Genetic Diversity of Asian Rice (Oryza sativa L.) Germplasm for Direct Seeding in Response to Soil Salinity Stress at Germination Stage

doi:10.21203/rs.3.rs-3456447/v1

Salinity is one of the major devastating factors restricting the rice yield particularly at germination stage. Direct seeding is an alternative approach to avoid the excessive water use for rice production. Till date, limited efforts have been conducted for rice germplasm to screen for direct seeding method against soil salinity stress. In present study, a total of 40 rice genotypes were screened using 59 microsatellite markers across the rice genome to identify the genetic diversity against salinity stress. The genotyping identified 287 alleles. The number of alleles averaged at 4.86 per locus, and ranged from 2 to 9. The polymorphic information content (PIC) ranged from 4–8. The markers RM21, RM481 RM566, RM488, RM9, RM217, RM333, RM242, RM209, RM38, RM539, RM475, RM267, RM279 and RM430 were found highly polymorphic with PIC value > 0.7 and contains the highest number of alleles (≥ 6). Model based and distance based population structure both inferred the presence of 3 clusters in studied rice germplasm. Based on cluster analysis, Shiroodi, Hashemi Tarom and BAS2000 were found as weak salt tolerant varieties. MR211 and MR219 are two Malaysian varieties found to be highly tolerant and have a high potential for direct seeding methods. AMOVA test suggested that 95% genetic diversity was within the population, which implies that significant genetic variation was present in rice germplasm to be used to select parents for future breeding programs.

Biological sciences/Genetics

Biological sciences/Plant sciences

Genetic diversity

Rice

Population structure

Salt tolerant

Direct seeding

Molecular markers

Germination stage

Rice is a staple crop that feeds half of the world’s population 1. The world’s population is expected to rise up to 9.6 billion by 2050 2. With this increasing population, it is imperative to increase the global rice production to combat the food security issues. This global demand needs to be achieved under increasing abiotic and biotic stresses caused by climate change. Among these abiotic stresses, salinity is a major factor that inhibits crop growth, development, and yield. More than 1000 million hectares of land is estimated to be saline or sodic, and between 25% and 30% of irrigated lands are salt-affected and commercially unproductive 3. Currently, 4.03 billion people (over 50% of the world’s population) living in 13 countries are severely affected by soil salinity. This number is expected to increase up to 5.02 billion by 2050 4. If the circumstances continues, half of the world’s current agricultural landscapes will no longer be available for cultivation by the year 2050 5. To cope with this problem there is a need to produce more food in limited agricultural land which is also seriously affected by salt.

Rice is an important grain crop especially in south Asia and South-east Asia and it is the most sensitive to salinity stress among cereals crops. It is reported that rice growth and yield is effected by minimum salinity stress of 30 mmol L^− 1 NaCl (electrical conductivity ~ 3 dS m^− 1) ⁶. Above 3 dS m^− 1, most modern, high-yielding rice varieties display a 12% reduction in yield per dS m^− 1 while 50% yield reduction have been documented at 6 dS m^{− 1 7}. Rice cultivation relies on direct seeding and transplanting method. Transplanting options are significantly more laborious, costly and difficult as compared to the direct seeding approach ⁸. Transplantation approach required the seedlings to germinate in a controlled greenhouse environment with absence of salt stress. Hence, this negates the need for rice germplasm to be tolerant to salt stress during germination when using transplantation method. Moreover, puddling and transplanting require large amount of water which is becoming scarce and expensive, making rice production less profitable. On the other hand, the direct seeding approach is less laborious and cheap method in increasing scarcity of water and farmers are changing their choice of rice establishment method toward direct seeding approach ⁹. But the direct seedling approach requires the rice to exhibit tolerant characteristic during its germination stage which has not been studied yet. The effects of salinity stress are significant on morpho-physiological parameters including reduction in seed germination ¹⁰. It is documented in one of the study that increase in soil salinity inhibited germination and also effects the early seedling growth of different rice varieties ¹¹.There is a dire need to screen the rice germplasm against salinity at germination stage so that it could be effectively used for direct seeding method.

