Distribution of SCoT-Based Populations Depict Genotypic Diversity of Six Stevia Germlines in Egypt

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Hashem, Rafat A. Eissa, AbdelRahman A. AbouEldahab, Ahmed ElFatih A. ElDoliefy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4636839/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The quick response (QR) codes produce unique patterns based on the black and white spots distribution. If germlines were ordered vertically in columns and alleles horizontally in rows, the presence (+ 1) and absence (0) of alleles could respectively be considered as the black and white spots. Consequently, the vertical and horizontal differential distribution of these black and white spots in a genotype can produce unique QR-like patterns (QRLP). The variation among these QRLP depends on the composition of alleles resembling the genetics embedded in the DNA. Accordingly, six stevia germlines were genotyped using 14 SCoT primers that generated 1320 allelic forms with 3.26% and 1.06% of uniquely positive and negative effects; respectively. Of the 1320 alleles (83.41% of polymorphs), 220 polymorphs encompassed 180–185 alleles representing the population size of effective interacting alleles (ne). The genetic diversity of SCoT was averaged across the observed number of alleles (Mean = 0.174; StDev = 0.44) and varied (Mean = 1.5; StDev = 0.35). Correspondingly, the Nei’s gene diversity (h) of observed heterozygosity (Mean = 0.27; StDev = 0.18) and the Shannon index (Mean = 0.41; StDev = 0.26) were different. Therefore, the gene/allele frequency that was discovered among the populations of SCoT loci varied (0.17, 0.33, 0.5, 0.67, 0.83, and 1). The dominant SCoT, in the current study, along with the unweighted pair-group of arithmetic average (UPGMA) analysis concluded four interacting ancestors configuring the genetics in the six stevia germlines. The study can be considered the first showing the SCoT marker as the best QRLP producer exclaiming the differential diversity despite the size of genotyped alleles. Marker Molecular QR-code Allelic Blockchain Popgene Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Foods low in both sugar and calories have become essential for athletes and health welfare. Leaves of Stevia rebaudiana, Bertoni (family: Asteraceae ) encompass steviol glycosides (SGs), which are 200–300 times sweeter than sucrose [ 1 ]. The SGs have a wide range of usages as herbal medicines (tonics for diabetic patients), food industry (drinks, bread, and fruit juices), and cosmetics (mouthwashes and toothpaste) [ 2 ]. Increasing demand for natural sweeteners has led growers worldwide to cultivate stevia on a large scale [ 3 ]. Large-scale cultivation of economic crops depends on allelic variation. If the allelic variation is limited, breeders essentially increase it by outcrosses to lower the inbreeding effect. To help breeders improve stevia and use it as a genetic source, the determination of allelic diversity within and among germlines is essential [ 4 ]. Molecular techniques provide effective tools for dissecting genetic diversity and population structure [ 5 ]. Genetic DNA markers are cheap, reliable, and unique for each species and independent of age, physiological conditions, and environmental factors [ 6 ]. For efficient conservation and further utilization of plant resources, several molecular markers have been developed such as random amplified polymorphic DNA (RAPD) [ 7 ], simple sequence repeat (SSR) [ 8 ], amplified fragment length polymorphism (AFLP) [ 9 ], sequence-related amplified polymorphism (SARP) [ 10 ], start codon-targeted (SCoT) [ 11 ] as well as inter-retrotransposon- amplified polymorphisms (IRAP), retrotransposon-microsatellite amplified polymorphism (REMAP) [ 12 ]. The SCoT is a dominant novel technique that uses a single primer with strong affinity, specificity, and reproducibility [ 13 ]. Six SCoT primers were used to evaluate genetic diversity in durum wheat using agro-morphological traits [ 14 ] and for population structure [ 15 , 16 ]. As well, 15 SCoT primers were used to assess diversity of 70 Iranian Triticum species [ 17 ], also for molecular characterization [ 18 , 19 ] in stevia [ 20 ], for plant variability and relationships [ 21 ], and for genotypic and phenotypic diversity [ 22 ]. Stevia plants have been characterized by high genetic diversity and described as a starting material for bio-products at both biochemical and molecular levels [ 23 ]. Molecular characterization can help determine species, breeding behavior, presence of gene flow, and allelic transfer within and between populations of the same or related species [ 24 ]. Molecular methods can also be combined with morphological techniques for more reliability. SCoT marker has been used as a genetic marker for cross-pollinated crops [ 25 ]. It also has been used as an immense tool for genome mapping, gene tagging, and forensic investigations [ 26 ] and for genetic diversity and phylogenetic analysis in stevia [ 16 ]. Further, SCoT focuses on gene regions that provide advantages over other markers types that amplify random spots on the DNA suiting more applications in genetic mapping [ 27 ]. The QR codes are information carriers with many advantages including the high recognition rate, the large amount of stored information, the low cost and simple operation [ 28 ]. However, QR may face some challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult and questionable [ 29 ]. Despite this debatable potential of QR codes, molecular biologists opt to integrate it in DNA barcoding technology to identify species based on standard DNA sequences starting form version 1, 10, and 40 (Fig. 1 -A). DNA barcoding technology depends on short standard DNA sequences for biodiversity, conservation genetics, and wildlife forensic studies [ 30 ]. For animal-based studies the mitochondrial cytochrome c oxidase subunit I ( CO1 ) gene has been accepted, while for plant-based, the two chloroplast genes, namely, rbcL and matK , were proposed [ 31 – 33 ]. The DNA intergenic transcribed spacer (ITS) regions, its subsequence (ITS2), and the psbA-trnH were evaluated [ 34 , 35 ]. This has created various one- and two-dimensional barcoding symbologies, nevertheless, at the DNA sequence level of the aforementioned standard genes. Therefore, the bioinformatic tools (such as the tree-based phylogenetic analysis) provided a solution to estimate divergence time between organisms and relationship among species. Though the most commonly methods for species identification is the Basic Local Alignment Search Tool (BLAST) followed by distance matrix computations [ 36 ]; there is a possibility that the phylogenetic tree-based methods (like Neighbor-joining (NJ) and/or the Maximum Likelihood) may produce the lowest accuracy due to unavailability of homologs in sequence databases [ 37 ]. There comes the affirmed need for QR codes to be intergrade with the molecular and bioinformatics tools. In the current study, 14 SCoT primers have successfully produced polymorphic and allelic forms at the genetic regions of six stevia germlines. Moreover, we provided a quantified evidence to the divergent distribution of variation observed in genotypic data generated by a dominant molecular marker (such as the SCoT). This molecular quantitative method of variation can be converted to an associated-QRLP as a unique tool of barcoding system. Moreover, the QRLP signature was digitalized using a script to produce QR codes preserving and recalling the allelic diversity in the genotyped DNA. This approach peace-mind the worries a researcher may have regarding the generated data about the breeding lines planned to be distributed. At any time and by running a very small number of DNA markers the researcher can recall the unique alleles characterizing each germline data and compare between any of them by the QR given to each germline he distributed to others breeders. Materials and Methods Plant Materials and DNA Preparation Six stevia germlines (Sponti, China1, HighSugar, ShouA3-2, Levan, and Shou2) were kindly provided by the Sugar Crops Research Institute, that belong to the ARC, Giza, Egypt. The stevia germlines are owned to the ARC with no license or agreement is needed to decree the usage of the germlines. The DNA was extracted from the fresh leaves of each germline based on the manufacturer manual of the Qiagen DNeasy Plant Mini Kit (Qiagen, USA). DNA concentration and quality were assessed using the NanoDrop spectrophotometer (TU 1880 Double Beam UV- VIS) and visualized using 1% agarose gel electrophoresis. SCoT PCR Preparation PCR reactions were performed in a total volume of 25µl that contained 1µl of DNA (50–80 ng), PCR reaction buffer (1X, v/v), MgCl 2 (2.5 mM), dNTPs (2 mM), SCoT primer (2.5 µmol each), 1 unit of Taq DNA polymerase (Fermentas, Szeged, Hungary) and ddH 2 O. Fourteen SCoT primers (Table 1 ) were used following [ 13 ]. PCR reactions were performed following the program settings in Table 2 . Propagation of The SCoT Marker Populations All PCR products were electrophoresed on 2% (w/v) of agarose gels. Amplified fragments were visually scored as present (1) and absent (0) using the molecular Imager Gel Doc XR system and Image Lab software (Bio-Rad, USA). A fragment was considered as unique positive (UP) if was present (1) for once in a germline, while was absent (0) across all other germlines. In contrarily, a fragment was considered a unique negative (UN) if was absent (0) for once in a germline, while was present (1) across all other germlines. For each SCoT fragment (allele), conversion to the allelic form and nomenclature was performed by combining the name of the SCoT primer and the corresponding molecular weight (bp) (ex. SCoT16-250). Generally, the non-random association of alleles (linkage disequilibrium-LD) at two loci has been studied. Mainly two reasons are discussed for LD, epistatic natural selection and random genetic drift [ 38 ]. Plausibly, markers among interrelated functional genes (which may be clustered to form supergenes such as MHC in man and mice) are considered as the observed LD caused by epistatic fitness interactions (natural selection) [ 39 , 40 ]. For exotic stevia germlines of non-Egyptian sources and the random mating type of stevia, the structured populations in Egypt could be affected by migration between sub-populations (colonies). Despite the limited effect of immigration on the recombination between loci in the finite germlines of stevia in Egypt (due to fewer heterozygotes), the LD may be created among loci of local colonies (germlines). Therefore, it is important to clarify the magnitude of LD among the loci of stevia germlines due to random drift when the stevia population was subdivided into six genetic sources (germlines) in Egypt. those germlines are used in the current study. However, results may suggest strong LD between colonies (germlines) when migration is limited. Table 1 The Molecular Weight of SCoT Unique Positive and Negative Amplicons Among the Six Stevia Germlines. Primer Sequence (3'-5') Germline MWUPB (bp) MWUNB (bp) Total SCoT6 CAATGGCTACCACTACAG Sponti 200 High Sugar 580 Total 2 0 2 SCoT9 ACAATGGCTACCACTGCC Sponti 750 1900 Chian1 600 Levan 1000 1200 Total 3 2 5 SCoT10 ACAATGGCTACCACCAGC Sponti 450 China1 1000 Total 2 0 2 SCoT13 ACCATGGCTACCACGGCA Sponti 550 China1 440 1060 1000 High Sugar 390 Shou2 520 900 800 Levan 620 Total 6 3 9 SCoT16 CCATGGCTACCACCGGCA Sponti 590 China1 450 ShouA3-2 490 Levan 520 700 Shou2 2200 High Sugar 210 Total 6 1 7 SCoT19 CCATGGCTACCACCGGCG Sponti 1500 China1 580 High Sugar 460 480 Total 1 3 4 SCoT20 CAACAATGGCTACCACGC ShouA3-2 1700 Total 1 0 1 SCoT24 CCATGGCTACCACCGCAG Total 0 0 0 SCoT28 CAACAATGGCTACCACCA Total 0 0 0 SCoT32 CAACAATGGCTACCACGC High Sugar 260 ShouA3-2 220 380 900 1200 Total 2 5 7 SCoT35 AACCATGGCTACCACCAC Sponti 180 China1 1200 1210 1400 1500 High Sugar 220 350 ShouA3-2 850 Total 6 2 8 SCoT36 CACCATGGCTACCACCAT Sponti 540 1000 China1 650 High Sugar 580 600 ShouA3-2 500 Levan 1600 800 Shou2 1200 Total 7 2 9 SCoT44 ACCATGGCTACCACCGAC China1 1600 0 Total 1 0 1 SCoT46 ACCATGGCTACCACCGCC Total 0 0 0 Total 37 18 55 MWUPB, molecular weight of unique positive bands. MWUNB, molecular weight of unique negative bands. Table 2 SCoT PCR Conditions Cycles Temperature (˚C) Time (min) # of Repetition for Cycles Cycle1:Initial Denaturation 94 5 Cycle1:Once Cycle2a:Denaturation 94 1 Cycle2:40 Cycle2b:Annealing 50 1 Cycle2:40 Cycle2c:Extension 72 1.5 Cycle2:40 Cycle3:Final Extension 72 7 Cycle3:Once Proposed Theorem of Creating The Populations Out of SCoT Loci The best simplified theory of current study, which is derived from the Wright's island model [ 41 ], is to consider a population (species as denoted by the Wright's island model) consisting of (n) colonies (sub-population/demes), where each colony ( i.e. , a stevia germline) consists of (N) breeding loci (effective size) that will be generated by the SCoT. Let (m) be the migration rate of loci among colonies (herein, the stevia germlines) per generation. In the island model, it is noted that every colony (a stevia germline) receives, in one generation, a fraction of (m) of its loci from the entire population of loci of stevia species around. So, based on [ 42 ], each colony (a stevia germline) is subject to extinction at a rate of (λ) per generation, and when extinction occurs to a colony (a stevia germline), it is immediately replaced by a line (a dose of loci) that is derived from single colony (other stevia germline) in the population. As expected, the value (mean) of nonrandom association (LD) in