Molecular Marker Based Assessment of Wheat Biodiversity for Conservation and Sustainable Breeding

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Albalawi, Amenah S. Al-otaibi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6959182/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Biodiversity and Conservation → Version 1 posted 11 You are reading this latest preprint version Abstract Background Wheat ( Triticum aestivum L.) is a vital cereal crop, providing a substantial share of dietary calories and protein. However, climate variability, including heat stress, drought, and irregular rainfall, poses serious challenges to wheat production, particularly in regions already experiencing food insecurity. Enhancing genetic diversity and stress tolerance is crucial for sustaining wheat yields under changing environmental conditions. However, intensive breeding for high-yielding cultivars has led to a reduced genetic base, limiting adaptability and resilience. Objectives This study aims to assess the genetic diversity of 40 wheat genotypes collected from diverse agro-climatic zones using a combination of molecular marker systems: simple sequence repeats (SSR), inter-simple sequence repeats (ISSR), and amplified fragment length polymorphism coupled with capillary electrophoresis (AFLP-CE). The goal is to characterize genetic relationships, detect polymorphisms, and provide insights to support conservation and sustainable breeding strategies. Methods Genotypic differentiation was analyzed using a combination of SSR, ISSR markers, and high-resolution fragment analysis of AFLP-CE to ensure comprehensive genome-wide polymorphism detection. The genetic relationships among wheat genotypes were inferred through similarity matrix analysis and hierarchical clustering, facilitating a robust framework for classification and selection. Results The integrated marker approach revealed substantial genetic variability among the studied wheat genotypes, reflecting diverse ecological adaptations. AFLP-CE enhanced the resolution of rare and informative polymorphisms, improving genotype discrimination. These results highlight the richness of genetic resources that can be harnessed to improve stress tolerance and yield stability. Conclusion This study underscores the effectiveness of combining SSR, ISSR, and AFLP-CE for the complete evaluation of wheat biodiversity. The findings support conservation and targeted breeding strategies for improved resilience and sustainable yield under climate variability. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Wheat ( Triticum aestivum L.) is a major worldwide cereal crop, contributing approximately 20% of total caloric and protein intake; thus, ensuring its genetic resilience is crucial for sustaining global food security (FAO, 2023). However, climate change presents considerable challenges to wheat production, particularly in areas vulnerable to heat stress, drought, and irregular rainfall patterns ( Pequeno et al., 2021 ). Global wheat yields are expected to fall by 1.9% by mid-century, with more severe reductions (15–16%) estimated in North Africa and South Asia, exacerbating food insecurity ( Pequeno et al., 2021 ; USDA, 2025). To sustain wheat productivity under increasing pressures of environmental conditions, breeding programs must enhance stress tolerance, yield stability, and genetic diversity ( Langridge et al., 2021 ). However, due to decades of intensive selection for high-yielding cultivars, the genetic base of wheat has narrowed, reducing its adaptability to the changing climatic conditions ( Reif et al., 2005 ; Voss-Fels et al., 2015 ). A comprehensive understanding of wheat biodiversity and genetic adaptation is therefore crucial for the development of climate-adaptable cultivars ( Lopes et al., 2015 ; Mondal et al., 2016 ). Egypt, with its diverse agro-climatic zones ranging from hot arid desert to semi-arid environments, serves as an ideal location for studying wheat adaptation to variable climates ( Tadesse et al., 2019 ; Hamada et al., 2022 ; Elkot et al., 2024 ). Molecular marker technologies provide efficient techniques for the genetic description of wheat biodiversity, supporting marker-assisted selection (MAS), and finding unique genotypes with adaptive traits. Among these, SSRs and ISSRs have been widely employed because of their specificity and genome coverage ( Landjeva et al., 2007 ; Soriano, 2020 ). In parallel, AFLP coupled with capillary electrophoresis (AFLP-CE) provides high-throughput, fine-scale resolution for identifying polymorphisms, particularly those that are undetected on gel-based platforms ( Papa et al., 2005 ). The use of AFLP-CE enhances marker sensitivity and reproducibility, making it especially valuable in complex genomes like wheat. Simple sequence repeats (SSR), inter-simple sequence repeats (ISSR), and amplified fragment length polymorphisms (AFLP) have all proven an outstanding ability in genetic differentiation and genome-wide polymorphism identification ( Landjeva et al., 2007 ; Noli et al., 2008 ). However, a comprehensive analysis using integrative and complementary multi - marker systems is still required to fill knowledge gaps and gain a precise evaluation of wheat genetic structure, aiding both conservation and breeding efforts. The present study aims to evaluate the genetic diversity of 40 wheat genotypes across different agro-climatic zones using SSR, ISSR, and AFLP-CE molecular markers. The inferred genetic relationships, based on similarity matrix analysis and hierarchical clustering, will provide a robust framework for genetic classification. Meanwhile, our findings will contribute to wheat conservation efforts and breeding programs aimed at improving stress tolerance and yield potential under climatic variability. MATERIALS AND METHODS Plant Materials and Field Trials: Field experiments were conducted over two consecutive growing seasons (2021/2022–2022/2023) at the Shandaweel Agricultural Research Station in Sohag Governorate, Egypt (26°33′N, 31°42′E). As specified in Table 1 and Figure 1, the present study evaluated forty wheat ( Triticum aestivum L.) landraces (G1–G40), collected from diverse agro-climatic regions across Egypt, including Upper Egypt, the New Valley, and North Sinai, by the Egyptian National Gene Bank. These genotypes were assigned accession numbers 111320 to 111370, reflecting their official registration in the national germplasm collection (Egyptian Gene Bank, 2024). To investigate genetic diversity and agronomic performance, the wheat genotypes were regenerated and evaluated under controlled field conditions. A randomized complete block design (RCBD) with three replicates was employed for the experiments to ensure reliable statistical analysis. Standard agronomic practices were implemented to ensure optimal growth conditions throughout both seasons. Each genotype was cultivated in six rows, each measuring 2.5 meters in length, with 20 cm spacing between rows. Individual plants within rows were spaced 20 cm apart. The field trials were conducted under conventional irrigation, with five irrigation cycles per season. Data on morphological and yield component traits were collected from ten randomly selected guarded plants per genotype. Where: Hot Arid : Extremely high temperatures and low precipitation. Hot Semi-Arid : High temperatures with slightly higher moisture availability than arid regions. Arid : Dry conditions with very low annual precipitation. Semi-Arid : Moderately dry conditions with some seasonal rainfall. DNA Extraction, Purification, and Quantification: Genomic DNA was extracted from the young leaves of each accession using the DNeasy Plant Mini Kit (QIAGEN, Hilden, Germany). The quantity and quality of the extracted DNA were determined using spectrophotometric measurements of UV absorbance at 260 nm and 280 nm on a Thermo Scientific NanoDrop 2000™ spectrophotometer. Simple Sequence Repeat Analysis: Simple Sequence Repeat (SSR) markers were utilized for genetic diversity assessment and polymorphism detection in the studied wheat genotypes. Ten SSR primers selected for their specificity to key wheat chromosomes and extensive genome coverage, were used for amplification (Table 2). The GrainGenes Database for Triticeae and Avena (http://wheat.pw.usda.gov) included the SSR primer sequences (Yao et al. 2022). PCR reactions were carried out in a 25 μl mixture consisting of 12.5 μl Master Mix (Sigma), 1 μl forward primer, 1 μl reverse primer (10 pmol/μl each), 2.5 μl template DNA (10 ng/μl), and 8 μl dH₂O, following the protocol outlined by Ibrahim et al. (2016). Amplification was performed using a Perkin-Elmer/GeneAmp® PCR System 9700 under the following thermal cycling conditions: initial denaturation at 94ºC for 5 minutes, followed by 40 cycles of denaturation at 94ºC for 30 