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Article

Comparative Genetic Diversity Assessment and Marker–Trait Association Using Two DNA Marker Systems in Rice (Oryza sativa L.)

by
Mohammed I. Al-daej
1,
Adel A. Rezk
1,2,*,
Mohamed M. El-Malky
3,
Tarek A. Shalaby
4,5 and
Mohamed Ismail
6
1
Department of Agricultural Biotechnology, College of Agricultural and Food Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
2
Plant Pathology Research Institute, Agriculture Research Center, Giza 12619, Egypt
3
Rice Research and Training Center, Field Crops Research Institute, Agricultural Research Center, Sakha 33717, Egypt
4
Department of Arid Land Agriculture, College of Agricultural and Food Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
5
Horticulture Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt
6
Genetics Department, Faculty of Agriculture, Menoufia University, Shibin El-Kom 6131567, Egypt
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(2), 329; https://doi.org/10.3390/agronomy13020329
Submission received: 12 December 2022 / Revised: 12 January 2023 / Accepted: 17 January 2023 / Published: 21 January 2023
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Abstract

:
In this paper, the genetic diversities of 12 rice genotypes (Oryza sativa L.), representing Indica, Japonica, and Indica–Japonica varieties, were assessed using twelve ISSR and five SSR markers. In addition, the rice genotypes were evaluated for 11 agro-morphological traits in a two-year trial. Association mapping was performed to detect any association between the DNA markers and the agro-morphological traits. An association analysis was conducted considering the relative kinship among the genotypes and accounting for the population structure using the unified mixed-model approach to avoid possible false-positive associations. Seventy-three alleles were collectively produced by ISSRs and SSRs, with an average of 6.3 and 2.8 alleles per locus, respectively. Both marker systems were informative, and the average polymorphism information content (PIC) was 0.222 and 0.352 for ISSRs and SSRs, respectively. The average expected heterozygosity (Hexp) was 0.264 for ISSRs compared to 0.457 for SSRs. After using the false discovery rate (FDR) method, the association analysis revealed a total of 12 significant marker–trait associations with six agro-morphological traits, including the no. of unfiled grains panicle−1, panicle length, panicle weight, the no. of panicles plant−1, the no. of tillers plant−1, and 1000-grain weight. ISSRs showed seven significant associations with five markers, while SSRs showed three significant associations with three markers. The phenotypic variance (R2) explained by each marker ranged between 29.2% for the ISSR marker HB11 (associated with 1000-grain weight) and 49.3% for the ISSR marker HB8 (associated with the no. of tillers plant−1). The identified marker–trait associations reported herein may improve the expected gain of future molecular-based rice-breeding programs, particularly those designed for improving grain-related or harvest-related traits.

1. Introduction

Rice (Oryza sativa L.) is a strategic crop worldwide; its consumption is steadily increasing due to its low cost and high nutrient levels [1]. There is a continuous increase in population growth across the globe, reaching 12% between 2010 and 2020, and this requires an increase in productivity of at least 13%, as well as an increase of 500 million tons of rice, to meet the demand for rice for the growing population (USDA 2022). The development of new rice varieties requires the assessment of parental genetic diversity to understand the genetic background of each variety, which helps in designing breeding programs and, subsequently, assists in achieving appropriate genetic structures in new varieties [2,3]. Additionally, the knowledge of different genetic variation analyses helps in monitoring germplasm progeny and in predicting possible genetic potentials. The information obtained through such monitoring/predicting procedures significantly enhances breeding programs, which ensure the efficient utilization of germplasm resources and an effective breeding system not only for rice but also for closely related crop species [4]. Quantitative traits are one of the most important tools used in assessing the phenotypic differences and similarities among cultivars in grain crops [5,6,7,8,9], and they significantly increase cross-performance in breeding programs.
Traditional breeding methodologies that are based on observed variations in traditional varieties and/or those that are based on controlled crosses rely primarily on the differences and similarities among genotypes in breeding program design; however, the extended periods of time required to attain the desired gain might hinder their use. Molecular breeding, in contrast, which incorporates the use of DNA markers in breeding programs, could accelerate the genotype screening process and shorten the time required to achieve the projected gain.
Different DNA markers have been used for many objectives in breeding programs, including for the identification of biotic- and abiotic-stress-related genes (e.g., disease resistance, salinity, drought, heat, and cold tolerance). Moreover, DNA markers are used extensively in genetic diversity assessments between varieties. There is a wide array of DNA markers with different usage objectives; however, their accuracy and economic strains—among other factors—play a key role in DNA marker choice. Inter-simple sequence repeats (ISSRs) and simple sequence repeats (SSRs) are among the most utilized DNA markers in genetic diversity evaluations [10,11,12,13]. The high polymorphisms of ISSRs and SSRs allow them to be used in phylogeny, genome mapping, evolutionary biology, genetic diversity, and gene tagging. ISSRs and SSRs have previously been shown to accurately estimate the extent of genetic diversity in rice [14,15,16]. One of the most important aspects of DNA markers is their potential associations with a specific trait that facilitates breeding programs. The association between a genetic marker and a phylotypic trait is usually determined by using the association mapping/genetics approach. Association genetics has recently become a popular tool in deciphering the genetic basis of complex traits in plants. It is preferable to traditional quantitative trait locus mapping in many aspects; for instance, it provides a relatively detailed genetic map within a short time. In addition, association genetics studies consider the population structure, a key factor in introducing false positives. The idea behind association genetics is that alleles are expected to be randomly associated within the genome; that is, alleles are in a linkage disequilibrium. The existence of successful association studies in many crop species demonstrates that it is a powerful tool in the fine mapping of complex traits and its usefulness in breeding programs for strategic crops. For instance, in previous studies on rice, a single SSR marker was found to be simultaneously associated with plant height, panicle length, and the number of grains per panicle [17,18]. Moreover, using SSRs, a total of 115 significant marker–trait associations were determined in rice landraces [19]. Although SSRs are the main markers used in association genetic analyses of grain crops compared to other markers, ISSRs are also used intermittently. In a previous study, an association analysis employing ISSRs was used in different plant species, such as pineapple, and the association analysis was able to determine the putative association between fiber quality and ISSR markers [20].
In this study, two DNA marker systems, ISSRs and SSRs, were used simultaneously to address the magnitude of genetic diversity in a group of rice genotypes and to detect the possible associations between the studied markers and agro-morphological traits using an association mapping approach, which will ultimately assist in future rice-breeding programs.