Direct seeding approach is the main production strategy in Aisan countries like Malaysian, Thailand, Vietnam, the Philippines, and Sri Lanka rice ⁹. Since the method was introduced in the late 1970s, it has successfully sustained rice production, and remained favourable over transplanting approach. This exclusively emphasized on the importance of selection for germplasm with agronomically favourable traits like salt tolerance among local varieties. Hence, performance of different varieties at germination stage in salinity stress environment must be screened effectively. On the other hand, transplantation approach requires less emphasis on rice growth abilities during its earliest vegetative stage. The aim of this study is to assess the salinity tolerance of rice germplasm at seed germination stage and to analyse the diversity against salinity tolerance at seed germination for direct seeding method. There are number of salinity tolerance varieties available for transplanting approach. But very limited germplasm is available for salinity tolerance at germination stage for directed seeding method. So, another objective of current study is to screen the germplasm at germination stage to be utilized for direct seeding method in rice.

Phenotypic diversity

The variation in the phenotypes is a key factor for crop improvement. The phenotypic traits were divided into two groups’ i.e., germination parameters and growth parameters.

Germination parameters

Wide range of diversity was observed in germination parameters. The trait wise phenotypic variation was depicted by histogram (Figure 1). FGP spread from 10 to 100%, and peaked in frequency between 90 and 100%. The distribution was poorly fit with clear left skew (Figure 1a). MGT spread from 1 to 8 days, which peak frequency for MGT between 3 and 4 days. The distribution was slightly left skewed (Figure 1b). Germination Index (GI) GI spread from 0.1 to 1, with peak frequency recorded between 0.9 and 1. The distribution was left skewed (Figure 1c). Germination Energy (GE) spread from 0 to 100%, with frequency between 90 and 100%. The central distribution was not normal and left skewed (Figure 1d). Peak Value (PV) spread from 0 to 3.5, with marginal mode frequency between 2 and 2.5. The distribution were normally distributed (Figure 1e). Germination Speed (GS) spread from 0 to 7 seed /day, with frequency between 2 and 3 seed /day. The distribution was slight right skewed (Figure 1f). GR spread from 0 to 7 % /day, with peak in frequency for GR between 6 and 7% /day. The distribution was right skewed (Figure 1g). Germination Capacity (GC) spread from 10 to 100%, with peak in frequency between 90 and 100%. The distribution was left skewed (Figure 1h). All the data was validated by the normality test (p-value < AD-value) (Table 1). Based on these results, the null hypothesis is rejected which assumed the data follows a normal distribution. All these parameters was tested statistically and result showed that germination parameters (FGP, MGT, GI, GE, GS, GR and GC) were found to be statistically significant at α = 0.1% (Table 1). Outliers for FGP, GI, GS, GR and GC were identified and illustrated by boxplot (Supplementary Fig S1). Based on above results, it is concluded that BAS2000 and Hashemi Tarom were identified as expressing weak tolerant phenotypes consistently (high MGT, low GI, GS, and FGP, nil GE).