seconds, annealing at primer-specific temperatures for 30 seconds, and extension at 72ºC for 40 seconds. A final extension at 72ºC for 7 minutes was applied to ensure complete fragment synthesis. Inter Simple Sequence Repeat Analysis Similarly, Inter Simple Sequence Repeat (ISSR) markers were employed to assess genetic diversity and detect polymorphisms among the examined wheat genotypes. Ten ISSR primers, synthesized by Sigma-Aldrich (USA), were used for amplification (Table 3). PCR reactions were conducted in a 25 μl mixture containing 12.5 μl Master Mix (Sigma), 2.5 μl primer (10 pmol/μl), 3 μl template DNA (10 ng/μl), and 7 μl dH₂O, following the protocol described by Ibrahim et al. (2019). Amplification was performed using a Perkin-Elmer/GeneAmp® PCR System 9700 with an initial denaturation at 94ºC for 5 minutes, followed by 40 cycles of denaturation at 94ºC for 1 minute, annealing at 45ºC for 1 minute, and extension at 72ºC for 1.5 minutes. A final extension step at 72ºC for 7 minutes was applied to ensure complete amplification. Amplified Fragment Length Polymorphism Analysis: The Amplified Fragment Length Polymorphism (AFLP) assay was performed according to the AFLP Plant Mapping protocol (Applied Biosystems, 2010). Genomic DNA was digested with the restriction enzymes EcoRI and MseI to generate modified restricted fragments for the pre-selective amplification step. Adaptors were ligated to the digested fragments using T4 DNA ligase (New England BioLabs, Ipswich, MA, USA), according to the manufacturer's instructions, to ensure efficient ligation. Selective amplification was conducted using fluorescence-labeled EcoRI primers and unlabeled MseI primers. Three primer combinations were utilized: Eco- AAG /Mse- CTC , Eco- ACT /Mse- CTT , and Eco- AAC /Mse- CAG (Table 4). The amplified fragments were prepared for electrophoresis by mixing 1 µL of the selective product with 12 µL Hi-Di™ formamide and 0.5 µL GeneScan™ 500 ROX™ standard (Applied Biosystems, USA). Prior to analysis, the mixture was denatured and loaded onto the Applied Biosystems 310 Genetic Analyzer, which employs a single-capillary system for high-resolution fragment separation using POP-4™ polymer. Fragment analysis was conducted using GeneMapper® software. Molecular data analysis: Throughout the assessment of SSR and ISSR marker results, each gel underwent a duplicate assessment to validate the results, whereas the clearly detectable bands were scored as present (1) or absent (0), while any faint bands were discarded. Binary data obtained from the molecular marker analysis was used to identify key parameters related to the effectiveness of molecular markers, such as polymorphism percentage (P%), polymorphic information content (PIC), marker index (MI), effective multiplex ratio (EMR), and resolving power. These analyses were executed utilizing the Online Marker Efficiency Calculator (https://irscope.shinyapps.io/iMEC/) as developed by Amiryousefi et al., (2018). Statistical and Multivariate Analysis: All the results of molecular marker analysis, including SSR, ISSR, and AFLP, were combined to create a comprehensive genetic profile of the studied wheat types. Using Jaccard’s coefficient (Jaccard, 1908), a similarity matrix was created to evaluate the pairwise genetic relationships. Meanwhile, the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) in Multi-Variate Statistical Package (MVSP) software (Version 3.13d; Kovach, 2002 ) was used to perform cluster analysis, which resulted in a dendrogram that illustrates the genetic relatedness based on polymorphic loci among the studied 40 wheat genotypes. Principal Component Analysis (PCA): The principal component analysis (PCA) was performed using PAST software (version 4.03; Hammer et al., 2001) to reduce dimensionality and capture main variations in the genetic dataset. The amount of variance explained by each main component was calculated to improve genotype differentiation. Heat Map Visualization: A heat map analysis was performed using ClustVis, a web-based tool for clustering and visualizing multivariate data (Metsalu & Vilo, 2015). Genetic similarity indices derived from SSR, ISSR, and AFLP markers were used to generate a standardized data matrix. RESULTS AND DISCUSSION Genotypic Differentiation Using SSR Marker Analysis: A comprehensive evaluation of genetic diversity among wheat genotypes was conducted using Simple Sequence Repeat (SSR) markers, revealing genetic diversity and genotype-specific correlations across nine chromosomes: 1A, 1B, 3A, 5A, 5B, 6B, and 7A (Table 5, Figure 2). 823 bands were amplified from 41 SSR amplicons with 10 SSR primers. These 41 alleles comprise 35 polymorphic and 6 monomorphic alleles., Fragment sizes varied widely, from 153-105 bp for Xgwm99 (SSR10, 1AL) to 1000-175 bp Xgwm369 (SSR12, 3A), indicating amplification across loci with different size ranges. This diversity in product size improves allele resolution and confirms the extensive genomic coverage of the used primer set. Five primers, WMS273 (SSR2, 1B), Xgwm371 (SSR4, 5B), Xgwm219 (SSR5, 6B), Xgwm99 (SSR10, 1AL), and wmc422 (SSR16, 7A) exhibited full polymorphism among the tested genotypes, confirming their high discriminatory potential. In contrast, Xgwm205a (SSR14, 5A) and Xgwm124 (SSR17, 1B) showed the lowest polymorphism levels (50%), limiting their utility in diversity analysis. Polymorphic alleles with band frequencies (B.F.) ranging from 0.25 to 0.9 were categorized as rare (BF ≤ 0.2), low (0.2 < BF ≤ 0.5), moderate (0.5 0.8), in addition to 2 unique bands. The highly informative loci included Xwmc596 (SSR9, 7A) (149 bp) and wmc422 (SSR16, 7A) (150 bp), each amplified in 90% of genotypes (B.F. = 0.9), making them suitable for diversity analysis, population structure assessment, and marker-assisted selection. Moderately frequent loci such as Xgwm234 (SSR13, 5B) (256 bp) (B.F. = 0.775), Xgwm99 (SSR10, 1AL) (113 bp) (B.F. = 0.725), and Xgwm371 (SSR4, 5B) (168 bp) (B.F. = 0.725) were also effective in discriminating among accessions. Conversely, low-frequency loci, including Xgwm371 (SSR4, 5B) (208 bp) (B.F. = 0.25) and Xgwm219 (SSR5, 6B) (176 bp) (B.F. = 0.275), suggest allele restriction to a subset of genotypes, indicating population-specific variations or alleles under selection pressure ( Han et al., 2015). Ten rare alleles (B.F. < 0.2) were found; for instance, WMS273 (SSR2, 1B) at 161 bp was found in only three genotypes (G13, G19, and G22), with a B.F. of 0.075, indicating that it is a rare allele with significant discriminating power. Other sorts of bands were found in just one to three genotypes, including WMS273 (SSR2, 1B) at 119 bp (B.F. = 0.05) and 161 bp (B.F. = 0.075); Xgwm369 (SSR12, 3A) at 155 bp (B.F. = 0.05); and Xgwm124 (SSR17, 1B) at 210 bp, demonstrating great discriminatory power. Similar alleles are critical for distinguishing distinct genotypes, such as Xgwm234 (SSR13, 5B) at 263 bp (B.F. = 0.2), Xgwm371 (SSR4, 5B) at 235 bp (B.F. = 0.15), Xgwm99 (SSR10, 1AL) at 105 bp (B.F. = 0.01), and wmc422 (SSR16, 7A) at 160 bp (B.F. = 0.075), and they may represent novel genetic changes important to marker-assisted selection or gene mapping. Additionally, two markers, Xgwm234 (SSR13, 5B) and wmc422 (SSR16, 7A), produced unique bands, indicating potential for genotype-specific allele identification. The identified monomorphic markers, each showing a band frequency of 1.0, indicating uniform allele presence across all 40 genotypes, including SSRs targeting Xwmc596 (SSR9) at (279 bp and 237 bp), Xgwm369 (SSR12) at 1000 bp, Xgwm234 (SSR13) at 217 bp, Xgwm205a (SSR14) at 145 bp, and Xgwm124 (SSR17) at 200 bp. While these markers do not contribute to genetic differentiation, they confirm conserved regions in the wheat genome (Han et al., 2015). Their presence suggests strong sequence conservation across tested lines, potentially representing core genome regions or housekeeping loci (Mullan et al., 2005). Regarding markers performance, Xgwm371 (SSR4, 5B), Xgwm219 (SSR5, 6B), Xwmc596 (SSR9, 7A), Xgwm99 (SSR10, 1AL), and Xgwm369 (SSR12, 3A), emerged as the most reliable, exhibiting high Polymorphism Information Content (PIC), Effective Multiplex Ratio (EMR), Marker Index (MI), and Discriminating Power (D). Polymorphic information content (PIC) values ranged from 0.1948 (SSR14) to 0.3750 (SSR13), with an average around 0.35-0.37, categorizing these markers as moderately informative (Qian et al., 2012). Higher PIC values observed in SSR4, SSR12, and SSR17 reflect their strong allelic diversity, enhancing their ability to differentiate genotypes. The observed heterozygosity (H.) values were consistent with the polymorphism levels, with SSR17 displaying the highest average heterozygosity (0.0062), suggesting a greater allelic diversity at this locus. Effective Multiplex Ratio (EMR) values were