Rice productivity is significantly impacted by a significant abiotic stress called heat stress. To adapt to current or future heat stress, it is necessary to understand the physiological basis of heat tolerance. Thus, the purpose of this study was to assess the physiological, morphological, and yield parameters of fifty traditional rice landraces when subjected to high temperature stress, as well as to identify SSR (Simple Sequence Repeat) markers associated with these traits. The experiment was designed in a completely randomised manner, with two treatment levels, control and high temperature stress (36 ± 2oC), each with three replications. Thirty SSR primers were used to analyze genetic diversity and population structure among 50 traditional rice landraces collected from Regional Agricultural Research Station, Pattambi, Kerala. The fifty traditional rice landraces were clustered using the average linkage method to determine their relationship to one another. The accessions were all grouped primarily into three clusters. According to principal component analysis, under high temperature conditions, the first principal component accounted for 24.602 percent of the variation, while the second component accounted for 20.587 percent of the variation. The accessions were clearly divided into three subpopulations based on population structure analysis. Under high temperature and control conditions, GLM (Generalized Linear Model) analysis revealed highly significant marker trait associations.RM5715, RM10793, and RM471 were among those that showed associations with multiple traits. Molecular markers and identified tolerant accessions can be used in breeding programmes to create high-yielding, temperature-tolerant rice landraces.
Research Article
Population Structure Analysis and Marker Trait Association in Traditional Rice (Oryza sativa L.) Landraces of Kerala under High Temperature Condition
https://doi.org/10.21203/rs.3.rs-3780092/v1
This work is licensed under a CC BY 4.0 License
Version 1
posted
You are reading this latest preprint version
Population structure
Molecular marker
High temperature
Marker trait association analysis
Rice landraces
Ninety percent of the world's production of rice comes from Asian nations, where it is an often-cultivated crop. The world produced 513.68 million tonnes of rice in 2022–2023, with China producing the most at 149 million tonnes, followed by India at 132 million tonnes. Rice consumed by nearly one-third of the global population accounting for about 80 per cent of their energy requirements(Yadhav et al. 2013; Garris et al. 2005; Anandan et al. 2011).In addition to being the staple grain of Asia, rice is quickly taking the lead as the main diet in Latin America and Africa (Prasad et al., 2020). Oryza satiava is the most widely grown species of the available approximately 11,000 types of rice (Fukagawa et al., 2019) and 100,000 landraces as well as improved cultivars. Existence of such a huge genetic diversity enables humans to improve the crop’s performance in spite of changing climatic scenarios and burgeoning population with high returns to farmers (Dominic and Thomas 2014).
The recent agricultural practices have distinguished complications arising from two interrelated aspects of global environmental change: the continuous rise in atmospheric CO2 concentrations and that simultaneous rise in temperatures. These Anthropocene epoch features due to fossil fuels combustion and human activity, have significant and varied impact on agricultural ecosystems. The ideal temperature for normal rice development is between 27 and 32°C. Fifth assessment of IPCC reported an increase in global mean temperature by 0.850C from the year 1880–2012, and as per report the global mean temperature is expected to increase 3–5 0C in Southeast Asia by the year 2100 (IPCC 2–14; Xu et al., 2021).
As a result, high temperature stress has become a major detriment to dry season rice productivity in subtropical countries. Tropical and subtropical countries, including India, Pakistan, Bangladesh, China, Thailand, Sudan, and a few other African countries, are clearly experiencing yield loss as a result of high temperature stress (Pradhan et al. 2016). An every 10C rise in temperature the yield of rice was expected to loss by 3.2–10% (Peng et al. 2004). High temperatures in both day and night have an impact on almost all stages of rice growth, from seedling to maturity and subsequent harvesting. Booting followed by flowering are thought to be the most temperature sensitive stages of development in rice (Shah et al. 2011; Ali et al. 2021). Exposure of rice genotypes to a temperature of above 350C for a period of hour can lead to panicle sterility. During flowering, high temperatures cause abnormal anther dehiscence, poor pollen germination, and shorter grain filling duration (Beena et al. 2018b; Beena et al. 2021b).
The identification of heat-tolerant genes and proteins, the physiological and biochemical characteristics of heat-stress-sensitive rice, and the molecular mechanisms underlying the heat-stress response are all required for breeding rice for thermotolerance (Janni et al., 2020; Raza et al., 2020). Farmers' landraces have a lot of genetic diversity since they haven't been subjected to subtle selection over a lengthy period of time. This helps landraces adapt to a wide range of agro-ecological settings, and they also offer unrivalled quality features, medicinal capabilities better environmental adaptations and are an irreplaceable source of highly co-adapted genotypes (Borba et al. 2009; Kumbhar et al. 2015). The amount genetic diversity present in the population determines the pace and magnitude of the genetic improvement i.e., genetic diversity is necessary to study the genetic structure of a population and to orientate effective strategies of germplasm conservation (Borba et al. 2010; Nithya et al., 2021; Amrutha et al., 2022). Usually, the genetic diversity in plants has been determined by physiological and morphological traits.
But relying on phenotypic assessment of plants for above mentioned traits is not reliable measure of genetic difference because of the influence of environmental factors (Seetharam et al. 2009). Highly reliable and effective tools for determining the genetic diversity in crop germplasm and evaluating evolutionary relationship is made possible by using DNA-based markers. DNA-based molecular markers can be used to determine the difference in genetic information of different individuals of known varieties as well as the identification and characterization of new germplasm (Beena et al., 2018c; Kumbhar et al. 2015; Rejeth et al., 2020).
Molecular markers such as restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), and random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) have become popular in recent years for evaluating genetic variation at the DNA level of crop species, allowing for a calculation of the degree of genetic connection between two individuals without the genotype complication–Interactions with the environment. Microsatellites or simple sequence repeats (SSRs) are a type of sequence repeat that is both reliable and inexpensive. Molecular markers that are inexpensive due to the fact that they are based on the polymerase chain reaction (PCR) and are codominant, show a lot of allelic diversity. They are found plentiful in rice and are widely spread across the genome (Bajracharya et al. 2006).More research on QTL mapping using diverse genetic resources and populations is required to address the means to improve heat tolerance in rice (Ye et al. 2015).
With this understanding, the current study was formulated to characterize the genetic diversity and the population structure using SSR markers in 50 landraces of rice to clarify the genetic relationship among these rice cultivars as an illustration of a closely related population. Using the phenotypic data under high temperature condition, we compared the results by principle component analysis and population structure analyses.
Plant material
The plant material for this study contained 50 traditional rice landraces which are collected from Regional Agricultural Research Station, Pattambi, Kerala, India presented to supplementary table 1.
Morpho - physiological characterization of rice landraces
A set of pot experiment was conducted to evaluate the selected 50 landraces for various morpho-physiological and yield traits when exposed to elevated temperature conditions. The experiment was conducted at protected cultivation facility available at COA-Vellayani, coordinates 8.4310E, 76.980N by following complete randomised design with two treatment levels, control and high temperature stress (36 ± 2oC). A healthy crop was established by following recommended package of practices as per KAU. Each landrace was replicated 6 times and each replication encompasses 5 plants. The 20 days old plants were transferred to temperature-controlled greenhouse forstress induction in later stages.
The sensitive stages viz., from flower initiation to maturity, high temperature stress was induced. During this time, the average maximum temperature was 35.5oC, and the average minimum temperature was 25.4oC, with maximum and minimum RH of 90.6 percent and 59.2 percent, respectively. Throughout the experiment, daily temperatures, including maximum and minimum temperatures, were recorded using a digital thermo-hygrometer under control and heat stress conditions. Days to first flowering and days was tracked for both control and stress-induced plants. Morphological and physiological parameters were measured at 25 days after the temperature stress was induced and correspondingly in control plants.
Observations recorded
Gaseous exchange parameters viz., photosynthetic rate, transpiration rate and stomatal conductanceand canopy temperature weremeasured at morning time between 9am and 11am using Portable Photosynthetic System (CIRAS-3, PP systems U.S.A). Days to 50% flowering at respective stage in all the landraces when 50% panicles were exerted in each replication. Spikelet fertility percentage is found from number of filled and total spikelet using following formula
Specific leaf area was calculated from control and stressed plants by selecting a fully expanded leaf. The area of index leaf was recorded with the help of graphical method then the sample was oven dried at 70oC for about 48 hours till constant weight was obtained. Specific leaf area was calculated from the following equation as 
Where, LA is the leaf area; DW is the dry weight.