Growth parameters

Wide range of diversity was observed in growth parameters. The trait wise phenotypic variation was depicted by histogram (Figure 2). Total Length (TL) spread from 1 to 10 cm, and peaked in frequency between 5 and 6 cm. The TL were normally distributed (Figure 2a). Shoot Length (SL) spread from 1 to 6.5 cm, with peaked frequency between 4.5 and 5 cm. The distribution was slightly left skewed (Figure 2b). Root Length (RL) spread from 0 to 3 cm, with peaked frequency between 0.5 and 1 cm. The distribution were poorly fit with clear right skew (Figure 2c). Vigor Index (VI) spread from 0 to 1,000, with peaked frequency between 500 and 600. The VI were normally distributed (Figure 2d). Shoot Fresh Weight (SFW) spread from 0 to 35 mg, with peaked in frequency between 15 and 20 mg (Figure 2e). Root Fresh Weight (RFW) spread from 0 to 25 mg, with peaked frequency between 0 and 5 mg. The distribution was poorly fit with clear right skew (Figure 2f). Shoot Dry Weight (SDW) spread from 0.5 to 6 mg, with peaked frequency for SDW between 4 and 5 mg. The distribution was poorly fit with clear left skew (Figure 2g). Root Dry Weight (RDW) spread from 0 to 2.5 mg, with peaked frequency between 1 and 1.5 mg. The distribution were poorly fit with clear right skew (Figure 2h). Normality test indicated that the null hypothesis was rejected (p-value < AD-value). (Table 1). Outliers for FGP, GI, GS, GR and GC were identified and illustrated by boxplot. Boxplot suggested an outlier which complemented the AD test (Supplementary Fig. S2). All growth parameters (TL, SL, RL, VI, SFW, RFW, SDW, and RDW) varied statistically significant at α = 0.1%. Based on the results, it was concluded that BAS2000 variety was severely affected by salt stress. MCHKAB variety also indicated minimal growth in comparison to other varieties investigated. Interestingly, MR varieties including MR219, MR263 and MR211 ranked higher in terms of traits that suggest salt tolerant attributes.

Clustering analysis based on normalised phenotypic traits suggested significant variation between three subgroups. However, in this clustering, the weak and highly tolerant varieties grouping were significantly smaller (Figure 3). Shiroodi, Hashemi Tarom and BAS2000 were clustered together, presented as weak tolerant varieties. MR211 and MR219 are two Malaysian varieties which were clustered as highly tolerant traits. 87.5% of the remaining varieties clustered into moderately tolerant varieties (Table 2).

Genotyping Analysis

These genotyping markers collectively find 287 alleles. The number of alleles ranged from 2- 9 among which RM403, RM559, RM343, RM596 and RM479 each had the lowest (2) number of alleles, while RM481 and RM21 both shared the highest (9) number of alleles. The number of alleles averaged at 4.86 per locus (Table 3). Fifty markers were assigned as highly polymorphic (PIC > 0.50), seven as moderately polymorphic (0.50 < PIC < 0.25) and two markers were not categorized as no PIC value was calculated for them. RM21 had the highest PIC value i.e., 0.88, while RM462 had the lowest PIC value i.e., 4.0 (Table 3). The average PIC value is 0.69.

Population structure analysis

The model based analysis using STRUCTURE HARVESTER successfully interrogated the dataset using the evano approach, since there were 10 replicate sets (≥ 3), which allowed K to be analysed (Earl 2012). The result showed the number of K groups which best fit the dataset was three (where maximum Δk = 33.64). The K verses Δk graph indicated the presence of three groups in the rice germplasm (Figure 4a). The presence of the three groups was also illustrated by triangle cluster diagram (Figure 4b). Moreover, the presence of these groups was confirmed by QQ plot and results provide the clear indication of three groups shown by three different colours (Figure 4c). The groups represented moderately tolerant (Group A), highly tolerant (Group B) and weakly tolerant (Group C) subpopulations.

Comparatively, the dendrogram generated by DARwin using the distance-based approach establish a similar conclusion on the population structure among the varieties (Figure 5). There were clear sub clades present with increasing dissimilarity between them. Therefore, similar to the findings by Nachimuthu et al. (2015) suggested that both population structure approaches demonstrated similarity between the genotypes clustering. The dendrograms were labelled as Group I, Group II and Group III clusters, which correspond to weakly tolerant, highly tolerant and moderately tolerant clustering, respectively.

Genetic diversity

AMOVA test suggested that 95% genetic diversity was within the population, which implies significant genetic variation between each grouped subpopulation with salinity tolerant characteristics (Figure 6). The variation between all forty varieties were significantly smaller (5 %) given a relatively high PIC average and wide geographical origin of the cultivars tested, but are accounted by the large variation between those exhibiting similar salinity tolerance.