highest for SSR9 (3.875) and SSR12 (3.325), suggesting efficient polymorphic detection across multiple loci. Corresponding Marker Index (MI) values for these primers (0.0074 and 0.0059, respectively) further emphasize their robustness, combining both informativeness and multiplexing efficiency (Jahnke et al., 2022). Discriminating power (D) and resolving power (Rp) analyses further reinforced the utility of SSR5, SSR12, and SSR16, which showed high D values (≥ 0.77) and Rp values (> 2.0), confirming their strong capacity to distinguish among individuals. Conversely, SSR14 and SSR17 exhibited low D (0.2358 and 0.7275) and very low Rp (0.5 and 0.1), indicating limited effectiveness in resolving genotypic differences. Overally, SSR4, SSR5, SSR9, SSR10, and SSR12 emerged as the most effective markers, demonstrating high polymorphism, PIC, EMR, MI, and resolving power. These findings recommend their application in future wheat genetic diversity assessments and marker-assisted selection programs. Xu et al. (2014) employed 34 SSR markers to find polymorphisms in wheat genotypes. Eight markers, including cfa2040, wmc273, and barc32, were discovered to be polymorphic between resistant and susceptible cultivars, allowing the discovery of the stripe rust resistance gene YrC591 for marker-assisted selection in wheat breeding projects. Wu et al. (2018) employed SSR and SNP markers to detect wheat genotype differences, identifying a chromosomal area associated with stripe rust resistance on chromosome 1B. The combination of SSR and KASP markers found the resistance gene YrH62, which is critical for wheat breeding selection. Mohi-Ud-Din et al. (2022) found 458 alleles in 56 wheat genotypes, with an average of 18.32 per locus, using 30 SSR markers. The polymorphism information content values varied between 0.434 and 0.932, with wmc177 and wms292 having 24 alleles apiece. This genetic diversity is critical for developing drought-tolerant wheat cultivars. Despite their advantages, SSR markers face certain limitations, including time-consuming primer development and relatively high costs, which may hinder broader application (Nicot et al., 2004). Nonetheless, their co-dominant inheritance, capacity to distinguish homozygous and heterozygous loci, and overall reliability make them ideal for genetic mapping, cultivar identification, and marker-assisted breeding (Kalia et al., 2011; Han et al., 2015). As demonstrated in this study and supported by Mohamed et al. (2023), SSR markers offer higher resolution than morphological traits in evaluating genetic diversity among Egyptian wheat genotypes. Detection of Genome-Wide Variation by ISSR Marker analysis: The ISSR marker system also demonstrated high utility for assessing genetic variation among the studied wheat genotypes, as illustrated in Table 6 and Figure 3. A total of 3,883 bands (from 124 amplicons) were amplified using ten ISSR primers. Of these, 84 bands were polymorphic, resulting in a mean polymorphism rate of 67.74%, indicating effective allelic diversity detection. Among the primers, ISSR-7 (92%), ISSR-14 (83%), H24 (83%), and ISSR-9 (80%) recorded the highest polymorphism percentages, highlighting their suitability for distinguishing genotypes. Conversely, H28 and ISSR-8 displayed lower polymorphism levels (27% and 36%, respectively), which limits their effectiveness in diversity studies. The polymorphism analysis of ISSR markers on wheat genotypes revealed a wide range of allelic diversity, with band frequencies ranging from 0.05 to 0.975. from these polymorphic alleles, 27 high-frequency alleles shared by the majority of wheat genotypes remained polymorphic as a result of continual amplification and varying molecular weight. ISSR5 at 1100 bp, ISSR9 at 1500 bp, ISSR15 at 600 bp, H24 at 970 bp and 1500 bp, H26 at 570 bp, and H28 at 1350 bp (each BF = 0.975) were mainly fixed in the population while staying different from monomorphic loci due to minor but significant genetic exclusions. In addition, 32 moderate-frequency alleles, such as ISSR5 at 830 bp, ISSR14 at 350 bp, and ISSR15 at 1500 bp, have frequencies ranging from 0.55 to 0.6, making them useful for differentiating subpopulations and marker-assisted breeding, as they offer a larger allele distribution with intermediate informativeness. Among the polymorphic alleles, 25 low-frequency alleles, including ISSR5-400 bp and H26-1800 bp, are markers of intermediate variation, useful for analyzing population structure and selection inspections. Alleles like ISSR6 at 530 bp, ISSR7 at 2400 bp, and H26 at 320 bp show variations between accessions. Eight rare alleles were identified, indicating highly discriminative loci in a few genotypes. These include ISSR14 at 460 bp (BF = 0.1), which is found in only two genotypes; ISSR9 at 250 bp (BF = 0.05), 500 bp (BF = 0.2), and 2300 bp (BF = 0.175); ISSR8 at 1000 bp (BF = 0.175); H24 at 650 bp and 350 bp (BF = 0.15 and 0.175, respectively); and H26 at 1650 bp (BF = 0.15), highlighting specific variability, potentially indicating adaptive or unique genotypic traits relevant for breeding. In contrast, the present investigation identified 40 monomorphic alleles in the 40 wheat genotypes using the ISSR primers (Table 3). including ISSR6, ISSR7, ISSR8, ISSR9, ISSR14, ISSR15, H24, H26, and H28 (Figure 3). Each allele has a band frequency of 1.0, indicating uniformity across all genotypes. These loci, such as ISSR6 at 1500 bp, ISSR8 at 1300 bp, ISSR14 at 940 bp, and H28 at 1900 bp, represent genomic regions with no detectable polymorphism in the population. The identified monomorphic bands, possibly from conserved genomic regions or housekeeping genes, confirm sequence conservation and low mutation rates in regions like ISSR15 at 2000 bp and H24 at 1250 bp, respectively. However, certain primers generate multiple monomorphic bands, suggesting potential primer affinity for tandem repeats or conserved SSR-rich regions (Kumar et al., 2020). PIC values ranged from 0.1364 (ISSR-5) to 0.5739 (ISSR-9). Based on criteria from Qian et al. (2012) and Chen et al. (2021), four primers, ISSR-9, H24, ISSR-8, and ISSR-14, were classified as highly informative (PIC > 0.5), underscoring their utility in wheat genetic research. Effective Multiplex Ratio (EMR) was highest in ISSR-6 (12.775), ISSR-H26 (11.775), and ISSR-15 (11.375), reflecting their ability to simultaneously detect multiple polymorphic loci. These values led to a high genotyping resolution, valuable for cultivar differentiation and breeding program support (Najaphy et al., 2011). Marker Index (MI) peaked at H26 (0.0074), followed by ISSR-15 (0.0062), and H24 (0.5606), reinforcing the high efficiency of these primers in capturing genomic variation. ISSR-9 and H24 also recorded the highest heterozygosity values (0.6467 and 0.5606), indicating rich allelic diversity. Discriminating power (D) was greatest for H26 (0.5206), ISSR-15 (0.3401), and H24 (0.2628), while resolving power (Rp), a key parameter in assessing marker resolution, was highest for H26 (4.75), ISSR-9 (4.65), and ISSR-15 (4.55). These results collectively affirm the utility of these primers for differentiating closely related genotypes (Xiao et al., 2006). Generally, ISSR primers ISSR-9, H24, H26, and ISSR-15 demonstrated superior performance across most of the estimated diversity parameters. These markers are particularly suitable for genetic diversity assessments, cultivar identification, and marker-assisted selection in wheat breeding. Using ISSR markers, Vaillancourt et al. (2008) found 41 polymorphic loci in wheat genotypes and amplified 196 bands from wheat, rye, and triticale DNA samples to demonstrate genetic heterogeneity in the examined accessions. Najaphy et al. (2011) revealed high genetic diversity by 86 bands from thirty wheat genotypes, with 69 bands (80.2%) being polymorphic, showing genetic variation among the examined cultivars and breeding lines. Mohamed et al. (2023) detected 153 ISSR markers, including 85 polymorphic markers, demonstrating high genetic variation among wheat genotypes, with an average polymorphism band per primer of 7.7. However, a notable limitation of ISSR markers is their dominant inheritance pattern, which prevents differentiation between homozygous and heterozygous loci, reducing their utility in fine-scale genetic mapping (Grover & Sharma, 2016). Despite this, their capacity to target both coding and non-coding regions makes them a valuable complement to co-dominant markers like SSRs and AFLPs, contributing to a more comprehensive evaluation of genetic diversity (Reddy et al., 2002). High-Resolution Fingerprinting with AFLP-CE Marker Analysis: As shown in Table 7, the Amplified Fragment Length Polymorphism) technique, coupled with the capillary electrophoresis system (AFLP-CE), and three selected primer combinations, was highly effective