Statistical analysis
Statistical analysis was done using the Grapes 1.0.0 web application designed by KAU (https://www.kaugrapes.com) (Gopinath et al. 2021). Averaging the data from three replicates was used to calculate all parameters. The data analysis design used was a completely randomized design (CRD).Using R software version 4.1.2 (R studio), the "FactomineR" (Husson et al., 2010), "factoextra," and "ggfortify" packages (R Development Core Team, 2008) were used to perform PCA for phenotypic traits recorded under residual moisture conditions.
Isolation of genomic DNA and PCR reaction
The CTAB method (Murray and Thompson 1980) is used for the isolation of DNA from young and stable leaves. The genomic DNA isolated from 50 rice landraces were analyzed and confirmed by agarose gel electrophoresis. The quality and quantity of DNA was determined using UV-Vis Spectrophotometer. PCR screening was carried out using thirty microsatellite SSR markers and the sequence was taken from the database GRAMENE (https://archive.gramene.org/markers/microsat/all-ssr.html). Their sequence is listed in the Supplementary table 2.
PCR reaction was performed in a 20 µl reaction mixture which consisted of genomic DNA (25 ng/ µl)-2.0 µl, 10X Taq assay buffer A-2.0 µl, dNTPs mix (2.5mM each)-1.5 µl, Taq DNA polymerase (1U)-0.3 µl, Forward primer (10 µM)-0.75 µl., Reverse primer (10 µM)-0.75 µl, autoclaved distilled water-12.7 µl. PCR reaction was carried out using Master Cycler gradient 5331-Eppendorf version 2.30.31-09, Germany. The thermal cycling was carried out with the following programme; Initial denaturation for 94oC for 5 min, Denaturation at 94oC for 45 seconds, Primer annealing at 55oC to 60oC for 30 seconds, Primer extension at 72oC for 30 seconds. These steps repeated for 35 cycles. Then final extension carried out at 72oC for 10 min, Incubation-4oC for infinity to hold the sample. On an agarose gel, the PCR products were separated. The documented SSR profiles were thoroughly examined for polymorphism in banding pattern differences between rice landraces.
PIC value is determined by the equation formulated by Botstein et al., 1980 which is used to predict the polymorphic information content (PIC), which accounts for the sensitivity and specificity of a locus or loci by taking into account not only the number of alleles demonstrated but also their frequency distribution.
Population structure analysis
The population structure of the 50 genotypes of rice landraces was evaluated using the Bayesian model-based scoring software STRUCTURE V2.3.4. By plotting the mean calculation of the data's posterior log probability [L(K)] against the given K value in StructureHarvester, the entire collection of findings obtained from that type of initial analysis were further examined to determine the optimum K value. The number of specific-populations data had been analyzed for a K value of 2–8 with performance parameters set as; burning time of 5000, MCMC reps 50,000, number of iterations as 5, the possibility of mixes and strongly linked allele frequency.The entire collection of findings obtained from that type of initial analysis was further examined in Structure Harvester to determine the optimum K value by plotting the mean calculation of the data's posterior log probability [L (K)] against the given K value. There was also a calculation of an arbitrary value called DK, which produced a significant peak at the appropriate K value.
Similarity coefficient and cluster analysis
Binary codes (0/1) were used to score the amplified gel images obtained from thirty primers. The presence of a band received as 1 and the absence of a band received as 0. The binary data generated for polymorphic markers for all fifty rice landraces was entered into the NTeditprogramme of the NTSYSpc version 2.10 software. Using the SHAN module of cluster analysis software, the similarity matrix was used to generate a dendrogram. The SIMQUAL method is being used to calculate the Jaccard's similarity parameters. The NTSYSpcprogramme version 2.10 was used to perform UPGMA grouping. Individuals can only cluster in this software; there is no way to screen or select them. It is the most common application of a matrix of similarities and differences.
Association mapping
Traditional rice landraces were used to conduct association mapping, and this analysis aids in the discovery of linked markers associated with the gene controlling the trait. TASSEL 5 software was used to calculate the associations between SSR markers and traits. For calculating the associations between the marker and the trait, two models were used: general linear model (GLM) and mixed linear model (MLM) based on Q-matrix based on population structure. Markers with P values of < 0.05 and r2 values of > 0.1 were considered significant.
Phenotypic variation for physio-morphological under control and high temperature conditions.
Under control and high temperature conditions, there was significant variation in physio-morphological and yield traits (Tables 1 and 2). Under controlled conditions, minimum days to 50% flowering was recorded by LN-9903-Chettivirippu (52 days) and maximum days to 50% flowering was recorded by Kalluruli (109 days), with a mean value of 80.5 days, similarly under high temperature conditions Mundakankutty exhibited 50% flowering at 50 DAS, whereas Kalluruli at 108 DAS. The trait specific leaf area varied from 208 to 573.53 cm2/g, whereas under stressed condition it varied from 196.55 to 580.26 cm2/g.Under controlled conditions, SPAD Chlorophyll meter reading ranged between 30.4 to 49.70 nmol/cm2 with an average of 40.05 nmol/cm2. Chuvannavellikannan and Kannamkulamban showed the maximum reading of 49.70 nmol/cm2 while Pallipurampokkali and Kokkan showed minimum reading of 30.4 nmol/cm2, whereas under high temperature condition the value varied from 24.20 to 46.20 nmol/cm2 with, Chuvannavellikannan and Kannamkulambanrecording maximum value of 46.20 nmol/cm2 while LN-9939-kochuvithu (shornad) and LB-2000-70-TCR-6982 showed minimum reading of 24.20 nmol/cm2.
|
Variable |
Days to 50% flowering (DAS) |
Specific leaf area (cm2) |
SPAD Chlorophyll meter reading (nmol/cm2) |
Photosynthetic rate (µmol CO2 m− 2 s− 1) |
Stomatal conductance (mmol H2O CO2 m− 2 s− 1) |
Transpiration rate (mmol H2O CO2 m− 2 s− 1) |
Canopy temperature |
No. of panicles per plant (panicles/ft2) |
Filled grains per panicle |
Unfilled grains per panicle |
Spikelet fertility (%) |
|---|---|---|---|---|---|---|---|---|---|---|---|
|
Minimum |
52 |
208 |
30.4 |
2.19 |
0.18 |
2.17 |
23.5 |
3 |
10 |
5 |
8.6 |
|
Maximum |
109 |
573.53 |
49.7 |
26.1 |
2.07 |
12.8 |
29.9 |
15 |
404 |
152 |
96.86 |
|
Mean |
80.5 |
390.765 |
40.05 |
14.145 |
1.125 |
7.485 |
26.7 |
9 |
207 |
78.5 |
52.73 |
|
Standard deviation |
9.867 |
38.724 |
3.936 |
4.979 |
0.329 |
1.539 |
1.164 |
2.07 |
65.11 |
26.669 |
15.425 |
|
Variable |
Days to 50% flowering (DAS) |
Specific leaf area (cm2) |
SPAD Chlorophyll meter reading (nmol/cm2) |
Photosynthetic rate (µmol CO2 m− 2 s− 1) |
Stomatal conductance (mmol H2O CO2 m− 2 s− 1) |
Transpiration rate (mmol H2O CO2 m− 2 s− 1) |
Canopy temperature |
No. of panicles per plant (panicles/ft2) |
Filled grains per panicle |
Unfilled grains per panicle |
Spikelet fertility (%) |
|---|---|---|---|---|---|---|---|---|---|---|---|
|
Minimum |
50 |
196.55 |
24.2 |
2.69 |
0.0917 |
1.71 |
24.6 |
5 |
2 |
8 |
2.98 |
|
Maximum |
108 |
580.26 |
46.2 |
34.9 |
4.2 |
18.5 |
30.4 |
24 |
261 |
207 |
60 |
|
Mean |
79 |
388.405 |
35.2 |
18.795 |
2.14585 |
10.105 |
27.5 |
14.5 |
131.5 |
107.5 |
31.49 |
|
Standard deviation |
10.078 |
56.139 |
3.867 |
5.708 |
0.48 |
2.779 |
0.946 |
2.98 |
31.156 |
36.01 |
6.722 |
Under controlled conditions, the highest PR was noticed from mundakan (26.1µmol CO2 m-2 s-1), similarly under high temperature conditions AL-2000-28-TCR-6983 and Chempan (34.90 µmol CO2 m-2 s-1) showed the maximum photosynthetic rate. Stomatal conductance and transpiration rate was found to be highest from Chengayama and Mundakankutty with values 2.07 mmol H2O CO2 m-2 s-1 and 12.80 mmol H2O CO2 m-2 s-1for stomatal conductance and transpiration rate respectively, similar under heat stress Ittikandan and Gendhakasalarecorded highest value for stomatal conductance(4.2 mmol H2O CO2 m-2 s-1)and transpiration rate (18.5 mmol H2O CO2 m-2 s-1). Similarly, canopy temperature under control conditions varied from 23.5 to 29.9, with genotypes champan and kathikannan recorded maximum value (29.90C), whereas under high temperature conditions pallipurampokkali and kokkan recorded maximum temperature of 30.40C.