2. Materials and Methods

2.1. Agro-Morphological Characteristics of Plant Materials and Their Evaluations in the Field

A total of 12 rice genotypes were selected from different types of rice (Indica, Japonica, and Indica–Japonica), as shown in Table 1. The Japonica type included Giza171, Giza172, Sakha104, Sakha103, and Giza159. The Indica type included Egyptian Yasmin, Giza181, and Giza182. The Indica–Japonica type included Giza179, Giza178, Hasswi-1, and Hasswi-2, with the last two varieties being from the Kingdom of Saudi Arabia (KSA). The studied varieties have distinct backgrounds; Giza 171, Giza 172, and Giza 159 are old Egyptian varieties, which also have a longer growing season, and Sakha104, Giza178, Sakha103, Giza181, Giza182, Egyptian Yasmin, and Giza179 are new Egyptian varieties. They represent diverse types of rice that belong to long-grain (Indica) and short-grain (Japonica) varieties. In addition, they are non-aromatic, except for Egyptian Yasmin. The selected varieties have two different origins; the first ten varieties in Table 1 originate from Egypt, while Hasswi-1 and Hasswi-2 originate from KSA. The selected varieties are still grown commercially in large areas, making them economically important to the Egyptian economy and local farmers. The Saudi rice genotypes are known for their drought tolerance, making them candidate parents for drought-tolerant attributes in breeding programs.

2.2. Field Trials

All rice genotypes were planted in a nursery during the summers of 2019 and 2020. When the seedlings were 25 days old, they were transplanted into a nursery with a Randomized Complete Block Design (RCBD) with three replications. Each plot contained five rows, and each row contained 25 plants, with a planting distance of 20 × 20 cm in a 5 m2 plot. Eleven traits, namely, duration (day), plant height (cm), number of tillers plant−1, number of panicles plant−1, panicle length (cm), panicle weight, 1000-grain weight, number of filled grains panicle−1, number of unfiled grains panicle−1, grain yield (ton ha−1), and leaf blast reaction, were measured. The measurement followed the Standard Evaluation System for Rice [21]. Grain yield (ton ha−1 ) was measured after the harvesting stage, with moisture at 14% in paddy rice in five rows. The grain yield (ton ha−1) was calculated as follows: 5 m2 multiplied by 200 m2. The blast reaction was recorded forty days after the sowing date. The typical blast lesions were scored according to the Standard Evaluation System, using a 0–9 scale as follows: 1–2 = resistant (R), 3 = moderately resistant (MR), 4–6 = susceptible (S), and 7–9 = highly susceptible (HS) [21].

2.3. Statistical Analysis of Agricultural Morphological Features

The agro-morphological traits were statistically combined over the two years according to the method described by Le Clerg et al. [22] and analyzed using an analysis of variance (ANOVA) computed by using GenStat version 15. The significant differences in means were examined at p < 0.05 using the least significant difference (LSD) test.

2.4. Agro-Morphological Trait Principal Component Analysis

The ClustVis website [23] was used to carry out a principal component analysis (PCA) on the 11 agro-morphological traits in the 12 rice genotypes in order to evaluate any possible grouping patterns. Many variables, for example, the 11 agro-morphological traits, produce redundancy in the dataset when traits are highly correlated. The PCA can summarize data and produce new orthogonal parameters named principal components [24]. In this analysis, multivariate data are transformed into main components, which describe how much variance is explained by each component using linear transformations.