Genetic variation provides the potential for a species to adapt to adverse environmental changes ¹². Genetic diversity is also the foundation for heterosis, which is widely employed in breeding of high yielding hybrid rice varieties. In the current study the phenotypic and genetic diversity of diverse germplasm was evaluated against salinity stress. Significant variations were revealed in parameters investigation for the forty varieties during seed germination. MR219 and MR211 were identified as varieties with the biggest potential as salt-tolerant varieties suitable for direct seeding. BAS2000, Hashemi Tarom and Shiroodi reported poor performance at seed germination and these varieties may be excluded from the germplasm suitable for direct seeding. The BAS2000, Hashemi Tarom were already known to be susceptible for salinity while the status of Shiroodi were not known. It was found in the study that these 3 varieties are highly susceptible for salinity and hence cannot be used for direct seeding method against salinity stress. On the other hand, based on growth parameters it was found that BAS2000 variety was severely affected by salt stress. MCHKAB variety also indicated minimal growth in comparison to other varieties investigated. The status of MCHKAB was not known before. Based on the results it was found to be moderately susceptible. Similar incidences in low TL, SL, RL, SFW, RFW, SDW and RDW were reported in tomato and cotton following salt stress treatments ^13,14. However, MR219 exhibited high TL, SL, RL, VI, SFW, RFW, SDW and RDW, thus suggesting high salt tolerant characteristics. Interestingly, MR varieties including MR219, MR263 and MR211 ranked higher in terms of traits that suggest salt tolerant attributes. These varieties can be used as parent for future breeding program as salt tolerant varieties for direct seeding approach.

Molecular markers, such as SSRs and SNPs, are effective tools to discover genetic diversity ¹⁵. Compared to other genetic markers, SSR markers have remarkable potential to discriminate between rice genotypes ^16,17. Classification of genotypes based on polymorphism markers is a prevailing mean for assessment of genetic variance ^15,17. In the current study the 59 polymorphic SSR markers were utilized to evaluate rice diversity. The survival of the population is dependent on their capacity to maintain adaptation with the environmental shifts (Nature Education 2014). The higher the number of alleles represent the higher the degree of genetic variability which correspond to the wider adaptability to the environment. In the current study, genotyping markers cumulatively sum to 287 alleles. The number of alleles investigated ranged from 2–9, with an average value of 4.86 allele per locus which suggests that there is favourable allelic diversity present. The PIC value indicates the degrees of allelic diversity ¹⁸. It is a principal factor in distinguishing the percentage of polymorphism of a marker at a specific locus; the higher PIC value is in SSR markers, the higher percentage of polymorphism is detected ¹⁷. It has been found that markers with PIC values higher than 0.7 were considered very high, and they can be employed to enlarge the genetic foundation of rice germplasm ¹⁶. In current study 50% (31) markers were found having PIC value > 0.7, which showed the these markers can be used in future breeding program for screening or identification of salt tolerant varieties for direct seeding methods ( Table 3). Our results are comparable with the previous study. In addition, SSR markers with PIC value of 0.5 or higher are considered as to be effective in discriminating the polymorphism rate and they have a potential in evaluation of genetic divergence ^17,19. In the current study, the more than 90% markers having the PIC value ≥ 0.5 and the average PIC value is 0.69 which showed that enough diversity was present in the rice germplasm to be effectively used in breeding program for selection of parents against salinity stress for direct seeding method (Table 3).

