =8 based on SSR markers banding pattern. K is the number of signi cant populations in each main group. Results were analyzed using Evanno's method implemented in Structure HARVESTER. The results showed that the maximum delta K was detected at K=5 (Fig: 1) therefore the number of sub-populations as obtained from the STRUCTURE analysis of the rice genotypes was observed to be ve. The grouping of rice genotypes based on sub-populations is shown in Plate 6.

Discussion
Exposure of plants to various environmental stresses result in general and speci c effects on plant growth and development, reduced productivity, and extensive crop losses worldwide. Drought and salinity together affect more than 10% of arable land, leading to more than 50% decline in the average yields of the major crops of the world (Roychoudhury et al., 2013). Drought-induced crop losses have signi cant economic impact, which is predicted to increase with global climate change (Marris, 2008;Battisti and Naylor, 2009). High temperature is also attering rice productivity to a great extent (Beena, 2013). Abiotic stress tolerance is governed by multiple loci and thus is multigenic in nature; therefore, adapting to variable environmental cues is a highly complex phenomenon (Yamaguchi-Shinozaki, K. and Shinozaki, 2006). Ali et al., (2017) stated that, low productivity of rice in rain fed areas is mostly correlated with decline in yield due to multiple abiotic stresses. Breeding programs are formulated so as to mitigate the effect of any single stress, whereas in eld condition multiple stresses occur simultaneously. This provides a platform to work on and to exploit the tolerance level of rice genotypes to multiple abiotic stresses and to validate the molecular markers reported for various abiotic stresses in rice.
Screening of rice genotypes for individual stress tolerance using paper towel method: In this study, there was signi cant variation for seedling vigour index and proline content among different levels of drought stress treatments. Seeds were not germinated under -7barPEG6000 and thus -5bar PEG 6000 was identi ed as the highest tolerating level of drought stress. Under this condition and less range of drought stress also, PTB-7, PTB-60 and PTB-35 were showed higher seedling vigour index. Similar study was conducted by Panda et al., (2019) and reported that among the folk rice genotypes of Odhisha, Nagina-22 showed highest seedling vigour index.
Among the treatments, -5barPEG 6000 recorded the highest average proline content. Lowest proline content was recorded in control condition. Same genotypes recorded the highest proline content under various levels of drought stress. Hsu et al, (2003) reported that drought stress induction by PEG results in proline production in rice and there is 25 fold increase in proline for every 12h under stress. Beena et al., (2012) also reported an increase in proline (89.6%) across the RIL's of IR20 x Nootripathu as compared to control. Islam et al., (2018) studied the drought tolerance of 18 genotypes using 0, 5, 10, 15 and 20% PEG6000 and reported that seed germination and relative growth of seedlings decreased under high concentration.
At highest level of salinity stress, 250mM NaCl, MO-16, Vyttila-3 and Vyttila-9 exhibited the highest seedling vigour index. There is gradual reduction in seedling vigour index from control to 250mM NaCl. Zhang et al. (2007) reported a similar kind of ndings in lucerne crop under salinity stress. High levels of sodium adversely in uence the acquisition of pottasium (Munns et al., 2010). There is signi cant difference in Na + /K + ratio among the treatments and genotypes for diverse salinity stress condition.
Among the treatments, 250mMNaCl recorded the highest average Na + /K + ratio of 5.53. Lowest Na + /K + ratio was recorded in control condition with an average of 0.588. Vytilla-6, Nagina-22 and NL-44 recorded lowest Na + / K + ratio, so as to reduce sensitivity of the plants to salinity is due to the failure to prevent Na + and Clfrom transpiration streams (Gorham et al., 1990). Similar results were reported by Bohra and Doer ing, (1993).
In the present study, highest tolerating level of temperature stress was 35 0 C. Seeds were not germinated at different temperature levels of 40°C, 45°C and 50°C. Seedling vigour index was reduced under temperature stress condition. The maximum seedling vigour index was observed in Nagina-22 at 35 0 C. Iioh, et al. (2014) reported that under temperature regimes seedling vigour index found to be decreasing with increasing temperature in rice, maize and sorghum. Among the varieties, cell membrane stability index was found to be maximum in Nagina-22 under temperature stress condition. Similar results were also reported by Prasad et al. (2006) and Beena et al., (2018).
Screening of rice genotypes for combination of stresses using paper towel method: Highest tolerated level of drought (D h ) and highest tolerated level of salinity stress (S h ) PTB-7 recorded the highest seedling vigour index, followed by Vyttila-9 and the least seedling vigour index was observed in PTB-35. Combined stresses at their highest tolerable levels resulted in decrease in seedling vigour index, due to reduced water availability for seedling growth and establishment (Babu and Rosaiah, 2017), whereas, at the highest tolerated level of temperature (T h ) and highest tolerated level of salinity (S h ), highest seedling vigour index was recorded by Nagina-22. This is followed by NL-44 and MO-16 which are on par with each other. High temperature stress may cause severe damage to the proteins, disturb their synthesis, inactivate major enzymes and damage membranes. Heat stress could also have major effects on the process of cell divisions (Smertenko et al., 1997). Mokhberdoran et al., (2009) reported that rice cultivar Kalat at seedling stage stated that the deleterious effects of NaCl and PEG were more pronounced with increase in temperature. Metabolic adjustments in response to unfavourable conditions are dynamic and multifaceted and not only depend on the type and strength of the stress, but also on the type of genotypes used for the study.
Simple sequence repeats (SSR) are the DNA markers of choice for rice genetic assessment (Oryza sativa L.) owing to their abundance, elevated polymorphism, co-dominance and easy agarose gel electrophoresis assays (Singh et al. 2010). In the present investigation microsatellite markers or SSR markers (Simple Sequence Repeats) were used to characterize and to assess abiotic stress tolerance among 20 rice genotypes. Beena, 2011 andPrince et al., 2015 also reported that identi cation of trait linked molecular markers can improve rice breeding programme. Our results were supported by other studies. RM 6100 is associated with heat tolerance at owering stage (Bharathkumar et al. 2014)  found in Saltol is considered as the superior marker for genetic diversity analysis. Rice genotypes with the Pokkali band type for locus RM 8094 marker were either highly tolerant or tolerant to salinity stress at the seedling stage (Nejad et al. 2008).
RM8085 was mapped on chromosome 1 and is linked to leaf rolling and leaf drying under drought stress (Salunkhe et al. 2011). The QTL on chromosome 9 is associated with spikelet fertility under stress and root and shoot traits (Yue et al. 2006). Major QTL on chromosomes 4 (Ctb1) and 8 (qCTB8) for cold tolerance at the booting stage were identi ed in a tropical japonica cultivar, Silewah, and markers have been used for introducing the tolerance gene (Ctb1) into japonica cultivars (Kuroki et al. 2007).
Five subpopulations obtained by STRUCTURE analysis were, SP1 consists of only one variety NL-44. It is NERICA Line from Africa, which is entirely different from indica varieties. SP2 consists of two varieties, Nagina-22 and Ptb-7 which have the common traits of drought and high temperature tolerance from previous studies (Beena et al., 2018c;Rejeth et al., 2020). In this study these two varieties showed salinity tolerance also. SP3 consists of varieties, Chomala, Ptb-35, Ptb-39, Vyttila-3, Vyttila -4, Vyttila-5, CR Dhan 307 and MO-16. In this group, except Chomala, all varieties are high yielding. SP4 consist of varieties-Ptb-60, Vyttila 10, Ptb-55, Ptb-30 and Vyttila-6. Among these, PTB60, PTB55 and PTB 30 are drought tolerant and Vyttila 10 and Vyttila 6 are salinity tolerant genotypes. SP5 consist of varieties Vyttila -9, Vytilla-8, Apo and Vyttila-5. SP4 and SP5 characterized by drought tolerant (Ptb-60, Ptb-55, Ptb-30, Apo) and saline tolerant varieties 6,8,9,10). This study also reveals the similar tolerance pattern or cross talk between salinity and drought. That may be the reason why saline and drought tolerant genotypes grouped in the same sub-population. Molecular and genomic studies have shown that several genes with various functions are induced by salinity, drought, and cold stresses, and that various transcription factors are involved in the regulation of stress-inducible genes. There are multiple stress perception and signaling pathways, some of which are speci c, but others may crosstalk at various steps. Recently, progress has been made in identifying components of signaling pathways involved in salt, drought, and cold stresses (Mishra et al., 2016).

