Reviews on Diversity Analysis of Paddy Genotypes by Using Marker

• Journal Listing
• Plants (Basel)
• v.8(11); 2019 Nov
• PMC6918417

Plants (Basel). 2019 November; 8(11): 471.

Assessment of the Genetic Diversity of Rice Germplasms Characterized by Black-Purple and Red Pericarp Color Using Simple Sequence Repeat Markers

Jae-Ryoung Park

¹Division of Plant Biosciences, School of Applied Biosciences, Higher of Agriculture and Life Scientific discipline, Kyungpook National Academy, Daegu 41566, Korea; moc.revan@29dci

Kyung-Min Kim

¹Division of Plant Biosciences, School of Applied Biosciences, Higher of Agronomics and Life Scientific discipline, Kyungpook National University, Daegu 41566, Korea; moc.revan@29dci

Received 2019 Oct 15; Accepted 2019 November i.

Abstract

The assessment of the genetic multifariousness within germplasm collections tin exist accomplished using uncomplicated sequence repeat (SSR) markers and association mapping techniques. The present study was conducted to evaluate the genetic multifariousness of a colored rice germplasm drove containing 376 black-purple rice samples and 172 red pericarp samples, conserved by Dong-A University. There were 600 pairs of SSR primers screened against 11 rice varieties. Sixteen informative primer pairs were selected, having high polymorphism information content (PIC) values, which were then used to assess the genetic multifariousness within the collection. A full of 409 polymorphic amplified fragments were obtained using the sixteen SSR markers. The number of alleles per locus ranged from eleven to 47, with an boilerplate of 25.6. The average Picture value was 0.913, ranging from 0.855 to 0.964. 4 hundred and nine SSR loci were used to summate Jaccard's distance coefficients, using the unweighted pair-grouping method with arithmetics mean cluster analysis. These accessions were separated into several distinctive groups respective to their morphology. The results provided valuable information for the colored rice breeding program and showed the importance of protecting germplasm resource and the molecular markers that can be derived from them.

Keywords: rice (Oryza sativa Fifty.), uncomplicated sequence repeat (SSR), cultivar identification, molecular marker, constitute variety protection