The marker RM493 is found to be closely linked with Saltol QTL located on chromosome 1 ²⁰. Saltol QTL is related with the homeostasis of Na?/K? within the cell and is especially responsive at the seedling stage. In the previous study it was found to have the 5 alleles with PIC value of 0.69 ²⁰. In our study the results are comparable with the previous finding and marker RM493 showed 6 alleles with PIC value of 0.72. These results showed that the markers with higher PIC value and greater number of alleles will be showed the greater diversity and have the ability to distinguish the between salt tolerant and salt susceptible genotypes. In the current study the RM21 showed highest number of alleles (9) and PIC value (0.88) followed by RM481 (alleles-9, PIC value- 0.86). Similarly, the marker RM566, RM488, RM9, RM217, RM333, RM242, RM209, RM38, RM539, RM475, RM267, RM279 and RM430 were found highly polymorphic with PIC > 0.7 and contains the highest number of alleles (≥ 6) (Table 3). Hence, they are important for future breeding program for selection of salt tolerant genotypes for direct seeding methods.

Normalised aggregate between the forty varieties based on germination and growth parameters categorize the germplasm into three subpopulations (Fig. 3). The clustering pattern in the present study indicated the existence of variability among rice genotypes. There were sufficient variations between the varieties for development of newly improved crosses sharing similar salinity tolerance criteria. Population structure analysis revealed three sub-populations (Fig. 1) indicating that enough genetic base exists among the studied rice genotypes. Further, the population structure analysis confirmed the clustering of the sampled genotypes in a similar group, suggesting that enough diversity is present for selection of parents for direct seeding method. Although, crosses of unrelated parents can be practiced for enlarging the genetic diversity to develop breeding populations. However, AMOVA revealed highly significant genetic differences (P ≤ 0.001) among the populations, among individuals (95%) and within individuals (5%) (Fig. 6). Variation of similar pattern has been reported in previous studies on rice germplasm ^21,22. AMOVA results suggest that a small collection within a given source will capture the genetic diversity present in the test genotypes. The presence of variability within and between the populations represents the possibility of making wide crosses for population development and to enhance genetic divergence in rice.

Salt-tolerant traits at every part of rice developmental stages are the important criteria for the next generation of elite rice varieties. MR219 and BAS2000 were identified as the highly tolerant and weakly tolerant varieties, correspondingly. Thus, based on the analysis for germination and growth parameters, MR219 exhibited as the most potential cultivar for successful direct seeding accession for local cultivation. At least fifty markers were highly informative to the traits of interest, especially RM21 with the highest PIC score of 0.876. Three subpopulations were inferred to make up the population structure between the varieties from both model based and distance based approaches. Therefore, it can be inferred that the width of the morphological and genotypic variation were sufficient to sustain the necessary diversity throughout the generations to develop new varieties from this population, that are able to withstand the salt stress conditions during direct seeding. Direct seeding of MR219 germplasm for rice cultivation showed extensive promise to survive salinity tolerance and hence, may be readily used by farmers for widespread cultivation. This variety, along with MR211 are among those varieties that are depicted as salt tolerant cultivars both phenotypically and genotypically and is readily available for immediate plantation. BAS2000, on the other hand showed the opposite performance. This variety will have the lowest survival past the vegetative stage as it will not be able to sustain the osmotic stress on its root system, if specifically grown through direct seeding method.

It has been confirmed that the experimental data collection, complied with relevant institutional, national, and international guidelines and legislation with appropriate permissions from authorities of Malaysian Agricultural Research and Development Institute (MARDI) and Department of Plant Beeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan. The seeds samples were provided and collected from Malaysian Agricultural Research and Development Institute (MARDI) and used with proper permission from authorities.

Plant materials

In total forty varieties were utilized in current experiment. The varieties were mixture of salt tolerant (used a control), moderately tolerant, moderately susceptible, and susceptible (used as control). Twenty varieties were elite varieties from Malaysia and sourced locally from Malaysian Agricultural Research and Development Institute (MARDI). Eight varieties originated from Pakistan, five varieties from Iran, five from Iraq, and one each from Philippines and India (Supplementary Table S1).