this model is zero at equilibrium. Thus the variance of the LD coefficient is created by random genetic drift in each colony (of a stevia germline). When the migration of loci is limited, random drift in each colony (a stevia germline) would dominate and the different types of loci would spread in different colonies (of stevia germlines) increasing the variance of LD. Therefore, the LD coefficient will follow the equations demonstrated by [ 38 ]. Briefly, if two loci in a population have the following rates of evolution. ϰ i = g 1i + g 2i and γ i = g 1i + g 3i ; Variables of the total population: G j = n Σ i = 1 g ji /n for j = 1 ~ 4, X = n Σ i = 1 ϰ i /n and Y = n Σ i = 1 γ i /n. Recombination: Rate = c/generation; Random genetic drift within a colony: effective population size = N; Migration (island model): Rate = m/generation; Extinction replacement of colonies: Rate = λ/generation; Mutation (symmetric, two-allele): Rate = υ /generation; α = 2(m + λ)/n; Number of colonies: n; Variables of the i -th colony g 1i = frequency (A 1 B 1 ), g 2i = frequency (A 1 B 2 ), g 3i = frequency (A 2 B 1 ) and g 4i = frequency (A 2 B 2 ). Then the LD coefficient would be defined at two levels within the colony (or the stevia germline) and between colonies (between the stevia germlines). Ending up with the LD coefficient of the total population as D = G 1 – XY . Interestingly, the subdivision occurring in the LD coefficient is analogous to the inbreeding coefficient of [ 41 ] in structure population [ 43 , 44 ]. Likewise, the analogy existed in two-locus properties of various mating schemes [ 45 ] and genetics of mitochondrial alleles [ 46 ] as an exotic/ migrating DNA different from alleles found in the nuclei. QR Coding Using Python Python includes a module ‘qrcode’ that facilitates the creation of QR codes. However, this module has certain limitations, such as the volume of data (column*rows matrix) to be encoded into a single QR code. To address such constraints, we developed a package “PopAllele” ( https://github.com/RaafatA/PopAllele ) to visualize the distribution of whole markers of the studied populations through the medium of the QR code. We considered one QR1 code for the distributed alleles (rows of genotypic data) in the studied populations. The second QR2 code considered the germlines (column of genotypic data) within the population of markers. Then, a third QR code was used to combine QR1 and 2 codes forming the unique pattern of distributed alleles across the assessed populations (Supplementary Fig. 1). This last QR code is used to recall diversity and acquired data results. QR-like Patterns and Barcoding Possibility DNA barcoding depends on specific sequences that are not amenable to information storage, recognition, and retrieval [ 30 ]. The two chloroplast genes, namely, rbcL and matK were proposed by the plant working groups of the Consortium for Barcode of Life ( http://www.barcodeoflife.org/ ) as core barcodes [ 33 ]. [ 30 ] was the first report directly converted DNA sequences into QR codes that could take either of the known versions (Fig. 1 -A). However, their data was based on ITS2, rbcL, matK, psbA-trnH, and CO1 sequences that get converted into QR codes after detailed comparison with 2D coding systems like Aztec Code, CodaBlock-F, Data Matrix, PDF417, PDF417 Truncated, QR2005 code, and QR codes. This means that in the model of [ 30 ], they used the variation in the nucleotide base paring (A, T, G, and C) coming out of the amplified sequences of only the barcoding genes ( ITS2, rbcL, matK, psbA-trnH, and CO1 ). However, our system does not have any limits affected by the size of the sequenced genes or the nucleotide bases converted into QR codes. Besides, our system directly uses the differential and observed patterns of existing/absent alleles (as in the 100 genotyped-wheat lines Fig. 1 -B). Therefore, our model depends on the variation of the alleles inherited in a stevia germline, not on the variation at the nucleotide bases that could be misread due to malfunctioning in the sequencing machine. In addition, the [ 30 ] model depends on the NCBI taxid organisms, if a germline is not taxed, the user will fail to create a QR code. Our model will use the allelic pattern distributed along the genotyped stevia germline as the pattern for the QR code to be associated uniquely to this specific germline. Analysis of SCoT Marker Populations The SCoT fragments were converted into allelic and genotypic forms using The Microsoft Excel and prepared as an input file to be used by the Popgene version 1.32 (1997) software. Popgene was used to analyze the genetic diversity and relationships among and within the six SCoT populations of markers/alleles generated based on the variable distribution of these alleles along the DNA of the six stevia germlines based on the weight of base paired size of the amplified fragment within each SCoT primer. The phylogeny tree was built using the of the cluster analysis of the unweighted pair group method with arithmetic averages (UPGMA). Besides, the Popgene software was used to estimate the gene diversity ( h ) Nei’s [ 47 , 48 ] and Shannon’s information index ( I ) [ 52 ] for the six SCoT populations of markers/alleles, which were used as dominant marker types. For diploid data analysis of SCoT markers distributed among the six stevia germlines calculations were based on population genetics parameters. Among these parameters were the allelic frequency (gene frequency at each locus excluding missing values), allelic number (counts with nonzero frequency), effective allelic number reciprocal homozygosity; [ 53 ], polymorphic loci (as percentage of all alleles regardless of allelic frequency), homogeneity test (constructs two-way contingency tables and apply chi-square (χ 2 ) and likelihood ratio (G 2 ) tests for homogeneity of gene frequencies across the populations, the tests were carried out for six groups correspond to the six stevia germlines), F -statistics (estimate Nei’s [ 48 ]) G ST and both G ST and G CS for six groups), gene flow (from estimated G ST F ST [ 49 ] for six groups), genetic distance [ 50 ] genetic identity and distance and Nei’s [ 47 ] unbiased genetic identity and genetic distance for six groups), and neutrality test (performs Ewens-Watterson test for neutrality using the algorithm given in [ 51 ]), two-locus LD (Burrows’ composite for LD between pairs of loci and (χ 2 ) tests for significance [ 54 ] for single group. Results Genotypic Distribution of SCoT Allelic Populations The 14 SCoT primers have successfully produced a total of 1320 allelic and genotypic forms as, which means in average 220 allelic forms for each cultivar. Among the 1320 amplicons, 162 alleles (12.27%) were non-polymorphic and 1101 alleles (83.41%) were polymorphic (Table 3 ). Out of 1320, 43 amplicons (3.26%) were unique positive (UP) and able to molecularly differentiate among the six stevia cultivars. Likely, the unique negative (UN) characterization was confirmed by 14 allelic (1.06%) morphs. Table 3 SCoT marker distribution among the six stevia genotypes SCoT Type Sponti Chaina1 High Sugar ShouA3-2 Levan Shou2 Total Unique Positive 8 8 7 8 6 6 43 Unique Negative 2 5 3 0 2 2 14 Non Polymorphic 27 27 27 27 27 27 162 Polymorphic 183 180 183 185 185 185 1101 Total 220 220 220 220 220 220 1320 A doughnut distribution of SCoT markers is emphasized among stevia genotypes in Figure (2). The differentially distributed alleles of marker types (polymorphic, non-polymorphic, UN, and UP) among the six stevia genotypes were presented in four circles ordered; respectively, from outside to inside direction. The stevia genotypes were emphasized in the doughnut graph by the different areas colored within each circle (marker type). For polymorphic alleles, the genotypes with the same number of alleles (70) were China1, High Sugar, and Shou2. However, similar numbers (27) of non-polymorphic SCoT alleles were distributed among all stevia genotypes. Differential distribution of SCoT markers was observed at the level of allelic type of UP and UN. For the type of UN, the genotypes Sponti and High Sugar have shown the same number (three) of distributed alleles. For the type of UP, the genotypes Levan and Shou2 have shown the same number (six) of distributed alleles. Noticeably, the highest number (71) of distributed alleles was scored to the genotype Sponti for the type of polymorphic alleles, while the lowest number (zero) was scored to the genotype ShouA3-2 for the markers of type UN. The Populations of SCoT Alleles Revealing The Differential Diversity For each stevia genotype, the 14 SCoT-based loci have generally produced 220 allelic forms. Of these, 193 alleles (86.46%) were polymorphic among the six stevia genotypes (Table 4 ). A total of 27 non-polymorphic and 57 monomorphic alleles were produced. The expected number of alleles per single SCoT locus has reached 15.7 alleles, where each SCoT locus on average can produce 14 polymorphic alleles. Approximately, two non-polymorphic and 4.1 monomorphic alleles were also expected for each SCoT locus (Table 4 ). Therefore, the average percentage of non-polymorphic alleles was 14.73%, and of the monomorphic alleles was 23.11% (Table 4 ). On one hand, seven SCoT loci (SCoT6, 10, 24, 28, 32, 44, and 46) have produced 100% polymorphism, whereas three of them (SCoT24, 28, and 46) have produced 0% of monomorphism. On the other hand, SCoT6, 13, 19, 35, and 36 have produced at least 63.64% of polymorphism and a maximum 46.43% of monomorphism, which is close to 50% of interacting and active alleles for segregating and genetics, this should be a potential good marker for genotyping and NGS studies. Especially that this high potential has been approved and generated form only 14 SCoT (primers). The SCoT6, 10, 20, and 44 have produced either one or two monomorphic alleles. Such loci were informative at the homozygosity level for the studied stevia germlines. This gives special importance to these four informative loci (SCoT6, 10, 20, and 44) out of the 14 due to the monomorphic representation of the amplified fragments among the stevia genotypes. Generally, the sizes of the alleles were ranged between 120 bp (SCoT9) and 2500 bp (SCoT28) (Table 4 ). Table 4 The size range of mono and polymorphic amplicons generated by 14 SCoT markers loci SCoT Primer TNA P NP MMA P% NP% MM% ASR (bp) SCoT6 5 5 0 2 100 0 40 200–580 SCoT9 16 12 4 5 75 25 31.25 120–1900 SCoT10 11 11 0 2 100 0 18.18 200–1000 SCoT13 28 27 1 13 96.43 3.57 46.43 390–1700 SCoT16 39 31 8 7 79.49 20.51 17.95 180–2200 SCoT19 11 7 4 4 63.64 36.36 36.36 180–1500 SCoT20 6 1 5 1 16.67 100 16.67 320–1700 SCoT24 16 16 0 0 100 0 0 180–2000 SCoT28 8 8 0 0 100 0 0 400–2500 SCoT32 14 14 0 5 100 0 35.72 200–1200 ScoT35 22 19 3 8 86.36 13.64 36.36 180–1500 SCoT36 28 26 2 9 92.86 7.14 32.14 220–1800 SCoT44 8 8 0 1 100 0 12.5 240–1600 SCoT46 8 8 0 0 100 0 0 300–1000 Average 15.7 14 2 4.1 86.46% 14.73% 23.11% 120–2500 Total 220 193 27 57 TNA = the total number of amplicons; P = Polymorphic; NP = Non polymorphic; MMA = Monomorphic amplicons; ASR = Amplicon size range. SCoT-Allele-Specific Characterizing Each Stevia Genotype The SCoT system has provided a strong genotypic and differential tool with a total of 55 unique allelic markers (Table 1 ). Both ScoT13 and 36 have produced the highest number (nine, each) of differential monomorphic alleles. Meanwhile, SCoT20 and 44 have produced the lowest number (one) of monomorphic alleles. SCoT35 has produced eight, while SCoT16 and 32 have each produced seven, and SCoT9 and 19 have respectively produced five and four alleles. Both SCoT6 and 10 have each produced two alleles. A total of 10 differentiating alleles were best characterizing the genotype Sponti among the other stevia genotypes. Amongst, eight unique alleles were as positive and two as negative. Most importantly were the alleles at the sizes of 590, 540, and 1000 bp that were respectively produced by SCoT16 and 36. All uniquely negative alleles for the Sponti genotype were at the molecular weight above 1000 bp (SCoT9 = 900 bp and SCoT19 = 1500 bp; Table 1 ). A total of 12 differentiating alleles were best characterizing the genotype China1 among the other stevia genotypes. Amongst, seven unique alleles were as positive and five as negative. Most importantly were the alleles at the sizes of 450 and 650 bp and respectively produced by SCoT16 and 36. A total of seven were four uniquely positive and three as negative alleles for the China1 genotype and above the molecular weight of 1000 bp (SCoT10, 13, 35, and 44; Table 1 ). A total of nine differentiating alleles were best characterizing the genotype High Sugar among other stevia genotypes. Amongst, six unique alleles were as positive and three as negative. Most importantly were the alleles at the sizes of 210 and 600 bp and respectively produced by SCoT16 and 36. No alleles for the High Sugar genotype were above the molecular weight of 1000 bp (Table 1 ). A total of eight differentiating alleles were all unique positive and best characterizing the genotype ShouA3-2 among the other stevia genotypes. Most importantly were the alleles at the sizes of 490 and 500 bp and respectively produced by SCoT16 and 36. Two alleles for the ShouA3-2 genotype were above the molecular weight of 1000 bp (SCoT20 = 1700 and SCoT32 = 1200 bp; Table 1 ). A total of seven differentiating alleles were best characterizing the genotype Levan among the other stevia genotypes. Amongst, five unique alleles were positive and two negative. Most importantly were the alleles at SCoT16 of the sizes of 520, 700 (positive), and 800 (negative) and