one. Introduction

Rice (Oryza sativa 50.) is one of the most widely cultivated crops in the earth; distributed beyond a diversity of climates and regions, information technology has developed a diverse array of genotypes and phenotypes. Koreans accept historically used rice every bit a major food source. Notwithstanding, in contempo years, both the production and consumption of rice have been in turn down. In 2017, rice production was 3.972 meg tons, downwards 5.3% from 4.197 million tons the previous yr, and the annual rice consumption per capita in 2017 was 70.9%, down 0.iv% from the previous yr. The continuous decrease in the production and consumption of rice in Korea means that the industry is not stable [one]. 1 reason for the annual decline in rice consumption is the increase in the number of people seeking meat and salad alternatives because of a preference for a westernized nutrition [2]. Therefore, to increase the consumption of rice in Korea, processed food products using a rice base accept been adult. Colored rice varieties such equally black-purple rice are peculiarly popular for this purpose because of their unique color and aroma [three,4]. To increase rice consumption and production, high-quality rice containing the functional materials preferred by producers and consumers are too cultivated [v]. Rice varieties are largely classified co-ordinate to the main areas of their cultivation, such as Oryza sativa L. ssp. japonica, Oryza sativa L. ssp. indica, and Oryza sativa Fifty. ssp. javanica [six,7,8]; these can exist utilized to farther breed new, high-quality varieties. Despite the affluence of rice genetic resource globally, the genetic similarity of rice varieties has increased, and the variety of species has continued to decrease, since only a few specific varieties are utilized for convenance [nine,ten]. In the process of producing new varieties, genetic diverseness was greatly reduced in both wild and cultivated rice. At present, various rice convenance institutes in Korea and abroad are trying to brood high-quality rice varieties; among the cultivars adult with the artificial hybridization method rather than the transgenic technique are "Tongilbyeo" and "Naepungbyeo" [xi,12]. These are usually made by crossing several dissimilar varieties of ssp. japonica. It is assumed that the currently developed varieties are genetically very like. Equally the minimum genetic altitude between the varieties is very small considering of the genetic resources used for cultivation, the varieties of rice in Korea are not very diverse, and seed disputes related to genotype use and the protection of breeders' rights oftentimes occur. To overcome these problems, we accept constructed a database for each species using molecular markers and established a genetic linkage to known varieties [xiii]. The International Union for the Protection of New Varieties of Plants (UPOV) besides provides guidelines for database construction of varieties using molecular markers [fourteen]. Assessing the diversity of rice genetic resources involves identifying phenotypes, analyzing biochemistry, and evaluating DNA diversity [15,sixteen]. The evaluation methods involving the identification of phenotypes and biochemical characteristics are non necessarily reliable, every bit they are environmentally influenced, labor intensive, and costly [17,18]. Even so, evaluating genetic diversity based on Dna is the most widely used evaluation method in terms of repeatability, stability, and reliability. Several techniques take been developed to analyze the genetic multifariousness of rice varieties based on DNA, such as the analysis of restriction fragment length polymorphism (RFLP) using brake enzymes, random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), simple sequence repeats (SSR), and single nucleotide polymorphisms (SNP) utilizing the polymerase concatenation reaction (PCR) [nineteen,20]. The RAPD and AFLP techniques are not easily reproducible and the markers are dominant, so at that place are many limitations in analyzing the relationship of genetic resources. However, since SSR markers exhibit loftier polymorphism and show co-ascendant condition, they tin be used to identify heterozygosity. SSR markers, also called Dna microsatellites, are regions of DNA (often forming part of the non-coding regions) where sequences of i to v nucleotides are repeated, and they are uniformly distributed in the genomes of about eukaryotes. The SSR sequences found in plants are frequently made up of AT and GA nucleotide repeats. The relationships of genetic resources take been analyzed using SSR markers in other crops such as melon (Cucumis melo Fifty.), watermelon (Citrullus lanatus), and corn (Zea mays) [21,22,23]. SSR markers representing the unabridged genome take been adult and commercialized for rice with an average distance of 3–iv cM between markers [17,24,25], simply few have been used to identify rice varieties to protect the breeders' rights. Colored rice has many antioxidants. At present, the development of colored rice with loftier antioxidant content is being made continuously. Therefore, in this study, if we synthetic a Dna database of colored rice gene source past using SSR markers, this study was expected to exist helpful for the comeback of colored rice varieties in the time to come. Also, in this study, we developed a core mark set for the identification of rice varieties using SSR markers and synthetic a Deoxyribonucleic acid profile database for rice varieties using the adult markers. To characterize the SSR markers with a high degree of polymorphism in the rice genetic resources, allele characteristics of 600 SSR markers reported in the rice genome database (http://www.gramene.org/) were used. The best markers were selected and analyzed for gene affinity with 548 colored rice genetic resources (376 black-purple rice samples, 172 ruddy pericarp rice samples) nerveless both at home and abroad.

2. Results

ii.1. Microsatellite Analysis

Assessing the genetic diversity of our resources is an essential process for efficiently preserving biodiversity and for characterizing and exploiting them. We used 600 microsatellite primers, reported in the rice genome database (http://www.gramene.org/), to select microsatellite markers suitable for rice germplasm identification and genetic diversity assessment. The microsatellite markers were amplified in 11 different varieties: "Hopum," "Hwayeong," "Dami," "Sindongjin," "Odae," "Chucheong," "Saechucheong," "Junam," "Ilpum," "Ilmi," and "Gopum." Polymorphisms of primers and characteristics of the alleles were examined later removing those that showed the dominant form, had no obvious bands, or had low repeatability when the PCR was performed; sixteen microsatellite markers were deemed suitable (KRF24, RM8085, RM72, KRF16, HvSSR02-86, RM18821, RM12834, RM20754, RM26063, RM333, RM15641, RM26730, RM19731, KRF15, KRF17, RM24946). The 16 microsatellite markers showed co-dominance, high repeatability, and high polymorphism rates and were selected to evaluate the multifariousness of the rice germplasm resources (Figure 1 and Table i). To investigate the genetic polymorphisms of the chromosomal genetic resources, the fluorescent labels FAM, VIC, NED, and PET were attached to the frontwards 5′-ends of the 16 microsatellite markers selected above. PCR and electrophoresis were carried out with the rice germplasm Deoxyribonucleic acid using an automated nucleotide sequencer, and the caste of polymorphism was examined. A total of 409 alleles were amplified with the selected microsatellite markers. The number of alleles constitute in each varietyranged from 11 to 47, with an average of 25.6 alleles per multifariousness. The polymorphism information content (PIC) values ranged from 0.855 to 0.964, and the average was very high at 0.913. Loftier Picture show values betoken that the selected microsatellite markers are efficient at evaluating a large number of genetic resources. In this study, the mean PIC value of the microsatellite markers used to assess the variety of rice genetic resource was 0.913, which was much higher than the Picture values of microsatellite markers used in other studies [26,27,28]. These results indicate that the microsatellite markers selected for use in this study are very suitable and efficient for assessing the genetic variety of many different rice genetic resources. Several studies accept been conducted to evaluate genetic variety using microsatellite markers in rice [17,24,25]. Withal, because of the abundance of rice genetic resources, and as merely a fraction of these resources accept been analyzed [29], it is necessary to conduct more such studies. In addition, since the PIC values of the microsatellite markers used in this study were much higher than those in other studies [30], it appears that the microsatellite markers used for the analysis of the 548 rice genetic resources in this written report could exist used more effectively to establish the genetic relationship of rice genetic resources and to create a database for each species.