2.5. Extraction of Genomic DNA and Genotyping with Genetic Markers

According to the method described by McCouch et al., frozen leaf tissue (200 mg) from each genotype was used to isolate genomic DNA. DNA was checked for quality and quantity on a 2.0% agarose gel [25]. The final DNA concentration was set at 25 ng/L for each sample. A total of 12 ISSR primers were used to genotype the 12 rice genotypes (Table 2). Polymerase chain reaction (PCR) was performed in a 25 µL reaction using the following components: 2.5 µL dNTPs (2.5 mM), 2.5 µL of MgCl2 (2.5 mM), 2.5 µL of buffer 10×, 3.0 µL of primer (10 pmol), 3.0 µL of template DNA (25 ng/L), and 1 µL of Taq polymerase (1 U/µL) (i-TaqTM). Thermocycler Bio-Rad C-1000 was used for the PCR reactions according to the following protocol: 4 min of initial denaturation at 94 °C; 45 cycles of 1 min at 94 °C, 1 min at 57 °C, and 2 min at 72 °C; and 5 min of a final extension at 72 °C. A TBE buffer and 1.5% agarose gel electrophoresis were used to separate the amplified products. Following ethidium bromide staining, band patterns were visualized on an ultraviolet transilluminator. A SizerTM-100 DNA marker (iNtRON Biotechnology, Seongnam, Kyonggi-do South Korea) [26] was used to gauge the size of the fragments. For each marker, the genotypes were examined to determine whether they contained a certain band (allele). The rice genotypes were also identified using nine SSR markers (Table 3). PCR was used to amplify the genomic DNA in 25 µL according to the following conditions: PCR-grade water and 0.1 U/µL Taq polymerase, 500 M dNTP, 100 mM KCl, 3 mM MgCl2, 10 pmol of each primer, and 1 µL of DNA (50 ng). Thermocycler Bio-Rad C-1000 was used to perform the amplifications under the following conditions: (1) 5 min of initial denaturation at 94 °C; (2) 30 s of initial denaturation at 94 °C; (3) 1 min of annealing the primers at 55 or 58 °C depending on the marker; and after 40 cycles of steps 2, 3, and 4, a final extension at 72 °C for 10 min. A 1.5% agarose gel stained with ethidium bromide was used to separate the fragments. Images were taken, and the sizes of the alleles were measured using the SizerTM-100 plus DNA Marker (iNtRON Biotechnology) [26]. The selected SSRs have previously been reported to be linked to different abiotic/abiotic stresses, nutritional contents, and physiological traits using the quantitative trait locus (QTL) approach and/or segregation analyses [27,28,29].

2.6. Molecular Characterization

Out of the nine SSR primers used for genotyping, four primers were monomorphic and, hence, excluded from the analysis (RM29, RM1216, RM 225, and RM247). For each marker system, that is, the ISSR and SSR marker systems, standard diversity indices, including the number of alleles, expected heterozygosity (Hexp), polymorphism information content (PIC), discriminating power (D), and resolving power (R), were calculated using the iMEC program [30]. Combining the ISSR and SSR data, a dendrogram of the 12 rice genotypes was generated based on Nei’s genetic distance [31], using the unweighted pair-group method with arithmetic averaging (UPGMA) as implemented using the NTSYS-pc version 2.1 software [32].

2.7. Association between Agro-Morphological Characteristics and Molecular Markers

An association genetic analysis was used to look for associations between the agro-morphological features and the DNA markers. First, to assess the population structure and to determine the number of genetically homogeneous groups, the model-based Bayesian clustering approach was used with the genotypic data of 17 markers (ISSRs and SSRs), along with information on the origin of each genotype, as implemented in STRUCTURE 2.3 [33]. The number of presumed clusters (K) varied between one and five. The program was run with a burn-in period of 105 iterations, followed by 105 data collection iterations, with five replicates. The number of clusters was determined by using the ∆K statistic [34].
Second, the pair-wise kinship coefficients among the genotypes were inferred based on the 17 markers (ISSRs and SSRs) according to Hardy 2003 [35] using SpaGeDi software [36]. The inferences of the population structure and kinship would be more accurate if several marker systems were combined [37]. To determine the marker–trait associations, the unified mixed-model approach [38] was used according to the following equation: y = Sα + Qv + Zu + e, where y, α, v, u, and e are the vectors of phenotypic observations, the marker effect (fixed), the population effect (fixed), the kinship effect (random), and the residual effect, respectively, and S, Q, and Z are the incidence matrices of 1 s and 0 s relating y to α, v, and u, respectively. False-positive results due to the population structure or familial links among the genotypes would be eliminated if the association model included both factors [39]. The analysis was performed using TASSEL version 5.0 (released in October 2018), and positive relationships were detected at the p < 0.05 level. To avoid any additional false-positive associations, all results were further corrected for multiple testing using the false discovery rate (FDR) method for multiple comparisons at p < 0.05. The FDR thresholds were calculated using Microsoft Excel.

3. Results

3.1. Analysis of Variance of Agro-Morphological Traits

The studied rice genotypes showed a wide range of agro-morphological traits performance (Table 4). The analysis of variance indicated highly significant differences between the varieties for all studied traits, except for panicle weight (Table 5). The independent ANOVA conducted for each year was not significant, so the average of the two years was used instead.

