Original Article
Md Shafikur Rahman, Md Kamrul Hasan Sohag, Lutfur Rahman, Md Shah -E- Alam, Ujjal kumar Nath, Md. Khairul Bashar
J Adv Biotechnol Exp Ther. 2020; 3(3): 182-193.

  • facebook
  • twitter
  • reddit
  • linkedin
 [View Full Article PDF]
  • facebook
  • twitter
  • reddit
  • linkedin
[View Crossref]
  • facebook
  • twitter
  • reddit
  • linkedin
 [View Full Article HTML]  
  • facebook
  • twitter
  • reddit
  • linkedin
[View Full Article DOI]

ABSTRACT: Genetic fingerprinting of 110 rice cultivars of Bangladesh was completed with five polymorphic microsatellite DNA markers such as RM153, RM251, RM333, RM335 and RM475. The amplified DNA fragments are known as alleles from Polymerase Chain Reaction (PCR) reactions were separated on 2% agarose gel electrophoresis system, subsequently visualized by high performance ultraviolet transilluminator. In all, 99 distinctive alleles averaging 19.80 alleles/locus from the entire utilized microsatellite loci were counted. Several diversity indexes such as Polymorphism Information Content (PIC), heterozygosity, and cluster analysis were computed in this quantitative investigation. Superior genetic differentiation and inferior gene flow values among the cultivars were revealed from the recorded genetic diversity study of PIC, Effective allele, Shannon index (I), Hardy-Weinberg equilibrium (HWE), Nei’s gene diversity (h), along with genetic differentiation-Fis and gene flow-Nm analysis. A total, 5995 varietal pairs were achieved all the way through alternative combinations of 110 rice cultivars where their Nei’s genetic distance (D) was ranged from zero to 2.832. Nei’s genetic-base an Unweight Pair Group Method of Arithmetic Means (UPGMA) diagram was assembled which eventually separated all the cultivars from each other according to their genetic distance and similarity. Thus, the finding of this study will expose such strategies to distinct all the wild relatives, cultivars and commercial varieties of rice or any other crop species having various genetic levels to facilitate further improvement and protection in future.

KEYWORDS: Genetic fingerprinting, Oryza sativa L., Plant variety protection, Bangladesh

Rice (Oryza sativa L.) having diploid chromosome number 2n=2x=24 belongs to the significant grass family Gramineae and subfamily Oryzoidae is considered one of the most cultivated annual cereals because almost 1/2 of the world’s human population consume rice every day as their predominant staple food [1]. It is occupied in the central position other than rest of the agricultural component to contribute Bangladesh’s national economy [2]. It is believed that Asian farmers have been maintaining the selection and domestication process of a wide range of rice cultivars since ancient [3]. The Gene bank of Bangladesh Rice Research Institute has accumulated near about 8,500 rice germplasm from different ecological and indigenous sources of Bangladesh of which 8,044 genotypes have been registered so far [4]. Great variations in rice genotypes have been noticed in Asia, more particularly in China as well as Indian subcontinent regarding morphological, biochemical and molecular aspects [5],[6]. Zhao [7] has reported on the subject of the origin of rice which has been accomplished  close to the northeastern part of India (Assam) and southwestern  part of China (Yunan), both are truly recommended to the subtropical upland of Himalayas [8]. Oryza rufipogon and Oryza nivara are the two parts of Javanica rice which are assumed feasible immediate progenitors of Oryza sativa [9].Interestingly, the Assam center (nearby of Bangladesh) is also considered for the center of origin of such Javanica rice.
Day by day the number of rice genotypes become raise which ultimately harder the flexibility to differentiate of rice cultivars on the premise of morphological and biochemical attributes [4]. Therefore several molecular markers such as SSR (Simple Sequence Repeat), RAPD (Random Amplified Polymorphic DNA), RFLP (Restriction Fragment Length Polymorphism), and AFLP (Amplified Fragment Length Polymorphism) etc. are widely utilized to be identified of particular cultivars [10], or quality seed of hybrid varieties [11] and for documentation of the released varieties in seed grain trade as well [12]. Thus DNA fingerprinting data is additionally one of the example which is being globally practiced for the legal evidence of DUS (Distinctness, Uniformity, and Stability) [13]–[20].
PCR-based assays, co-dominant inheritance pattern, and elevated multi allelic variation/polymorphism are the major dynamic factors which steer the microsatellites/SSRs as the precious genetic markers of choice for the breeders [21]–[23]. The motif of the hyper variable microsatellites/SSRs are fairly located and abundantly well distributed all over the rice genome [24]–[28]. Hence, microsatellites/SSRs are also becoming efficient tools for the breeders as well as geneticists to incorporate genetic maps of rice having enormous wealth of diverge genetic variation [29]–[32]. Still, more than 50,000 microsatellites/SSRs have been designed in between Indica and Japonica rice accessions which are being contributed to construct genetic map for characterization and documentation of rice [30],[33]–[36]. Based on the above scientific reports, such characterization and documentation process have been recently utilized in several varieties/landraces/cultivars/wild types of Oryza sativa, Triticum aestivum, Zea mayes, Saccharum officinarumBrassica napus, Glycine max, Solanum tuberosum, Corchorus capsularis, and other crop species of Bangladesh [13]–[19],[37]. However, in this study, the genetic fingerprinting techniques were utilized through five SSR markers for the protection of 110 local rice genotypes grown in Bangladesh. Moreover, the genetic fingerprinting techniques of this research can be further exploited for the protection and establishment of Intellectual Property Rights (IPR) of other crop species of Bangladesh.