Polymorphism of four simple sequence echo (SSR) markers, RM20754, HVSSR2-86, RM15641, and RM19731. The amplified polymerase chain reaction (PCR) products were loaded on the HAD-GT12™ Genetic Analyzer Organisation and analyzed using Biocalculator Data Analysis Software. (a): RM20754, (b): HVSSR2-86, (c): RM15641, (d): RM19731; Lane 1: Hopum, 2: Hwayeong, 3: Dami, 4: Sindongjin, 5: Odae, half-dozen: Chucheong, 7: Saechucheong, 8: Junam, 9: Ilpum, 10: Ilmi, 11: Gopum.

Tabular array i

Repeat motif, no. of alleles, and polymorphism information content (PIC) value of microsatellite markers selected for genetic label of rice cultivars and germplasms.

No.	SSR Primers	Repeat Motif	Chromosome Number	Annealing Temperature (°C)	PCR Product Size (bp)	No. of Alleles	PIC Value
i	KRF24	concluding repeat	7	55	213–238	22	0.902
2	RM8085	ag	ane	55	105–144	xx	0.915
3	RM72	(tat)5c(att)xv	viii	55	151–207	20	0.916
4	KRF16	aac	9	55	160–253	27	0.963
5	HvSSR02-86	aac	2	55	187–328	47	0.970
half-dozen	RM18821	tct	5	55	150–205	17	0.876
7	RM12834	aga	2	55	186–260	25	0.862
8	RM20754	aat	6	55	151–219	22	0.955
ix	RM26063	ct	11	55	225–283	27	0.952
10	RM333	aat	x	55	156–312	35	0.964
eleven	RM15641	aat	3	55	163–247	31	0.894
12	RM26730	ctt	11	55	98–157	11	0.781
thirteen	RM19731	tta	6	55	339–429	31	0.964
14	KRF15	atg	eight	55	126–175	18	0.878
15	KRF17	tatc	2	55	243–405	41	0.956
16	RM24946	atg	10	55	317–369	15	0.855
Total						409.00	14.604
Mean						25.56	0.913