3.2. Principal Component Analysis

The PCA indicated that the first three principal components (PCs) explained approximately 90% of the phenotypic variance among the genotypes. The relative discriminating power of the PCA was high in PC1 (65.8%), followed by PC2 (13.7%) and PC3 (10.5%). The variations in PC1, PC2, and PC3 were dominated by duration and plant height; however, the no. of unfiled grains contributed to the variation in PC1 and PC2 only (vector loading with an asterisk, Table 6). Moreover, the no. of filled grain contributed to the variations in both PC1 and PC3. Only the first two PCs were considered for the generation of a scatter plot of the rice genotypes along the PCs (Figure 1). The distribution of the 12 genotypes along PC1 and PC2 indicated the presence of distinct grouping. For instance, the Saudi-origin genotypes (Hasswi-1 and Hasswi-2) were grouped away from the Egyptian-origin genotypes. Moreover, the old Egyptian varieties were grouped away from the new Egyptian varieties.

3.3. Analysis of Genetic Variations and the Efficiency of Markers

Across all 12 rice genotypes, a total of 63 alleles and 14 alleles, with an average of 6.3 and 2.8 alleles per locus, were produced by ISSRs and SSRs, respectively (Table 7). The expected heterozygosity (Hexp) ranged from 0.105 to 0.482 and from 0.395 to 0.5, with an average of 0.264 and 0.457 for ISSRs and SSRs, respectively. Moreover, the polymorphism information content (PIC) ranged from 0.099 to 0.366 and from 0.317 to 0.375, with an average of 0.222 and 0.352 for ISSRs and SSRs, respectively. The discriminating power (D) detected for ISSRs ranged between 0.109 and 0.649, with an average of 0.312, while that of SSRs ranged between 0.761 and 0.931, with an average of 0.849. The estimated resolving power (R) ranged from 0.500 to 4.00 and from 0.667 to 2.00, with averages of 1.733 and 1.433 for ISSRs and SSRs, respectively. Samples of the ISSR and SSR marker banding patterns are presented in Figure 2. A dendrogram based on Nei’s (1972) genetic distance of the 12 rice genotypes is presented in Supplementary Figure S1.

3.4. Association between Molecular Markers and Agro-Morphological Traits

The studied genotypes showed a low population structure as inferred from STRUCTURE software using the ISSR and SSR data. Using the ∆K statistic approach, only two genetic homogenous groups representing the 12 genotypes were defined, with equal assignment membership coefficients in each group (Supplementary Figure S2). Moreover, the studied genotypes showed a relatively low kinship (< 0.55), as determined using the method described by Hardy 2003 [35], and approximately 60% of the pairwise kinship estimates were < 0.05, while 90% of the relative kinship estimates were < 0.25 (Supplementary Figure S3).
Following the inclusion of the population structure (Q) and kinship (Z) matrices in the association model, a total of 12 significant marker–trait associations (p < 0.05) for six agro-morphological features were discovered after FDR correction. The traits included the number of panicles plant−1, the number of tillers on plant−1, and the weight of 1000 grains (gm) for the number of panicles plant−1 (Table 8). The number of unfiled grains in panicle−1, panicle length (cm), the number of tillers plant−1, the number of panicles plant−1, panicle weight (gm), the number of unfiled grains panicle−1, and 1000-grain weight (gm) were all significantly associated with ISSRs. A total of three SSRs were significantly associated with three features: panicle length (cm), tiller plant−1, and panicle weight (gm). Using ISSRs, HB8 showed a significant association with four agronomic traits. The phenotypic variance (R2) explained by each marker was moderate and ranged between 25.2% for the SSR marker RM 217 (associated with 1000-grain weight) and 49.3% for the ISSR marker HB8 (associated with the no. of tillers plant−1).

4. Discussion

4.1. Principal Component Analysis

A principal component analysis (PCA) was performed to determine the possible effects of each agro-morphological trait on the variations in rice genotypes. PCA is a well-known multivariate statistical approach used to identify the minimum number of components, which explain the maximum variability in a dataset through dimension reduction [40]. Assessing such effects is crucial in rice-breeding programs in order to determine the phenotypic diversity among breeding materials. In such an analysis, genotypes are ranked based on individual PC scores [41]. The application of PCA is widely used to determine the morphological diversity within and between rice germplasms for different objectives [42,43]. The results show that the first two PCs were enough to explain the majority (79.5%) of the total variation among the rice genotypes. The first two PCs are considered the most important in revealing the variations among genotypes, and the characteristics associated with each one are the most important for differentiating among genotypes [43]. Similar findings were reported by Soe et al. [44], where maturity, plant height, and grain yield, among other quantitative traits, accounted for a substantial amount of variability among rice genotypes. Moreover, the use of PCA [45] in a previous study was able to identify two primary PCs that accounted for 80% of the total variance of 13 agronomic traits, where maturity and seed length showed the highest discriminatory power among the studied traits. In the current study, duration, plant height, and the no. of filled grains/panicle showed the highest loads on PC1 and PC2. The current study illustrates that duration, plant height, the no. of filled grains panicle−1, and the no. of unfiled grains are the traits that contributed the most to the variation among the genotypes in the first two PCs. Thus, the contribution of these traits to the total variance among genotypes makes them ideal candidates when selecting parents in breeding programs.
The PCA scatterplot effectively separated the genotypes not only based on their origin but also based on some of their agronomic traits. For instance, Hasswi-1 and Hasswi-2 (Saudi varieties) showed the maximum plant height, the lowest no. of filled grains panicle−1, and the highest no. of unfiled grains panicle−1 as compared to the other genotypes. Our results are partially in line with previous reports on rice landraces, where PCA revealed that plant height contributed substantially to the variation among genotypes [42]. However, Suvi et al. [46] indicated that PCA was able to identify different traits that contributed to discrimination among Tanzanian rice genotypes, including the number of tillers, the number of panicles per plant, panicle length, and grain yield [46]. Disparity among different studies is expected and probably associated with the genetic background of the genotypes of interest.