Germination and growth parameters

To calculate the germination and its related parameters seed were grown in 5 mL of 175 mM salt solution in a 6 cm petri dish. Ten seeds for each replicate were used to calculate the average data. The salinity concentration was determined by previous findings by Zainul Alam (2016), which found the threshold to effect the germination and growth of rice. A total of eight germination and related parameters were recorded which are as follow: Final germination percentage (FGP), mean germination time (MGT), germination index (GI), germination energy (GE), peak value (PV) (or peak period of germination in other literature), germination speed (GS), germination rate (GR) and germination capacity (GC). The formula for measuring each parameter is given in the supplementary file (Supplementary Table S2)

For growth parameters, seeds were grown were grown in greenhouse conditions between 27^oC to 30^oC and subjected to 175 mM NaCl treatment in order to assess the effect of salt stress on rice growth parameters. Eight growth parameters were calculated which included: Total length (TL), shoot length (SL), root length (RL), shoot and root fresh weight (SFW and RFW), shoot and root dry weight (SDW and RDW), and vigour index (VI). The formula for measuring each parameter is given in the supplementary file (Supplementary Table S2)

Statistical analysis

The germination parameters and growth parameters were calculated by using Microsoft excel. The data were then statistically analysed using R 3.4.1 by running analysis of variance (ANOVA) test (R Core Team 2017).

Genomic DNA Isolation

20 mg of plant samples were taken and sterilized using absolute ethanol and grounded in 1.5 mL eppendorf tube. The genomic DNA (gDNA) from fresh leaves was extracted using optimised protocols by Ikeda et al. (2001). The quality was determined by running DNA on agarose gel electrophoresis and quality was measured by nanodrop (ND1000, Thermo Scientific, USA).

Genotyping by SSR

Polymerase chain reaction (PCR) was performed using 59 microsatellite markers distributed across all twelve chromosomes. The markers were selected based on their relatedness to salt tolerance in rice (Supplementary Table S3). The PCR was run on ABI thermal cycler (ProFlex PCR) with following conditions: initial denaturing temperature at 94ºC for 5 min, followed by 40 cycles of 94 °C for 45sec, specific temperature 55-60 °C for each primer for 30 sec, 72 °C for 60 sec with a final extension at 72 °C for 10 min. Efficiency of primer pairs in terms of their ability to yield unique and genotype specific allele(s) was assessed by computing the Polymorphism percent as PP = [un/tn] × 100, where un and tn represent number of unique alleles and total number of alleles detected amongst the varieties, respectively ²³ Amplified PCR products were run on 2-3% agarose gel and band were visualized under UV light. The amplified products were then recorded in the binary data format: 1 for presence and 0 for absence. Each amplified band against each marker is termed as an allele. This binary data was further used for assessment of the genetic diversity in the form of allelic variation which was assessed by calculating the polymorphic information content²⁴ ²⁴ of the primer pairs as PICi=1-∑j=1kP2ij, where, k is the total number of alleles detected for a marker; Pij is the frequency of the jth allele for ith marker and summation extends over k alleles.

Population Structure

The binary genotypic data and the phenotypic parameters were analysed for identification of the population structure. These data were then further interrogated for Jaccard’s genetic distance matrix using DARwin 6.0.014. Population structure was assessed using (i) model based and (ii) distance based approach ²⁵. The former approach was addressed using STRUCTURE 2.3.4 program ²⁶. STRUCTURE Harvester was then used to extract the results for consecutive interpretation ²⁷. The run length and Markov Chain Monte Carlo (MCM) length were set to 150,000 and burning length 150,000 replication, as described by ²⁵.

On the other hand, the distance approach utilised pair wise distance algorithm calculating dissimilarity matrix using DARwin 6 software. The genetic dissimilarity was calculated from the binary genotypic data using dice coefficient with 1000 bootstrap. The bootstrap value followed the same recommendation suggested by Nachimuthu et al. (2015). An unweighted neighbour joining tree was constructed under the same bootstrap conditions to determine the association and hence validate the number of subpopulation in the germplasm. Analysis of Molecular Variance (AMOVA) between the clusters identified by distance-based approach were analysed using GenAlEx 6.503 ²⁸. The populations were set based on k value obtained, with number of region set as 1 and 999 permutations, following the default setting for binary haploid input data.