2.2. Hierarchical Cluster Analysis

The unweighted pair-grouping method with arithmetical boilerplate (UPGMA) dendrogram was constructed using the MEGA7 program and Jaccard'due south distance coefficients using the results of the PCR with the 16 microsatellite markers selected from the 548 colored rice genetic resources (Figure S1). The analysis classified the 548 rice genetic resources into 4 major groups (I, Ii, III, and Iv) with seven subgroups (I-1, I-2, I-3, II, Iii, IV-one, and IV-2). In addition, as the black-purple rice and red pericarp rice were non clearly distinguished from each other, they were mixed and classified according to the ecology conditions of the areas where each genetic resources was grown (Figure ii). The 548 rice genetic resources used in this written report included 337 varieties in Group I, thirteen varieties in Group II, 144 varieties in Group Three, and 54 varieties in Group IV. Group I included 18 varieties from Malaysia, 5 from Myanmar, two from Vietnam, two from Bhutan, five from Sri Lanka, 95 from Republic of indonesia, 43 from Japan, 78 from China, 3 from Cambodia, iii from Taiwan, 26 from Thailand, two from Islamic republic of pakistan, 23 from the Philippines, and 32 from Korea. Group I included most of Asia and could be divided into 3 subgroups (I-one, I-two, I-three). Subgroup I-ane included mostly South Asian countries such equally Malaysia, Indonesia, and Cambodia. Subgroup I-2 included Due east Asian countries such as Japan and Korea, and Subgroup I-3 included Malaysia, Bhutan, Sri Lanka, Indonesia, Nihon, Red china, Thailand, the Philippines, and South Korea. Group II contained ane variety from Republic of indonesia, eleven from Nihon, and ane from Korea. Group 3 included one diverseness from Malaysia, two from Vietnam, 13 from Republic of indonesia, 41 from Japan, 51 from Red china, two from Cambodia, one from Thailand, 2 from the Philippines, and 31 from Korea. Group 3 deemed for about 86% of the rice genetic resources of Nippon, China, and Korea. In Group IV, Korean rice genotypes constituted the bulk, with ane variety from Myanmar, 1 from Indonesia, iv from Japan, 10 from China, ane from Cambodia, one from Thailand, 1 from the Philippines, and 35 from Korea. Group IV was also divided into two subgroups (4-one, Iv-two); Subgroup Iv-1 had one variety from Indonesia, one from Japan, 5 from Cathay, one from Cambodia, one from Thailand, and 31 from Korea. In Subgroup 4-2, there was one diversity from Myanmar, three from Japan, five from China, i from the Philippines, and four from South korea. Subgroup 4-2 contained rice genes from East Asian countries including Japan, China, and Korea. The UPGMA analysis classified most of the Southeast Asian rice genetic resources into Group I. Rice genetic resource of Japan were included in Group II, and Group Iii independent genetic resource from Japan, China, and Korea. In Group IV, most of the samples were confirmed as Korean rice genetic resource. These groupings were based on areas where each rice genetic resource was grown, mainly Southeast Asia and East asia. Southeast Asia, because of its proximity to the equator, represents a tropical climate with high temperatures throughout the year, and it is influenced by the monsoon season with frequent precipitation, except in the dry season. Therefore, information technology was confirmed that the ssp. indica varieties, suitable for this surroundings, were cultivated in Southeast Asia. On the opposite, the East Asian region has iv distinct seasons, jump, summer, autumn, and winter, and has many plains; in this climate, the ssp. japonica varieties are cultivated rather than ssp. indica. An analysis of the dendrogram of the microsatellite marking genotypes from this written report confirmed that similar rice varieties were in the same groups, and like varieties of crosses were used for the cultivation of growth environments and breeds. When the dendrogram was fabricated by the UPGMA analysis, Grouping I contained genetic resources cultivated in Southeast Asian countries, such equally Indonesia, Malaysia, the Philippines, and Thailand. Groups I, Iii, and Four included genes cultivated in the East Asian countries of Japan, China, and Korea. These results were divided according to the genetic resources of the maternal and paternal families from which the varieties were developed equally well as the results of other researchers and the genotype of rice or the environment of the region where the cultivars were grown [28,31]. Therefore, it is expected that more effective breeding results will be obtained by selection based on the maternal or paternal samples when cultivating a new brood for a new environment.

Unweighted pair-group method with arithmetical average (UPGMA) dendrogram of 548 rice germplasms based on 16 SSR markers using MEGA7 program. The cherry-red color in the boundary indicates rice genotypes that have cherry-red seeds and the black color indicates rice genotypes that have blackness seeds. Blackness and scarlet rice are not clearly distinguished and mixed in big groups.