4.2. Genetic Variation and Marker Efficiency

To better understand the genetic diversity of the studied rice genotypes, two different marker systems were used: ISSRs and SSRs. The two marker systems have previously been used to assess the genetic variability of rice for different purposes [14,47,48]. Different DNA markers are distributed distinctively across the genome and, therefore, are expected to exhibit different genetic diversity patterns [49], providing more insight into rice germplasm diversity. The total number of alleles detected using ISSRs was higher than that detected using SSRs; however, the expected heterozygosity (Hexp) detected using SSRs was two-fold higher than that detected using ISSRs. The studied ISSR and SSR markers were reasonably informative (0.50 > PIC > 0.25), while SSRs exhibited a PIC that was higher than that of ISSRs. Similar results have previously been reported in basmati rice using SSRs (an average PIC = 0.480 in [50] and an average PIC = 0.372 in [51]). The SSRs showed a higher discrimination power (D) than ISSRs. The discrimination power represents the efficiency of a marker to differentiate between closely related genotypes; moreover, discrimination power has the potential to aid in efficient assessments of different types of markers [52]. The average allele frequency across the ISSR loci was 0.81 compared to 0.35 obtained for the SSR loci. However, ISSRs showed a slightly higher resolving power (R) than SSRs, confirming their ability to detect levels of variation between individuals [53]. The heterozygosity, discriminatory, and resolution power differences between the two marker systems, ISSRs and SSRs, might be explained by their abundance across the genome. SSR markers target the functional regions of the open-reading frame (ORF), while ISSR markers are spread out across the entire genome. Since different marker systems target different regions, multiple marker systems could provide comprehensive information on the rice genome. Moreover, ISSRs and SSRs have different modes of inheritance; however, the combined use of selected SSR and ISSR markers could provide a detailed insight into the similarities/disparities among genotypes with similar origins [54]. The use of ISSRs and SSRs collectively in assessing genetic diversity is well-documented in different crops, including rice [14] and barley [55]. Although the used genotypes did not represent a comprehensive rice collection, they provided insight into the local genotypes’ genetic diversities using different DNA markers.

4.3. Association of Agro-Morphological Features with Molecular Markers

Association analyses play an important role in identifying the associations between a genetic marker and an individual’s phenotype. Many factors affect the accuracy of any association genetic analysis and need to be accounted for. For instance, false-positive associations may arise due to significant populations and/or familial structures. In the current study, the 12 rice genotypes showed a weak-to-no-population structure, and the maximum number of homogeneous groups was two (K = 2). Additionally, the relative kinship among the genotypes was relatively low, suggesting that the familial structure was also insignificant and, hence, did not contribute to false-positive associations [56]. The association analysis was therefore expected to reveal the likely donor genotypes/populations required for crop improvement in breeding operations. In the current study, more associations were detected between ISSRs and the agro-morphological traits than between SSRs and the agro-morphological traits. Most of the previously reported association studies reported in rice used SSR markers with different agro-morphological traits and disease resistances (e.g., blast resistance) [48,57]. The observed pattern of higher marker–trait associations detected by ISSR is, in part, related to the higher allele frequency, which, in turn, affects the statistical power of genetic association studies. In the current study, the average allele frequency across the ISSR loci was 0.81 compared to 0.35 obtained across all the SSR loci; this pattern may affect the detection power of significant associations, which is expected to be higher at an allele frequency exceeding 0.50. Similar findings have also been previously reported in barley, where a total of 47 marker–trait associations were detected with ISSR compared to 37 detected with SSR [55]. Moreover, Shirmohammadli et al. [58] detected positive associations between ISSRs and grain characteristics in brown and white rice in normal and drought environments.
Most of the marker–trait associations identified herein represent associations between markers and panicle and grain properties. For instance, RM209 showed a significant association with panicle length, which has previously been reported to be associated with flag leaf angle and length and plant compactness [2], traits that are positively correlated with panicle length. The same marker has also been previously reported to be associated with heat tolerance [59]. The significant association between RM211 and two different traits related to panicle and tiller properties highlights its importance in rice-breeding programs, especially as it has previously been reported to be associated with nutritional content [60]. Moreover, RM217, which was associated with panicle length, has previously been reported to be associated with days to 50% flowering and blast resistance [27]. An independent study conducted by Oladosu et al. [61] reported significant positive correlations between panicle length, 1000-grain weight, and days to flowering, which possibly explains—in part—the association between RM217 and panicle length. This genetic association provides a platform for candidate marker–trait association combinations, which is considered the most significant goal of any association study.
Typically, association studies result in several positive associations; however, only a handful of these associations are significant at a nominal significance level, varying across studies. So, it is essential to replicate these findings and to validate the estimates of the allelic effects [62].
However, replicating phenotype–genotype associations is not always possible for many reasons. For instance, it is difficult to replicate the environment in which the traits were initially measured, and non-representative sample sizes and non-comprehensive phenotyping also create challenges.
In previous studies, a total of 14 and 12 rice genotypes were assessed using ten and four SSRs to identify the possible genetic associations between SSRs and seed nutritional content and genetic diversity in rice [63,64].
Although the current study identified a few associations in a limited number of genotypes, it provides the first step in marker–trait association in local Egyptian rice germplasms.
The future of similar research requires more representations of local genotype collections and agro-morphological assessments across multiple environments.