at the SCoT36 of 1600 bp (positive). A total of three alleles were all as uniquely positive for the Levan genotype and above the molecular weight of 1000 bp (SCoT9 = 1000 and 1200, and SCoT36 = 1600 bp; Table 1 ). A total of four differentiating alleles were best characterizing the genotype Shou2 among the other stevia genotypes. Amongst, two alleles each were unique positive and negative. Most importantly were the alleles at the sizes of 2200 and 1200 bp and respectively produced by the SCoT16 and 36 above the molecular weight of 1000 bp (Table 1 ). Diploid SCoT Diversity Revealing Genetic Identity and Distance Parameters of genetic diversity of SCoT as the dominant marker system were presented based on the average of the observed number (na) of alleles between one and two alleles (Mean = 0.174; StDev = 0.44). The genic variation [ 55 ] observed among the different alleles of SCoT loci has effectively differentiated the six stevia genotypes. The effective number (ne) of alleles [ 56 ] varied as 1, 1.39, 1.8, and 2 (Mean = 1.5; StDev = 0.35) among the different germlines. Correspondingly, the gene diversity (h) [ 48 ], where observed heterozygosity (Ht) in the six groups of SCoT loci was varied in values as 0, 0.28, 0.44, and 0.5 (Mean = 0.27; StDev = 0.18). Meanwhile, Shannon's information (I) index [ 57 ] also varied in values (0, 0.45, 0.64, and 0.69) (Mean = 0.41; StDev = 0.26). Besides, the gene/allele frequency that was discovered among the SCoT loci varied as well in values (0.17, 0.33, 0.5, 0.67, 0.83, and 1). Unbiased values of genetic identity and distance (Supplementary File 2-A and B; respectively) were estimated among the six different groups of generated SCoT loci and presented as doughnut distribution in Figure (3). Generally, the values of genetic distance (Fig. 3 -A) ranged between 0.27 and 0.5, while the values of genetic identity (Fig. 3 -B) ranged between 0.61 and 0.76. The doughnut graph included six circles representing the six stevia genotypes from the outer to the inner direction respectively as Shou2, Levan, ShouA3-2, High Sugar, China1, and Sponti. The differential areas colored within each circle (genotype) represent the percentages of genetic distances distributed between the two encountered genotypes labeled to the denoted percentage in the box (Fig. 3 -A). Likewise, for each circle (genotype) in Fig. 3 -B, though the colored areas represent the percentage of genetic identity score between the two encountered genotypes labeled to the denoted percentage in the box. For example, the genotype Shou2 showed differential genetic distance scored as 49% with Sponti, 50% with China1, 40% with High Sugar, 39% with ShouA3-2, and 35% with Levan. This indicates an expected close genetic relation among the common ancestors of Shou2 and Levan. The genotype Sponti showed 37% of genetic distance with the genotype China1, 39% with High Sugar, 32% with ShouA3-2, 44% with Levan, and 49% with Shou2. This indicates, a lower to moderate genetic relationship between the aforementioned genotypes and Sponti. At the level of genetic identity, the Shou2 genotype showed 61% identity with the genotype Sponti, 60% with China1, 67% with High Sugar, 78% with ShouA3-2, and 71% with Levan. The genotype Sponti showed 69% with China1, 68% with High Sugar, 73% with ShouA3-2, 64% with Levan and 61% with Shou2. Phylogeny Tree Revealing The Genic Relationships Among The Stevia Genotypes A dendrogram based on [ 50 ] genetic distances was drawn using the of UPGMA method and presented in Figure (4). The two genotypes High Sugar and ShouA3-2 have the smallest genetic distance (13.5%). The highest genetic distance (19.9%) was scored to the genotype China1. The two genotypes Levan and Shou2 have the same genetic distance (17.3%) away from that of the other four stevia genotype. The genotype Sponti had a genetic distance (17.7%) linked between the highly distant genotype (China1) and the two closely related genotypes (Shou2 and Levan) and the other two lower related genotypes (ShouA3-2 and High Sugar). This means that the genotype Sponti is more related (0.4%) to the two genotypes Levan and Shou2, followed by the genotype China1 (2.2) and more distant from High sugar and ShouA3-2 genotypes (4.2%). More emphasis can be drawn on the four ancestors expected for the six stevia germlines and their genetic relationships. For example, the two genotypes Shou2 and China1 may have a common ancestor that was genetically distant (4.2) from those of other stevia genotypes. Likewise, the two genotypes Sponti and High Sugar may have a common ancestor that was genetically distant (4.2) from those of other stevia genotypes. Moreover, the ancestor of the genotype Levan was more related (3.9) to that of ShouA3-2, while was distant (5.9) from that of China1 and Shou2. Discussion Molecular Genotyping Tools Revealing Genetic Diversity Whenever homologous genes are tandemly arranged in (short) chromosomal regions, then transfer of gene segments will occur between loci based on evolution through gene conversion or doubled unequal crossing-over [ 58 , 59 ]. Expectedly, the transfer of gene segments will occur among genes of different loci and support the evolution of a supergene family of Bodmer theory [ 39 ]. Therefore, gene identity among alleles is roughly 90% of amino acid identity, while it is 85% among genes of different loci [ 60 ]. This means that gene homology between different loci is slightly lower than that among alleles. Likewise at the level of LD, where gene segments transfer between different loci as a mutation effect on LD. This supports a modification made by [ 38 ] to the proposal of Bodmer Silver and Hood [ 61 ] that each marker contains a cluster of many loci (multigene family), of which one would be expressed. Usually, DNA markers are applied on multiple populations or groups of plant germlines (RIL, NIL, DH, or magic). However, in the current study, one population of SCoT (1320 fragments) has indirectly been converted to a generic population of alleles reflect the hidden genetic diversity embedded in six stevia germlines. SCoT has produced high information content about the loci that can be used for genotyping (monomorphism) and differentiating (polymorphism) the different stevia germlines. SCoT alleles have produced various and wide ranges of percentages (0–46% ) of monomorphism. On one hand, this indicates the ability of the SCoT technique to execute the type of loci with a type of low information content to be avoided in the studies including crosses among stevia germlines. On the other hand, some loci have a low number of monomorphisms (two alleles), which could be emphasized by some reasonable homozygosity (for such alleles) or the presence of null alleles. Plants depend on high variation in their DNA to survive environmental changes. Heterozygosity in plants requires strong and codominant DNA marker tools. Herein, SCoT as a DNA marker belongs to the dominant type, with very low potential for digging behind heterozygosity. Despite such fact, SCoT system tools herein have provided some indirect information about the existence of null alleles and the expected amount of heterozygosity. In a study comparing between efficiency of marker types (ISSR and SSR), ISSR has provided the highest genetic variability [ 62 ]. When eight ISSR markers were used, the Shannon’s index was 0.26 [ 63 ], while when seven primers were used, Shannon’s index was 0.48 [ 62 ]. Herein, the Shannon’s index was 0.69 when 14 primers of SCoT were used. Besides, these fluctuations in the results among the different studies can be emphasized by the fact that the stevia germlines were diverse in the origins of each germline. It is thought that the closely related populations reflect lower Shannon’s index. As expected when plants were related to similar populations [ 63 ], the Shannon index was produced in low values. In a study that included two populations of Finnish roseroot [ 64 ], Shannon’s index values generated within the populations were lower (0.31 and 0.23) than those calculated among populations (0.34). This may suggest higher diversity for SCoT marker than ISSR marker despite the number of primers. At the level of number of primers used for each marker type (ISSR, SSR, SCoT) in the different studies and how it affects the Shannon’s index. It was evident that when 12 primers [ 65 ] were used, Shannon’s index was 0.17–0.33, when 13 primers [ 66 ] were used, it was 0.07–0.22, likewise, when13 primers [ 67 ] were used, it was 0.08–0.24, while when 14 primers (in the current study) were used, it was averaged as 0.41. This suggests that Shannon’s index reflects the true genetic diversity that existed in the DNA of plant germplasm despite the number of primers used to estimate it. Depicted Genetic Diversity Among Stevia Germlines Based on 1000 simulated sampling among the 1320 amplicons in the current study, the overall Ewens-Watterson test for neutrality ([ 51 ]) has produced about 78 polymorphic bands with an L95 value of 0.5 and U95 value of 0.72 (SE = 0.009 for both L95 and U95). These values support the high level of natural selection practiced on the genetic composition of distributed SCoT loci among the six populations of stevia. Gene flow [ 68 ] and expected heterozygosity (Hs) were estimated as zero, which indicates that the population of SCoT loci in each single genotype were independent form other genotypes. In addition, only 14 SCoT loci (primers) were allocated on stevia DNA, generating almost 50% monomorphism, which reflects the existence of active alleles/regions for new recombination. Such alleles are very informative to study genetics and segregating traits opt to discover heterozygosity. Potentially, SCoT is a good marker for diagnostics, genotyping, and/or NGS studies. According to Table (3), the high number of alleles (ex. 31 for SCoT16) in the current study reflects the high genetic diversity compared to other marker types [ 69 , 70 , 62 ]. Observed heterozygosity (Ho) in the current study ranged between 0.167 and 1.0, while expected heterozygosity (He) (genetic diversity) ranged between 0.278 and 0.5 for most loci of two alleles. Most importantly, if the current potential of SCoT has been proved by only 14 primers, what if more SCoT primers were used? The current study produces the first easy tool to genotype the six main stevia germlines in Egypt through specific SCoT alleles. For example, the germline Sponti can be characterized through some genotypic banding pattern tools. However, through the especially associated fragments of SCoT in the current study, only one fragment produced by either the SCoT13 at the size of 550 bp or the SCoT16 at the size of 590 bp will be sufficient to identify Sponti away of other germlines (Table 2 ). SCoT system as a diagnostic germline tool provides various characterizing fragments suiting multiple availabilities of SCoT in any laboratory. Molecular markers provide a tool of multiple banding patterns to characterize specific cultivars/germlines [ 71 ]. Conclusion The protection of crop germlines, encompassing biologically important traits and/or breeding values, is one of the top priorities of worldwide intellectual properties (IP) rights. Many roles and tools have been created in this perspective. If the current potential of SCoT as a dominant marker has been extended through the 14 presented primers, what if more SCoT primers were used? The unique allele-specific association, in the current study for each germline of stevia, is a practical application satisfying this objective. Most notably, the application of the two SCoT16 and 36 markers on the six stevia germlines (Sponti, China1, High Sugar, ShouA3-2, Levan, and Shou2) respectively discovered 10, 12, nine, eight, seven, and four unique alleles. Most importantly those at the sizes of 590, 540, and 1000 bp (Sponti); 450 and 650 bp (China1); 210 and 600 bp (High Sugar); 490 and 500 bp (ShouA3-2); 520, 700, 800, and 1600 bp (Levan); 2200 and 1200 bp (Shou2). Supportively, the genetic diversity and distances among and between the six populations of SCoT alleles were calculated. These diversities and distances have confirmed the diverged lineages and evolutionary ancestors linking these six stevia germlines and were not discovered before. As a result, the phylogenetic discrimination has presented the common structured genetics among the six stevia germlines in Egypt. Generally, electronic data needs to be converted into encrypted forms with specified keys to encode this encryption, the presented tool (PopAllele) compress, encrypt, and encode the genotypic data in a modified form to be stored in blockchain networks. PopAllele enables breeders to eliminate redundancy in generated genotypic data and store informative parts into retrieval and encrypted QR-forms reserving germlines IP rights when distributed. Declarations Acknowledgment: Authors thanks Hisham A. Ashry and Eslam S. ElShahed for their technical assistance. Data Availability Statement: “The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request, while the developed a package “PopAllele” can be accessed (https://github.com/RaafatA/PopAllele).” Ethics Approval and Consent to Participate : The plants used in the study comply with relevant institutional, national, and international guidelines and legislation and are owned to the Agricultural Research Center (ARC), who encompass the Agricultural Genetic Engineering Research Institute (AGERI) and the Sugar Crops Research Institute (SCRI), where the experiments and the study were taken place. The stevia plants ( Stevia rebaudiana ) used in this study are not species at risk of extinction nor belong to the International Union for Conservation of Nature (IUCN) red list index of threatened species or the endangered species of wild fauna and flora. Therefore, the genotypes of stevia used in the study complies with the ARC, national, and IUCN guidelines. The material used in this study was based upon work supported by the authors. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s). Authors Contribution : Conceptualization of the research article, writing original draft, reviewing, editing of all manuscript versions and validation of analyzed data by Ahmed ElFatih ElDoliefy . Data collection and main analysis by Ahmed ElFatih ElDoliefy and Mai M. Hashem . Methodology by Ahmed ElFatih ElDoliefy , Rafat A. Eissa . Software analysis by Ahmed ElFatih ElDoliefy , Rafat A. Eissa , and AbdelRahman A. AbouEldahab . Python language and scripting by Rafat A. Eissa . Formal analysis by Ahmed ElFatih ElDoliefy , Rafat A. Eissa , and AbdelRahman A. AbouEldahab . Data curation and statistical analysis Ahmed ElFatih ElDoliefy . Visualization by Ahmed ElFatih ElDoliefy , Rafat A. Eissa . All authors have read and agreed to the published version of the manuscript. Funding and Competing Interests : “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript and they have no relevant financial or non-financial interests to disclose and no competing interests.” References Kaplan, B.; K. Turgut. 2019. Improvement of rebaudioside A diterpene glycoside content in Stevia rebaudiana Bertoni using clone selection. Turk. J. Agric. For., 43, 232–240. Goyal, S.K.; Samsher E.T. and R.K. Goyal. 2010. Stevia ( Stevia rebaudiana ) a bio-sweetener: A review. Int. J. Food Sci. Nutr., 61, 1–10. Šic Žlabur, J.; Vo´ca, S.; Dobriˇcevi´c, N.; Ježek, D.; Bosiljkov, T.; M Brnˇci´c. 2013. Stevia rebaudiana Bertoni-A review of nutritional and biochemical properties of natural sweetener. Agric. Conspec. Sci., 78, 25–30. 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SLU, Plant Breeding and Biotechnology (until 121231). https://stud.epsilon.slu.se/2049/. Zhou, C. J., Zhu Song, H., Hua Li, J., Wei Sun, J., De Jin, M., Wen Zhang, Q., and Wang, B. (2005). Evaluation of genetic diversity and germ plasm identification of 44 species, clones, and cultivars from 5 sections of the genus Populus based on amplified fragment length polymorphism analysis. Plant Molecular Biology Reporter, 23 (1), 39–51. https://doi.org/10.1007/BF02772645. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.jpg Supplementary Figure 1: Workflow of The PopAllele Package Scripting Allelic QR. The initial input is a CSV file encompassing genotyping data. The first stage involves preprocessing this data to ascertain its appropriate format and eliminate any redundancy. Subsequently, the data undergoes an encoding and compression process, resulting in the generation of two distinct QR codes. The final step involves the combining of these two QR codes into a singular QR code. SupplementaryFile2A.png SupplementaryFile2B.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4636839","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332693576,"identity":"d1c994b6-5d93-4efa-af05-c261fd498b59","order_by":0,"name":"Mai M. Hashem","email":"","orcid":"","institution":"Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC)","correspondingAuthor":false,"prefix":"","firstName":"Mai","middleName":"M.","lastName":"Hashem","suffix":""},{"id":332693577,"identity":"5d3f97f9-9582-4d6a-84af-553c4c1017e4","order_by":1,"name":"Rafat A. Eissa","email":"","orcid":"","institution":"Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC)","correspondingAuthor":false,"prefix":"","firstName":"Rafat","middleName":"A.","lastName":"Eissa","suffix":""},{"id":332693578,"identity":"11ed1776-7811-48d4-b561-daa1bf85bed9","order_by":2,"name":"AbdelRahman A. AbouEldahab","email":"","orcid":"","institution":"Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC)","correspondingAuthor":false,"prefix":"","firstName":"AbdelRahman","middleName":"A.","lastName":"AbouEldahab","suffix":""},{"id":332693579,"identity":"98cdb062-1ad6-4f05-b99d-0dd54ea52a8f","order_by":3,"name":"Ahmed ElFatih A. ElDoliefy","email":"data:image/png;base64,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","orcid":"","institution":"Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC)","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"ElFatih A.","lastName":"ElDoliefy","suffix":""}],"badges":[],"createdAt":"2024-06-25 13:35:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4636839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4636839/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61299232,"identity":"5347660a-e353-4f28-a89c-6f74b02e2956","added_by":"auto","created_at":"2024-07-29 08:41:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1060869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Black and White Spots Between QR and Genotypic Patterns.\u003c/strong\u003e \u003cstrong\u003ePanel\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, the different versions of QR patterns from 1, 10 and 40. \u003cstrong\u003ePanel B\u003c/strong\u003e, the differential distribution of alleles/markers (in rows) showed as present (white) and absence (black) in some genotypic data, where columns represents the germlines in a unique black and white distribution of marker alleles (rows) providing a resembling QR-like pattern (QRLP) similar to those presented in panel A.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/c77a31edf641ffa5426d5ec7.png"},{"id":61299229,"identity":"1a8824ec-5d8e-470d-8d91-e11616071247","added_by":"auto","created_at":"2024-07-29 08:41:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":467807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDoughnut Distribution of SCoT Markers.\u003c/strong\u003e Circles from outer to inner direction represent the four SCoT marker types (non-polymorphic, polymorphic, unique negative, and unique positive). The different colored areas of each circle represent the percentage of this marker type in each of the six stevia genotypes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/aa42cbe47e8982b003d9ff99.png"},{"id":61301068,"identity":"d540c30c-5479-4e1d-901c-bbefeb4fb876","added_by":"auto","created_at":"2024-07-29 08:57:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":652956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDoughnut distribution of percentage of similarity among stevia cultivar.\u003c/strong\u003e Circles from outer to inner direction represent the different cultivars. The different colored areas of each circle represent the genetic distances (A) and genetic identities (B). Values of colored areas within a circle represent the percentages of similarity based on diversity estimates of SCoT allelic markers among the six stevia cultivars.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/629e3018e493ff1002216ddb.png"},{"id":61299231,"identity":"563dfcb2-f27e-448c-a2d5-d009b159be66","added_by":"auto","created_at":"2024-07-29 08:41:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogeny Tree Based on Nei's Genetic Distance and The UPGMA Method.\u003c/strong\u003e A modified neighbor procedure of PHYLIP software (Version 3.5) was used to illustrate the genetic relationship among the six stevia germlines. Numbers at nodes indicate the bootstrap values and at the top of the lines are root distances.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/703085fc54b39dc5350b24dc.jpeg"},{"id":65156364,"identity":"6996422c-5a87-44c3-a19e-5da194174cb9","added_by":"auto","created_at":"2024-09-24 08:09:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3764749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/02f5c424-86d2-450d-bec5-69916daa2714.pdf"},{"id":61299230,"identity":"154abd25-99cc-4a10-8d60-89414fb6c165","added_by":"auto","created_at":"2024-07-29 08:41:45","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":219358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1: Workflow of The PopAllele Package Scripting Allelic QR.\u003c/strong\u003e The initial input is a CSV file encompassing genotyping data. The first stage involves preprocessing this data to ascertain its appropriate format and eliminate any redundancy. Subsequently, the data undergoes an encoding and compression process, resulting in the generation of two distinct QR codes. The final step involves the combining of these two QR codes into a singular QR code.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/1b7f64eef6cabd3762c37cd4.jpg"},{"id":61299235,"identity":"cd38892b-19e9-4ef5-bf13-d73bd5fcf793","added_by":"auto","created_at":"2024-07-29 08:41:45","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":192330,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2A.png","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/3d17141917feacf7827339e2.png"},{"id":61300299,"identity":"b8f40355-c1bb-4a0f-a8db-feacc6668816","added_by":"auto","created_at":"2024-07-29 08:49:45","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":211656,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2B.png","url":"https://assets-eu.researchsquare.com/files/rs-4636839/v1/9f7ea3b16b823f18d66fb3f0.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distribution of SCoT-Based Populations Depict Genotypic Diversity of Six Stevia Germlines in Egypt","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFoods low in both sugar and calories have become essential for athletes and health welfare. Leaves of \u003cem\u003eStevia rebaudiana, Bertoni\u003c/em\u003e (family: \u003cem\u003eAsteraceae\u003c/em\u003e) encompass steviol glycosides (SGs), which are 200\u0026ndash;300 times sweeter than sucrose [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The SGs have a wide range of usages as herbal medicines (tonics for diabetic patients), food industry (drinks, bread, and fruit juices), and cosmetics (mouthwashes and toothpaste) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Increasing demand for natural sweeteners has led growers worldwide to cultivate stevia on a large scale [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Large-scale cultivation of economic crops depends on allelic variation. If the allelic variation is limited, breeders essentially increase it by outcrosses to lower the inbreeding effect. To help breeders improve stevia and use it as a genetic source, the determination of allelic diversity within and among germlines is essential [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Molecular techniques provide effective tools for dissecting genetic diversity and population structure [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Genetic DNA markers are cheap, reliable, and unique for each species and independent of age, physiological conditions, and environmental factors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For efficient conservation and further utilization of plant resources, several molecular markers have been developed such as random amplified polymorphic DNA (RAPD) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], simple sequence repeat (SSR) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], amplified fragment length polymorphism (AFLP) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], sequence-related amplified polymorphism (SARP) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], start codon-targeted (SCoT) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] as well as inter-retrotransposon- amplified polymorphisms (IRAP), retrotransposon-microsatellite amplified polymorphism (REMAP) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The SCoT is a dominant novel technique that uses a single primer with strong affinity, specificity, and reproducibility [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Six SCoT primers were used to evaluate genetic diversity in durum wheat using agro-morphological traits [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and for population structure [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As well, 15 SCoT primers were used to assess diversity of 70 Iranian \u003cem\u003eTriticum\u003c/em\u003e species [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], also for molecular characterization [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in stevia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], for plant variability and relationships [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and for genotypic and phenotypic diversity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Stevia plants have been characterized by high genetic diversity and described as a starting material for bio-products at both biochemical and molecular levels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Molecular characterization can help determine species, breeding behavior, presence of gene flow, and allelic transfer within and between populations of the same or related species [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Molecular methods can also be combined with morphological techniques for more reliability. SCoT marker has been used as a genetic marker for cross-pollinated crops [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It also has been used as an immense tool for genome mapping, gene tagging, and forensic investigations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and for genetic diversity and phylogenetic analysis in stevia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Further, SCoT focuses on gene regions that provide advantages over other markers types that amplify random spots on the DNA suiting more