Phenotypic variation for yield attributing traitsunder control and high temperature conditions.
LN-9939-kochuvithu (shornad) and Kalluruli recorded highest number of panicles per plant (15), whereas under high temperature treatment LN-9939-kochuvithu (shornad) and LB-2000-70-TCR-6982 (24) recorded highest number of panicles per plant. Significant variation was found among filled grains per panicle, Chuvanna vellikannan and Kannamkulamban recorded maximum filled grains per panicle (404) under control conditions, whereas under high temperature conditions maximum filled grains per panicle was found among Mullanchanna (261).Unfilled grains per panicle ranged between 5 and 152 with Chuvannavellikannan and Kannamkulamban(152) recorded maximum, whereas under high temperature condition, maximum number of unfilled grains per panicle recorded in Mundakan (207). Under controlled conditions, significant variation recorded among spikelet fertility, which ranged from 8.60 to 96.86% with a mean of 52.73%. Kunjukunjuand Navara (black) (96.86%) recorded maximum spikelet fertility, whereas under high temperature Kuruva and Mundakakutty (sel5) (60%) recorded maximum spikelet fertility while Vellathondi and Karanellu (2.98%) recorded lowest.
Relatedness among phenological, physiological and yield traits under stress
Principal component analysis – Control conditions
Phenotypic data for eleven morpho-physiological and yield traits from fifty traditional rice landraces were used to generate a genotype by trait biplot graph (Fig. 1) for analysis with the first two principal components. The first principal component accounted for 28.295 percent of the variation, while the second component accounted for 19.94 percent of the variation. Among the eleven morpho-physiological and yield traits, SCMR, PR, Gs, TR, and UFGP contributed to the highest diversity (Table 3). The first quadrant (top left) consists of 14 rice landraces and 2 variables, SF and DFF. The 2nd (top right) quadrant contains of 12 rice landraces and 6 variables, FGP, UFGP, SCMR, Gs, PR and CT. The 3rd (bottom right) quadrant contains of 13 rice landraces and 2 variables, SLA and TR. The 4th (bottom left) quadrant contains of 11 rice landraces and 1 variable ie., NPP.
|
Variables |
PC1 (28.295%) |
PC2 (19.94%) |
|---|---|---|
|
DFF |
-0.237 |
0.491 |
|
SLA |
0.116 |
-0.5 |
|
SCMR |
0.365 |
0.242 |
|
PR |
0.391 |
0.042 |
|
Gs |
0.474 |
0.126 |
|
TR |
0.466 |
-0.226 |
|
CT |
0.061 |
0.067 |
|
NPP |
-0.12 |
-0.342 |
|
FGP |
0.024 |
0.414 |
|
UFGP |
0.303 |
0.286 |
|
SF |
-0.302 |
0.088 |
Principal component analysis – High temperature conditions
Phenotypic data for eleven morpho-physiological and yield traits from fifty traditional rice landraces were used to generate a genotype by trait biplot graph (Fig. 2) for analysis with the first two principal components. The first principal component accounted for 24.602 percent of the variation, while the second component accounted for 20.587 percent of the variation. Among the eleven morpho-physiological and yield traits, PR, Gs, and TR contributed to the highest diversity (Table 4). The first quadrant (top left) consists of 15 rice landraces and 2 variables, SF and NPP. The 2nd (top right) quadrant contains of 13 rice landraces and 5 variables, FGP, DFF, Gs, TR and CT. The 3rd (bottom right) quadrant contains of 10 rice landraces and 2 variables, UFGP and PR. The 4th (bottom left) quadrant contains of 12 rice landraces and 2 variables, SCMR and SLA.
|
Variables |
PC1 (24.602%) |
PC2 (20.587%) |
|---|---|---|
|
DFF |
0.267 |
-0.156 |
|
SLA |
-0.141 |
0.193 |
|
SCMR |
-0.166 |
0.025 |
|
PR |
0.45 |
0.015 |
|
Gs |
0.467 |
-0.231 |
|
TR |
0.49 |
-0.132 |
|
CT |
0.052 |
-0.458 |
|
NPP |
-0.242 |
-0.07 |
|
FGP |
0.093 |
-0.363 |
|
UFGP |
0.27 |
0.518 |
|
SF |
-0.277 |
-0.502 |
Correlation analysis
Correlation analysis was performed between various morphological, physiological, and yield-related traits under control condition (Fig. 3). The analyzed data revealed that significant positive correlation for the transpiration rate with SCMR (R = 0.35), photosynthetic rate (R = 0.38) and stomatal conductance (0.73). Also, significant positive correlation found for the spikelet fertility with filled grains per panicle (R = 0.34).Strong negative correlation was found between spikelet fertilityand unfilled grains per panicle (R = -0.64).
Correlation analysis was performed between various morphological, physiological, and yield-related traits under high temperature condition (Fig. 4). The analyzed data revealed that significant positive correlation for the transpiration rate with photosynthetic rate (R = 0.42) and stomatal conductance (R = 0.88). Strong negative correlation was found between spikelet fertility and unfilled grains per panicle (R = -0.63).
Genotypic relatedness among traditional rice landraces
Jaccard's similarity coefficient was calculated using NTSYSpc software based on the DNA banding pattern of 50 traditional rice landraces and 30 SSR markers. The genetic similarity coefficients of these 50 traditional rice landraces ranged between 0.18 and 0.9091.Maximum genetic similarity (0.9091) was shown by Ponkurukaand Pallipurampokkali which belongs to cluster 2 and subpopulation 1; as well as LN-9904-Kochuvithu (Thamarakulam) and Kalluruli which belongs to cluster 2 and subpopulation 1. Based on the phenotypic data such as photosynthetic rate, transpiration rate, number of panicles per plant, and spikelet fertility percentage, these varieties showed similar characteristics whereas, minimum genetic similarity (0.18) was shown by Jeerakasala (pottisseri) and Chenellu. Since these rice landraces show least similarity they found in different cluster and subpopulation.
The 50 rice landraces were clustered into three distinct clusters in the dendrogram (Fig. 5) based on genotypic data obtained from the banding pattern of 50 rice landraces using 30 SSR primers. The phylogenetic tree was constructed with the UPGMA cluster analysis software NTSYSpc. The UPGMA cluster evaluation of the matrix for genetic similarities resulted in a dendrogram, which was then divided into three major clusters.
Polymorphism information content
The calculated polymorphic information content (PIC) value is shown in (Table 5). For the detection of Polymorphic Information Content values in rice, twenty-two polymorphic SSR primers were used across fifty rice landraces. The PIC values for markers ranged from 0 to 0.4928. RM10793 (0.4928) was the primer with the highest PIC value, followed by RM310 (0.492) and RM5749 (0.48) and RM556 (0.1302) was the primer with lowest PIC value. The overall average PIC value was found to be 0.40329.
|
Sl. No. |
Marker |
PIC value |
Sl. No. |
Marker |
PIC value |
|---|---|---|---|---|---|
|
1. |
RM122 |
0.3542 |
12. |
RM413 |
0.455 |
|
2. |
RM310 |
0.492 |
13. |
RM222 |
0.4712 |
|
3. |
RM471 |
0.4422 |
14. |
RM320 |
0.4278 |
|
4. |
RM242 |
0.3648 |
15. |
RM237 |
0.3942 |
|
5. |
RM6100 |
0.4598 |
16. |
RM556 |
0.1302 |
|
6. |
RM3586 |
0.1436 |
17. |
RM3475 |
0.4758 |
|
7. |
RM490 |
0.455 |
18. |
RM470 |
0.4032 |
|
8. |
RM337 |
0.275 |
19. |
RM259 |
0.4488 |
|
9. |
RM10793 |
0.4928 |
20. |
RM5715 |
0.4662 |
|
10. |
RM5749 |
0.48 |
21. |
RM3351 |
0.4608 |
|
11. |
RM473 |
0.3942 |
22. |
RM246 |
0.3848 |
|
Average |
0.40329 |
||||
Population structure analysis
The genetic composition of various populations was analysed using the population structure of 50 traditional rice landraces based on banding results provided by SSR markers. The number K denotes the number of significant populations in each main group. Ten runs were performed for each k ranging from 2 to 8, and the results were analyzed using Evanno's method as implemented in Structure HARVESTER. Based on the highest delta K value obtained from STRUCTURE HARVESTER, the total number of subpopulations was estimated to be three (Fig. 6 and Fig. 7).