5. Conclusions

Genetic diversity is a key factor in any rice-breeding program. The twelve rice genotypes showed significant differences among their agro-morphological traits, an indication of their possible inclusion in successful breeding programs. Further, the PCA successfully identified traits that contributed significantly to the variations among the rice genotypes. ISSR and SSR markers were used to assess the genetic diversity in the 12 rice genotypes, which had different genetic backgrounds. Although ISSRs showed a greater number of alleles than SSRs, SSRs outperformed ISSRs in discrimination among the genotypes. Most successful breeding programs rely on the associations between a genetic marker and a trait of interest; hence, assessing significant marker–trait associations is crucial. In the current study, we used the association mapping approach to identify possible marker–trait associations between ISSRs and SSRs and rice agro-morphological traits. Association mapping has become a powerful tool in mining genes within specific donor populations or elite germplasms compared to traditional linkage mapping. In the current study, significant marker–trait associations were identified using two different marker systems that could be further used in breeding programs to improve the yield of rice, since they are mainly associated with yield component traits.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy13020329/s1, Figure S1: UPGMA dendrogram based on Nei’s (1972) genetic distance between 12 rice genotypes generated from SSR and ISSR data; Figure S2: STRUCTURE analysis of 12 rice genotypes using ISSR and SSR data. (a) Number of groups best supported by ΔK approach (i.e., K = 2). STRUCTURE output for K = 2, with two different colors representing two different groups. Each bar represents an individual genotype, and the different colors represent the estimated membership coefficients of every genotype in each identified group (Q matrix); Figure S3: Distribution of pairwise relative kinship across 12 rice genotypes using ISSR and SSR data. Each bar represents the percentage of genotypes in different kinship range classes.

Author Contributions

Conceptualization and visualization, M.M.E.-M. and T.A.S.; resources and methodology, M.I.A.-d., M.M.E.-M. and A.A.R.; software, T.A.S. and M.I.; validation, M.I., M.M.E.-M. and T.A.S.; investigation, T.A.S. and M.I.A.-d.; data curation, M.I., M.M.E.-M. and A.A.R.; writing—original draft and preparation, M.M.E.-M., M.I. and M.I.A.-d.; writing review and editing, T.A.S. and A.A.R.; funding acquisition, M.I.A.-d. and A.A.R.. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Deputyship for Research & Innovation, Ministry of Education, Saudi Arabia, grant number (INST102).

Data Availability Statement

Not applicable.

Acknowledgments

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education, Saudi Arabia, for funding this research work through the project number (INST102).

Conflicts of Interest

The authors declare no conflict of interest.