in identifying genetic diversity among 40 wheat genotypes. Each primer pair produced a large number of amplicons (264 to 425), almost all of which were polymorphic. This demonstrates the excellent ability of the AFLP-CE technique to capture the genome-wide variation at high resolution. The E AAC –M CAG combination produced the highest total number of amplicons (425), all of which were polymorphic, including 65 unique amplicons. It also had the highest resolving power (Rp = 179.65) and discriminating power (D = 0.9273), indicating a greater capacity to detect uncommon alleles and distinguish between genotypes. Although it had the lowest average heterozygosity (2.32 × 10⁻⁵), this might be due to marker saturation or allelic fixation within certain regions. E AAG –M CTC followed closely, producing 391 total amplicons and 33 unique ones, with a high effective multiplex ratio (EMR = 129.90) and Rp = 159.30, indicating its broad genome coverage and informative value. The high polymorphism rate (99.49%) reinforces its use in assessing inter-genotypic variation. E ACT –M CTT , while yielding fewer total amplicons (264), had the highest average heterozygosity (3.99 × 10⁻⁵) and a PIC value of 0.3323, making it valuable for detecting minor allelic differences. Its strong discriminating power (D = 0.9093) suggests its usefulness in genotypic differentiation even with fewer loci. Overall, the integration of AFLP-CE with capillary electrophoresis enabled precise detection of amplified loci with high polymorphic potential. The combination of high PIC, EMR, and resolving power across primer sets confirms the reliability of this platform in molecular characterization of wheat germplasm. These results support the adoption of AFLP-CE in marker-assisted selection, genetic diversity studies, and pre-breeding programs targeting complex traits (Papa et al., 2005). The AFLP (Amplified Fragment Length Polymorphism) approach, when paired with capillary electrophoresis technology (AFLP-CE), provides an enhanced platform for high-resolution fragment analysis, improved reproducibility, and multiplexing efficiency (Papa et al., 2005; Blignaut et al., 2013). Unlike traditional gel-based AFLP technique, which can introduce electrophoretic variability, the use of the Applied Biosystems 310 Genetic Analyzer in this study allowed for precise peak separation using single-capillary electrophoresis and POP-4 polymer technology (Wenz et al., 1998; Blignaut et al., 2013). This technique guarantees precise and uniform DNA movement, decreasing signal variability that often affects manual polyacrylamide gel electrophoresis (Papa et al., 2005; Blignaut et al., 2013). The incorporation of GeneMapper software improved the AFLP procedure by automating peak identification, fragment size, and allele binning, reducing subjective bias in AFLP scoring and enhancing data uniformity (Holland et al., 2008). Furthermore, fluorescence-based detection increased the sensitivity and multiplexing capability of the AFLP approach, allowing for simultaneous examination of numerous AFLP peaks (Schwarz et al., 2000). The absence of post-PCR gel handling significantly reduced contamination hazards, boosting overall data integrity and increasing the dependability of the AFLP technique in high-throughput genetic screening and genome-wide diversity studies (Papa et al., 2005; Blignaut et al., 2013). AFLP markers also provided valuable insights into genotypic differentiation across contrasting agro-climatic zones. Genotypes from Semi-Arid regions (Sohag, Assiut, and Beni Suef) exhibited the highest number of unique AFLP peaks, suggesting the presence of selection-driven genomic variations. Additionally, wheat genotypes from North Sinai (G40) displayed distinct AFLP patterns, potentially reflecting genetic adaptations to salinity stress and fluctuating moisture availability (Hazen et al., 2002). The combination of AFLP with capillary electrophoresis enabled the detection of exceptionally high polymorphism levels (99.49%–100%), confirming its effectiveness in wheat molecular breeding and conservation programs (Papa et al., 2005). These results underscore the superior capacity of AFLP markers for high-resolution genetic fingerprinting, making them a powerful tool for assessing genetic variability and guiding marker-assisted selection (MAS) strategies. Integration of SSR, ISSR, and AFLP Markers for Sustainable Wheat Breeding: The integration of SSR, ISSR, and AFLP markers in this study provided an intensive and complementary approach to analyzing wheat biodiversity. This resulted in higher accuracy for detecting genome-wide variations and locus-specific polymorphisms (Noli et al., 2008; Malik et al., 2012; Costa et al., 2016). Meanwhile, SSR markers, due to their co-dominant nature and trait association potential, are widely applied in trait-specific selection, such as drought tolerance, disease resistance, and grain quality (Han et al., 2015). ISSR markers facilitate the detection of genome-wide polymorphisms, which is valuable in identifying genotypic responses to abiotic stress (Reddy et al., 2002; Mohamed et al., 2023). AFLP markers, despite their complexity, contribute significantly to mapping multigenic traits, like resistance to biotic diseases and yield stability (Bai et al., 1999; Guo et al., 2003; Liu et al., 2019). The combined marker analysis is particularly advantageous for marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping,which in turn leads to the identification of climate-resilient genotypes and the preservation of genetic diversity, which are important aspects of sustainable wheat improvement. However, while the integration of these markers enhances genetic resolution, it also presents practical challenges such as data consistency, higher operating costs, and technical demands (Agarwal et al., 2008; Soriano, 2020). Along with the integration of molecular marker techniques, future investigations should benefit from the availability of single-nucleotide polymorphism (SNP) arrays and genotyping-by-sequencing (GBS). Combining these high-throughput technologies with marker-assisted selection (MAS) and genome-wide association studies (GWAS) is critical for enhancing breeding efficiency and accelerating genetic gains (Jiangfeng et al., 2014; Rasheed & Xia, 2019). Genetic Similarity Matrix: Assessing Relationships Among Wheat Genotypes The study of wheat genotypes using molecular marker techniques revealed significant genetic variations, highlighting the importance of multi-locus fingerprinting in understanding population structure and diversity in wheat landraces. The pairwise similarity utilizing Jaccard coefficients ranged from 0.253 to 0.827 (table 8), indicating a broad spectrum of genetic distances and the presence of closely related and genetically divergent members. This variability can be attributed to the rich evolutionary history, local adaptation, and complex seed exchange networks in Egyptian wheat germplasm (Balfourier et al., 2019). According to earlier research, closely related wheat varieties cluster together based on molecular markers (Peng et al., 2000; Fufa et al., 2005). The highest similarity (J = 0.827) was found between G28 and G29, which were collected from surrounding villages in Sohag Governorate. This strongly indicates that similarly adapted regional landraces have almost identical genetic histories, most likely due to localized seed circulation, shared environmental pressures, and limited external genetic inflow, as previously reported for closely adapted regional landraces (Ijaz & Khan, 2009). Another relatively high similarity (J = 0.698) was found between G38 (Assiut) and G39 (Beni Suef), two landraces from Upper Egypt but different governorates. This inter-regional similarity could reflect a common ancestral lineage or recent seed exchange between the regions, emphasizing the dynamic nature of traditional wheat cultivation systems in Upper Egypt. By contrast, G19 and G22, both from Upper Egypt, showed only moderate similarity (J = 0.443). This finding contradicts the assumption of regional homogeneity within Upper Egypt and indicates the presence of sub-regional genetic differentiation. Such divergence may arise from distinct evolutionary pathways, segregated seed populations, or variation in farming practices across villages, as is commonly observed in traditional farming systems. The lowest similarity values were recorded between G3 and G35 (J = 0.253) and between G1 and G34 (J = 0.254), indicating substantial genetic divergence. These genotypes may differ in ecogeographic origin, genetic background, or selection pressure, possibly due to genetic drift, historical isolation, or introgression from different breeding pools (Zhou et al., 2018). Their genetic distinctiveness enhances their potential as unique sources of alleles