Author’s contributions:

A.A (Alia Anwar) collected and analyse the data, writes the first draft of the paper. J.T ( Javaria Tabassum) helps in data collection and review the first draft of manuscript. H.A (Haider Ali) helps in the figures preparation. R.S.A (Rosa Sanchez Luca) critically review the manuscript. G.J (Ghulam Jilani) helps in the data analysis. NS (Nur Shuhadah Binti Mohd Saad) conduct the experiment and collect the data. Q. A ( Qurban Ali) provide the support to the statistical analysis. MA (Muhammad Ashfaq) critically reviewed the manuscript. MAJ (Muhammad Arshad Javed) formulized the idea of research and critically review the manuscript.

Funding:

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Field studied:

No filed studied was involved in the current study. No, wild, threaten or lethal species were utilized in the study which required the specific methods to follow.

Data availability

All the necessary data is provided in the supplementary file. However, if someone required more details of analysis of data it could be available from the corresponding author on reasonable request.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permission

All the material is owned by the corresponding author. No permission is required for specific institution or third party.

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Table 1: Analysis of variance (ANOVA) results for germination and growth parameters of forty Malaysian rice varieties.

	FGP	MGT	GI	GE	PV	GS	GR	GC
Mean	86.92	3.95	0.87	68.08	1.61	2.73	5.80	83.17
Median	95.00	3.60	0.95	80.00	1.67	2.85	6.34	90.00
Anderson-Darling Normality Test
AD-value	13.8920	2.1206	13.8920	6.4223	0.3258	0.7669	13.8550	10.6010
P-value	< 2.20x10^-16	2.03x10^-5	< 2.20x10^-16	7.48x10^-16	0.5110	0.0450	< 2.20x10^-16	< 2.20x10^-16
				ANOVA
*Varieties* (DF = 39)
SS	42492	198.44	4.249	122126	30.3100	152.40	108.2000	52397
MS	1089.6	5.088	0.10896	3131.4	0.7773	3.908	2.7740	1344
F-value	11.9900	24.3	11.99	32.39	n/a	42.03	1.9570	13.44
P-value	< 2x10^-16***	< 2x10^-16***	< 2x10^-16***	< 2x10^-16***	n/a	< 2x10^-16***	0.0058**	< 2x10^-16***
*Error* (DF = 80)
SS	7267	16.75	0.727	7733	n/a	7.44	113.4000	8000
MS	90.8	0.209	0.00908	96.7	n/a	0.093	1.4170	100

DF = Degree of Freedom, SS = Sums of Square, MS = Means of Square

Table 2: Normalised aggregate for different phenotypic parameters with regard to their salt tolerance characteristics.

Germination Parameters
Group	FGP	GR	MGT	GI	GE	GS	GC	PV
WT	-2.93	-0.40	1.73	-2.93	-2.07	-1.93	-2.72	-1.48
MT	0.23	0.01	-0.10	0.23	0.13	0.13	0.20	0.10
HT	0.42	0.44	-0.75	0.42	0.83	0.54	0.56	0.50
Growth Parameters
Group	TL	SL	RL	SFW	RFW	SDW	RDW	VI
WT	-1.21	-1.09	-1.16	-1.37	-0.79	-1.28	-1.07	-1.78
MT	-0.01	0.02	-0.06	0.03	-0.10	0.05	-0.03	0.05
HT	1.93	1.21	2.76	1.54	3.00	1.01	2.14	1.77

Note: WT: weakly tolerant; MT: moderately tolerant; HT: highly tolerant

Table 3: Genotyping analysis across twelve chromosomes using 59 microsatellite markers.