Conclusion
Under eld conditions drought and heat stress occur in combination. Simultaneous occurrence of multiple stresses, increase the deleterious effect, such that the effect considerably exceeds the simple additive effects of the action alone. It has been suggested that variability in temperature extremes and water de cit events will be more critical in future climates. The present study based on phenotypic and genotypic analysis, the variety N-22 was chosen the best tolerant variety for the combined stress salinity and temperature. PTB-7 was selected as the most tolerant variety for combined drought and salinity condition. Among the markers, RM6100, RM5749 and RM26212 were validated as markers linked to high temperature tolerance. RM8094 and RM1287 were validated as markers linked to salinity tolerance in rice. These markers can differentiate the tolerant genotypes from susceptible ones. Population structure analysis revealed the genetic relatedness and grouped these varieties into ve subgroups. Natural stress tolerance is a very complex process involving numerous metabolites and metabolic pathways. Analyses of metabolic adjustments of plants with different levels of stress tolerance is very important.

Declarations ACKNOWLEDGMENT
Authors are thankful to Department of Plant Physiology and Department of Plant Biotechnology College of Agriculture Vellayani, for providing facilities to conduct this research work and gratefully acknowledges Kerala Agricultural University for providing the fund to carry out this research programme.

Con ict of interest
All authors don't have any con ict of interest regarding this article.