for crop improvement, particularly for adaptive traits like drought tolerance or pest resistance (Mondal et al., 2016). As depicted in figure 4, the UPGMA dendrogram constructed from the Jaccard similarity matrix, reveals notable genetic diversity among the 40 wheat genotypes collected from a wide range of agro-climatic regions in Egypt. The similarity ranged approximately from 0.25 to 0.83, reflecting a broad spectrum of genetic divergence and highlighting the rich heritage of wheat germplasm preserved across different environmental and geographic zones . The dendrogram delineates the genotypes into two main clusters, each consisting of several sub-clusters, with distinguishable correlations to ecogeographic origin. The first cluster, which is genetically distinct from genotypes from the semi-arid and arid regions, includes several genotypes from Sohag Governorate, particularly from Al Monshaah, Sohag city, and Akhmim districts (e.g., G16–G29), along with entries from the New Valley region (e.g., G11–G15). Notably, G22 (Kombedar, Al Monshaah), G19 (Al Salaah, Sohag), and G17 (Shandawil, Sohag) represent some of the most genetically distinct genotypes, clustering separately at low similarity coefficients. Their divergence suggests that these local landraces, collected from isolated rural areas, have preserved unique alleles that may be attuned to specific drought conditions, salinity levels, or selective cultural influences. Genotypes such as G14 and G15 (Farafrah, New Valley) and G26–G29 (Al Monshaah villages) form tight sub-clusters, suggesting shared ancestry and localized adaptation within the oases of the Western Desert and the central semi-arid locations of Upper Egypt. Interestingly, G27 (Awlaad Harron) and G28 (Nagaa Abd El Azeem Salem) are closely grouped, suggesting genetic homogeneity among samples from proximate villages within Al Monshaah . These findings reinforce the importance of semi-arid and arid zones, especially Sohag and New Valley, as central locations for genetic uniqueness and conservation priorities . The second cluster, which is the larger cluster, has genetically similar genotypes from hot arid and hot semi-arid regions. This second cluster encompasses most of the genotypes from Aswan, Luxor, Qena, Assiut, Beni Suef, and North Sinai, many of which likely underwent modern selection, informal seed exchange, or adaptation to similar agro-ecological niches. Genotypes from Aswan (G1–G3) and Luxor (G4–G6), both categorized under the Hot Arid Region, formed a sub-cluster with moderate similarity, indicating shared traits potentially shaped by high temperature and low precipitation stress conditions . Entries from Qena (G7–G10) and New Valley (G11–G13) grouped relatively closely, underscoring the genetic proximity between genotypes from adjacent semi-arid governorates . G33 (Al Sawamaa Sharq, Akhmim) and G2 (Aswan) clustered tightly with other Upper Egyptian entries like G1 and G3, suggesting gene flow or varietal dissemination across the Nile Valley corridor . A tight sub-cluster including G36 (Bany Aysh, Gerga), G37 (Al Zaraby, Abo Teeg, Assiut), G38 (Assiut city), and G39 (Sayan Abu Ali, Beni Suef) displayed the highest Jaccard similarity values, indicating minimal genetic distance. These may represent improved cultivars or genetically uniform varieties that have diffused widely through national seed systems or farmer networks . Notably, G40 (Rafah, North Sinai) clustered within this group, suggesting that even distant North Sinai shares a genetic affinity with Upper Egyptian materials, potentially due to varietal introductions or shared breeding stock. The observed clustering patterns, shaped by both geographic isolation and shared environmental pressures, underscore the importance of preserving region-specific germplasm, particularly from underrepresented areas such as Sohag, New Valley, and North Sinai. Genotypes like G22 (Kombedar) and G19 (Al Salaah), which lie at the borders of the dendrogram, should be prioritized for in situ conservation and GenBank safeguarding, as they harbor rare or divergent alleles valuable for climate resilience . From a breeding perspective, the high divergence among genotypes from different governorates presents an excellent opportunity to exploit heterosis by crossing distant pairs (e.g., G22 × G39 or G17 × G36). Moreover, the tight clustering of certain genotypes highlights redundancy risks, emphasizing the need to avoid overrepresentation of genetically similar lines in breeding pools . The geographic structuring of genetic diversity revealed in this study holds critical value for conservation planning, germplasm management, and sustainable breeding strategies aimed at enhancing wheat productivity and resilience in arid and semi-arid environments . PCA Biplot Analysis of Genetic Similarity: The principal component analysis (PCA) biplot (Figure 5) provides a comprehensive overview of the genetic structure of wheat genotypes. The PCA results complement molecular results by reducing the genetic data into key dimensions that explain the observed variation (Chou et al., 2022). The provided biplots illustrate distributions of genotypes and marker contributions to enable the detection of clusters, outliers, and high-contribution markers. The spatial distribution of genotypes in the PCA scatter plot aligns with the dendrogram, reinforcing the robustness of the clustering analysis. The first two components cumulatively explained a substantial portion of the variance, with Component 1 distinguishing genotypes along a gradient likely related to regional adaptation, while Component 2 captured orthogonal variation patterns. A prominent cluster along Component 1 comprised genotypes from the hot arid and semi-arid regions of Upper Egypt, such as G1, G2 (Aswan), G3 (Al Sayala), G4, G6 (Luxor), G7, and G10 (Qena). Their tight grouping suggests a high degree of genetic similarity, potentially reflecting convergent selection pressure in similarly hot environments. Genotypes G11 and G13 (Dakhla Oasis, New Valley Governorate), from the arid zone, are also shown to be close to this cluster, indicating shared allelic characteristics adapted to harsh desert climates. In contrast, genotypes from Sohag, especially those from the Al Monshaah district (e.g., G22, G23, G24, G25, G26, G27, G28, and G29), formed a separate sub-cluster oriented more toward Component 2. Their separation implies intra-regional variability, likely due to micro-environmental conditions or farmer selection practices within villages like Al Kawamel Kebly, Al Zook Al Gharbyea, and Kombedar. Genotypes G34 (Al Zook Al Sharkhya, Sohag) and G39 (Sayan Abu Ali, Beni Suef) were positioned in the lower-left quadrant of the biplot, distinctly apart from the central mass. This indicates their unique genetic background, possibly due to geographic isolation or limited germplasm exchange. G40, from Rafah in North Sinai, also displayed considerable divergence, suggesting an adaptation to the cooler and possibly more Mediterranean-influenced coastal climate. The vectors overlaid on the biplot represent trait or marker loadings, pointing to the traits driving genotype dispersion. Genotypes like G28 (Nagaa Abd El Azeem Salem) and G20 (Al Shoryfat, Sohag) appeared aligned with longer vectors, suggesting strong associations with the underlying variables, potentially representing adaptive or agronomic traits. Heatmap Analysis of Genetic Similarity: The heatmap clustering (Figure 6) provides a visual synthesis of pairwise genetic similarity among the 40 wheat genotypes based on the Jaccard coefficient matrix. The hierarchical clustering dendrograms aligned along both axes corroborate the patterns detected in PCA and UPGMA analyses but further emphasize subgroup structures and levels of intragroup homogeneity. Distinct blocks of high similarity are indicated by warm color gradients (red to orange), while cool tones (light to dark blue) denote dissimilarity. A prominent diagonal line represents perfect self-similarity, as expected, while square clusters along the diagonal highlight groups of closely related genotypes. One such cluster is observed among genotypes from Sohag (Al Monshaah district), including G22 (Kombedar), G23 (Al Zook Al Gharbyea), G24, G27 (Al Kawamel Kebly to Awlaad Harron), and G28 (Nagaa Abd El Azeem Salem). These genotypes exhibited strong within-group similarity (bright red-orange blocks) and minimal divergence, which likely reflects localized farmer selection and seed exchange within the same ecological and cultural context. Similarly, G1 and G3 (Aswan and Al Sayala) formed another compact, high-similarity block, reinforcing their close genetic relationship previously seen in PCA space. Their proximity, both geographically and genetically, suggests either a common ancestral landrace or shared selection