2.3. Population Structure

A model-based program chosen Construction v2.3.four was used to make up one's mind the genetic relationship of the 548 colored rice genetic resources. In club to analyze the colored rice genetic resources, ΔK was chosen with the height alpha parameter. Population structure of the 192 germplasm lines was analyzed by a Bayesian based approach. The estimated membership fractions of 192 accessions for unlike values of one thousand ranged between 2 and 5 (Figure 3). The log likelihood revealed past structure showed the optimum value every bit 2 (K = two). Still, when the G value was set to 2, 548 colored rice cistron sources could not be effectively distinguished. Considering 548 colored rice cistron sources have a very various genetic pool. Thus, when the K value was fix to the side by side higher K = 3, 4, vii, the population structure was confirmed. When the Grand value was set to 7, 548 colored rice genes were classified into the optimal population structure. The seven groups were divided co-ordinate to the environments in which the colored rice genetic resources were grown and the type of classification. In addition, analysis of the population structure distinguished whether the colored rice genetic resources belonging to the group were pure or admixtures; varieties with a probability value greater than 0.lxxx were considered pure. Most of the colored rice genetic resources belonging to cluster 7 were pure, and about of the colored rice genetic resources belonging to the remaining subgroups were admixtures (Figure 4). Most of the colored rice genetic resource used in this study were in the course of admixtures, significant that the colored rice genetic resources were not high in purity but derived through many recombination and crossbreeding events. Population structure analysis showed that the group of colored rice genetic resources was like that of the dendrogram for the UPGMA analysis based on genetic distances. When the dendrogram was drawn through the UPGMA analysis, information technology was divided into four groups with vii subgroups. The population structure assay showed the highest value at Grand = 7, so the 548 colored rice genetic resources were divided into seven groups. Population structure analysis showed that the classification of colored rice genetic resource, the geographical location, and the goal of breeding were significantly influenced by the genetic structure of rice genotypes.

Inference of number of clusters in rice germplasm collection based on 16 SSR markers using STRUCTURE program. The Bayesian model in the Structure v2.three.4 program was used to examination 1000 (number of clusters) using the parameter of burn-in/Markov chain Monte Carlo (MCMC): 100,000/250,000 iterations. An ad hoc quantity (ΔK) [31] was calculated for each K to detect the best number of clusters (Yard = 7).

Construction plot presenting 7 clusters of rice germplasm collection. The y-centrality indicates the estimated membership coefficients for each individual. Each variety's genome is represented past a single vertical line, which is partitioned into colored segments in proportion to the estimated membership in the 7 clusters.

2.4. Principal Component Assay (PCA)

PCA is an analytical method that finds the best representations of the differences of each dataset and distinguishes the data past each element. In other words, PCA is a method in which when data are presented as axes, the axis with the greatest variance is set as the start main component, and the axis with the second largest variance is displayed equally a diagram. When PCA was performed to confirm the degree of genetic diverseness of the 548 colored rice genetic resources, all the genetic resources were uniformly distributed in four quadrants and were not shifted to whatsoever i identify (Figure 5). Ii-dimensional data obtained through PCA analysis resembled the results of the dendrogram and population structure analyses, and about varieties were distributed in all quadrants. When the UPGMA dendrogram was created, the colored rice genetic resources belonging to Subgroup I-ane were uniformly distributed in the first and second quadrants of the diagram, and those of Subgroup I-2 were distributed in the third quadrant. Subgroup I-iii appeared in the lower-left corner of the fourth quadrant, and Group Ii appeared in the upper-left quadrant. Group III was shown in the lower half of Quadrant 1 and Quadrant 4. Subgroup Four-1 was uniformly shown in the starting time and second quadrants like Group I, and Subgroup IV-2 was shifted to the lower right of the tertiary quadrant. This PCA can exist used to allocate genotypes of colored rice genetic resource based on molecular morphological changes as well as constitute morphological data. However, the PCA only finds the all-time representatives of the differences in genetic resource in the population; it does not provide data on the number of populations or subgroups of populations. In addition, PCA is based on morphological markers and it analyzes group structure. Therefore, PCA is less authentic than other methods. Since PCA showed very high variability in the principal component of this report, this information may be helpful in identifying related genotypes if used with population analysis. It can also be useful to distinguish between subgroups of populations involved in classifying genotypes. In add-on, when comparing the PCA with the dendrogram, they showed similar tendencies and their general and group compositions were similar.