applications in genetic mapping [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe QR codes are information carriers with many advantages including the high recognition rate, the large amount of stored information, the low cost and simple operation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, QR may face some challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult and questionable [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Despite this debatable potential of QR codes, molecular biologists opt to integrate it in DNA barcoding technology to identify species based on standard DNA sequences starting form version 1, 10, and 40 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-A). DNA barcoding technology depends on short standard DNA sequences for biodiversity, conservation genetics, and wildlife forensic studies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For animal-based studies the mitochondrial cytochrome c oxidase subunit I (\u003cem\u003eCO1\u003c/em\u003e) gene has been accepted, while for plant-based, the two chloroplast genes, namely, \u003cem\u003erbcL\u003c/em\u003e and \u003cem\u003ematK\u003c/em\u003e, were proposed [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The DNA intergenic transcribed spacer (ITS) regions, its subsequence (ITS2), and the \u003cem\u003epsbA-trnH\u003c/em\u003e were evaluated [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This has created various one- and two-dimensional barcoding symbologies, nevertheless, at the DNA sequence level of the aforementioned standard genes. Therefore, the bioinformatic tools (such as the tree-based phylogenetic analysis) provided a solution to estimate divergence time between organisms and relationship among species. Though the most commonly methods for species identification is the Basic Local Alignment Search Tool (BLAST) followed by distance matrix computations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; there is a possibility that the phylogenetic tree-based methods (like Neighbor-joining (NJ) and/or the Maximum Likelihood) may produce the lowest accuracy due to unavailability of homologs in sequence databases [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. There comes the affirmed need for QR codes to be intergrade with the molecular and bioinformatics tools. In the current study, 14 SCoT primers have successfully produced polymorphic and allelic forms at the genetic regions of six stevia germlines. Moreover, we provided a quantified evidence to the divergent distribution of variation observed in genotypic data generated by a dominant molecular marker (such as the SCoT). This molecular quantitative method of variation can be converted to an associated-QRLP as a unique tool of barcoding system. Moreover, the QRLP signature was digitalized using a script to produce QR codes preserving and recalling the allelic diversity in the genotyped DNA. This approach peace-mind the worries a researcher may have regarding the generated data about the breeding lines planned to be distributed. At any time and by running a very small number of DNA markers the researcher can recall the unique alleles characterizing each germline data and compare between any of them by the QR given to each germline he distributed to others breeders.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003ePlant Materials and DNA Preparation\u003c/h2\u003e\n \u003cp\u003eSix stevia germlines (Sponti, China1, HighSugar, ShouA3-2, Levan, and Shou2) were kindly provided by the Sugar Crops Research Institute, that belong to the ARC, Giza, Egypt. The stevia germlines are owned to the ARC with no license or agreement is needed to decree the usage of the germlines. The DNA was extracted from the fresh leaves of each germline based on the manufacturer manual of the Qiagen DNeasy Plant Mini Kit (Qiagen, USA). DNA concentration and quality were assessed using the NanoDrop spectrophotometer (TU 1880 Double Beam UV- VIS) and visualized using 1% agarose gel electrophoresis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eSCoT PCR Preparation\u003c/h2\u003e\n \u003cp\u003ePCR reactions were performed in a total volume of 25\u0026micro;l that contained 1\u0026micro;l of DNA (50\u0026ndash;80 ng), PCR reaction buffer (1X, v/v), MgCl\u003csub\u003e2\u003c/sub\u003e (2.5 mM), dNTPs (2 mM), SCoT primer (2.5 \u0026micro;mol each), 1 unit of Taq DNA polymerase (Fermentas, Szeged, Hungary) and ddH\u003csub\u003e2\u003c/sub\u003eO. Fourteen SCoT primers (Table \u003cspan\u003e1\u003c/span\u003e) were used following [\u003cspan\u003e13\u003c/span\u003e]. PCR reactions were performed following the program settings in Table \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003ePropagation of The SCoT Marker Populations\u003c/h2\u003e\n \u003cp\u003eAll PCR products were electrophoresed on 2% (w/v) of agarose gels. Amplified fragments were visually scored as present (1) and absent (0) using the molecular Imager Gel Doc XR system and Image Lab software (Bio-Rad, USA). A fragment was considered as unique positive (UP) if was present (1) for once in a germline, while was absent (0) across all other germlines. In contrarily, a fragment was considered a unique negative (UN) if was absent (0) for once in a germline, while was present (1) across all other germlines. For each SCoT fragment (allele), conversion to the allelic form and nomenclature was performed by combining the name of the SCoT primer and the corresponding molecular weight (bp) (ex. SCoT16-250).\u003c/p\u003e\n \u003cp\u003eGenerally, the non-random association of alleles (linkage disequilibrium-LD) at two loci has been studied. Mainly two reasons are discussed for LD, epistatic natural selection and random genetic drift [\u003cspan\u003e38\u003c/span\u003e]. Plausibly, markers among interrelated functional genes (which may be clustered to form supergenes such as MHC in man and mice) are considered as the observed LD caused by epistatic fitness interactions (natural selection) [\u003cspan\u003e39\u003c/span\u003e, \u003cspan\u003e40\u003c/span\u003e]. For exotic stevia germlines of non-Egyptian sources and the random mating type of stevia, the structured populations in Egypt could be affected by migration between sub-populations (colonies). Despite the limited effect of immigration on the recombination between loci in the finite germlines of stevia in Egypt (due to fewer heterozygotes), the LD may be created among loci of local colonies (germlines). Therefore, it is important to clarify the magnitude of LD among the loci of stevia germlines due to random drift when the stevia population was subdivided into six genetic sources (germlines) in Egypt. those germlines are used in the current study. However, results may suggest strong LD between colonies (germlines) when migration is limited.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe Molecular Weight of SCoT Unique Positive and Negative Amplicons Among the Six Stevia Germlines.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequence (3\u0026apos;-5\u0026apos;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGermline\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMWUPB (bp)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMWUNB (bp)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAATGGCTACCACTACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACAATGGCTACCACTGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChian1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACAATGGCTACCACCAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCATGGCTACCACGGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShou2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCATGGCTACCACCGGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShouA3-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShou2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCATGGCTACCACCGGCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAACAATGGCTACCACGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShouA3-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCATGGCTACCACCGCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAACAATGGCTACCACCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAACAATGGCTACCACGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShouA3-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAACCATGGCTACCACCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShouA3-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCACCATGGCTACCACCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSponti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Sugar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShouA3-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShou2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCATGGCTACCACCGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCoT46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACCATGGCTACCACCGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eMWUPB, molecular weight of unique positive bands. MWUNB, molecular weight of unique negative bands.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSCoT PCR Conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCycles\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemperature (˚C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime (min)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e# of Repetition for Cycles\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle1:Initial Denaturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle1:Once\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle2a:Denaturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle2:40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle2b:Annealing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle2:40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle2c:Extension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle2:40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle3:Final Extension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycle3:Once\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eProposed Theorem of Creating The Populations Out of SCoT Loci\u003c/h2\u003e\n \u003cp\u003eThe best simplified theory of current study, which is derived from the Wright\u0026apos;s island model [\u003cspan\u003e41\u003c/span\u003e], is to consider a population (species as denoted by the Wright\u0026apos;s island model) consisting of (n) colonies (sub-population/demes), where each colony (\u003cem\u003ei.e.\u003c/em\u003e, a stevia germline) consists of (N) breeding loci (effective size) that will be generated by the SCoT. Let (m) be the migration rate of loci among colonies (herein, the stevia germlines) per generation. In the island model, it is noted that every colony (a stevia germline) receives, in one generation, a fraction of (m) of its loci from the entire population of loci of stevia species around. So, based on [\u003cspan\u003e42\u003c/span\u003e], each colony (a stevia germline) is subject to extinction at a rate of (\u0026lambda;) per generation, and when extinction occurs to a colony (a stevia germline), it is immediately replaced by a line (a dose of loci) that is derived from single colony (other stevia germline) in the population. As expected, the value (mean) of nonrandom association (LD) in this model is zero at equilibrium. Thus the variance of the LD coefficient is created by random genetic drift in each colony (of a stevia germline). When the migration of loci is limited, random drift in each colony (a stevia germline) would dominate and the different types of loci would spread in different colonies (of stevia germlines) increasing the variance of LD. Therefore, the LD coefficient will follow the equations demonstrated by [\u003cspan\u003e38\u003c/span\u003e]. Briefly, if two loci in a population have the following rates of evolution.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1722241716.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eϰ\u003csub\u003ei\u003c/sub\u003e = g\u003csub\u003e1i\u003c/sub\u003e + g\u003csub\u003e2i\u003c/sub\u003e and \u003cem\u003e\u0026gamma;\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;g\u003csub\u003e1i\u003c/sub\u003e + g\u003csub\u003e3i\u003c/sub\u003e; Variables of the total population: G\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e = \u003csup\u003en\u003c/sup\u003e\u0026Sigma; \u003csub\u003ei = 1\u003c/sub\u003e g\u003csub\u003eji\u003c/sub\u003e/n for \u003cem\u003ej\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u0026thinsp;~\u0026thinsp;4, \u003cem\u003eX\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003csup\u003en\u003c/sup\u003e\u0026Sigma; \u003csub\u003ei = 1\u003c/sub\u003e ϰ\u003csub\u003ei\u003c/sub\u003e/n and \u003cem\u003eY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003csup\u003en\u003c/sup\u003e\u0026Sigma; \u003csub\u003ei = 1\u003c/sub\u003e \u003cem\u003e\u0026gamma;\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e/n.