Raising of rice seedlings and extraction of DNA
Genetic Resource and Seed (GSD) division of Bangladesh Rice Research Institute (BRRI) was provider of 108 cultivar’s seeds and the seeds of rest two cultivars named ACI-1 and Alok-932024 were supplied by ACI Pvt. Ltd of Bangladesh. Genetic fingerprinting lab of department of Genetics and Plant Breeding (GPB), Bangladesh Agricultural University (BAU), Bangladesh was being concerned to grow these seedlings as well as this experiment to be conducted. Genomic DNA of each cultivar was extracted from 2-5 inner succulent shoots of two-weeks-old fresh seedlings germinated in sterile petridishes. In that case, Rahman et al., [13]–[19],[37],[38] illustrated modified CTAB (cetyl trimethyl ammonium bromide) DNA extraction method supported by Aljanabi and Martinez [39] was followed to isolate genomic DNA.

Quantification and optimization of DNA concentration
UV-absorption spectrophotometer (Spectronic® Genesis™) was utilized at 260nm absorption to measure the purity and concentration of DNA which was subsequently estimated and converted into 25ng/µl with TE buffer, and finally stored at 4oC before to amplify with SSR primers.

Selection of polymorphic SSR markers for rice genotypes
A total, 50 SSRs comprising on all the twelve chromosomes of rice were obtained. A set of seventeen SSR primers described previously [38] were selected from them by surveying allelic polymorphism data from the available rice genome database (http://www.gramene.org) as illustrated by Rahman et al.,  [13]–[19],[37]. At first, three to five of those primers were tested through ten randomly selected genotypes setting with the recommended PCR thermal profile [13]–[19]. The expected ranges (base pairs length) PCR product was then validated based on the most excellent response to amplify the target genomic region of the template DNA. In such a way five SSR primers viz., RM153, RM251, RM333, RM335 and RM475 which depict 5, 3, 10, 4, and 2 of rice chromosome [36] were preferred by visualizing clear and predictable amplified alleles, and finally employed for SSR analysis in this study (Figure 1). The selected primers were subsequently run with all 110 cultivars at once which displayed clear and repeatable polymorphic bands.

Polymerase chain reaction (PCR) amplification profile for SSRs
Eppendorf oil-free thermal cycler gradient was utilized in this PCR reaction. Approximate 15 μl reaction volume consists of 50ng sample DNA (2.0 µl), 10x PCR Buffer (3.0 µl), 1 µM of each forward and reverse SSR primer (1.0 µl), 0.25 mM dNTPs (1.5 µl), 1 unit ampli Taq DNA polymerase (0.5 µl), and nuclease free double distilled deionized water (6.0 µl) were utilized to perform PCR reaction. PCR settings were carried out by the conditions described by Panaud et al., [34],[36] with minor modifications suggested by previous research [13]–[19],[37] as follows: 95°C for 5 min (an initial denaturation) followed by entire 35 cycles,  95°C for 40 sec (denaturation), 55°C for 30 sec (annealing) and 72°C for 1 min (elongation/extension), then a final elongation/extension cycle at 72°C for 7 min. Amplified PCR reactions were then stored at –20oC for further utilization.

Electrophoresis and inspection of banding patterns
Top vision submarine horizontal electrophoresis system (BIORAD Sequencing Cell) was utilized to electrophoresis the PCR reactions (Figure 1). Prior to electrophoresis, each 07 µL amplified PCR aliquot and 3 µL of loading buffer [38] were mixed gently. This mixture was then loaded on 2% agarose gel, and placed into the submarine horizontal gel chamber with 1x TBE running buffer (Trizma base, boric acid and EDTA; pH 8.0). A five microlitre (5 µL) 100 bp standard DNA (Gene ruler, Fermentas®) ladder was added in both left and right side of the gel to compare the molecular weight of the amplified PCR products of each cultivar. PCR mixtures (10 μl) were subjected to electrophoresis at 100V and 50W for 2 hrs 40 mins. The electrophoresis sample was then kept as photographic image by the camera polaroid gel documentation system (UVP, BioDoc-ItSystem).