Principal component analysis (PCA) of 548 rice germplasms based on 16 SSR markers using R program. The colors shown in this figure represent the same genetic resource as the colors in Figure 5. The rice genotypes belonging to cluster i are indicated by cherry; cluster 2: blue; cluster 3: green; cluster 4: violet; cluster 5: orange; cluster 6: black; and cluster vii: brown.

three. Discussion

16 SSR markers were used for the assessment of colored rice genetic diverseness. Each marker can distinguish merely one or two varieties, but using multiple marking set up simultaneously can assess the genetic diversity of numerous gene resources. In this study, when UPGMA dendrograms of the 548 colored rice genetic resources were created using Jaccard'south distance coefficients and the MEGA7 program, they were divided into 4 big groups (I, 2, Three, and IV) and vii subgroups (I-1, I-2, I-3, 2, 3, IV-one, and IV-2). Moreover, when analyzing the population structure using the STRUCTURE v2.iii.4 program, it was confirmed that as in the previous results, optimal separation occurred when the 548 colored rice genetic resources were divided into seven subgroups [32]. In the analysis of the colored rice population structure using the UPGMA dendrogram and STRUCTURE programs, it was confirmed that it was divided into seven groups in all analysis. The results were obtained when cess genetic variety based on genetic distance and model-based population construction. In improver, when analyzing the population structure using the Construction program, it was confirmed that the colored rice gene sources were genetically mixed. The reason why the genetic fabric is then mixed is because convenance continues to develop varieties with loftier yield and quality. Since this breeding will non terminate at present and volition go on in the future, the mix of genetic resources will continue to increase. When the UPGMA dendrogram was created, Group I independent evenly colored rice genetic resources from Southeast Asian countries such as the Philippines, Indonesia, Thailand, Korea, Prc, and Nippon. Group II included about of the Japanese colored rice genetic resources, and Group III included the rice genetic resource of East Asian countries including Japan, China, and Korea. Finally, Group IV included most of the Korean colored rice genetic resource. When the selected microsatellite markers were used to analyze the populations of rice genetic resources, some of the varieties were classified as identical. It was presumed that these results were obtained because rice genetic resources are composed of the same maternal or paternal material and the genetic resources should be judged by either comparing the morphological characteristics or increasing the number of microsatellite markers. In this study, 16 microsatellite markers were newly selected from the 600 pairs of microsatellite primers reported in the rice genome database (http://www.gramene.org/); they showed loftier polymorphism and excellent repeatability of the pattern of alleles from the PCR results. The results of the analysis of the 548 colored rice genetic resources, the black-regal rice and red pericarp rice, using these microsatellite markers showed that the colors were not clearly distinguishable but that the resources were clearly grouped according to plant taxonomic characteristics, breeding lineage, and cultivation environment of rice. Therefore, the database of Dna profiles constructed through the selected microsatellite markers can be used not only to evaluate the characteristics of rice genetic resources only also to select the controls for cultivar-protected varieties. There are many genetic resource that have not been identified by the microsatellite markers used in this study and in that location have been many contempo reports of new SNP markers [33], highlighting the necessity to proceed to develop new markers, specific to varieties, by comparing and analyzing morphological characteristics.

4. Materials and Methods

iv.1. Institute Fabric

In this report, 548 colored rice genetic resource (376 black-royal rice samples, 172 red pericarp rice samples) collected from national and international sources were used as belittling materials for microsatellite markers (Tabular array S1).

4.ii. Genomic DNA Extraction

Genomic DNA was isolated using a NucleoSpin^®Found Two (Cat. 740 770.250, Macherey–Nagel GmbH & Co., KG, Deutsch, Düren, Deutschland) kit. The extracted genomic DNA was quantitated at 260 nm using a spectrophotometer, and the DNA concentration was confirmed past electrophoresis on 1.5% agarose gel. The Deoxyribonucleic acid was diluted to a final concentration of 20 ng/μL using sterilized water and then used for SSR analysis. The concentration and quality of the obtained genomic Deoxyribonucleic acid samples were estimated with an ultramicrospectrophotometer Nano Drop (True cat. ND-2000, ThermoFisher SCIENTIFIC, Seoul, South Korea).