pressures due to the extreme hot, arid climate of Aswan. The New Valley genotypes, G11 (Bashandy), G12, G13 (El Qasr), G14, and G15 (Farafrah), though slightly more dispersed, still formed a moderately cohesive cluster, consistent with their adaptation to the arid Western Desert conditions. Outlier genotypes again stood apart in this heatmap, G34 (Al Zook Al Sharkhya, Sohag) and G39 (Sayan Abu Ali, Beni Suef) clustered weakly with others and displayed mostly cool-toned interactions, emphasizing their unique allelic profiles. G40 (Rafah, North Sinai) formed a distinct branch with limited similarity to other genotypes, which supports its divergent adaptation to the Mediterranean coastal agro-ecology and potential genetic isolation from Upper Egyptian wheat pools. The heatmap also exposed internal differentiation within broader governorates. For instance, Sohag genotypes such as G16 (Sohag City), G19 (Al Salaah), and G33 (Al Sawamaa Sharq) showed lower similarity with other Sohag landraces, implying that intra-governorate environmental variation and localized seed systems contribute significantly to genetic structuring. Collectively, the heatmap enriches the understanding of genotype relationships by illustrating fine-scale clustering and suggesting candidate genotypes for breeding crosses. Specifically, crossing highly divergent genotypes such as G40 (Rafah) or G34 (Sohag) with cluster-bound landraces (e.g., G22 and G28) could maximize heterosis and trait recombination potential. In summary, the integration of similarity matrix, dendrogram, PCA, and heatmap analysis provides a comprehensive assessment of genetic diversity among wheat genotypes. The observed clustering patterns highlight genetic subpopulations essential for wheat breeding and conservation strategies. The identification of genetically diverse and outlier genotypes offers valuable insights into potential parental lines for hybridization, ensuring the sustainable enhancement of wheat biodiversity and resilience in future breeding programs. Factors Shaping Genetic Diversity in Wheat Genotypes: The genetic biodiversity observed among the 40 wheat genotypes analyzed in this study reflects a complex interplay of several factors, such as environmental selection pressures, geographical origins, agronomic practices, historical gene flow, genetic drift, and molecular evolution. Over centuries, these components have contributed to wheat adapting to a wide range of environments and cropping strategies (Sahri et al., 2014). Understanding these aspects is crucial for enhancing genetic conservation, promoting sustainable breeding practices, and enhancing wheat resistance to changing climate conditions. The integration of SSR, ISSR, and AFLP markers in this study provided a comprehensive framework for assessing genetic diversity, achieving correct genotypic differentiation and genome-wide polymorphism identification, which are required for marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping. Environmental Selection Pressures and Genetic Adaptation: Strengthening the link between molecular data and agronomic performance is essential for translating genetic diversity into sustainable yield and food security outcomes. The adaptation of wheat genotypes to diverse agro-climatic zones in Egypt has been a major driver of genetic diversity (Hamada et al., 2022). The genotypes analyzed in this study were sourced from Hot Arid, Hot Semi-Arid, Arid, and Semi-Arid regions, each imposing distinct selective pressures that shape genotypic differentiation (He et al., 2019; Terwisscha van Scheltinga et al., 2021; Hamed et al., 2022). Wheat populations in the Hot Arid and Hot Semi-Arid zones (e.g., Aswan, Luxor, and Qena) are subjected to high temperatures and prolonged drought periods, leading to overexpression of the genes involved in water-use efficiency, root system development, and heat tolerance (Tricker et al., 2018). The existence of unique SSR alleles and AFLP polymorphism peaks in these genotypes indicates that they include adaptive genomic changes associated with abiotic stress tolerance (Huang et al., 2016; Du et al., 2023). Similarly, wheat genotypes from the Arid region (New Valley: G11–G15) demonstrated distinct ISSR banding patterns, indicating genetic divergence caused by high temperature variations and low soil fertility. The comprehensive genome coverage of ISSR markers allowed for the identification of stress-responsive loci, which are crucial for increasing wheat performance in difficult desert settings (Kumar et al., 2024). In contrast, genotypes from Semi-Arid regions (e.g., Sohag, Assiut, and Beni Suef) experience seasonal temperature variations and moderate water availability, leading to the selection of genotypes with increased photosynthetic efficiency, enhanced nutrient uptake, and greater yield stability (Wasaya et al., 2021; Koua et al., 2022). The AFLP analysis of these genotypes revealed higher levels of genetic differentiation, confirming that environmental adaptation plays a major role in shaping genetic variation (Sasanuma et al., 2002). These findings highlight the necessity of implementing breeding strategies to develop resilient wheat varieties to climate change while maintaining high productivity (Mondal et al., 2016; Yadav et al., 2022). Geographic Isolation, Gene Flow, and Historical Breeding Influences In addition to the environmental selection pressures and the adaptation of wheat cultivars, geographic isolation and gene flow impact the genetic structure of wheat populations, since they lead to determining the extent of allelic exchange (Luo et al., 2007; Rufo et al., 2019). Genotypes from distantly isolated regions (e.g., Dakhla and Farafrah in New Valley) exhibited higher genetic differentiation due to restricted cross-pollination and localized selection pressures (Bhuri, 2013; Li et al., 2000). Limited seed exchange and farmer selection in these regions may have contributed to higher genetic uniqueness, as observed in the presence of specific ISSR and AFLP polymorphisms (Bhuri & Upadhyay, 2013; Balfourier et al., 2019). Conversely, some genotypes showed genetic similarities across different regions, indicating possible historical gene flow through seed trade, farmer migration, or cross-regional breeding efforts (Balfourier et al., 2019; Scott et al., 2019). For example, wheat genotypes from Sohag (Semi-Arid) and Luxor (Hot Semi-Arid) shared common AFLP markers, suggesting historical introgression of adaptive alleles between these populations. Understanding these patterns of gene flow is critical for the conservation of genetic resources and breeding programs that aim for the optimization of trait introgression for sustainable wheat production (Royo et al., 2021). CONCLUSION The present study provides a comprehensive assessment of the genetic diversity of 40 wheat genotypes using SSR, ISSR, and AFLP molecular markers and provides insights regarding their genetic structure, phylogenetic relationships, and their potential for breeding applications. The genetic similarity matrix revealed considerable variability, highlighting the impact of environmental selection pressures, geographic origin, and historical breeding practices on wheat populations. The adaptation of genotypes to agro-climatic zones, especially in Hot Arid and Semi-Arid environments, has enabled stress tolerance selection, crucial for developing climate-resilient wheat cultivars. Geographic isolation and gene flow also play a role in detecting genetic diversity, emphasizing the need to conserve both elite and underutilized germplasm for breeding programs. Future research should incorporate next-generation sequencing and genome-wide association studies to improve breeding accuracy and genetic improvement. Declarations Acknowledgment: The authors would like to acknowledge the Deanship of Graduate Studies and Scientific Research, Taif University for funding this work. Authors’ Contributions: Conceptualization: Shafik D. Ibrahim (SDI), Ahmed F. Abd El-Hakim (AFA),Mohammed Alqurashi (MA), Salman Aloufi (SA), Doha A. Albalawi (DAA), Amenah S. Al-Otaibi (ASA), Basmah M. Alharbi (BMA) Fahad Mohammed Alzuaibr (FMA), Hanan Khalaf Anazi (HKA), Siham M. Al-Balawi (SMA). Collection and Data Evaluation: SDI. Methodology – Laboratory and Greenhouse Experiments: SDI, AFA. Data Analysis: AFA, SDI. Manuscript Preparation (Drafting): AFA, SDI. Review and Editing: MA, SA, DAA, ASA, BMA, FMA, HKA, SMA. Overall guidance: AFA. All authors have read and approved the final version of the manuscript. Funding : The authors would like to acknowledge the Deanship of Graduate Studies and Scientific Research, Taif University for funding this work. Data availability All data generated or analyzed during this study are included in this published article. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details College of Science, Taif University- Saudi Arabia, Agricultural Genetic Engineering Research Institute (AGERI), Agriculture Research Center (ARC)-Egypt, Faculty of Science, University of Tabuk-Saudi Arabia, Faculty of Agriculture, Ain Shams University- Egypt, Faculty of Agriculture, Al-Azhar University, Egypt. References Agarwal, M., Shrivastava, N., & Padh, H. (2008). Advances in molecular marker techniques and their applications in plant sciences. 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Genotype No. Barcoding No. Source in Egypt Governorate District Village Hot Arid Region: G1 111320 Aswan Aswan Aswan G2 111321 Aswan Aswan Aswan G3 111322 Aswan Al Noba Al Sayala G4 111323 Luxor Esna Tuffnice G5 111324 Luxor Esna Danffic G6 111325 Luxor Esna Danffic Hot Semi-Arid Region: G7 111326 Qena Qeft Qeft G8 111327 Qena Nag HamadI Al HalfayaKebly G9 111328 Qena Nag HamadI Al Rahmanya G10 111329 Qena Qena Dandrah Arid Region: G11 111330 New Valley Dakhla Bashandy G12 111331 New Valley Dakhla El Qasr G13 111332 New Valley Dakhla El Qasr G14 111333 New valley Farafrah Farafrah G15 111334 New valley Farafrah Farafrah Semi-Arid Region: G16 111335 Sohag Sohag Sohag G17 111336 Sohag Sohag Shandawil G18 111337 Sohag Sohag Jazirat-Shandawil G19 111338 Sohag Sohag Al Salaah G20 111340 Sohag Sohag Al Shoryfat G21 111341 Sohag Sohag AwlaadNaseer G22 111342 Sohag Al Monshaah Kombedar G23 111343 Sohag Al Monshaah Al Zook Al Gharbyea G24 111344 Sohag Al Monshaah Al Kawamel Kebly G25 111345 Sohag Al Monshaah Al Kawamel Kebly G26 111346 Sohag Al Monshaah Al Horyzat Al Gharbeya G27 111347 Sohag Al Monshaah AwlaadHarron G28 111348 Sohag Al Monshaah NagaaAbd El Azeem Salem G29 111349 Sohag Al Monshaah Kombedar G30 111351 Sohag Idfa Idfa G31 111353 Sohag Akhmim Al deyabat G32 111354 Sohag Akhmim Arab Al Atawla G33 111356 Sohag Akhmim Al SawamaaSharq G34 111359 Sohag Al Monshaah Al Zook Al sharkhya G35 111362 Sohag Gerga Gerga G36 111363 Sohag Gerga BanyAysh G37 111364 Assiut Abo teeg Al Zaraby G38 111365 Assiut Assiut Assiut G39 111369 BeniSwif BeniSwif Sayan Abu Ali G40 111370 North Sinai Rafah Rafah Table 2: SSR primer details, including marker name, chromosomal location, annealing temperature, and primer sequences. Primer Code Marker Name Chr.* Ta (°C)* Fwd (5′–3′)* Rev (5′–3′)* SSR2 WMS273 1B 55 ATTGGACGGACAGATGCTTT AGCAGTGAGGAAGGGGATC SSR4 Xgwm371 5B 58 GACCAAGATATTCAAACTGGCC AGCTCAGCTTGCTTGGTACC SSR5 Xgwm219 6B 60 GATGAGCGACACCTAGCCTC GGGGTCCGAGTCCACAAC SSR9 Xwmc596 7A 61 TCAGCAACAAACATGCTCGG CCCGTGTAGGCGGTAGCTCTT SSR10 Xgwm99 1AL 57 AAGATGGACGTATGCATCACA GCCATATTTGATGACGCATA SSR12 Xgwm369 3A 55 CTGCAGGCCATGATGATG ACCGTGGGTGTTGTGAGC SSR13 Xgwm234 5B 55 GAGTCCTGATGTGAAGCTGTTG CTCATTGGGGTGTGTACGTG SSR14 Xgwm205a 5A 60 CGACCCGGTTCACTTCAG AGTCGCCGTTGTATAGTGCC SSR16 wmc422 7A 51 GACTACTGAACTGGAGAGTGTG GCATTAGAATTTGGAGTTTGGAG SSR17 Xgwm124 1B 60 GCCATGGCTATCACCCAG ACTGTTCGGTGCAATTTGAG Abbreviations: Chr.*: Chromosome; Ta (°C)* ; Annealing Temperature (°C); Fwd (5′–3′)*; Forward Sequence (5’ - 3’); Rev (5′–3′)* ; Reverse Sequence (5’ - 3’). Table 3: ISSR primer sequences and details. Primer Code Sequence ISSR-05 5'-GTGTGTGTGTGTGTGTG-3' ISSR-06 5'-CGCGATAGATAGATAGATA-3' ISSR-07 5'-GACGATAGATAGATAGATA-3' ISSR-08 5'-AGACAGACAGACAGACGC-3' ISSR-09 5'-GATAGATAGATAGATAGC-3' ISSR-14 5'-CTCCTCCTCCTCCTCTT-3' ISSR-15 5'-CTCTCTCTCTCTCTCTRG-3' H24 5'-AGAGAGAGAGAGAGAGYC-3' H26 5'-AGAGAGAGAGAGAGAGYG-3' H28 5'-HVHTGTGTGTGTGTGTGT-3' Table 4: AFLP Adapter and Primer Sequences. Name Sequence (5’-3’) Adapters EcoRI Adapter 5’-CTCGTAGACTGCGTACC-3’ 3’-CATCTGACGCATGGTTAA-5’ MseI Adapter 5’-GACGATGAGTCCTGAG-3’ 3’-TACTCAGGACTCAT-5’ Primers MseI Primer GATGAGTCCTGAGTAA MseI + CTC GATGAGTCCTGAGTAACTC MseI + CTT GATGAGTCCTGAGTAACTT MseI + CAG GATGAGTCCTGAGTAACAG EcoRI Primer GACTGCGTACCAATTC EcoRI + AAG (Ned) GACTGCGTACCAATTCAAG EcoRI + ACT (Fam) GACTGCGTACCAATTCACT EcoRI + AAC (Fam) GACTGCGTACCAATTCAAC Table (5): performance parameters of SSR primers used for genetic diversity analysis of the studied wheat genotypes. Primer code Fragment size Total Amplicons T. AB Monomorphic Amplicons (MB) Polymorphic Amplicons (PB) Unique Bands (UB) P % PIC EMR MI H H. Avg D Rp SSR2 161-106 5 82 0 5 0 100.00 0.3668 2.05 0.0050 0.4838 0.0024 0.8331 1.7 SSR4 235-119 5 103 0 5 0 100.00 0.3748 2.575 0.0064 0.4996 0.0025 0.7360 2.45 SSR5 201-176 3 43 0 3 0 100.00 0.3541 1.075 0.0041 0.4599 0.0038 0.8735 2.15 SSR9 279-149 6 155 2 4 0 66.67 0.3528 3.875 0.0074 0.4575 0.0019 0.5839 2.15 SSR10 153-105 4 75 0 4 0 100.00 0.3740 1.875 0.0058 0.4980 0.0031 0.7818 1.85 SSR12 1000-175 7 133 1 6 0 85.71 0.3744 3.325 0.0059 0.4988 0.0018 0.7753 2.85 SSR13 263-217 4 80 1 3 1 75.00 0.3750 2 0.0063 0.5000 0.0031 0.7516 0.9 SSR14 160-145 2 70 1 1 0 50.00 0.1948 1.75 0.0048 0.2188 0.0027 0.2358 0.5 SSR16 160-140 3 40 0 3 1 100.00 0.3457 1 0.0037 0.4444 0.0037 0.8908 0.4 SSR17 210-200 2 42 1 1 0 50.00 0.3744 1.05 0.0065 0.4988 0.0062 0.7275 0.1 Total 41 823 6 35 2 * Amplicon: A DNA fragment amplified and detected across the 40 genotypes via SSR marker technique. **Abbreviations: PIC* : Polymorphic Information Content, EMR* : Effective Multiplex Ratio, H* : Heterozygosity H. Avg* : Average Heterozygosity, MI* : Marker Index, D* : Discriminatory Power, Rp* : Resolving Power. Table (6): performance parameters of ISSR primers used for genetic diversity analysis of the studied wheat genotypes. Primer Code Fragment Size Total Amplicons T. AB Monomorphic Amplicons (MB) Polymorphic Amplicons (PB) Unique Bands (UB) P % PIC EMR H H. Avg. MI D Rp ISSR-5 423-1903 10 368 2 8 0 80% 0.1364 9.2 0.1472 0.0004 0.0034 0.1538 1.6 ISSR-6 371-1541 14 511 6 8 0 57% 0.1469 12.775 0.1597 0.0003 0.0036 0.1675 2.05 ISSR-7 266-2684 13 338 1 12 0 92% 0.4432 1 0.5250 0.5250 0.5250 0.2538 3.4 ISSR-8 364-2526 11 362 7 4 0 36% 0.4604 1 0.5168 0.5168 0.5168 0.1090 1.6 ISSR-9 282-1581 10 251 2 8 0 80% 0.5739 1 0.6467 0.6467 0.6467 0.1924 4.65 ISSR-14 310-1961 12 368 2 10 0 83% 0.4539 1 0.5078 0.5078 0.5078 0.1949 2.55 ISSR-15 218-1319 14 455 4 10 0 71% 0.2583 11.375 0.3047 0.0005 0.0062 0.3401 4.55 H24 219-1493 12 330 2 10 0 83% 0.4937 1 0.5606 0.5606 0.5606 0.2628 3.65 H26 226-2184 17 472 6 11 0 65% 0.3351 11.775 0.4258 0.0006 0.0074 0.5206 4.75 H28 302-1806 11 428 8 3 0 27% 0.3799 1 0.4790 0.4790 0.4790 0.0320 0.45 Total 124 3883 40 84 0 * Amplicon: A DNA fragment amplified and detected across the 40 genotypes via ISSR marker technique. **Abbreviations: PIC* : Polymorphic Information Content, EMR* : Effective Multiplex Ratio, H* : Heterozygosity H. Avg* : Average Heterozygosity, MI* : Marker Index, D* : Discriminatory Power, Rp* : Resolving Power. Table (7): Efficiency Parameters of CE-AFLP Primer Combinations for Assessing Genetic Diversity Among 40 Wheat Genotypes. Primer Combination Total Amplicons Monomorphic Amplicons (MF) Polymorphic Amplicons (PF) Unique Amplicons (UF) Polymorphism (%) PIC Heterozygosity (H) Avg. Heterozygosity (H Avg.) EMR MI D Rp E AAG -M CTC 391 2 389 33 99.49% 0.3453 0.4437 2.83698E-05 129.9 0.0037 0.8896 159.3000 E ACT -M CTT 264 0 264 26 100.00% 0.3323 0.4209 3.98586E-05 79.5 0.0032 0.9093 123.1000 E AAC -M CAG 425 0 425 65 100.00% 0.3163 0.3939 2.31723E-05 114.625 0.0027 0.9273 179.6500 * Amplicon: A DNA fragment amplified and detected across the 40 genotypes via CE-AFLP. **Abbreviations: PIC : Polymorphic Information Content, EMR : Effective Multiplex Ratio, H : Heterozygosity H. Avg : Average Heterozygosity, MI : Marker Index, D : Discriminatory Power, Rp : Resolving Power. Table 8: Similarity matrix of the examined 40 wheat genotypes based on combined ISSR, SSR, and AFLP marker data. G 1 G 2 G 3 G 4 G 5 G 6 G 7 G 8 G 9 G1 0 G1 1 G1 2 G1 3 G1 4 G1 5 G1 6 G1 7 G1 8 G1 9 G2 0 G2 1 G2 2 G2 3 G2 4 G2 5 G2 6 G2 7 G2 8 G2 9 G3 0 G3 1 G3 2 G3 3 G3 4 G3 5 G3 6 G3 7 G3 8 G3 9 G4 0 G 1 1.000 G 2 0.485 1.000 G 3 0.352 0.439 1.000 G 4 0.456 0.595 0.475 1.000 G 5 0.454 0.545 0.481 0.620 1.000 G 6 0.435 0.543 0.433 0.602 0.658 1.000 G 7 0.437 0.525 0.462 0.619 0.690 0.607 1.000 G 8 0.526 0.532 0.427 0.550 0.602 0.624 0.612 1.000 G 9 0.450 0.609 0.459 0.607 0.625 0.627 0.647 0.629 1.000 G1 0 0.471 0.543 0.490 0.542 0.551 0.546 0.591 0.649 0.585 1.000 G1 1 0.421 0.515 0.419 0.633 0.594 0.613 0.612 0.554 0.652 0.554 1.000 G1 2 0.446 0.532 0.431 0.576 0.658 0.609 0.667 0.684 0.583 0.582 0.597 1.000 G1 3 0.465 0.559 0.415 0.555 0.588 0.695 0.603 0.639 0.568 0.587 0.594 0.691 1.000 G1 4 0.379 0.445 0.323 0.460 0.445 0.492 0.500 0.502 0.484 0.489 0.488 0.494 0.543 1.000 G1 5 0.390 0.467 0.353 0.470 0.464 0.502 0.490 0.502 0.486 0.523 0.517 0.519 0.558 0.684 1.000 G1 6 0.351 0.439 0.343 0.439 0.412 0.453 0.429 0.444 0.454 0.478 0.469 0.485 0.481 0.544 0.593 1.00 0 G1 7 0.304 0.380 0.325 0.386 0.360 0.387 0.385 0.377 0.391 0.400 0.407 0.391 0.413 0.445 0.460 0.583 1.000 G1 8 0.343 0.427 