Genotyping of alleles and data analysis
DNA FRAG v3.03 computer software [39] was utilized to measure the most profoundly amplified alleles per loci (Figure 1) by using 100bp recognizing size standard DNA ladder [40]. The individual unambiguous DNA fragments were referred as alleles of the respective SSR markers. The allele frequency data (DNA fragment) was exported as diploid datasheet arrangement (AA, AB, CC and so on) on  POPGENE v1.31 computer program [42], and therefore, utilized for the several statistical analysis including “observed number of alleles-Na [42]”, “effective number of alleles-Ne” [43], “allelic diversity index (PIC=1-∑Xi2, where Xi  indicates the  frequency  of  the ith allele), Shannon’s Information index-I [44]”, “Hardy-Weinberg equilibrium (He and Ho of Levene [45] and Gene flow-Nm)”, “Nei’s gene diversity index (h) [46]”, “chi-square & probability index”, and “Wright’s fixation index-Fis [47]”. POPGENE v1.31 software was also applied to estimate genetic distance and similarity among the genotypes.  Finally, an UPGMA (Unweighted Pair Group Method of Arithmetic Means) phylogenetic tree (Figure 2) was assembled by means of Nei’s [48] genetic distance (D) which was visualized via Treeview computer software [49]. The generated cluster on UPGMA diagram (Figure 2) was then used to explain the relationships among the cultivars in this study.

Allele frequency and allelic diversity index (PIC)
Using 5 polymorphic loci in 110 cultivars of rice, a complete of 99 alleles were found in this quantitative investigation where RM335 revealed the foremost observed alleles (25) followed by RM333 (21), RM251 (20), RM475 (19) and RM153 (14) as shown in Table 1. The Highest effective number (Ne) of alleles (19.852) was also found in RM335 (Table 2). The diversity index of alleles or Polymorphism Information Content (PIC=1-∑Xi2) values usually reflect particular allele diversity into a species [50]. The average PIC value was 0.864 with the number ranging from 0.879 (RM153) to 0.949 (RM335) (Table 1).

Genetic variation statistics
Aspect of allele frequency of each cultivars and total cultivars were taken into consideration to calculate the comprehensive Shannon’s Information Index (I) which is fluctuated from 2.284 (RM153) to 3.080 (RM335). Fixation index (Fis) (a measure of genetic differentiation) was recorded from 0.687 to 0.958 having 0.838 average values (Table 2). Highest gene flow (Nm) was estimated through RM251 microsatellite loci (0.046) while RM153 showed the lowest gene flow (0.005) in this study (Table 2).
Table 1. Fingerprinting Alleles and polymorphism information content (PIC) of five SSR loci across 110 rice cultivars
Table 2. Summary statistics of the utilized genetic diversity parameters in the study

Across 110 rice varieties, RM251 (0.290) yielded the very best average heterozygosity (HO) in current study followed by RM475 (0.172), RM335 (0.154), RM333 (0.090) and RM153 (0.036) (Table 2). Highest heterozygosity can be explained as a result of length and distance of RM251 marker on the genetic map relative to centromere [14].

Varietal identifications
Comparative SSR profiles and DNA molecular weight (band position) against five SSR primers (Figure 1), all the varieties were distinguished from each other with a minimum of single and/or arrangement of five primers (Table 3).
Table 3. Distinction of 110 rice cultivars all the way through SSR band positions
Figure 2. UPGMA phylogenetic tree based on Nei’s [63] genetic distance showing the genetic relationship among 110 rice cultivars  (Group, A= Transplant Aman, B= Broadcast Aman, C= Boro and Jhum)

Analysis of genetic distance and phylogenetic tree
The summary of Nei’s genetic distance (D) from 5995 varietal pairs among 110 rice cultivars varied from zero to 2.832. Out of these varietal pairs, 58.87% (3529) showed no genetic distance [11]. Such genetic distance and similarity in this study separated all the 110 cultivars into several clusters (“a” to “x”) at once on the UPGMA diagram (Figure 2).