\u003c/p\u003e\n \u003cp\u003eRecombination: Rate\u0026thinsp;=\u0026thinsp;c/generation; Random genetic drift within a colony: effective population size\u0026thinsp;=\u0026thinsp;N; Migration (island model): Rate\u0026thinsp;=\u0026thinsp;m/generation; Extinction replacement of colonies: Rate\u0026thinsp;=\u0026thinsp;\u0026lambda;/generation; Mutation (symmetric, two-allele): Rate\u0026thinsp;=\u0026thinsp;\u0026upsilon; /generation; \u0026alpha;\u0026thinsp;=\u0026thinsp;2(m\u0026thinsp;+\u0026thinsp;\u0026lambda;)/n; Number of colonies: n; Variables of the \u003cem\u003ei\u003c/em\u003e-th colony g\u003csub\u003e1i\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;frequency (A\u003csub\u003e1\u003c/sub\u003eB\u003csub\u003e1\u003c/sub\u003e), g\u003csub\u003e2i\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;frequency (A\u003csub\u003e1\u003c/sub\u003eB\u003csub\u003e2\u003c/sub\u003e), g\u003csub\u003e3i\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;frequency (A\u003csub\u003e2\u003c/sub\u003eB\u003csub\u003e1\u003c/sub\u003e) and g\u003csub\u003e4i\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;frequency (A\u003csub\u003e2\u003c/sub\u003eB\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e\n \u003cp\u003eThen the LD coefficient would be defined at two levels within the colony (or the stevia germline) and between colonies (between the stevia germlines). Ending up with the LD coefficient of the total population as D\u0026thinsp;=\u0026thinsp;G\u003csub\u003e1\u003c/sub\u003e \u0026ndash; \u003cem\u003eXY\u003c/em\u003e. Interestingly, the subdivision occurring in the LD coefficient is analogous to the inbreeding coefficient of [\u003cspan\u003e41\u003c/span\u003e] in structure population [\u003cspan\u003e43\u003c/span\u003e, \u003cspan\u003e44\u003c/span\u003e]. Likewise, the analogy existed in two-locus properties of various mating schemes [\u003cspan\u003e45\u003c/span\u003e] and genetics of mitochondrial alleles [\u003cspan\u003e46\u003c/span\u003e] as an exotic/ migrating DNA different from alleles found in the nuclei.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eQR Coding Using Python\u003c/h2\u003e\n \u003cp\u003ePython includes a module \u0026lsquo;qrcode\u0026rsquo; that facilitates the creation of QR codes. However, this module has certain limitations, such as the volume of data (column*rows matrix) to be encoded into a single QR code. To address such constraints, we developed a package \u0026ldquo;PopAllele\u0026rdquo; (\u003cspan\u003e\u003cspan\u003ehttps://github.com/RaafatA/PopAllele\u003c/span\u003e\u003c/span\u003e) to visualize the distribution of whole markers of the studied populations through the medium of the QR code. We considered one QR1 code for the distributed alleles (rows of genotypic data) in the studied populations. The second QR2 code considered the germlines (column of genotypic data) within the population of markers. Then, a third QR code was used to combine QR1 and 2 codes forming the unique pattern of distributed alleles across the assessed populations (Supplementary Fig. 1). This last QR code is used to recall diversity and acquired data results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eQR-like Patterns and Barcoding Possibility\u003c/h2\u003e\n \u003cp\u003eDNA barcoding depends on specific sequences that are not amenable to information storage, recognition, and retrieval [\u003cspan\u003e30\u003c/span\u003e]. The two chloroplast genes, namely, \u003cem\u003erbcL\u003c/em\u003e and \u003cem\u003ematK\u003c/em\u003e were proposed by the plant working groups of the Consortium for Barcode of Life (\u003cspan\u003e\u003cspan\u003ehttp://www.barcodeoflife.org/\u003c/span\u003e\u003c/span\u003e) as core barcodes [\u003cspan\u003e33\u003c/span\u003e]. [\u003cspan\u003e30\u003c/span\u003e] was the first report directly converted DNA sequences into QR codes that could take either of the known versions (Fig. \u003cspan\u003e1\u003c/span\u003e-A). However, their data was based on \u003cem\u003eITS2, rbcL, matK, psbA-trnH, and CO1\u003c/em\u003e sequences that get converted into QR codes after detailed comparison with 2D coding systems like Aztec Code, CodaBlock-F, Data Matrix, PDF417, PDF417 Truncated, QR2005 code, and QR codes. This means that in the model of [\u003cspan\u003e30\u003c/span\u003e], they used the variation in the nucleotide base paring (A, T, G, and C) coming out of the amplified sequences of only the barcoding genes (\u003cem\u003eITS2, rbcL, matK, psbA-trnH, and CO1\u003c/em\u003e). However, our system does not have any limits affected by the size of the sequenced genes or the nucleotide bases converted into QR codes. Besides, our system directly uses the differential and observed patterns of existing/absent alleles (as in the 100 genotyped-wheat lines Fig. \u003cspan\u003e1\u003c/span\u003e-B). Therefore, our model depends on the variation of the alleles inherited in a stevia germline, not on the variation at the nucleotide bases that could be misread due to malfunctioning in the sequencing machine. In addition, the [\u003cspan\u003e30\u003c/span\u003e] model depends on the NCBI taxid organisms, if a germline is not taxed, the user will fail to create a QR code. Our model will use the allelic pattern distributed along the genotyped stevia germline as the pattern for the QR code to be associated uniquely to this specific germline.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eAnalysis of SCoT Marker Populations\u003c/h2\u003e\n \u003cp\u003eThe SCoT fragments were converted into allelic and genotypic forms using The Microsoft Excel and prepared as an input file to be used by the Popgene version 1.32 (1997) software. Popgene was used to analyze the genetic diversity and relationships among and within the six SCoT populations of markers/alleles generated based on the variable distribution of these alleles along the DNA of the six stevia germlines based on the weight of base paired size of the amplified fragment within each SCoT primer. The phylogeny tree was built using the of the cluster analysis of the unweighted pair group method with arithmetic averages (UPGMA). Besides, the Popgene software was used to estimate the gene diversity (\u003cem\u003eh\u003c/em\u003e) Nei\u0026rsquo;s [\u003cspan\u003e47\u003c/span\u003e, \u003cspan\u003e48\u003c/span\u003e] and Shannon\u0026rsquo;s information index (\u003cem\u003eI\u003c/em\u003e) [\u003cspan\u003e52\u003c/span\u003e] for the six SCoT populations of markers/alleles, which were used as dominant marker types.\u003c/p\u003e\n \u003cp\u003eFor diploid data analysis of SCoT markers distributed among the six stevia germlines calculations were based on population genetics parameters. Among these parameters were the allelic frequency (gene frequency at each locus excluding missing values), allelic number (counts with nonzero frequency), effective allelic number reciprocal homozygosity; [\u003cspan\u003e53\u003c/span\u003e], polymorphic loci (as percentage of all alleles regardless of allelic frequency), homogeneity test (constructs two-way contingency tables and apply chi-square (\u0026chi;\u003csup\u003e2\u003c/sup\u003e) and likelihood ratio (G\u003csup\u003e2\u003c/sup\u003e) tests for homogeneity of gene frequencies across the populations, the tests were carried out for six groups correspond to the six stevia germlines), \u003cem\u003eF\u003c/em\u003e-statistics (estimate Nei\u0026rsquo;s [\u003cspan\u003e48\u003c/span\u003e]) G\u003csub\u003eST\u003c/sub\u003e and both G\u003csub\u003eST\u003c/sub\u003e and G\u003csub\u003eCS\u003c/sub\u003e for six groups), gene flow (from estimated G\u003csub\u003eST\u003c/sub\u003e F\u003csub\u003eST\u003c/sub\u003e [\u003cspan\u003e49\u003c/span\u003e] for six groups), genetic distance [\u003cspan\u003e50\u003c/span\u003e] genetic identity and distance and Nei\u0026rsquo;s [\u003cspan\u003e47\u003c/span\u003e] unbiased genetic identity and genetic distance for six groups), and neutrality test (performs Ewens-Watterson test for neutrality using the algorithm given in [\u003cspan\u003e51\u003c/span\u003e]), two-locus LD (Burrows\u0026rsquo; composite for LD between pairs of loci and (\u0026chi;\u003csup\u003e2\u003c/sup\u003e) tests for significance [\u003cspan\u003e54\u003c/span\u003e] for single group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenotypic Distribution of SCoT Allelic Populations\u003c/h2\u003e \u003cp\u003eThe 14 SCoT primers have successfully produced a total of 1320 allelic and genotypic forms as, which means in average 220 allelic forms for each cultivar. Among the 1320 amplicons, 162 alleles (12.27%) were non-polymorphic and 1101 alleles (83.41%) were polymorphic (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Out of 1320, 43 amplicons (3.26%) were unique positive (UP) and able to molecularly differentiate among the six stevia cultivars. Likely, the unique negative (UN) characterization was confirmed by 14 allelic (1.06%) morphs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSCoT marker distribution among the six stevia genotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSponti\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChaina1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Sugar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eShouA3-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevan\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eShou2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique Negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon Polymorphic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e162\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolymorphic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1101\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1320\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA doughnut distribution of SCoT markers is emphasized among stevia genotypes in Figure (2). The differentially distributed alleles of marker types (polymorphic, non-polymorphic, UN, and UP) among the six stevia genotypes were presented in four circles ordered; respectively, from outside to inside direction. The stevia genotypes were emphasized in the doughnut graph by the different areas colored within each circle (marker type). For polymorphic alleles, the genotypes with the same number of alleles (70) were China1, High Sugar, and Shou2. However, similar numbers (27) of non-polymorphic SCoT alleles were distributed among all stevia genotypes. Differential distribution of SCoT markers was observed at the level of allelic type of UP and UN. For the type of UN, the genotypes Sponti and High Sugar have shown the same number (three) of distributed alleles. For the type of UP, the genotypes Levan and Shou2 have shown the same number (six) of distributed alleles. Noticeably, the highest number (71) of distributed alleles was scored to the genotype Sponti for the type of polymorphic alleles, while the lowest number (zero) was scored to the genotype ShouA3-2 for the markers of type UN.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe Populations of SCoT Alleles Revealing The Differential Diversity\u003c/h2\u003e \u003cp\u003eFor each stevia genotype, the 14 SCoT-based loci have generally produced 220 allelic forms. Of these, 193 alleles (86.46%) were polymorphic among the six stevia genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A total of 27 non-polymorphic and 57 monomorphic alleles were produced. The expected number of alleles per single SCoT locus has reached 15.7 alleles, where each SCoT locus on average can produce 14 polymorphic alleles. Approximately, two non-polymorphic and 4.1 monomorphic alleles were also expected for each SCoT locus (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Therefore, the average percentage of non-polymorphic alleles was 14.73%, and of the monomorphic alleles was 23.11% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On one hand, seven SCoT loci (SCoT6, 10, 24, 28, 32, 44, and 46) have produced 100% polymorphism, whereas three of them (SCoT24, 28, and 46) have produced 0% of monomorphism. On the other hand, SCoT6, 13, 19, 35, and 36 have produced at least 63.64% of polymorphism and a maximum 46.43% of monomorphism, which is close to 50% of interacting and active alleles for segregating and genetics, this should be a potential good marker for genotyping and NGS studies. Especially that this high potential has been approved and generated form only 14 SCoT (primers). The SCoT6, 10, 20, and 44 have produced either one or two monomorphic alleles. Such loci were informative at the homozygosity level for the studied stevia germlines. This gives special importance to these four informative loci (SCoT6, 10, 20, and 44) out of the 14 due to the monomorphic representation of the amplified fragments among the stevia genotypes. Generally, the sizes of the alleles were ranged between 120 bp (SCoT9) and 2500 bp (SCoT28) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe size range of mono and polymorphic amplicons generated by 14 SCoT markers loci\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT Primer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNP%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMM%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eASR (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e200\u0026ndash;580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e120\u0026ndash;1900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e200\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e390\u0026ndash;1700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e180\u0026ndash;2200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e180\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e320\u0026ndash;1700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e180\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e400\u0026ndash;2500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e200\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScoT35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e180\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e220\u0026ndash;1800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e240\u0026ndash;1600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCoT46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e300\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e15.