Subpopulation 1 (SP1) consist of 18 rice landraces; LN-9975-Vyttilla (Thuravoor), Ponkuruka, LN-9904-Kochuvithu (Thamarakulam), JJK – 2000–195 – TCR – 6975,Punchakayama, Pallipurampokkali, LN – 9939-Kochuvithu (Shornad), Mullanchanna, JJK – 2000–206 – TCR-6981, Kalluruli, Chempan, Karanellu, S.K (TPM), JJK – 2000-193-TCR-6974, Chenkayama (Ambalapara), Kathikannan, Kunjukunju, and Vellathondi.According to the phenotypic data the spikelet fertility of this subpopulation is found to range between 11–25% in high temperature condition, photosynthetic rate ranged between 10–25 µmolCO2m-2s-1, transpiration rate ranged between 6–10 mmolH2OCO2m-2sec-1, and number of panicles per plant ranged from 7–15, thus this subpopulation is considered to be high temperature susceptible.
Subpopulation 2 (SP2) consist of 15 rice landraces; Chenellu, Jeerakachembavu, Kannamkulamban, Gendhakasala, Vellachettadi, Kuttadan, Chettadi, AL-2000-28-TCR-6983, Chuvannavellikannan, Mullan puncha, LN-9903 Chettivirippu, Chenkayama, Rakthasali, Jeerakasala (pottisseri), Mundakakutty (sel5). According to the phenotypic data the spikelet fertility of this subpopulation is found to range between < 25% in high temperature condition, photosynthetic rate ranged between 5–10 µmolCO2m-2s-1, transpiration rate ranged between 0–5 mmolH2OCO2m-2sec-1, and number of panicles per plant ranged from 5–10, thus this subpopulation is considered to be highly susceptible to high temperature.
Subpopulation 3 (SP3) consist of 17 rice landraces; Mundakan, Chengazhayama, LB-2000-28-TCR-6985, Kuruva, Ittikandan, Champan, LN-9937-Cherumallaram (Vatharam), Mangalapuram, Navara (Black), Chuvanna IR8(Thodupuzha), Cherukumbalam Ⅱ, Cheruvellari, Kokkan, Kuruthachitteni, Mundakankutty, LB-2000-70-TCR-6982 andVellari. Phenotypic traits such as photosynthetic rate, transpiration rate, number of panicles per plant, and spikelet fertility percentage were used to identify landraces that were tolerant to high temperature.According to the phenotypic data the spikelet fertility of this subpopulation is found to range between 20–30% in high temperature condition, photosynthetic rate ranged between 15–30 µmolCO2m-2s-1, transpiration rate ranged between 8–15 mmolH2OCO2m-2sec-1, and number of panicles per plant ranged from 11–20, thus this subpopulation is considered to be moderately heat tolerant. From the above-mentioned data moderately heat tolerant rice landraces found were LB-2000-28-TCR-6985, Kuruva, Ittikandan, Champan, LN-9937-Cherumallaram (Vatharam), Cheruvellari, Kokkan, Kuruthachitteni, Vellari.
Association of molecular markers with phenotypic traits under both control and high temperature condition
Molecular markers associated with morpho-physiological and yield traits under control conditions was displayed in Table 6. There were 23 associations found for 11 different traits. RM471 located at chromosome 4 was found to be associated with days to 50%flowering with a p value 0.01607 and R2 value 0.11717. RM470 found at chromosome 4 was associated with specific leaf area with a p value 0.0034 and R2 value 0.16842 which is highly significant. RM3475 located at chromosome 1 was found to be associated with SPAD Chlorophyll meter reading with a p value 0.07028 and R2 value 0.06803, stomatal conductance (p value 0.06369 and R2 value 0.07127), unfilled grains per panicle (p value 0.00532 and R2 value 0.15382). Among these associations, RM3475 linkage with unfilled grains per panicle with p value 0.00532 and R2 value 0.15382 was highly significant. RM413 located at chromosome 5 was found to be associated with photosynthetic rate with a p value 0.0111 and R2 value 0.12947 which is significant. RM10793 located at chromosome 1 was associated with transpiration rate (p value 0.0392 and R2 value 0.08738) RM3351 found at chromosome 4 was linked with no. of panicles per plant (p value 0.02528 and R2 value 0.10203). RM337 present at chromosome 8 was found to be associated with Canopy temperature with p value 0.04983 and R2 value 0.0794 which is highly significant. RM3351 located at chromosome 5 was associated with filled grains per panicle with p value 0.00289 and R2 value 0.17374. RM237 located at chromosome 1 was found to be associated with spikelet fertility with p value 0. 01419 and R2 value 0.12131, which is highly significant.
|
Sl. No. |
Trait |
Marker Name |
Chromosome number |
GLM |
||
|---|---|---|---|---|---|---|
|
F value |
P value |
R2 value |
||||
|
1 |
Days to 50%flowering |
RM471 |
4 |
6.23766 |
0.01607 |
0.11717 |
|
2 |
SLA |
RM470 |
4 |
9.51868 |
0.0034 |
0.16842 |
|
3 |
SCMR |
RM3475 |
1 |
3.43075 |
0.07028 |
0.06803 |
|
4 |
Photosynthetic rate |
RM413 |
5 |
6.99018 |
0.0111 |
0.12947 |
|
5 |
Stomatal conductance |
RM3475 |
1 |
3.60694 |
0.06369 |
0.07127 |
|
6 |
Transpiration rate |
RM10793 |
1 |
4.49987 |
0.0392 |
0.08738 |
|
7 |
Canopy temperature |
RM337 |
8 |
4.05353 |
0.04983 |
0.0794 |
|
8 |
No.of panicles/plant |
RM471 |
4 |
5.34 |
0.02528 |
0.10203 |
|
9 |
Filled grains/panicle |
RM3351 |
5 |
9.88302 |
0.00289 |
0.17374 |
|
10 |
Unfilled grains/panicle |
RM3475 |
1 |
8.54347 |
0.00532 |
0.15382 |
|
11 |
Spikelet fertility% |
RM237 |
1 |
6.48845 |
0.01419 |
0.12131 |
The similar associations among rice landraces was mentioned in Table 7. There were 23 associations found for 11 different traits. RM10793 located at chromosome 1 was found to be associated with days to 50%flowering with a p value 0.04712 and R2 value 0.08249. RM259 found at chromosome 1 was associated with specific leaf area with a p value 0.00732 and R2 value 0.14187 which is highly significant. RM3586 located at chromosome 3 was found to be associated with SPAD Chlorophyll meter reading with a p value 0.01977 and R2 value 0.10101 which is significant. RM470 located at chromosome 4 was found to be associated with photosynthetic rate with a p value 0.02362 and R2 value 0.1072 which is significant. RM5715 found at chromosome 12 was associated with stomatal conductance (p value 0.0695 and R2 value 0.06587), transpiration rate (p value 0.003 and R2 value 0.17033) and no. of panicles per plant (p value 0.07252 and R2 value 0.07018). Among these associations, RM5715 linkage with transpiration rate with p value 0.003 and R2 value 0.17033 was highly significant. RM5749 present at chromosome 4 was found to be associated with Canopy temperature with p value 0.00228 and R2 value 0.18252 which is highly significant. RM246 located at chromosome 1 was associated with filled grains per panicle with p value 0.18212 and R2 value 0.03882. RM471 found at chromosome 4 was associated with unfilled grains per panicle with p value 0.07578 and R2 value 0.06671. RM3351 located at chromosome 5 was found to be associated with spikelet fertility with p value 0. 02838 and R2 value 0.10109, which is highly significant.