All the utilized polymorphic SSR markers recorded a complete of 99 unique alleles (Table 1) which was significantly higher than the total number of alleles reported by the several previous researches [13]–[19],[33]. On an average it yielded 19.80 alleles per primer with an effect of 38.81%. In agreement with earlier works [13]–[19],[33] reported a total of 18 alleles [16],[17] through analyzing with three primers (RM11; RM151 and RM153) and 78 [15] alleles with five primers (RM1; RM151; RM153; RM334 and RM335), respectively, while running on diverse ecotypes of rice genotypes of Bangladesh from the protected rice materials of the BRRI. In those investigations, PIC values were recorded 0.670; 0.707; 0.698 [16],[17] & 0.862; 0.923; 0.831; 0.865 and 0.910, respectively [15].  In another study,  a total of 238 rice accessions (Indica and Japonica) by using entire ten microsatellite markers were investigated by Yang and his associates [51] where they observed maximum 25 exclusive alleles. Genotypes under the low PIC value study represent closely related variants, while superior PIC indicates considerable enormous diversity, which is ideal for the development of new variants as well [52]. The frequency of short tandem repeats of microsatellites as well as their repeat sequences have a command on the quantity of amplified alleles and their resultant PIC values  of the experimental genotypes [35],[36],[53],[54]. In addition, Ni et al., revealed from his investigation that more extensive repeats including GA- sequenced repeats acquiesce more quantity of distinctive alleles with superior PIC standards [54]. In contrast, it has been suggested that the motif of (CTT)n and amplified AT-affluent trinucleotide repeats also exhibits adequate and greater polymorphism of alleles [35]. RM333 primer containing (CTT)n motif was one of the most instructional SSR marker because it gave 6-7 distinctive alleles and standard PIC range in Temnykh’s experiment [36]. In support of RM335 [(CTT)20] SSR primers, 25 unique alleles with 0.910 PIC numeral were recorded which were the foremost alleles and the maximum PIC numeral in this investigation. The PIC valuation is considered as the discriminating strength of a promising marker to the genetic diversity study of the breeding materials selection program for the breeders because it regulates the frequency of observed and effective alleles of a particular DNA marker [55]–[57]. However, remarkably elevated PIC (0.879 to 0.949) values in this investigation indicated that the chosen markers have the required properties to be used in this DNA fingerprinting research among the 110 rice cultivars grown in Bangladesh[58]. However, the observation of this study was partially supported by the points of accuracy and usefulness from the above discussions.
Location of particular DNA genetic markers on the precise chromosomes, frequencies and size of alleles through their PIC numeral are given in Table 1. Mutation and chromosomal crossover are the two general events of heredity which are usually occupied at distal proximity from the centromere of the chromosome. They usually effort the formation of abundant alleles and eventually diversity of a specific locus [13]–[16],[18],[19],[37]. 24.7cM (chromosome 5); 79.1 cM (chromosome 3); 110.4cM (chromosome 10); 21.5cM (chromosome 4) and 92.5cM (chromosome 2) are the located positions of RM153; RM251; RM333; RM335 and RM475 primers on rice chromosome [36]. These primers were applied in the current observation of 110 rice cultivars including 2 hybrids and one variety of Jhum cultivation system. Overall gene flow values and genetic diversity were  observed in several populations of Oryza officinalis and computed 0.316 and 0.442, respectively, by examining of entire 14 microsatellite markers [59]. A wide range of allele frequency along with PIC value, major genetic variation in sense of observed and expected heterozygosity (Ho and He) were detected in this investigation. The observed and expected heterozygosity (Ho and He) values were estimated from 0.036 (RM155) to 0.290 (RM 251), and from 0.883 (RM155) to 0.954 (RM335), respectively. Superior expected heterozygosity (He) content than the observed heterozygosity in this study indicated that the selected SSR markers were remarkably abundant informative for the DNA fingerprinting among the rice cultivars [60],[61]. Partial consistent in theses’ observations were computed in several Bangladeshi local rice cultivars by a number of previous research groups [4],[17],[38]. Greater genetic variation and a lower gene flow value in 110 rice varieties argued that the most studied varieties in this experiment were landraces [38].
In these research materials, a total of 92, 15, 02 and 01 cultivars are recommended for transplant aman (T. Aman), broadcast aman (B. Aman), boro and jhum ecotype by BRRI [14]. In diallel fashion, a total of 5995 varietal pairs were possible among 110 cultivars where 2466 (41.13%) varietal pairs were computed as to be prominent genetic distance (Nei’s genetic distance-D) with each other [14]. In a previous study, while analyzing 94 varieties of six different ecotypes of rice grown in Bangladesh, a total of 4371 varietal pairs were computed of which 37% appeared nil genetic distance, and merely 1% showed highest genetic distance (2.583) [15],[62]. This closeness may be possible due to the genetic make-up of the locus for which the primers were responsible to distinguish along with low ecotype variation. Among the 5995 varietal pairs, only 0.917% appeared highest (2.832) genetic distance in this research. However, the superior genetic distance (D) is often observed while the cultivars or any genotypes were occupied from the landraces or wild relatives, in one side, and the high-yielding varieties (HYVs) on the opposite side in their crossing events [38].  Thus, the variation between highest and lowest genetic distance among the 110 cultivars proved their existence of variability. The resulting such genetic variability of the cultivars can be applied as a parent material in the future variety improvement programs to seek out the most efficient cultivars for further crossing or breeding.
However, Nei’s genetic distance (D) while analyzed on the UPGMA dendrogram considering 110 cultivars at a time, the dendogram separated the varieties, Dhul Abiz, Biropa and Bhor Gelam (Cluster “a”) from other 107 cultivars (Cluster “b”). Cluster “b” subsequently separated into sub-cluster “c” (Dumai Sail and Jol kumari) and sub-cluster “d” containing other 105 rice varieties. Sub-cluster subsequently formed other sub-clusters namely, “e”, “f”, “g”, “h” and so on (Figure 2). The varieties, as for example, Jol Kumari, Sandik sail, Jhul Kata, Thakor, Aalok 932024 and Tulu Sail were found in different sub-clusters “c”, “g”, “i”, “o”, “w” and “x”, respectively, due to their genetic distance. The major sub-clusters (“u”-“x”) were found to cover 39 of the 110 cultivars starting from ACI 1 to Lal Amon, all of which are traditional rice varieties of Bangladesh except ACI 1 and Aalok 932024.  UPGMA dendrogram within the groups; Transplant Aman (A), Broadcast Aman (B), Boro and Jhum (C) are given in Figure 2. As two Boro varieties (ACI 1 and Aalok 932024) and one Jhum variety (Ful Badam) were used in this experiment it was not possible to analyze them individually: they were therefore combined for a single analysis. An attempt was made to distinguish the varieties as their ecotype situations. The UPGMA dendrogram was constructed for this purpose (Figure 2). The results showed that the ecotypes have distinct clusters to represent Jhum and BRRI Accessions have formed the unique cluster different from all others as expected. The groups Transplant Aman (T. Aman) and Broadcast Aman (B. Aman) formed two closely linked sub-sub-clusters under one sub-cluster, while showing distinct difference from these sub-groups. Jhum formed a unique cluster and Boro a sub-cluster.
In these 110 cultivars, all the cultivars were distinguished from one another with either through 1st, 2nd, 3rd, 4th, and 5th SSR primers (Table 3) and also through qualitative and quantitative traits of Breeders [18]. Many varieties had similar names, which had created a number of problems related to final characterization. The varieties Moisha Mida (T. Aman), Moisha Mira (T. Aman), Moisa Mira (B. Aman); Kala Gura (B. Aman), Kala Gora (T. Aman) and Thakor (T. Aman), Thakur Dhan (T. Aman) have similar names but when studied by both qualitative and genetic fingerprinting those showed distinct differences [14].