#	Marker	Chr*	Repeat Motif	Number of Alleles	PIC
1.	RM462	1	(GA)12	3	0.40
2.	RM283	1	(GA)18	3	0.61
3.	RM493	1	(CTT)9	6	0.72
4.	RM9	1	(GA)15GT(GA)2	8	0.83
5.	RM488	1	(GA)17	8	0.84
6.	RM403	1	(GA)8	2	0.50
7.	RM279	2	(GA)16	6	0.73
8.	RM561	2	(GA)11	4	0.70
9.	RM526	2	(TAAT)5	4	0.63
10.	RM327	2	(CAT)11(CTT)5	3	0.64
11.	RM475	2	(TATC)8	6	0.77
12.	RM423	2	(TTC)9	4	0.70
13.	RM231	3	(CT)16	5	0.70
14.	RM489	3	(ATA)8	4	0.60
15.	RM517	3	(CT)15	5	0.76
16.	RM7	3	(GA)19	5	0.75
17.	RM563	3	(CCT)6	3	0.66
18.	RM261	4	C9(CT)8	5	0.70
19.	RM317	4	(GC)4(GT)18	4	0.60
20.	RM471	4	(GA)12	5	0.75
21.	RM559	4	(AACA)6	2	0.50
22.	RM267	5	(GA)21	6	0.77
23.	RM574	5	(GA)11	5	0.63
24.	RM430	5	(GA)25	6	0.72
25.	RM161	5	(AG)20	6	0.64
26.	RM343	6	(CAT)5(CAC)5CAT(CAC)4	2	0.50
27.	RM539	6	(TAT)21	6	0.87
28.	RM527	6	(GA)17	5	0.72
29.	RM540	6	(AG)16	5	0.72
30.	RM454	6	(GCT)8	4	0.67
31.	RM217	6	(CT)20	8	0.83
32.	RM481	7	(CAA)12	9	0.86
33.	RM500	7	(AAG)9	5	0.70
34.	RM560	7	(CT)12	4	0.66
35.	RM505	7	(CT)12	3	0.54
36.	RM18	7	(GA)4AA(GA)(AG)16	4	0.68
37.	RM502	8	(TG)10	4	0.66
38.	RM404	8	(GA)33	5	0.72
39.	RM433	8	(AG)13	5	0.50
40.	RM337	8	(CTT)4-19-(CTT)8	3	0.66
41.	RM38	8	(GA)16	7	0.73
42.	RM316	9	(GT)8-(TG)9(TTTG)4(TG)4	5	0.79
43.	RM105	9	(CCT)6	5	0.76
44.	RM566	9	(AG)15	8	0.85
45.	RM242	9	(CT)26	7	0.81
46.	RM201	9	(CT)17	4	0.65
47.	RM333	10	(TAT)19(CTT)19	8	0.83
48.	RM269	10	(GA)17	5	0.64
49.	RM474	10	(AT)13	5	0.78
50.	RM596	10	(GAC)10	2	0.47
51.	RM20B	11	(ATT)n	3	N/A
52.	RM332	11	(CTT)5-12-(CTT)14	5	0.65
53.	RM479	11	(TC)9	2	0.50
54.	RM209	11	(CT)18	7	0.77
55.	RM21	11	(GA)18	9	0.88
56.	RM19	12	(ATC)10	6	N/A
57.	RM117	12	(AG)7	3	0.70
58.	RM463	12	(TTAT)5	3	0.60
59.	RM512	12	(TTTA)5	3	0.57

Note: Chromosome (Chr): represents the position in which the respective marker is mapped on the chromosomes.

No competing interests reported.

AASupplementaryFile.docx

Genetic Diversity of Asian Rice (Oryza sativa L.) Germplasm for Direct Seeding in Response to Soil Salinity Stress at Germination Stage

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Abstract

Figures

Introduction

Results

Discussion

Conclusion

Materials and methods

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

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