|
Sl. No. |
Trait |
Marker Name |
Chromosome number |
GLM |
||
|---|---|---|---|---|---|---|
|
F value |
P value |
R2 value |
||||
|
1 |
Days to 50%flowering |
RM10793 |
1 |
4.17183 |
0.04712 |
0.08249 |
|
2 |
SLA |
RM259 |
1 |
7.90887 |
0.00732 |
0.14187 |
|
3 |
SCMR |
RM3586 |
3 |
5.85091 |
0.01977 |
0.10101 |
|
4 |
Photosynthetic rate |
RM470 |
4 |
5.49741 |
0.02362 |
0.1072 |
|
5 |
Stomatal conductance |
RM5715 |
12 |
3.46161 |
0.0695 |
0.06587 |
|
6 |
Transpiration rate |
RM5715 |
12 |
9.87077 |
0.003 |
0.17033 |
|
7 |
Canopy temperature |
RM5749 |
4 |
10.49426 |
0.00228 |
0.18252 |
|
8 |
No.of panicles/plant |
RM5715 |
12 |
3.38549 |
0.07252 |
0.07018 |
|
9 |
Filled grains/panicle |
RM246 |
1 |
1.83784 |
0.18212 |
0.03882 |
|
10 |
Unfilled grains/panicle |
RM471 |
4 |
3.30746 |
0.07578 |
0.06671 |
|
11 |
Spikelet fertility% |
RM3351 |
5 |
5.13796 |
0.02838 |
0.10109 |
Characterization and quantification of genetic diversity among different germplasm is a major goal for effective genetic enhancement for crop productivity. Genetic diversity based on the morphological characters possesses several limitations as they are influenced by environmental factors, and do not have the resolution power for the differentiation between closely related genotypes. The drastic temperature changes in recent years have increased the frequency of extreme weather events such as heat waves and drought, with serious consequences for rice yield (Ye et al. 2015).
Phenotypic trait variation among traditional rice landraces towards high temperature tolerance
All the genotypes showed early flowering under temperature stress condition. During the flowering period, daily temperatures above 30°C or daily maximum temperatures above 35°C will result in poor anther dehiscence and lower pollen production, resulting in a small number of germinating pollen grains on the stigma and a low rate of fertilisation. Similar study conducted by Xiao et al. (2011a) and Jagadish et al. (2015),who reported the ill effects of high temperature condition. Yoshida et al. (1981) and Pravallika et al. (2020) also reported similar results. Flowering time can be manipulated by the foliar spray of methyl jasmonate when rice plants were exposed to higher temperatures (Raghunath and Beena, 2021; Raghunath et al. 2021; Lakshmi et al. 2022).
As a response to high temperatures, the net photosynthetic rate in current study was reduced. Lu et al. (2013) reported a reduction in photosynthesis under the high temperature treatments. High temperature stress may indeed disrupt the movement of water, ions, and organic solutes across the cell, interfering with photosynthesis and respiration and thus increasing the ratio of respiration to photosynthesis (Fahad et al. 2019). The high temperature tolerant landraces showed a higher photosynthetic rate when compared to other landraces under high temperature. In the present study increase in transpiration rate and stomatal conductance was reported under high temperature condition in rice landraces. The increase in transpiration rate and stomatal conductance was due to the higher opening of stomata under higher temperature condition in rice (Egeh et al. 1992), further confirmed by the works of Beena et al. (2018a). These results were confirmed by the studied conducted by Yun-Ying et al., 2009; Reshma et al., 2021; Stephen et al., 2022a. In this study, a reduction in the Chlorophyll content was found under high temperature condition which was found to be similar to the studies conducted by Kumar et al. (2014) and SH (2011).The reduction in chlorophyll content is due to the degradation of chlorophyll under high temperature. Stuerz and Asch (2019) reported an increased specific leaf area under high temperature condition due to increased humidity, a similar result was reported from mean performance of current landraces.
Mean panicle number increased under high temperature condition which was supported by the results reported by Fahad et al. (2016). Number of filled grains under high temperature condition was found to be drastically decreased. Julia and Dingkuhn (2013) and Stephen et al., (2022b) found similar results that the decrease in fertile pollen grains on the stigma might result in the decrease in grain number in each panicle. Number of unfilled grains under high temperature condition was found to be increased. From this study it was reported that the plants when exposed to high temperature, reduction in spikelet fertility was observed. Extreme temperatures cause spikelet sterility in rice and thus yield losses. Prasanth et al. (2017) found that high temperatures at the heading stage drastically decreased anther dehiscence as well as pollen fertility rate, resulting in fewer pollen on stigma and, as a result, lower spikelet fertility and rice yield.
Phenotypic relatedness among traditional rice landraces
Principal component analysis revealed that morpho-physiological and yield traits such as SCMR, PR, Gs, TR, and UFGP contributed to the highest diversity under control condition. While under high temperature condition PR, Gs, and TR contributed to the highest diversity. Cluster analysis classified the traditional rice landraces to separate groups based on their phenotypic data. Similar analysis were reported by Nachimuthu et al. (2014) in rice, which identified days to 50% flowering, days to maturity plant height, number of filled grains, spikelet fertility, panicle length, and grain length as the most important for classifying variation. Mahendran et al. (2015) studied principal component analysis of rice germplasm accessions under high temperature stress, in which they found that wide genetic variability exists in this rice germplasm accessions.
Genotypic relatedness among traditional rice landraces
The 50 rice landraces were clustered into three distinct clusters in the dendrogram based on genotypic data, Cluster I, II and III encompasses 14, 21 and 15 landraces respectively. Jayabalan et al. (2019) studied genetic diversity among 47 rice landraces using 28 SSR markers, resulted in 5 main clusters using UPGMA clustering method. Yadav et al. (2013) reported two clusters among 9 rice genotypes using 100 SSR markers.
The genetic similarity coefficients of these 50 traditional rice landraces ranged between 0.18 and 0.9091. Maximum genetic similarity (0.9091) was shown by Ponkurukaand Pallipurampokkali which belongs to cluster 2 and subpopulation 1; as well as LN-9904-Kochuvithu (Thamarakulam) and Kalluruli which belongs to cluster 2 and subpopulation 1. Based on the phenotypic data such as photosynthetic rate, transpiration rate, number of panicles per plant, and spikelet fertility percentage, these varieties showed similar characteristics. Minimum genetic similarity (0.18) was shown by Jeerakasala (pottisseri) and Chenellu. Since these rice landraces show least similarity they found in different cluster and subpopulation.A wide range of coefficients of similarity between these genotypes suggested the presence of significant genetic variation among the genetic stocks studied. The narrow range of coefficients of similarity between these genotypes suggested that the analysed genotypes shared few genetic similarities. The current findings are consistent with the structural analysis, and they belong to the same cluster and population based on phenotypic and molecular details.
Rashmi et al. (2017) studied the genetic diversity in 65 rice accessions using 20 SSR markerswere divided into two major clusters, cluster I and cluster II, based on the dissimilarity coefficient (0.15). Cluster I was further subdivided into two minor sub-groups with dissimilarity coefficients, IA and IB (0.28). Clusters IA and IB were further subdivided into two subgroups: IA-1 and IA-2 (0.31) and IB-1 and IB-2 (0.32). With a dissimilarity coefficient of 0.16, the second main cluster was further subdivided into two minor subgroups, IIA and IIB.The Jaccard’s dissimilarity coefficient ranges from one to zero, with close to one indicating high similarity and close to zero indicating high dissimilarity. The average dissimilarity coefficient ranges between 0.77 and 0.51. Sajib et al. (2012) conducted genetic diversity analysis among 12 aromatic rice landraces using 24 SSR markers, among them observed five major genetic clusters.
Polymorphism Information Content (PIC) Value
For polymorphic markers, the PIC value ranged from 0 to 0.4928. The marker with the highest PIC value was RM10793, followed by RM310 and RM5749. Similar studies conducted by Ram et al. (2007) used 25 microsatellite (SSR) markers distributed across the rice genome to investigate genetic diversity among 35 rice accessions, which included 19 landraces, 9 cultivars, and 7 wild relatives for that the PIC ranged from 0.498 (RM 265) to 0.89 (RM 206), with an average of 0.707. Markers in this study had an average PIC value of 0.40329 and showed relatively high polymorphism, implying that the SSR markers included in this study were highly informative towards genetic studies and are extremely useful in differentiating the polymorphic frequency of a marker at a specific locus.
Population structure analysis
The results of population structure analysis reveals, Subpopulation 1 (SP1) consist of 18 rice landraces, subpopulation 2 (SP2) with 15 and subpopulation 3 (sp3) with 17 landraces, exhibiting wide variations for spikelet fertility, photosynthetic rate and transpiration rate. Similar study conducted by Mishra et al. (2019) found the genetic diversity and population structure analysis of Asian and African aromatic rice genotypes, in which they classified the 35 rice accessions into 3 subpopulations. Kumbhar et al. (2015) used a set of 50 rice genotypes which comprised of improved varieties, landraces and local selections for population structure analysis, which resulted in 5 subpopulations. Babu et al. (2014) investigated the population structure and genetic diversity of 82 rice genotypes collected from various Asian countries, including India, using 39 microsatellite loci, with the 82 genotypes classified into four subpopulations. Das et al. (2013) studied the population structure among 91 rice accessions from north eastern India using 23 SSR markers, which resulted in 4 subpopulations.