In this study, the registered local rice cultivars grown in Bangladesh were exploited to distinct each and every rice cultivar based on identification of the DNA band patterns by means of specific primers, generally termed as genetic fingerprinting. All the utilized cultivars in this research were distinguished from one another with either 1st, 2nd, 3rd, 4th, or 5th SSRs. It is important to note that some of the varieties of traditional types had very similar names, which usually gave an understanding of repeats, but it was interestingly found to be distinctly different from one another due to molecular data. An example is the varieties named Moisa Mira, Moisha Mira, Moisha Mida, Thakor, Thakur Dhan, both pairs of which were distinct from one another on molecular traits. Therefore, these were not repeated but individually distinct cultivars or land races of rice collected from different source areas of Bangladesh by BRRI scientists at different times. However, Intellectual Property Rights (IPR) and Plant Variety Protection (PVP) of wild relatives, landraces, cultivars and commercial varieties of Bangladeshi rice will be guided from the outcomes of this research.   Such series of works on more rice genetic materials as well as other crop species should be done as a regular study by the genetic resource centers of different institutes in collaboration with universities, where government should give adequate financial and special manpower support with appropriate incentives for those who will lead the program.

Authors express thanks to the DANIDA for supporting this research work through the Agriculture Extension Component/Seed Wing of the Ministry of Agriculture, Government of the People’s Republic of Bangladesh. In the same line, they extend thanks to Mr. Anwar Faruqe, Former Joint Secretary MoA and Former Director General of the Seed Wing, for his support and keen interest in the project activities.

LR, MSEA and MKB were involved in conception and design of the experiments. MSR and MKHS contributed to perform the experiments. LR, UKN, MSR and MKHS contributed to drafting the article. LR and MSR contributed to revising it critically for important intellectual content. MSR made the final approval of the version to be published.

Authors declared that they have no conflict of interest.