References

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Agronomy 13 00329 g001 550
Figure 1. Biplot of principal component analysis (PCA), based on the first two principal components (PC1 and PC2), showing the relationship among the 12 rice genotypes using 11 agro-morphological traits.
Figure 1. Biplot of principal component analysis (PCA), based on the first two principal components (PC1 and PC2), showing the relationship among the 12 rice genotypes using 11 agro-morphological traits.
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Figure 2. Samples of ISSR (left panel) and SSR (left panel) banding patterns obtained from 12 rice genotypes with primers HB15 (a), 49A (b), 44B (c), RM224 (d), RM211 (e), RM3873 (f). M is 100 bp DNA marker, and numbers 1 to 12 are rice genotypes.
Figure 2. Samples of ISSR (left panel) and SSR (left panel) banding patterns obtained from 12 rice genotypes with primers HB15 (a), 49A (b), 44B (c), RM224 (d), RM211 (e), RM3873 (f). M is 100 bp DNA marker, and numbers 1 to 12 are rice genotypes.
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Table
Table 1. List of rice genotypes used in phenotyping and DNA fingerprinting.
Table 1. List of rice genotypes used in phenotyping and DNA fingerprinting.
No.GenotypesParentageOriginTypeGrain Shape
1Giza171Nahda (Pure line selection)/Calady40EgyptianJaponicaShort
2Egyptian Yasmin(Jasmin85) IR841-67EgyptianIndicaLong
3Giza172Nahda/KinmazaEgyptianJaponicaShort
4Giza179Gz1368/IRAT112EgyptianI.-J1.Short
5Giza181IR28/IR22EgyptianIndicaLong
6Giza182Giza181/IR39422-163-247-2-2-3EgyptianIndicaLong
7Sakha104Gz4096-8-1/Gz4100-9-1EgyptianJaponicaShort
8Sakha103Giza177/Suwwon349EgyptianJaponicaShort
9Giza178Giza175/Milyange49EgyptianI.-J.Short
10Giza159Giza14/AgamiM1EgyptianJaponicaShort
11Hasswi-1ExoticKingdom of Saudi ArabiaI.-J.Short
12Hasswi-2IRI112/Hasswi-1Kingdom of Saudi ArabiaI.-J.Short
1 I.-J.= Indica–Japonica.
Table
Table 2. List of ISSR primers used in rice genotyping.
Table 2. List of ISSR primers used in rice genotyping.
No.NameSequence (5′-3′)
1A44CTC TCT CTC TCT CTC TAG
2B44CTC TCT CTC TCT CTC TGC
3A49CAC ACA CAC ACA AG
4B49CAC ACA CAC ACA GG
5B89CACACACACACAGT
6A89CACACACACACACA
7HB8GAGAGAGAGAGAGG
8HB15GTGGTGGTGGC
9HB9GTGTGTGTGTGTGG
10HB12CAC CAC CAC GC
11HB11GTG TGT GTG TGT TGT CC
12HB13GAG GAG GAG GC
Table
Table 3. List of SSR primers used in rice genotyping.
Table 3. List of SSR primers used in rice genotyping.
No.PrimerPrimer Sequence (5′-3′)Repeat MotifAnnealing
Temperature (°C)		Ch. No.Expected
Product Size (bp)
1FRM209ATATGAGTTGCTGTCGTGCG(CT)185511134–150
1RRM209CAACTTGCATCCTCCCCTCC
2FRM217ATCGCAGCAATGCCTCGT(CT)20556133
2RRM217GGGTGTGAACAAAGACAC
3FRM224ATCGATCGATCTTCACGAGG(AAG)8(AG)125511130–157
3RRM224TGCTATAAAAGGCATTCGGG
4FRM211CCGATCTCATCAACCAACTG(TC)3A(TC)18552159–189
4RRM211CTTCACGAGGATCTCAAAGG
5FRM3873GCTAGCTAGGACCGACATG(GA)50581215
5RRM3873CCTCCTCCTTATCCTCC
6FRM29CAGGGACCCACCTGTCATAC(GA)7-18(GA)5(AG)4552250
6RRM29AACGTTGGTCATATCGGTGG
7FRM1216TTCCCCAATGGAACAGTGAC(AG)1450173–80
7RRM1216GGGTCTACCACCCGATCTC
8FRM225TGCCCATATGGTCTGGATG(CT)18556126–164
8RRM225GAAAGTGGATCAGGAAGGC
9FRM247TAGTGCCGATCGATGTAACG(CT)165512180
9RRM247CATATGGTTTTGACAAAGCG
Ch. No. = chromosome number.
Table
Table 4. Mean performances of agronomic and associated traits of 12 rice genotypes in 2019 and 2020.
Table 4. Mean performances of agronomic and associated traits of 12 rice genotypes in 2019 and 2020.
GenotypeDuration (Day)Plant Height (cm)No. of Tillers Plant−1Panicle Length (cm)No. of Panicles
Plant−1		Panicle Weight (g)
Giza171152.2135.6727.824.425.172.61
Egyptian Yasmin140.70132.3227.7323.3624.533.62
Giza172152.78132.324.6323.2123.13.62
Giza179124.1298.1525.6724.3625.053.45
Giza181126.17102.0721.8826.4821.173.39
Giza182126.0896.3024.9522.1622.483.62
Sakha104134.87105.9523.2724.5821.003.35
Sakha103127.23102.5722.5022.4120.523.21
Giza178133.9098.5826.6023.4123.633.49
Giza159148.30142.1525.0325.4618.432.80
Hasswi-1154.12143.4720.8720.5016.171.92
Hasswi-2154.52144.1320.5721.415.802.02
LSD0.05 *3.823.314.280.711.470.34
Genotype1000-grain weight (g)No. of filled grains panicle−1No. of unfiled Grains panicle−1Grain yield (t ha−1)Blast reaction
Giza17125.49137.6719.336.387.16
Egyptian Yasmin27.37145.0211.77.661.4
Giza17226.66138.420.237.556.00
Giza17925.05168.439.3810.412.1
Giza18126.45156.387.3810.112.03
Giza18227.90170.67.3810.482.03
Sakha10427.71150.1213.8810.455.96
Sakha10325.70146.2310.7710.022.03
Giza17822.93146.612.5210.262.06
Giza15924.43114.5240.455.606.00
Hasswi-123.58112.0843.823.507.1
Hasswi-222.70106.3037.733.767.00
LSD0.05 *1.325.679.680.410.25
* LSD0.05 = least significant differences of means (p < 0.05).
Table