Molecular markers associated with morpho-physiological and yield components in traditional rice landraces
RM471 located at chromosome 4, found to be related to days to 50% flowering, number of panicles per plant and unfilled grains per panicle. Similar results were found by Li et al. (2011) that the marker RM471 was associated with mainly three traits (grain yield, kernels/branch, and plant weight). Xiao et al. (2011b) also found a quantitative trait locus (QTL) in chromosome 4 which is linked to pollen fertility (qPF4).RM3586 located at chromosome 3, found to be related to SPAD chlorophyll meter reading. Lang et al. (2015), Waghmare et al. (2021) and Zhang et al. (2009) reported that rice marker RM3586 is linked with heat tolerance. RM337 located at chromosome 8, found to be related to canopy temperature. Kotla et al. (2013) reported that RM337 is associated with QTL that which controls for plant height and days to flowering. Solis et al. (2018) detected that RM337 is lined with QTL which controls grain yield.
RM10793 located at chromosome 1, found to be related to days to 50%flowering and transpiration rate. Aliyu et al. (2011) found that RM 10793 is the best candidate for screening rice germplasm for SALTOL QTL on chromosome 1. RM10793 is also linked with QTL that controlling the salt tolerance character (Chowdhury et al., 2016). RM5749 located at chromosome 4, found to be related to canopy temperature. Waghmare et al. (2021) reported that RM5749 has heat tolerance in rice i.e., it has a strong linkage with spikelet fertility under heat stress. RM413 located at chromosome 5, found to be related to photosynthetic rate. Ashkani et al. (2013) reported that marker RM413 is linked to rice leaf blast disease resistance gene. RM237 located at chromosome 1, found to be related to spikelet fertility. Huong et al. (2020) reported that the marker RM237 have been linked with salinity tolerance. Xu et al. (2016) found that RM237 is associated withplant height and also linked with gene (DGL1) that encodes cell and organ elongation in rice. RM3475 located at long arm chromosome 1, found to be related to stomatal conductance, unfilled grains per panicle and SCMR. Zhang et al. (2012) reported that the marker RM3475 is linked with flo6 gene which is associated with floury endosperm.
RM470 located at chromosome 4, found to be related to photosynthetic rate and specific leaf area. Ramchander et al. (2016) reported that marker RM470 is associated with transpiration rate under stress condition. Muthukumar et al. (2015) reported that RM470 is associated with number of grains and spikelet fertility. RM259 located at chromosome 1, found to be related to specific leaf area. Marker RM259 has been reported to be linked with qDTY1.2 controlling grain yield (Kumar et al. 2017). Zhu et al. (2004) reported that RM259 is associated with blast resistance. Wang et al. (2014) found that this marker is linked with root traits. RM5715 located at chromosome 12, found to be related to stomatal conductance, transpiration rate and number of panicles per plant. Beena et al. (2021a) reported that RM5715 is associated with gene expressing root volume.
RM3351 located at chromosome 5, found to be related to spikelet fertility. Lee et al. (2005) reported that the marker RM3351 is associated with thermo-sensitive male sterility. Lou et al. (2014) reported that RM3351 is associated with percentage of single exerted stigma. RM246 located at chromosome 1, found to be related to filled grains per panicle. Subashri et al. (2009) reported that RM246 is associated with drought resistance could be determined by identifying QTLs associated with leaf rolling, panicle exertion, plant height, panicle length, senescence, and biological yield under moisture stress conditions.
High temperature is a significant abiotic stress that affects pollination, fertilization, spikelet fertility, grain yield and photosynthesis in rice. Based on physiological and yield traits LB-2000-28-TCR-6985, Kuruva, Ittikandan, Champan, LN-9937-Cherumallaram (Vatharam), Cheruvellari, Kokkan, Kuruthachitteni, Vellari were found to be moderately heat tolerant varieties.SLA, PR, TR, CT, and SF were the morpho-physiological and yield traits that contributed the most diversity under high temperature conditions.SSR markers RM5715, RM10793, RM471, RM237 and RM3351 were found to be in close proximity to a number of phenotypic traits and can be used for marker-assisted selection to enhance phenotypic traits in rice landraces grown in high temperatures.
Acknowledgements The authors thank Dean, College of Agriculture, Vellayani for extending full support in providing facilities for the research work.
Author contribution: BR conceptualized the study. Manuscript was written by SS, MChLN and BR; phenotypic work was done by BR and genotypic work was done by SS. Data analysis and figure were constructed by SS, MChLN and NN. All authors reviewed and approved the manuscript.
Conflict of interest: All authors don’t have any conflict of interest regarding this article.
Ethical approval: Not applicable.
Funding: University fellowship of student.
Availability of data and materials: Seed materials will be available after signing MoU with university.
- Ali, B.S.A., Beena R. and Stephen, K. 2021. Combined effect of high temperature and salinity on growth and physiology of rice (Oryza sativa L.). Agricultural Research Journal. 58(5): 783-788. 10.5958/2395-146X.2021.00111.3
- Aliyu R, Adamu AK, Muazu S, Alonge SO, Gregorio GB (2011) Tagging and validation of SSR markers to salinity tolerance QTLs in rice (Oryza spp). In: International conference on biology, environment and chemistry IPCBEE; 1
- Amrutha, V., Shanija, S., Beena, R., Nithya, N., Jaslam, M.P.K., Soni, K.B. and Viji, M.M. 2022. Population structure analysis and marker trait association in selected set of Indian tomato (Solanum lycopersicum L.) varieties under high temperature condition. Genetic Resources and Crop Evolution. 69(1): 183-207, DOI:10.1007/s10722-021-01216-2.
- Anandan A, Rajiv G, Eswaran R, Prakash M (2011). Genotypic variation and relationships between quality traits and trace elements in traditional and improved rice (Oryza sativa L.) genotypes. J Food Sci 76(4): 122–130
- Ashkani S, Rafii MY, Rahim HA, Latif MA (2013) Mapping of the quantitative trait locus (QTL) conferring partial resistance to rice leaf blast disease. Biotechnollett 35(5): 799-810
- Babu BK, Meena V, Agarwal V, Agrawal P K (2014) Population structure and genetic diversity analysis of Indian and exotic rice (Oryza sativa L.) accessions using SSR markers. Mol Biol Rep 41(7): 4329-4339
- Bajracharya J, Steele KA, Jarvis DI, Sthapit BR, Witcombe JR (2006) Rice landrace diversity in Nepal: Variability of agro-morphological traits and SSR markers in landraces from a high-altitude site. Field Crops Res 95(2): 327–335
- Beena R, Vighneswaran V, Sindhumole P, Narayankutty MC, Voleti SR (2018a) Impact of high temperature stress during reproductive and grain filling stage in rice ORYZA. Int J Rice 55(1): 126-133
- Beena R., Veena V., Jaslam MPK, Nithya N. and Adarsh V.S. 2021b.Germplasm innovation for high temperature tolerance from traditional rice accessions of Kerala using genetic variability, genetic advance, path coefficient analysis and principal component analysis. Journal of crop science and biotechnology.24(5):555-566. https://doi.org/10.1007/s12892-021-00103-7
- Beena, R., Silvas, K., Nithya,N., Manickavelu, A., Sah, R.P., Abida, P.S., Sreekumar, J., Jaslam,P.M., Rejeth, R., Jayalekshmy, V.G., Roy, S., Manju, R.V., Mary Viji, M.M. and Siddique, K.M. 2021a. Association mapping of drought tolerance and agronomic traits in rice (Oryza sativa L.) landraces. BMC Plant Biology: 21(1):1-21. 10.1186/s12870-021-03272-3
- Beena, R., Veena,V., and Narayankutty, M. C. 2018b. Evaluation of rice genotypes for acquired thermo-tolerance using Temperature Induction Response technique. Oryza-An International Journal on Rice. 55(2):285-291.
- Beena,R., Praveenkumar, V.P., Vighneswaran, V. and Narayankutty, M.C. 2018c. Bulked line analysis: A useful tool to identify microsatellite markers linked to drought tolerance in rice. Indian Journal of Plant Physiology: 23(1)-7-15
- Borba TC, de O, Brondani RPV, Breseghello F, Coelho ASG, Mendonça JA, Rangel PHN, Brondani C (2010) Association mapping for yield and grain quality traits in rice (Oryza sativa L.). Genet Mol Biol 33(3): 515–524.