[1]   Wennberg A. Food and Agriculture Organization of the United Nations. In: Encyclopedia of Toxicology: Third Edition.  2014. DOI: 10.1016/B978-0-12-386454-3.00988-X
[2]   Anonymous. National Workshop on Rice Research and Extension-2002. Feeding the extra millions by 2025. Bangladesh Rice Research Institute Gazipur. 2002:1.
[3]   Jackson MT. Protecting the heritage of rice biodiversity. Geo Journal. 1995; 35(3): 267–274. DOI: 10.1007/BF00989134
[4]   Siddique MA, Khalequzzaman M, Islam MM, Fatema K, Latif MA. Molecular characterization and genetic diversity in geographical indication (GI) rice (Oryza sativa L.) cultivars of Bangladesh. Revista Brasileira de Botanica. 2016; 39(2): 631–640. DOI: 10.1007/s40415-016-0271-1
[5]   Nadir S, Xiong HB, Zhu Q, Zhang XL, Xu HY, Li J, et al. Weedy rice in sustainable rice production. A review. Agron Sustain Dev. Agronomy for Sustainable Development. 2017; Springer-Verlag France. DOI: 10.1007/s13593-017-0456-4
[6]   Qiu J, Zhou Y, Wang Y, Mao L, Ye C, Wang W, et al. Genomic variation associated with local adaptation of weedy rice during de-domestication. Nature Communication. 2017; 8: 15323. DOI: 10.1038/ncomms15323
[7]   Zhao Z. New data and new issues for the study of origin of rice agriculture in China. Archaeological and Anthropological Sciences. 2010; 2(2): 99-105. DOI: 10.1007/s12520-010-0028-x
[8]   Choi JY, Platts AE, Fuller DQ. The rice paradox: Multiple origins but single domestication in Asian Rice. Molecular Biology and Evolution. 2017; 34(4): 969-979. DOI: 10.1093/molbev/msx049
[9]   Chang TT. The origin, evolution, cultivation, dissemination, and diversification of Asian and African rices. Euphytica. 1976; 25(1): 425-441. DOI: 10.1007/BF00041576
[10] Mailer RJ, Scarth R, Fristensky B. Discrimination among cultivars of rapeseed (Brassica napus L.) using DNA polymorphisms amplified from arbitrary primers. Theoretical and Applied Genetics: International Journal of Plant Breeding Research. 1994; 87(6): 697-704. DOI: 10.1007/BF00222895
[11] Marshall P, Marchand MC, Lisieczko Z, Landry BS. A simple method to estimate the percentage of hybridity in canola (Brassica napus) F1 hybrids. Theoretical and Applied Genetics. 1994; 89(7-8): 853-858. DOI: 10.1007/BF00224508
[12] Bligh HFJ, Blackhall NW, Edwards KJ, McClung AM. Using amplified fragment length polymorphisms and simple sequence length polymorphisms to identify cultivars of brown and white milled rice. In: Crop Science; 1999: 39(6); 1715-1721. DOI: 10.2135/cropsci1999.3961715xer
[13] Rahman L, Nath UK, Molla MR, Islam MN, Siddique MA, Rahman MS, et al. Plant Varieties of Bangladesh: Morphological and Molecular Characterization. Vol 4. Seed Wing, Ministry of Agriculture, Government of the People’s Republic of Bangladesh; 2010; 4: 550.
[14] Rahman L, Rahman MS, Nath UK, Bashar MK, Sohag MKH. Plant Varieties of Bangladesh: Morphological and Molecular Characterization Vol 3. Published by Seed Wing, Ministry of Agriculture, Government of the People’s Republic of Bangladesh. 2009; 3: 394. DOI: 10.13140/RG.2.1.3062.6640
[15] Rahman L, Rahman MS, Islam MN, Islam MS. Rahman L, Islam MN, Rahman MS, Islam MS. Plant Varieties of Bangladesh: Morphological and Molecular Characterization. Vol 2. Published by Seed Wing, Ministry of Agriculture, Govt of the People’s Republic of Bangladesh; 2008; 2: 300. https://www.researchgate.net/publication/286146036_PLANT_VARIETIES_OF_BANGLADESH_Morphological_and_Molecular_Characterization_Volume-2 .
[16] Rahman L, Molla MR, Sultana S, Islam MN, Ahmed NU, Rahman MS, et al. PLANT VARIETIES OF BANGLADESH: Morphological and Molecular Characterization. Vol 1. Published by Seed Wing, Ministry of Agriculture, Government of the Peoples’ Republic of Bangladesh; 2007; 1: 486. DOI: 10.13140/RG.2.1.3849.0968
[17] Rahman MS, Molla MR, Alam MS, Rahman L. DNA fingerprinting of rice (Oryza sativa L.) cultivars using microsatellite markers. Australian Journal of Crop Science. 2009; 3(3): 122-128. ISSN: 18352693
[18] Rahman M, Sohag M, Rahman L. Distinctness of 110 rice (Oryza sativa L.) varieties of Bangladesh through morphological traits. Journal of the Bangladesh Agricultural University. 2014; 12(1): 29-36. DOI: 10.3329/jbau.v12i1.21236
[19] Alam MK, Rahman MS, Mustakima QKJ, Sohag MKH, Ahmed NU, Nath UK, et al. SSR based molecular diversity study in local rice (Oryza sativa L.) genotypes of Bangladesh. International Journal of Innovative Research. 2016; 1(2): 17–24. http://www.irsbd.org/welcome/full_article/29
[20] Ahmed MSU, Khalequzzaman M, Bashar MK, Shamsuddin AKM. Agro-Morphological, Physico-Chemical and Molecular Characterization of Rice Germplasm with Similar Names of Bangladesh. Rice Science. 2016. DOI: 10.1016/j.rsci.2016.06.004