Table 5. Analysis of variance for agro-morphological traits in 2019 and 2020.
Table 5. Analysis of variance for agro-morphological traits in 2019 and 2020.
Mean Sum of Squares (MSS)
S.O.V.dfDuration (Day)Plant Height (cm)No. of Tillers Plant−1Panicle Length (cm)No. of Panicles Plant−1Panicle Weight (g)
Replication22.170.7543.741.0540.980.026
Year14.61 ns1.93 ns1.46 ns0.34 ns0.48 ns0.044 ns
Genotype11886.47 **2448.82 **37.16 **17.43 **62.68 **2.26
Error445.4674.2537.2870.17070.8320.04388
Mean sum of squares (MSS)
S.O.V.df1000-grain
weight (g)		No. of filled
Grains panicle−1		No. of unfiled
Grainspanicle−1		Grain yield
(t ha−1)		Blast
reaction
Replication23.087118.1448.660.015560.005
Year10.0025 ns1.89 ns41.05 ns0.0155 ns0.001 ns
Genotype1112.14 **2608.84 **1078.14 **49.02 **35.73 **
Error440.71212.8333.990.0680.023
S.O.V. = source of variation, df = degree of freedom, ns = not significant, ** highly significant at 0.01 level.
Table
Table 6. First three principal components and their loadings of 11 agro-morphological traits of the 12 rice genotypes.
Table 6. First three principal components and their loadings of 11 agro-morphological traits of the 12 rice genotypes.
PC1PC2PC3
Duration (day)0.320*0.4300.590
Plant height (cm)0.5200.2300.310
No. of tillers plant−1−0.1100.1200.000
Panicle length (cm)0.000−0.0800.010
No. of panicles plant−1−0.1000.0500.050
Panicle weight (g)−0.0100.020−0.010
1000-grain weight (g)−0.0200.0200.110
No. of filled grains panicle−1−0.600−0.2800.710
No. of unfiled grains0.490−0.8100.160
Grain yield t ha−1−0.0700.0200.010
Blast reaction0.0600.0100.090
* Numbers in bold, vector loading ≥ 0.3, represent the importance of the trait in contributing to variations among genotypes.
Table
Table 7. Polymorphism of ISSRs and SSRs in 12 rice genotypes.
Table 7. Polymorphism of ISSRs and SSRs in 12 rice genotypes.
MarkerLocusNo. of AllelesHexpPICDR
ISSR44A40.1170.1100.1220.500
49A60.1290.1210.1350.833
98A70.2280.2020.2461.167
49B70.4820.3660.6494.000
44B60.1050.0990.1090.667
HB880.2910.2490.3242.833
HB990.4660.3580.6063.000
HB1160.3890.3130.4611.833
HB1250.1800.1640.1921.000
HB1550.2550.2220.2801.500
Total63
Mean6.30.2640.2200.3121.733
SSRRM20920.5000.3750.7610.667
RM21720.5000.3750.7611.333
RM22430.4440.3460.8951.333
RM21140.3950.3170.9311.833
RM387330.4440.3460.8952.000
Total14
Mean2.80.4570.3520.8491.433
Hexp = expected heterozygosity; PIC = polymorphism information content; D = discriminating power; R = resolving power.
Table
Table 8. Association between ISSR and SSR markers and agro-morphological traits in 12 rice genotypes showing the marker, significance level (p), and its attributable percent of phenotypic variance (R2). Listed associations were considered significant at p ≤ 0.05 and were verified by using the false discovery rate method.
Table 8. Association between ISSR and SSR markers and agro-morphological traits in 12 rice genotypes showing the marker, significance level (p), and its attributable percent of phenotypic variance (R2). Listed associations were considered significant at p ≤ 0.05 and were verified by using the false discovery rate method.
Marker SystemTraitp-ValueR2
ISSR44ANo. of unfiled grainspanicle−10.0290.452
49APanicle length (cm)0.048 ns0.393
98APanicle length (cm)0.0430.406
HB8No. of tillers plant−10.0200.493
No. of panicles plant−10.0270.433
Panicle weight(gm)0.0360.433
No. of unfiled grains panicle−10.0290.452
HB11No. of panicles plant−10.0360.403
1000-grain weight (gm)0.0280.292
SSRRM209Panicle length (cm)0.0350.434
RM211No. of tillers plant−10.0380.419
No. of panicles plant−10.046 ns0.375
Panicle weight (gm)0.0350.436
RM217Panicle length (cm)0.0350.434
1000-grain weight (gm)0.047 ns0.252
ns = Rejected significant associations by FDR test.
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Al-daej, M.I.; Rezk, A.A.; El-Malky, M.M.; Shalaby, T.A.; Ismail, M. Comparative Genetic Diversity Assessment and Marker–Trait Association Using Two DNA Marker Systems in Rice (Oryza sativa L.). Agronomy 2023, 13, 329. https://doi.org/10.3390/agronomy13020329

AMA Style

Al-daej MI, Rezk AA, El-Malky MM, Shalaby TA, Ismail M. Comparative Genetic Diversity Assessment and Marker–Trait Association Using Two DNA Marker Systems in Rice (Oryza sativa L.). Agronomy. 2023; 13(2):329. https://doi.org/10.3390/agronomy13020329

Chicago/Turabian Style

Al-daej, Mohammed I., Adel A. Rezk, Mohamed M. El-Malky, Tarek A. Shalaby, and Mohamed Ismail. 2023. "Comparative Genetic Diversity Assessment and Marker–Trait Association Using Two DNA Marker Systems in Rice (Oryza sativa L.)" Agronomy 13, no. 2: 329. https://doi.org/10.3390/agronomy13020329

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