- Borba TC, de O, Mendes C, dos A, Guimaraes EP, Brunes TO, Fonseca JR, Brondani RV, Brondani C (2009) Genetic variability of Brazilian rice landraces determined by SSR markers. PesquiAgropecu Bras 44(7): 706–712
- Botstein D, White RL, Skolnick M, and Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet. 32(3): 314–331.
- Chowdhury AD, Haritha G, Sunitha T, Krishnamurthy SL, Divya B, Padmavathi G, Ram T, Sarla N (2016) Haplotyping of rice genotypes using simple sequence repeat markers associated with salt tolerance. Rice Sci 23(6): 317-325
- Das B, Sengupta S, Parida S, Roy B, Ghosh M, Prasad M, Ghose T (2013) Genetic diversity and population structure of rice landraces from Eastern and North Eastern States of India. BMC Genet 14(1): 71
- Dominic VJ, Thomas J (2014) Assessment of genetic diversity in pokkali (vtl) cultivars of rice using simple sequence repeat (SSR) markers. Ind J Sci Res and Tech 2(5):134-139
- Egeh AO, Ingram KT, Zamora OB (1992) High temperature effects on leaf gas exchange of four rice cultivars. Philipp J Crop Sci 17:21-26
- Fahad S, Adnan M, Hassan S, Saud S, Hussain S, Wu C, Wang D, Hakeem KR, Alharby HF, Turan V, Khan MA (2019) Rice responses and tolerance to high temperature. In: Advances in Rice Research for Abiotic Stress Tolerance, Woodhead Publishing, Cambridge, pp 201-224
- Fahad S, Hussain S, Saud S, Hassan S, Ihsan Z, Shah AN, Wu C, Yousaf M, Nasim W, Alharby H, Alghabari F (2016) Exogenously applied plant growth regulators enhance the morpho-physiological growth and yield of rice under high temperature. Front Plant Sci 7: 1250
- Fukagawa, Naomi K.; Ziska, Lewis H. (2019). Rice: Importance For Global Nutrition. Journal Of Nutritional Science AndVitaminology, 65(Supplement), S2-S3. Doi:10.3177/Jnsv.65.S2
- Garris AJ, Tai TH, Coburn J, Kresovich S, McCouch S (2005) Genetic structure and diversity in Oryza sativa L. Genetics 169(3): 1631–1638
- Gopinath PP, Parsad R, Joseph B, Adarsh VS (2021) GrapesAgri1: collection of shiny apps for data analysis in agriculture. J Open Sour Softw 6(63):3437
- Huong CT, Anh TTT, Tran HD, Duong VX, Trung NT, Dang Khanh T, Dang Xuan T (2020) Assessing salinity tolerance in rice mutants by phenotypic evaluation alongside simple sequence repeat analysis. Agriculture 10(6): 191
- Husson F, Josse J, Pages J. 2010. Principal component methods-hierarchical clustering-partitional clustering: why would we need to choose for visualizing data. Applied Mathematics Department, 1-17.
- IPCC, Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change, Synthesis report, Geneva, Switzerland, 2014.
- Jagadish SVK, Murty MVR, Quick WP (2015) Rice responses to rising temperatures–challenges, perspectives and future directions. Plant cell environ 38(9): 1686-1698
- Janni, M., Gullì, M., Maestri, E., Marmiroli, M., Valliyodan, B., Nguyen, H.T. and Marmiroli, N., 2020. Molecular and genetic bases of heat stress responses in crop plants and breeding for increased resilience and productivity. Journal of Experimental Botany, 71(13), pp.3780-3802.
- Jayabalan S, Pulipati S, Ramasamy K, Jaganathan D, Venkatesan SD, Vijay G, Kumari K, Raju K, Hariharan GN, Venkataraman G (2019) Analysis of genetic diversity and population structure using SSR markers and validation of a Cleavage Amplified Polymorphic Sequences (CAPS) marker involving the sodium transporter OsHKT1;5 in saline tolerant rice (Oryza sativa L.) landraces. Gene 713: 12
- Julia C, Dingkuhn M (2013) Predicting temperature induced sterility of rice spikelets requires simulation of crop-generated microclimate. Eur J Agron 49: 50-60
- Kotla A, Agarwal S, Yadavalli VR, Vinukonda VP, Chakravarthi Dhavala VN, Neelamraju S (2013) Quantitative trait loci and candidate genes for yield and related traits in Madhukar x Swarna RIL population of rice. J Crop Sci Biotechnol 16(1): 35-44
- Kumar AN, Vijayalakshmi C, Vijayalakshmi D (2014) Chlorophyll and chlorophyll fluorescence as influenced by combined heat and drought stress in rice. Bioscience 1461
- Kumar K, Rani K, Meena RK, Jain RK, Jain S (2017) Molecular marker analysis of selected rice lines aerobic traits under water limited conditions. The Bioscan 13(1): 237-243
- Kumbhar SD, Kulwal PL, Patil JV, Sarawate CD, Gaikwad AP, Jadhav AS (2015) Genetic diversity and population structure in landraces and Improved Rice Varieties from India. Rice Sci 22(3): 99–107
- Lakshmi, G., Beena, R., Soni K.B., Viji M.M.. and Uday, C.J. 2022. Exogenously applied plant growth regulator protects rice from heat induced damage by modulating plant defense mechanism. Journal of Crop Science and Biotechnology. https://doi.org/10.1007/s12892-022-00162-4.
- Lang NT, Ha PTT, Tru PC, Toan TB, Buu BC, Cho YC (2015) Breeding for heat tolerance rice based on marker-assisted backcrosing in Vietnam. Plant Breed Biotechnol 3(3): 274-281
- Lee DS, Chen LJ, Suh HS (2005) Genetic characterization and fine mapping of a novel thermo-sensitive genic male-sterile gene tms6 in rice (Oryza sativa L.). Theor appl genet 111(7): 1271-1277
- Li X, Yan W, Agrama H, Jia L, Shen X, Jackson A, Moldenhauer K, Yeater K, McClung A, Wu D (2011) Mapping QTLs for improving grain yield using the USDA rice mini-core collection. Planta 234(2): 347-361
- Lou J, Yue GH, Yang WQ, Mei HW, Luo LJ, Lu HJ (2014) Mapping QTLs influencing stigma exertion in rice. Bulg J Agric Sci 20: 1450-1456
- Lu GH, Wu YF, Bai WB, Bao MA, Wang CY, Song JQ (2013) Influence of high temperature stress on net photosynthesis, dry matter partitioning and rice grain yield at flowering and grain filling stages. J Integr Agric 12(4): 603-609
- Mahendran R, Veerabadhiran P, Robin S, Raveendran M (2015) Principal component analysis of rice germplasm accessions under high temperature stress. Int j agric sci res 5(3): 355-359
- Mishra A, Kumar P, Shamim M, Tiwari KK, Fatima P, Srivastava D, Singh R, Yadav P (2019) Genetic diversity and population structure analysis of Asian and African aromatic rice (Oryza sativa L.) genotypes. J Genet 98(3): 92
- Murray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic acid Res 8(19): 4321-4326
- Muthukumar C, Subathra T, Aiswarya J, Gayathri V, Babu RC (2015) Comparative genome-wide association studies for plant production traits under drought in diverse rice (Oryza sativa L.) lines using SNP and SSR markers. Curr sci 109(1): 139-147
- Nachimuthu VV, Robin S, Sudhakar D, Raveendran M, Rajeswari S, Manonmani S (2014) Evaluation of rice genetic diversity and variability in a population panel by principal component analysis. Indian J Sci Technol 7(10): 1555-1562
- Nithya, N., Beena, R., Abida, P.S., Sreekumar, J., Roy, S., Jayalekshmi, V.G., Manju,R.V. and Viji,M.M. 2021. Genetic diversity and population structure analysis of bold type rice collection from Southern India. Cereal Research Communications. 49(2): 311-328. https://doi.org/10.1007/s42976-020-00099-w.
- Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Centeno GS, Khush GS, Cassman KG (2004) Rice yields decline with higher night temperature from global warming Proc Natl Acad Sci 101(27): 9971-9975
- Pradhan SK, Barik SR, Sahoo A, Mohapatra S, Nayak DK, Mahender A, Meher J, Anandan A, Pandit E (2016) Population structure, genetic diversity and molecular marker-trait association analysis for high temperature stress tolerance in rice. PloS one 11(8): 23
- Prasad, G. S. V.; Padmavathi, G.; Suneetha, K.; Madhav, M. S.; Muralidharan, K. (2020). Assessment of diversity of Indian aromatic rice germplasm collections for morphological, agronomical, quality traits and molecular characters to identify a core set for crop improvement. CABI Agriculture and Bioscience, 1(1), 13–. doi:10.1186/s43170-020-00013-8
- Prasanth VV, Babu MS, Basava RK, Tripura Venkata VGN, Mangrauthia SK, Voleti SR, Neelamraju S (2017) Trait and marker associations in Oryza nivara and O rufipogon derived rice lines under two different heat stress conditions. Front Plant Sci 8: 1819
- Pravallika, K., Arunkumar, C., Vijayakumar, A., Beena,R and Jayalekshmi, V.G. 2020. Effect of high temperature stress on seed filling and nutritional quality of rice (Oryza sativa L.). Journal of Crop and Weed, 16(2): 18-23
- R Development Core Team (2008) R: A Language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. URL http://www.R-project.org.
- Raghunath, M.P. and Beena R. 2021. Manipulation of flowering time to mitigate high temperature stress in rice (Oryza sativa L.). Indian Journal of Agricultural Research. 10.18805/IJARe.A-5707.
- Raghunath, M.P., Beena, R., Vipin, M., Viji, M.M., Manju, R.V. and Roy, S. 2021. High temperature stress mitigation in rice: foliar application of plant growth regulators and nutrients. Journal of Crop and Weed. 17(1): 34-47.
- Ram, S.G., Thiruvengadam, V. and Vinod, K.K. 2007. Genetic diversity among cultivars, landraces and wild relatives of rice as revealed by microsatellite markers. J Appl Genet 48: 337–345
- Ramchander S, Raveendran M, Robin S (2016) Mapping QTLs for physiological traits associated with drought tolerance in rice (Oryza sativa L.). J Investig Genom 3(3): 52
- Rashmi D, Bisen P, Saha S, Loitongbam B, Singh S, Singh PK (2017) Genetic diversity analysis in rice (Oryza sativa L.) accessions using SSR markers. Int J Agric Environ Biotechnol 10(4): 457-467
- Raza, Q., Riaz, A., Bashir, K. and Sabar, M., 2020. Reproductive tissues-specific meta-QTLs and candidate genes for development of heat-tolerant rice cultivars. Plant Molecular Biology, 104, pp.97-112.
- Rejeth, R., Manikanta, Ch.L.N., Beena,R., Roy, S., Manju,R.V. and Viji,M.M. 2020. Water stress mediated root trait dynamics and identification of microsatellite markers associated with root traits in rice (Oryzasativa L.). Physiol Mol Biol Plants. 26(6):1225-1236. http:/ doi.org/10.1007/s12298-020-00809.
- Reshma, M., Beena, R., Viji, M.M., Manju, R.V. and Roy, S. 2021. Validation of temperature induction response technique on combined effect of drought and heat stress in rice (Oryza sativa L.). Journal of Crop and Weed. 17(2): 119-128. https://doi.org/10.22271/09746315.2021.v17.i2.1461
- Sajib AM, Hossain M, Mosnaz ATMJ, Hossain H, Islam M, Ali M, Prodhan SH (2012) SSR marker-based molecular characterization and genetic diversity analysis of aromatic landraces of rice (Oryza sativa L.). J BioSciBiotechnol 1(2): 107-116
- Seetharam K, Thirumeni S, Paramasivam K (2009) Estimation of genetic diversity in rice (Oryza sativa L.) genotypes using SSR markers and morphological characters. Afr J Biotechnol 8(10): 2050-2059
- SH, HS (2011) Effect of photosynthetic characteristic and dry matter accumulation of rice under high temperature at heading stage. Afr J Agric Res 6(7): 1931-1940
- Shah F, Huang J, Cui K, Nie L, Shah T, Chen C, Wang K (2011) Impact of high-temperature stress on rice plant and its traits related to tolerance. J Agric Sci 149(5): 545-556
- Solis J, Gutierrez A, Mangu V, Sanchez E, Bedre R, Linscombe S, Baisakh N (2018) Genetic mapping of quantitative trait loci for grain yield under drought in rice under controlled greenhouse conditions. Front chem 5: 129
- Stephen K, Beena R, Kiran AG, Shanija S, Saravanan R (2022a). Changes in physiological traits and expression of key genes involved in sugar signaling pathway in rice under high temperature stress. 3 Biotech. 12(2):183. https://doi.org/10.1007/s13205-022-03242-y.
- Stephen K, Beena R, Neethu M, Shanija S 2022b. Identification of heat-tolerant rice genotypes and their molecular characterisation using SSR markers. Plant Science Today. ISSN 2348-1900 Vol x(x): xx–xx: (1-12) https://doi.org/10.14719/pst.1639
- Stuerz S and Asch F (2019) Responses of rice growth to day and night temperature and relative air humidity-Dry matter, leaf area, and partitioning. Plants 8(11): 521
- Subashri M, Robin S, Vinod KK, Rajeswari S, Mohanasundaram K, Raveendran TS (2009) Trait identification and QTL validation for reproductive stage drought resistance in rice using selective genotyping of near flowering RILs. Euphytica 166(2): 291-305
- Waghmare SG, Sindhumole P, Mathew D, Shylaja MR, Francies RM, Abida PS, Narayanankutty MC (2021) Identification of QTL linked to heat tolerance in rice (Oryza sativa L.) using SSR markers through bulked segregant analysis. Electron J Plant Breed 12(1): 46-53
- Wang K, Cui K, Liu G, Xie W, Yu H, Pan J, Huang J, Nie L, Shah F, Peng S (2014) Identification of quantitative trait loci for phosphorus use efficiency traits in rice using a high density SNP map. BMC genet 15(1): 1-15
- Xiao Y, Pan Y, Luo L, Zhang G, Deng H, Dai L, Liu X, Tang W, Chen L, Wang GL (2011b) Quantitative trait loci associated with seed set under high temperature stress at the flowering stage in rice (Oryza sativa L.). Euphytica 178(3): 331-338
- Xiao YH, Yi P, Luo LH, Deng HB, Zhang GL, Tang WB, Chen LY (2011a) Quantitative trait loci associated with pollen fertility under high temperature stress at flowering stage in rice (Oryza sativa). Rice Sci 18(3): 204-209
- Xu FF, Liang JIN, Huang Y, Chuan T, Chen YL, Bao JS (2016) Association mapping of quantitative trait loci for yield-related agronomic traits in rice (Oryza sativa L.). J Integr Agric 15(10): 2192-2202
- Xu, Y., Chu, C. and Yao, S., 2021. The impact of high-temperature stress on rice: Challenges and solutions. The Crop Journal, 9(5), pp.963-976.
- Yadav S, Singh A, Singh MR, Goel N, Vinod KK, Mohapatra T, Singh AK (2013) Assessment of genetic diversity in Indian rice germplasm (Oryza sativa L.): Use of random versus trait-linked microsatellite markers. J Genet 92(3): 545–557
- Ye C, Tenorio FA, Argayoso MA, Laza MA, Koh HJ, Redoña ED, Jagadish KS, Gregorio GB (2015) Identifying and confirming quantitative trait loci associated with heat tolerance at flowering stage in different rice populations. BMC genet 16(1): 1-10
- Yoshida S, Satake T, Mackill DS (1981) High-temperature stress in rice. IRRI Research Paper Series 67: 15
- Yun-Ying C, Hua D, Li-Nian Y, Zhi-Qing W, Li-Jun L, Jian-Chang Y (2009) Effect of high temperature during heading and early filling on grain yield and physiological characteristics in indica rice. Acta Agron Sin 35(3): 512-521
- Zhang D, Wu J, Zhang Y, Shi C (2012) Phenotypic and candidate gene analysis of a new floury endosperm mutant (osagpl2-3) in rice. Plant Mol Biol Rep 30(6): 1303-1312
- Zhang GL, Chen LY, Xiao GY, Xiao YH, Chen XB, Zhang ST (2009) Bulked segregant analysis to detect QTL related to heat tolerance in rice (Oryza sativa L.) using SSR markers. Agric Sci China 8(4): 482-487
- Zhu M, Wang L, Pan Q (2004) Identification and characterization of a new blast resistance gene located on rice chromosome 1 through linkage and differential analyses. Phytopathology 94(5): 515-519
No competing interests reported.