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e86.46%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e14.73%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e23.11%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e120\u0026ndash;2500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e193\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;the total number of amplicons; \u003cb\u003eP\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Polymorphic; \u003cb\u003eNP\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Non polymorphic; \u003cb\u003eMMA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Monomorphic amplicons; \u003cb\u003eASR\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Amplicon size range.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSCoT-Allele-Specific Characterizing Each Stevia Genotype\u003c/h2\u003e \u003cp\u003eThe SCoT system has provided a strong genotypic and differential tool with a total of 55 unique allelic markers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Both ScoT13 and 36 have produced the highest number (nine, each) of differential monomorphic alleles. Meanwhile, SCoT20 and 44 have produced the lowest number (one) of monomorphic alleles. SCoT35 has produced eight, while SCoT16 and 32 have each produced seven, and SCoT9 and 19 have respectively produced five and four alleles. Both SCoT6 and 10 have each produced two alleles.\u003c/p\u003e \u003cp\u003eA total of 10 differentiating alleles were best characterizing the genotype Sponti among the other stevia genotypes. Amongst, eight unique alleles were as positive and two as negative. Most importantly were the alleles at the sizes of 590, 540, and 1000 bp that were respectively produced by SCoT16 and 36. All uniquely negative alleles for the Sponti genotype were at the molecular weight above 1000 bp (SCoT9\u0026thinsp;=\u0026thinsp;900 bp and SCoT19\u0026thinsp;=\u0026thinsp;1500 bp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 12 differentiating alleles were best characterizing the genotype China1 among the other stevia genotypes. Amongst, seven unique alleles were as positive and five as negative. Most importantly were the alleles at the sizes of 450 and 650 bp and respectively produced by SCoT16 and 36. A total of seven were four uniquely positive and three as negative alleles for the China1 genotype and above the molecular weight of 1000 bp (SCoT10, 13, 35, and 44; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of nine differentiating alleles were best characterizing the genotype High Sugar among other stevia genotypes. Amongst, six unique alleles were as positive and three as negative. Most importantly were the alleles at the sizes of 210 and 600 bp and respectively produced by SCoT16 and 36. No alleles for the High Sugar genotype were above the molecular weight of 1000 bp (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of eight differentiating alleles were all unique positive and best characterizing the genotype ShouA3-2 among the other stevia genotypes. Most importantly were the alleles at the sizes of 490 and 500 bp and respectively produced by SCoT16 and 36. Two alleles for the ShouA3-2 genotype were above the molecular weight of 1000 bp (SCoT20\u0026thinsp;=\u0026thinsp;1700 and SCoT32\u0026thinsp;=\u0026thinsp;1200 bp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of seven differentiating alleles were best characterizing the genotype Levan among the other stevia genotypes. Amongst, five unique alleles were positive and two negative. Most importantly were the alleles at SCoT16 of the sizes of 520, 700 (positive), and 800 (negative) and at the SCoT36 of 1600 bp (positive). A total of three alleles were all as uniquely positive for the Levan genotype and above the molecular weight of 1000 bp (SCoT9\u0026thinsp;=\u0026thinsp;1000 and 1200, and SCoT36\u0026thinsp;=\u0026thinsp;1600 bp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of four differentiating alleles were best characterizing the genotype Shou2 among the other stevia genotypes. Amongst, two alleles each were unique positive and negative. Most importantly were the alleles at the sizes of 2200 and 1200 bp and respectively produced by the SCoT16 and 36 above the molecular weight of 1000 bp (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiploid SCoT Diversity Revealing Genetic Identity and Distance\u003c/h2\u003e \u003cp\u003eParameters of genetic diversity of SCoT as the dominant marker system were presented based on the average of the observed number (na) of alleles between one and two alleles (Mean\u0026thinsp;=\u0026thinsp;0.174; StDev\u0026thinsp;=\u0026thinsp;0.44). The genic variation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] observed among the different alleles of SCoT loci has effectively differentiated the six stevia genotypes. The effective number (ne) of alleles [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] varied as 1, 1.39, 1.8, and 2 (Mean\u0026thinsp;=\u0026thinsp;1.5; StDev\u0026thinsp;=\u0026thinsp;0.35) among the different germlines. Correspondingly, the gene diversity (h) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], where observed heterozygosity (Ht) in the six groups of SCoT loci was varied in values as 0, 0.28, 0.44, and 0.5 (Mean\u0026thinsp;=\u0026thinsp;0.27; StDev\u0026thinsp;=\u0026thinsp;0.18). Meanwhile, Shannon's information (I) index [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] also varied in values (0, 0.45, 0.64, and 0.69) (Mean\u0026thinsp;=\u0026thinsp;0.41; StDev\u0026thinsp;=\u0026thinsp;0.26). Besides, the gene/allele frequency that was discovered among the SCoT loci varied as well in values (0.17, 0.33, 0.5, 0.67, 0.83, and 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnbiased values of genetic identity and distance (Supplementary File 2-A and B; respectively) were estimated among the six different groups of generated SCoT loci and presented as doughnut distribution in Figure (3). Generally, the values of genetic distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A) ranged between 0.27 and 0.5, while the values of genetic identity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B) ranged between 0.61 and 0.76. The doughnut graph included six circles representing the six stevia genotypes from the outer to the inner direction respectively as Shou2, Levan, ShouA3-2, High Sugar, China1, and Sponti. The differential areas colored within each circle (genotype) represent the percentages of genetic distances distributed between the two encountered genotypes labeled to the denoted percentage in the box (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A). Likewise, for each circle (genotype) in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B, though the colored areas represent the percentage of genetic identity score between the two encountered genotypes labeled to the denoted percentage in the box. For example, the genotype Shou2 showed differential genetic distance scored as 49% with Sponti, 50% with China1, 40% with High Sugar, 39% with ShouA3-2, and 35% with Levan. This indicates an expected close genetic relation among the common ancestors of Shou2 and Levan. The genotype Sponti showed 37% of genetic distance with the genotype China1, 39% with High Sugar, 32% with ShouA3-2, 44% with Levan, and 49% with Shou2. This indicates, a lower to moderate genetic relationship between the aforementioned genotypes and Sponti. At the level of genetic identity, the Shou2 genotype showed 61% identity with the genotype Sponti, 60% with China1, 67% with High Sugar, 78% with ShouA3-2, and 71% with Levan. The genotype Sponti showed 69% with China1, 68% with High Sugar, 73% with ShouA3-2, 64% with Levan and 61% with Shou2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePhylogeny Tree Revealing The Genic Relationships Among The Stevia Genotypes\u003c/h2\u003e \u003cp\u003eA dendrogram based on [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] genetic distances was drawn using the of UPGMA method and presented in Figure (4). The two genotypes High Sugar and ShouA3-2 have the smallest genetic distance (13.5%). The highest genetic distance (19.9%) was scored to the genotype China1. The two genotypes Levan and Shou2 have the same genetic distance (17.3%) away from that of the other four stevia genotype. The genotype Sponti had a genetic distance (17.7%) linked between the highly distant genotype (China1) and the two closely related genotypes (Shou2 and Levan) and the other two lower related genotypes (ShouA3-2 and High Sugar). This means that the genotype Sponti is more related (0.4%) to the two genotypes Levan and Shou2, followed by the genotype China1 (2.2) and more distant from High sugar and ShouA3-2 genotypes (4.2%). More emphasis can be drawn on the four ancestors expected for the six stevia germlines and their genetic relationships. For example, the two genotypes Shou2 and China1 may have a common ancestor that was genetically distant (4.2) from those of other stevia genotypes. Likewise, the two genotypes Sponti and High Sugar may have a common ancestor that was genetically distant (4.2) from those of other stevia genotypes. Moreover, the ancestor of the genotype Levan was more related (3.9) to that of ShouA3-2, while was distant (5.9) from that of China1 and Shou2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Genotyping Tools Revealing Genetic Diversity\u003c/h2\u003e \u003cp\u003eWhenever homologous genes are tandemly arranged in (short) chromosomal regions, then transfer of gene segments will occur between loci based on evolution through gene conversion or doubled unequal crossing-over [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Expectedly, the transfer of gene segments will occur among genes of different loci and support the evolution of a supergene family of Bodmer theory [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, gene identity among alleles is roughly 90% of amino acid identity, while it is 85% among genes of different loci [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This means that gene homology between different loci is slightly lower than that among alleles. Likewise at the level of LD, where gene segments transfer between different loci as a mutation effect on LD. This supports a modification made by [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] to the proposal of Bodmer Silver and Hood [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] that each marker contains a cluster of many loci (multigene family), of which one would be expressed. Usually, DNA markers are applied on multiple populations or groups of plant germlines (RIL, NIL, DH, or magic). However, in the current study, one population of SCoT (1320 fragments) has indirectly been converted to a generic population of alleles reflect the hidden genetic diversity embedded in six stevia germlines. SCoT has produced high information content about the loci that can be used for genotyping (monomorphism) and differentiating (polymorphism) the different stevia germlines. SCoT alleles have produced various and wide ranges of percentages (0\u0026ndash;46% ) of monomorphism. On one hand, this indicates the ability of the SCoT technique to execute the type of loci with a type of low information content to be avoided in the studies including crosses among stevia germlines. On the other hand, some loci have a low number of monomorphisms (two alleles), which could be emphasized by some reasonable homozygosity (for such alleles) or the presence of null alleles.\u003c/p\u003e \u003cp\u003ePlants depend on high variation in their DNA to survive environmental changes. Heterozygosity in plants requires strong and codominant DNA marker tools. Herein, SCoT as a DNA marker belongs to the dominant type, with very low potential for digging behind heterozygosity. Despite such fact, SCoT system tools herein have provided some indirect information about the existence of null alleles and the expected amount of heterozygosity. In a study comparing between efficiency of marker types (ISSR and SSR), ISSR has provided the highest genetic variability [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. When eight ISSR markers were used, the Shannon\u0026rsquo;s index was 0.26 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], while when seven primers were used, Shannon\u0026rsquo;s index was 0.48 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Herein, the Shannon\u0026rsquo;s index was 0.69 when 14 primers of SCoT were used. Besides, these fluctuations in the results among the different studies can be emphasized by the fact that the stevia germlines were diverse in the origins of each germline. It is thought that the closely related populations reflect lower Shannon\u0026rsquo;s index. As expected when plants were related to similar populations [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], the Shannon index was produced in low values. In a study that included two populations of Finnish roseroot [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], Shannon\u0026rsquo;s index values generated within the populations were lower (0.31 and 0.23) than those calculated among populations (0.34). This may suggest higher diversity for SCoT marker than ISSR marker despite the number of primers. At the level of number of primers used for each marker type (ISSR, SSR, SCoT) in the different studies and how it affects the Shannon\u0026rsquo;s index. It was evident that when 12 primers [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] were used, Shannon\u0026rsquo;s index was 0.17\u0026ndash;0.33, when 13 primers [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] were used, it was 0.07\u0026ndash;0.22, likewise, when13 primers [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] were used, it was 0.08\u0026ndash;0.24, while when 14 primers (in the current study) were used, it was averaged as 0.41. This suggests that Shannon\u0026rsquo;s index reflects the true genetic diversity that existed in the DNA of plant germplasm despite the number of primers used to estimate it.