[21] Thomson MJ, Septiningsih EM, Suwardjo F, Santoso TJ, Silitonga TS, McCouch SR. Genetic diversity analysis of traditional and improved Indonesian rice (Oryza sativa L.) germplasm using microsatellite markers. Theoretical and Applied Genetics. 2007; 114(3): 559–568. DOI: 10.1007/s00122-006-0457-1
[22] Kalia RK, Rai MK, Kalia S, Singh R, Dhawan AK. Microsatellite markers: An overview of the recent progress in plants. Euphytica. 2011. DOI: 10.1007/s10681-010-0286-9
[23] Nadeem MA, Nawaz MA, Shahid MQ, Doğan Y, Comertpay G, Yıldız M, et al. DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnology and Biotechnological Equipment. Taylor and Francis Ltd.. 2018. DOI: 10.1080/13102818.2017.1400401
[24] Akagi H, Yokozeki Y, Inagaki A, Fujimura T. Microsatellite DNA markers for rice chromosomes. Theoretical and Applied Genetics. 1996; 93(7): 1071-1077. DOI: 10.1007/BF00230127
[25] McCouch SR, Chen X, Panaud O, Temnykh S, Xu Y, Cho Y, et al. Microsatellite marker development, mapping and applications in rice genetics and breeding. Plant Molecular Biology. 1997; 35(1-2): 89-99. DOI: 10.1007/978-94-011-5794-0_9
[26] Siddique MA, Rashid ESMH, Khalequzzaman M, Bashar MK, Khan LR. Molecular characterization and genetic diversity in T. Aman landraces of rice (Oryza sativa L.) Using microsatellite markers. Thai Journal of Agricultural Science. 2014; 47(4): 211–220. ISSN: 00493589
[27] Thomson MJ, Zhao K, Wright M, McNally KL, Rey J, Tung C, et al. High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Molecular Breeding. 2012; 29(4): 875–886. DOI: 10.1007/s11032-011-9663-x
[28] Somaratne LHMYK, Abayawickrama ASMT, Wickramasinghe IP, Samarasinghe WLG. Estimating Out-Crossing Rate of Bg 379-2 Using Morphological Markers and Confirmation by Molecular Markers. Rice Science. 2012; 19(2): 166–168. DOI: 10.1016/S1672-6308(12)60036-5
[29] McCouch SR, Chen X, Panaud O, Temnykh S, Xu Y, Cho Y, et al. Microsatellite marker development, mapping and applications in rice genetics and breeding. Plant Molecular Biology. 1997; 35(1-2): 89-99. DOI: 10.1007/978-94-011-5794-0_9
[30] Chen X, Temnykh S, Xu Y, Cho YG, McCouch SR. Development of a microsatellite framework map providing genome-wide coverage in rice (Oryza sativa L.). Theoretical and Applied Genetics. 1997; 95(4): 553-567. DOI: 10.1007/s001220050596
[31] Ramu P, Kassahun B, Senthilvel S, Kumar CA, Jayashree  B, Folkertsma RT,  et al. Exploiting rice-sorghum synteny for targeted development of EST-SSRs to enrich the sorghum genetic linkage map. Theoretical and Applied Genetics. 2009; 119(7): 1193-1204. DOI: 10.1007/s00122-009-1120-4
[32] Parida SK, Dalal V, Singh AK, Singh NK, Mohapatra T. Genic non-coding microsatellites in the rice genome: Characterization, marker design and use in assessing genetic and evolutionary relationships among domesticated groups. BMC Genomics. 2009; 10. DOI: 10.1186/1471-2164-10-140
[33] Akagi H, Yokozeki Y, Inagaki A, Fujimura T. Microsatellite DNA markers for rice chromosomes. Theoretical and Applied Genetics. 1996; 93(7): 1071-1077. DOI: 10.1007/BF00230127
[34] Panaud O, Chen X, McCouch SR. Development of microsatellite markers and characterization of simple sequence length polymorphism (SSLP) in rice (Oryza sativa L.). Molecular and General Genetics MGG. 1996; 252(5): 597-607. DOI: 10.1007/s004380050267
[35] Temnykh S, Park WD, Ayres N, Cartinhour S, Hauck N, Lipovich L, et al. Mapping and genome organization of microsatellite sequences in rice (Oryza sativa L.). Theoretical and Applied Genetics. 2000; 100(5): 697-712Theor Appl Genet. 2000. DOI: 10.1007/s001220051342
[36] Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S. Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): Frequency, length variation, transposon associations, and genetic marker potential. Genome Research. 2001; 11(8): 1441-1452. DOI: 10.1101/gr.184001
[37] Rahman L, Alam MS, Hassan L, Alam MS, Bashar MK, Rahman MS, et al. Rice Varieties of Bangladesh: Morphological and Molecular Characterization. Vol 1. Published by Seed Wing, Ministry of Agriculture, Government of the People’s Republic of Bangladesh; 2008. DOI: 10.13140/RG.2.1.3160.9684
[38] Rahman M, Sohag M, Rahman L. Microsatellite based DNA fingerprinting of 28 local rice (Oryza sativa L.) varieties of Bangladesh. J Bangladesh Agric Univ. 2010; 8(1): 7-17. DOI: 10.3329/jbau.v8i1.6391
[39] Aljanabi S. Universal and rapid salt-extraction of high quality genomic DNA for PCR- based techniques. Nucleic Acids Res. 1997; 25(22): 4692-4693. DOI: 10.1093/nar/25.22.4692
[40] Nash J.H.E. DNAfrag, Version 3.03. Institute for biological sciences, National Research Council of Canada, Ottawa, Ontario, Canada. 1991.