4.iii. SSR Genotyping

In order to select SSR markers effective for colored rice genetic resource, the 600 SSR primer sets reported in the rice genome database (http://www.gramene.org/) were used to carry out PCR on the "Hopum," "Hwayeong," "Dami," "Sindongjin," "Odae," "Chucheong," "Saechucheong," "Junam," "Ilpum," "Ilmi," and "Gopum" rice varieties. The primers with high repeatability, high ring reproducibility, and a high degree of polymorphism were commencement selected. Primers having a high PIC value and a loftier heterozygosity value were selected among the primers selected beginning. Finally, the selected SSR primer sets were distributed evenly on the rice chromosome. Gene amplification products by PCR were analyzed past electrophoresis using Genetic Analyzer (HAD-GT12TM Organization, eGEnE, Irvine, CA, USA), and the differences in the diverse alleles were analyzed using a reckoner plan (Biocalculator 2.0). The PCR amplification results were used to construct a Deoxyribonucleic acid profile database for the 548 colored rice genetic resources. The PCR reaction mixture contained fifty μg of genomic Deoxyribonucleic acid, 0.i μM of SSR primer, and 2.0 μL of dNTP mixture (2.5 mM), and 1.0 U of HS Prime Taq polymerase (Cat. G-7002, Genet Bio, Daejeon, South korea, fifty mM KCl, twenty mM Tris-HCl, pH 8.0, 2.0 mM MgCl 2) was added with distilled water to arrange the total volume to 30 μL. PCR amplification (C1000, BioRad, Hercules, CA, U.s.a.) was performed by denaturation at 94 °C for xxx s, annealing at 55 °C for 30 south, and extension at 72 °C for 45 southward, for a total of forty cycles. After completion of the PCR, 3.0 μL of the PCR distension product was electrophoresed on a 2.5% agarose gel to confirm the PCR distension. Later confirming amplification, 1.5 μL of the PCR amplification product was diluted in 150 μL of distilled water. To estimate the size of the alleles of the chromogenic genes co-ordinate to the SSR markers, 1.0 μL of the PCR distension product, 10 μL of deionized formamide, and 0.25 μL of size marker (LIZ500 size standard) were mixed. After denaturation at 94 °C for 2 min, electrophoresis was performed using an automatic sequencer (Genetic Analyzer 3130XL, Applied Biosystems, Foster City, CA, Usa). And the size of amplified alleles was measured using the Gene Mapper (version 3.seven) programme (Applied Biosystems, Foster Urban center, CA, The states). The number of alleles and the size of alleles were determined for each marker.

four.4. Genetic Diversity Analysis

Motion picture values were calculated using the following formula to investigate the diversity of microsatellite markers:

where n is the number of alleles analyzed per marker and Pij is the frequency of the jth common band pattern among the bands of marking i. [34]. Microsatellite assay was used to select alleles with high reproducibility and high polymorphism as markers. NTSYSpc (version 2.10) [35] was performed co-ordinate to dominant marking scoring (nowadays = ane, absent-minded = 0), and genetic similarity values were calculated according to the Jaccard'due south method [36], followed by population analysis using UPGMA [37]. The population structure was deduced from the model-based programme STRUCTURE v2.3.4 [38] and performed over the range of K = 2 to 10. The final Chiliad value was adamant using Evanno's ΔK method [39]. Allelic frequencies of each microsatellite mark and each variety were calculated and used for PCA. PCA assay was performed using the R program (ver. three.ii.iii).

Acknowledgments

This work was supported by a grant from the Next-Generation BioGreen 21 program (Projection No. PJ01368401), Rural Development Assistants, Democracy of Korea and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018RC1B5047863).

Supplementary Materials

The following are bachelor online at https://www.mdpi.com/2223-7747/8/11/471/s1, Figure S1: Dendrogram of 548 rice varieties based on xvi SSR markers. The scale at the bottom is Jaccard's coefficient of similarity. Table S1: List of rice cultivars and germplasms assayed for genetic characterization using microsatellite markers.

Author Contributions

Conceptualization, J.-R.P.; methodology, J.-R.P. and W.-T.Y.; formal analysis, W.-T.Y.; investigation, J.-R.P., W.-T.Y., Y.-S.Yard., H.-N.K.; writing-original draft preparation, J.-R.P. and West.-T.Y.; writing-review and editing, J.-R.P. and W.-T.Y.; projection administration, Thousand.-M.Yard. and D.-H.K.

Funding

This work was supported by a grant from the Next-Generation BioGreen 21 program (Project No. PJ01368401), Rural Development Administration, Republic of Korea and the National Research Foundation of Korea (NRF) grant funded past the Korea government (MSIT) (No. 2018RC1B5047863).

Conflicts of Interest

The authors declare no conflict of interest.

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