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDepicted Genetic Diversity Among Stevia Germlines\u003c/h2\u003e \u003cp\u003eBased on 1000 simulated sampling among the 1320 amplicons in the current study, the overall Ewens-Watterson test for neutrality ([\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]) has produced about 78 polymorphic bands with an L95 value of 0.5 and U95 value of 0.72 (SE\u0026thinsp;=\u0026thinsp;0.009 for both L95 and U95). These values support the high level of natural selection practiced on the genetic composition of distributed SCoT loci among the six populations of stevia. Gene flow [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and expected heterozygosity (Hs) were estimated as zero, which indicates that the population of SCoT loci in each single genotype were independent form other genotypes. In addition, only 14 SCoT loci (primers) were allocated on stevia DNA, generating almost 50% monomorphism, which reflects the existence of active alleles/regions for new recombination. Such alleles are very informative to study genetics and segregating traits opt to discover heterozygosity. Potentially, SCoT is a good marker for diagnostics, genotyping, and/or NGS studies. According to Table\u0026nbsp;(3), the high number of alleles (ex. 31 for SCoT16) in the current study reflects the high genetic diversity compared to other marker types [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Observed heterozygosity (Ho) in the current study ranged between 0.167 and 1.0, while expected heterozygosity (He) (genetic diversity) ranged between 0.278 and 0.5 for most loci of two alleles. Most importantly, if the current potential of SCoT has been proved by only 14 primers, what if more SCoT primers were used? The current study produces the first easy tool to genotype the six main stevia germlines in Egypt through specific SCoT alleles. For example, the germline Sponti can be characterized through some genotypic banding pattern tools. However, through the especially associated fragments of SCoT in the current study, only one fragment produced by either the SCoT13 at the size of 550 bp or the SCoT16 at the size of 590 bp will be sufficient to identify Sponti away of other germlines (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). SCoT system as a diagnostic germline tool provides various characterizing fragments suiting multiple availabilities of SCoT in any laboratory. Molecular markers provide a tool of multiple banding patterns to characterize specific cultivars/germlines [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe protection of crop germlines, encompassing biologically important traits and/or breeding values, is one of the top priorities of worldwide intellectual properties (IP) rights. Many roles and tools have been created in this perspective. If the current potential of SCoT as a dominant marker has been extended through the 14 presented primers, what if more SCoT primers were used? The unique allele-specific association, in the current study for each germline of stevia, is a practical application satisfying this objective. Most notably, the application of the two SCoT16 and 36 markers on the six stevia germlines (Sponti, China1, High Sugar, ShouA3-2, Levan, and Shou2) respectively discovered 10, 12, nine, eight, seven, and four unique alleles. Most importantly those at the sizes of 590, 540, and 1000 bp (Sponti); 450 and 650 bp (China1); 210 and 600 bp (High Sugar); 490 and 500 bp (ShouA3-2); 520, 700, 800, and 1600 bp (Levan); 2200 and 1200 bp (Shou2). Supportively, the genetic diversity and distances among and between the six populations of SCoT alleles were calculated. These diversities and distances have confirmed the diverged lineages and evolutionary ancestors linking these six stevia germlines and were not discovered before. As a result, the phylogenetic discrimination has presented the common structured genetics among the six stevia germlines in Egypt. Generally, electronic data needs to be converted into encrypted forms with specified keys to encode this encryption, the presented tool (PopAllele) compress, encrypt, and encode the genotypic data in a modified form to be stored in blockchain networks. PopAllele enables breeders to eliminate redundancy in generated genotypic data and store informative parts into retrieval and encrypted QR-forms reserving germlines IP rights when distributed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eAuthors thanks \u003cstrong\u003eHisham A. Ashry\u003c/strong\u003e and \u003cstrong\u003eEslam S. ElShahed\u003c/strong\u003e for their technical assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003e\u0026ldquo;The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request, while the developed a package \u0026ldquo;PopAllele\u0026rdquo; can be accessed (https://github.com/RaafatA/PopAllele).\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The plants used in the study comply with relevant institutional, national, and international guidelines and legislation and are owned to the Agricultural Research Center (ARC), who encompass the Agricultural Genetic Engineering Research Institute (AGERI) and the Sugar Crops Research Institute (SCRI), where the experiments and the study were taken place. The stevia plants (\u003cem\u003eStevia rebaudiana\u003c/em\u003e) used in this study are not species at risk of extinction nor belong to the International Union for Conservation of Nature (IUCN) red list index of threatened species or the endangered species of wild fauna and flora. Therefore, the genotypes of stevia used in the study complies with the ARC, national, and IUCN guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe material used in this study was based upon work supported by the authors. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e: Conceptualization of the research article, writing original draft, reviewing, editing of all manuscript versions and validation of analyzed data by Ahmed ElFatih \u003cstrong\u003eElDoliefy\u003c/strong\u003e. Data collection and main analysis by Ahmed ElFatih \u003cstrong\u003eElDoliefy\u003c/strong\u003e and Mai M. \u003cstrong\u003eHashem\u003c/strong\u003e. Methodology by Ahmed ElFatih \u003cstrong\u003eElDoliefy\u003c/strong\u003e, Rafat A. \u003cstrong\u003eEissa\u003c/strong\u003e. Software analysis by Ahmed ElFatih \u003cstrong\u003eElDoliefy\u003c/strong\u003e, Rafat A. \u003cstrong\u003eEissa\u003c/strong\u003e, and AbdelRahman A. \u003cstrong\u003eAbouEldahab\u003c/strong\u003e. Python language and scripting by Rafat A. \u003cstrong\u003eEissa\u003c/strong\u003e. Formal analysis by Ahmed ElFatih \u003cstrong\u003eElDoliefy\u003c/strong\u003e, Rafat A. \u003cstrong\u003eEissa\u003c/strong\u003e, and AbdelRahman A. \u003cstrong\u003eAbouEldahab\u003c/strong\u003e. Data curation and statistical analysis Ahmed ElFatih \u003cstrong\u003eElDoliefy\u003c/strong\u003e. Visualization by Ahmed ElFatih \u003cstrong\u003eElDoliefy\u003c/strong\u003e, Rafat A. \u003cstrong\u003eEissa\u003c/strong\u003e. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and Competing Interests\u003c/strong\u003e: \u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript and they have no relevant financial or non-financial interests to disclose and no competing interests.\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKaplan, B.; K. Turgut. 2019. Improvement of rebaudioside A diterpene glycoside content in \u003cem\u003eStevia\u003c/em\u003e\u003cem\u003erebaudiana\u003c/em\u003e Bertoni using clone selection. Turk. J. Agric. For., 43, 232\u0026ndash;240. \u003c/li\u003e\n \u003cli\u003eGoyal, S.K.; Samsher E.T. and R.K. Goyal. 2010. 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Establishment and application of a method for chromosome ploidy identification and genome size estimation using flow cytometry in Ziziphus jujuba. J Agric Biotechnol 26:511\u0026ndash;520. https://doi.org/10.3969/j.issn.1674-7968.2018.03.017. \u003c/li\u003e\n \u003cli\u003eParfitt DE, Yonemori K, Honsho C, Nozaka M, Kanzaki S, Sato A, Yamada M (2015) Relationships among Asian persimmon cultivars, astringent and non-astringent types. Tree Genet Genomes 11:24\u003c/li\u003e\n \u003cli\u003eJing Y. Zhang D. Wang X. Tang W. Wang W. Huai J. Xu G. Chen D. Li Y. Lin R. (2013 ). Arabidopsis chromatin remodeling factor PICKLE interacts with transcription factor HY5 to regulate hypocotyl cell elongation . Plant Cell 25 : 242 \u0026ndash;256. \u003c/li\u003e\n \u003cli\u003eYang, Y., Yang, T., \u0026amp; Jing, Z. (2015). Genetic diversity and taxonomic studies of date plum (\u003cem\u003eDiospyros\u003c/em\u003e\u003cem\u003elotus\u003c/em\u003e L.) using morphological traits and SCoT markers. 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C., Ameziane, N., de Vries, Y., Rooimans, M. A., Sheng, Q., Pals, G., Errami, A., Gluckman, E., Llera, J., Wang, W., Livingston, D. M., Joenje, H., \u0026amp; de Winter, J. P. (2007). Fanconi anemia is associated with a defect in the BRCA2 partner PALB2. Nature Genetics, 39 (2), 159\u0026ndash;161. https://doi.org/10.1038/ng1942.\u003c/li\u003e\n \u003cli\u003eMcDermott, J., \u0026amp; McDonald, B.A. (1993). Gene Flow in Plant Pathosystems. Annual Review of Phytopathology, 31, 353-373.\u003c/li\u003e\n \u003cli\u003eZini, E., Clamer, M., Passerotti, S. et al. Eight novel microsatellite DNA markers in \u003cem\u003eRhodiola\u003c/em\u003e\u003cem\u003erosea\u003c/em\u003e L. Conserv Genet 10, 1397 (2009). https://doi.org/10.1007/s10592-008-9704-0. \u003c/li\u003e\n \u003cli\u003eKylin, M. (2010, November 29). Genetic diversity of Roseroot (Rhodiola rosea L.) from Sweden, Greenland and Faroe Islands [Second cycle, A2E]. SLU, Plant Breeding and Biotechnology (until 121231). https://stud.epsilon.slu.se/2049/. \u003c/li\u003e\n \u003cli\u003eZhou, C. J., Zhu Song, H., Hua Li, J., Wei Sun, J., De Jin, M., Wen Zhang, Q., and Wang, B. (2005). Evaluation of genetic diversity and germ plasm identification of 44 species, clones, and cultivars from 5 sections of the genus Populus based on amplified fragment length polymorphism analysis. Plant Molecular Biology Reporter, 23 (1), 39\u0026ndash;51. https://doi.org/10.1007/BF02772645. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Marker, Molecular, QR-code, Allelic, Blockchain, Popgene","lastPublishedDoi":"10.21203/rs.3.rs-4636839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4636839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quick response (QR) codes produce unique patterns based on the black and white spots distribution. If germlines were ordered vertically in columns and alleles horizontally in rows, the presence (+\u0026thinsp;1) and absence (0) of alleles could respectively be considered as the black and white spots. Consequently, the vertical and horizontal differential distribution of these black and white spots in a genotype can produce unique QR-like patterns (QRLP). The variation among these QRLP depends on the composition of alleles resembling the genetics embedded in the DNA. Accordingly, six stevia germlines were genotyped using 14 SCoT primers that generated 1320 allelic forms with 3.26% and 1.06% of uniquely positive and negative effects; respectively. Of the 1320 alleles (83.41% of polymorphs), 220 polymorphs encompassed 180\u0026ndash;185 alleles representing the population size of effective interacting alleles (ne). The genetic diversity of SCoT was averaged across the observed number of alleles (Mean\u0026thinsp;=\u0026thinsp;0.174; StDev\u0026thinsp;=\u0026thinsp;0.44) and varied (Mean\u0026thinsp;=\u0026thinsp;1.5; StDev\u0026thinsp;=\u0026thinsp;0.35). Correspondingly, the Nei\u0026rsquo;s gene diversity (h) of observed heterozygosity (Mean\u0026thinsp;=\u0026thinsp;0.27; StDev\u0026thinsp;=\u0026thinsp;0.18) and the Shannon index (Mean\u0026thinsp;=\u0026thinsp;0.41; StDev\u0026thinsp;=\u0026thinsp;0.26) were different. Therefore, the gene/allele frequency that was discovered among the populations of SCoT loci varied (0.17, 0.33, 0.5, 0.67, 0.83, and 1). The dominant SCoT, in the current study, along with the unweighted pair-group of arithmetic average (UPGMA) analysis concluded four interacting ancestors configuring the genetics in the six stevia germlines. The study can be considered the first showing the SCoT marker as the best QRLP producer exclaiming the differential diversity despite the size of genotyped alleles.\u003c/p\u003e","manuscriptTitle":"Distribution of SCoT-Based Populations Depict Genotypic Diversity of Six Stevia Germlines in Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 08:41:40","doi":"10.21203/rs.3.rs-4636839/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d99deea2-86bc-4654-b4be-50a4b2ae4ba9","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-24T08:09:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 08:41:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4636839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4636839","identity":"rs-4636839","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}