[41] Nash JHE, Villegas A, Kropinski AM, Aguilar-Valenzuela R, Konczy P, Mascarenhas M, et al. Genome sequence of adherent-invasive Escherichia coli and comparative genomic analysis with other E. coli pathotypes. BMC Genomics. 2010; 11(1): 667. DOI: 10.1186/1471-2164-11-667
[42] Yeh FC, Boyle J. POPGENE, the user-friendly softare for population genetic analysis. Molecular Biology and Biotechnology. 1997; 434: 724–31. DOI: 10.1038/nature03466
[43] Kimura M, Crow JF. The numer of alleles that can be maintained. Genetics. 1964; 49(4): 725–738. ISSN: 00166731
[44] Lewontin RC. The apportionment of human diversity. In: The Concept of Race in Natural and Social Science. 2014; 7-15. Taylor and Francis. DOI: 10.1007/978-1-4684-9063-3_14
[45] Levene H. On a Matching Problem Arising in Genetics. The Annals of Mathematical Statistics.1949; 20(1): 91–94. DOI: 10.1214/aoms/1177730093
[46] Nei M. Genetic Distance. In: Brenner’s Encyclopedia of Genetics: Second Edition. Elsevier Inc., 2013: 248-250. DOI: 10.1016/B978-0-12-374984-0.00615-X
[47] Nei M. F‐statistics and analysis of gene diversity in subdivided populations. Annals of Human Genetics. 1977; 41(2): 225–233. DOI: 10.1111/j.1469-1809.1977.tb01918.x
[48] Nei M. Analysis of Gene Diversity in Subdivided Populations Masatoshi Nei Center for Demographic and Population. Proc Nat Acad Sci USA. 1973; 41(2): 225–233. DOI: 10.1111/j.1469-1809.1977.tb01918.x
[49] Evening M. Adobe Photoshop CS6 for Photographers. 2012. DOI: 10.4324/9780240526126
[50] Nei M. Genetic Distance between Populations. The American Naturalist. 1972; 106(949): 283–292. DOI: 10.1086/282771
[51] Page RDM. Treeview: An application to display phylogenetic trees on personal computers. Bioinformatics. 1996;12(4):357-358. DOI: 10.1093/bioinformatics/12.4.357
[52] Yu JK, Dake TM, Singh S, Benscher D, Li W, Gill B, et al. Development and mapping of EST-derived simple sequence repeat markers for hexaploid wheat. Genome. 2004; 47(5): 805–818. DOI: 10.1139/G04-057
[53] Yang GP, Saghai Maroof MA, Xu CG, Zhang Q, Biyashev RM. Comparative analysis of microsatellite DNA polymorphism in landraces and cultivars of rice. MGG Molecular & General Genetics. 1994; 245(2): 187-194. DOI: 10.1007/BF00283266
[54] Khalequzzaman M, Islam MZ, Siddique MA, Prince MFRK, Rashid ESMH, Ahamed MS. Genetic Diversity in AUS Rice Landraces of Bangladesh using SSR Markers. Bangladesh Journal of  Plant Breeding and Genetics. 2017; 30(1); 11–20. DOI: 10.3329/bjpbg.v30i1.36529
[55] Debouck DG. Managing Plant Genetic Diversity. Crop Science. 2003; 43(2): 749-750. DOI:10.2135/cropsci2003.749a
[56] Ni J, Colowit PM, Mackill DJ. Evaluation of genetic diversity in rice subspecies using microsatellite markers. Crop Science. 2002; 42(2): 601-607. DOI: 10.2135/cropsci2002.6010
[57] Sharmin A, Hoque ME, Haque MM, Khatun F. Molecular Diversity Analysis of Some Chilli (Capsicum spp.) Genotypes Using SSR Markers. American Journal of Plant Sciences. 2018; 9(03): 368-379. DOI: 10.4236/ajps.2018.93029
[58] Cao T, Duprez E, Borden KLB, Freemont PS, Etkin LD. Ret finger protein is a normal component of PML nuclear bodies and interacts directly with PML. Journal of Cell Science. 1998; 111(10): 1319–1329. ISSN: 00219533
[59] Mia MM, Rahman S, Islam MM, Begum SN, Hassan L. Molecular characterization of rice genotypes for Zinc biosynthetic gene(s) using microsatellite simple sequence repeat (SSR) markers. Asian Journal of Medical and Biological Research. 2015; 1(2): 187–197. DOI: 10.3329/ajmbr.v1i2.25611
[60] Hossain MM, Islam MM, Hossain H, Jaime A, Silva TD, Komamine A, et al. Genetic Diversity Analysis of Aromatic Landraces of Rice (Oryza sativa L.) by Microsatellite Markers. Genes, Genomes and Genomics. 2012; 6(1): 42-47. Retrieved from http://www.globalsciencebooks.info/Online/GSBOnline/images/2012/GGG_6(SI1)/GGG_6(SI1)42-47o.pdf
[61] Gao LZ. Microsatellite variation within and among populations of Oryza officinalis (Poaceae), an endangered wild rice from China. Molecular Ecology. 2005; 14(14): 4287-4297. DOI: 10.1111/j.1365-294X.2005.02758.x
[62] Sharma R, Kumar B, Arora R, Ahlawat S, Mishra AK, Tantia MS. Genetic diversity estimates point to immediate efforts for conserving the endangered Tibetan sheep of India. Meta Gene. 2016; 8: 14-20. DOI: 10.1016/j.mgene.2016.01.002
[63] Warzecha J, Oczkowicz M, Rubis D, Fornal A, Szmatoła T, Bugno-Poniewierska M. An evaluation of the genetic structure of geese maintained in Poland on the basis of microsatellite markers. Animals. 2019; 9(10): 737. DOI: 10.3390/ani9100737
[64] Rahman L, Islam MN , Rahman MS, Islam MS,  Alam MS and Bashar MK. Characterization of 94 Rice (Oryza sativa L.) varieties of Bangladesh Based on Microsatellite Loci. Bangladesh Journal of Agricultural Science. 2008; 35(1): 97-112.