0.314 0.435 0.419 0.468 0.446 0.478 0.456 0.455 0.474 0.493 0.479 0.514 0.562 0.630 0.607 1.000 G1 9 0.386 0.388 0.289 0.379 0.387 0.401 0.379 0.407 0.388 0.392 0.403 0.425 0.420 0.430 0.430 0.414 0.424 0.607 1.000 G2 0 0.375 0.420 0.359 0.445 0.476 0.443 0.462 0.457 0.457 0.440 0.458 0.487 0.476 0.484 0.549 0.463 0.420 0.588 0.523 1.000 G2 1 0.313 0.381 0.373 0.423 0.406 0.386 0.418 0.383 0.413 0.384 0.397 0.396 0.383 0.398 0.436 0.455 0.412 0.474 0.430 0.555 1.000 G2 2 0.312 0.328 0.270 0.346 0.341 0.338 0.353 0.352 0.337 0.358 0.377 0.353 0.376 0.425 0.437 0.373 0.361 0.454 0.443 0.450 0.389 1.000 G2 3 0.361 0.451 0.363 0.447 0.497 0.491 0.470 0.539 0.462 0.469 0.449 0.531 0.527 0.513 0.525 0.464 0.411 0.548 0.509 0.600 0.484 0.455 1.000 G2 4 0.384 0.444 0.389 0.494 0.488 0.470 0.474 0.455 0.473 0.460 0.463 0.485 0.456 0.477 0.479 0.443 0.381 0.489 0.443 0.563 0.540 0.396 0.580 1.000 G2 5 0.340 0.422 0.373 0.473 0.465 0.454 0.447 0.437 0.435 0.425 0.418 0.433 0.419 0.437 0.459 0.433 0.387 0.488 0.439 0.567 0.513 0.374 0.555 0.667 1.000 G2 6 0.298 0.384 0.335 0.404 0.402 0.388 0.412 0.375 0.392 0.360 0.384 0.392 0.398 0.434 0.388 0.405 0.387 0.446 0.412 0.558 0.473 0.377 0.489 0.495 0.526 1.000 G2 7 0.323 0.400 0.334 0.432 0.412 0.398 0.422 0.410 0.422 0.400 0.441 0.433 0.404 0.424 0.434 0.457 0.395 0.489 0.446 0.523 0.513 0.371 0.566 0.546 0.529 0.523 1.000 G2 8 0.334 0.395 0.351 0.420 0.418 0.389 0.402 0.392 0.414 0.395 0.422 0.412 0.403 0.410 0.463 0.415 0.390 0.493 0.467 0.572 0.551 0.420 0.514 0.543 0.539 0.535 0.566 1.000 G2 9 0.350 0.394 0.350 0.427 0.409 0.395 0.392 0.389 0.414 0.396 0.432 0.414 0.409 0.423 0.466 0.414 0.398 0.493 0.474 0.570 0.554 0.426 0.520 0.553 0.539 0.519 0.539 0.827 1.000 G3 0 0.285 0.366 0.346 0.390 0.367 0.367 0.378 0.358 0.384 0.351 0.380 0.380 0.379 0.388 0.369 0.404 0.395 0.430 0.375 0.447 0.404 0.329 0.417 0.447 0.464 0.455 0.480 0.480 0.462 1.000 G3 1 0.280 0.355 0.351 0.357 0.351 0.367 0.360 0.325 0.356 0.338 0.343 0.345 0.360 0.373 0.356 0.373 0.350 0.366 0.335 0.408 0.399 0.296 0.370 0.385 0.391 0.425 0.387 0.421 0.423 0.474 1.000 G3 2 0.284 0.366 0.360 0.370 0.370 0.368 0.397 0.378 0.377 0.399 0.350 0.395 0.412 0.410 0.402 0.401 0.421 0.416 0.357 0.447 0.425 0.341 0.419 0.409 0.431 0.439 0.408 0.431 0.433 0.486 0.435 1.000 G3 3 0.397 0.467 0.380 0.481 0.469 0.472 0.505 0.553 0.516 0.498 0.469 0.539 0.498 0.425 0.414 0.407 0.348 0.403 0.365 0.401 0.357 0.309 0.459 0.405 0.388 0.350 0.375 0.378 0.382 0.376 0.376 0.388 1.000 G3 4 0.254 0.303 0.280 0.306 0.301 0.322 0.313 0.326 0.306 0.313 0.315 0.323 0.329 0.371 0.359 0.376 0.416 0.394 0.324 0.363 0.352 0.328 0.344 0.339 0.361 0.377 0.343 0.373 0.358 0.417 0.377 0.516 0.321 1.000 G3 5 0.275 0.292 0.253 0.320 0.302 0.303 0.314 0.299 0.305 0.292 0.317 0.296 0.310 0.358 0.340 0.343 0.347 0.385 0.373 0.367 0.355 0.346 0.344 0.339 0.340 0.371 0.352 0.381 0.392 0.393 0.351 0.395 0.323 0.421 1.000 G3 6 0.295 0.370 0.294 0.360 0.366 0.380 0.363 0.347 0.384 0.361 0.415 0.340 0.373 0.403 0.436 0.412 0.385 0.445 0.386 0.425 0.397 0.383 0.407 0.389 0.401 0.384 0.419 0.439 0.439 0.412 0.373 0.398 0.348 0.439 0.494 1.000 G3 7 0.300 0.350 0.293 0.373 0.350 0.350 0.376 0.337 0.363 0.359 0.370 0.336 0.354 0.426 0.404 0.404 0.394 0.450 0.409 0.439 0.471 0.388 0.416 0.415 0.426 0.424 0.436 0.439 0.453 0.396 0.404 0.419 0.344 0.410 0.420 0.589 1.000 G3 8 0.291 0.329 0.286 0.338 0.327 0.338 0.342 0.335 0.349 0.351 0.375 0.338 0.358 0.406 0.398 0.422 0.393 0.443 0.396 0.428 0.422 0.414 0.415 0.382 0.385 0.396 0.404 0.424 0.416 0.384 0.368 0.399 0.340 0.410 0.423 0.555 0.645 1.000 G3 9 0.300 0.330 0.286 0.339 0.333 0.339 0.343 0.326 0.318 0.355 0.362 0.335 0.351 0.393 0.397 0.400 0.370 0.412 0.359 0.420 0.405 0.387 0.399 0.382 0.383 0.394 0.396 0.404 0.403 0.370 0.357 0.399 0.308 0.413 0.406 0.573 0.636 0.698 1.000 G4 0 0.268 0.347 0.294 0.347 0.333 0.343 0.352 0.315 0.344 0.367 0.367 0.331 0.333 0.391 0.388 0.391 0.384 0.403 0.347 0.389 0.421 0.371 0.402 0.380 0.398 0.390 0.405 0.390 0.401 0.357 0.368 0.388 0.327 0.413 0.387 0.551 0.617 0.623 0.632 1.000 Additional Declarations No competing interests reported. Supplementary Files SSR21.tif SSR22.tif SSR52.tif SSR102.tif SSR122.tif SSR122.tif SSR142.tif SSR172.tif SSR132.tif SSR41.tif SSR42.tif SSR51.tif SSR91.tif SSR92.tif SSR101.tif SSR121.tif SSR161.tif SSR161.tif SSR141.tif SSR131.tif SSR162.tif SSR171.tif ISSR51.tif ISSR52.tif ISSR61.tif ISSR62.tif ISSR71.tif ISSR72.tif ISSR81.tif ISSR82.tif ISSR91.tif ISSR92.tif ISSR141.tif ISSR142.tif ISSRH241.tif ISSRH242.tif ISSRH262.tif ISSRH282.tif ISSR151.tif ISSRH261.tif ISSR152.tif ISSRH281.tif AFLPReportanalysis.xlsx ISSRReportanalysis.xlsx SSRReportanalysis.xlsx Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Biodiversity and Conservation → Version 1 posted Editorial decision: Revision requested 14 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviews received at journal 23 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 14 Jul, 2025 Submission checks completed at journal 24 Jun, 2025 First submitted to journal 23 Jun, 2025 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. 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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-6959182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485404451,"identity":"019ff5cb-0200-47c3-834a-8cb188d6c02b","order_by":0,"name":"Mohammed Alqurashi","email":"","orcid":"","institution":"Taif University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Alqurashi","suffix":""},{"id":485404452,"identity":"44ba8e7b-9538-4ce0-9f32-55959f6526c5","order_by":1,"name":"Salman Aloufi","email":"","orcid":"","institution":"Taif University","correspondingAuthor":false,"prefix":"","firstName":"Salman","middleName":"","lastName":"Aloufi","suffix":""},{"id":485404453,"identity":"ba833118-4348-4d64-959a-b5a06cf47c99","order_by":2,"name":"Doha A. 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08:20:08","extension":"xlsx","order_by":45,"title":"","display":"","copyAsset":false,"role":"supplement","size":24097,"visible":true,"origin":"","legend":"","description":"","filename":"SSRReportanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6959182/v1/bed3b11cab955925b3d591e0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular Marker Based Assessment of Wheat Biodiversity for Conservation and Sustainable Breeding","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is a major worldwide cereal crop, contributing approximately 20% of total caloric and protein intake; thus, ensuring its genetic resilience is crucial for sustaining global food security \u003cb\u003e(FAO, 2023).\u003c/b\u003e However, climate change presents considerable challenges to wheat production, particularly in areas vulnerable to heat stress, drought, and irregular rainfall patterns \u003cb\u003e(\u003c/b\u003ePequeno et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Global wheat yields are expected to fall by 1.9% by mid-century, with more severe reductions (15\u0026ndash;16%) estimated in North Africa and South Asia, exacerbating food insecurity \u003cb\u003e(\u003c/b\u003ePequeno et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cb\u003eUSDA, 2025).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo sustain wheat productivity under increasing pressures of environmental conditions, breeding programs must enhance stress tolerance, yield stability, and genetic diversity \u003cb\u003e(\u003c/b\u003eLangridge et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, due to decades of intensive selection for high-yielding cultivars, the genetic base of wheat has narrowed, reducing its adaptability to the changing climatic conditions \u003cb\u003e(\u003c/b\u003eReif et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Voss-Fels et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A comprehensive understanding of wheat biodiversity and genetic adaptation is therefore crucial for the development of climate-adaptable cultivars \u003cb\u003e(\u003c/b\u003eLopes et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mondal et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Egypt, with its diverse agro-climatic zones ranging from hot arid desert to semi-arid environments, serves as an ideal location for studying wheat adaptation to variable climates \u003cb\u003e(\u003c/b\u003eTadesse et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hamada et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Elkot et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMolecular marker technologies provide efficient techniques for the genetic description of wheat biodiversity, supporting marker-assisted selection (MAS), and finding unique genotypes with adaptive traits. Among these, SSRs and ISSRs have been widely employed because of their specificity and genome coverage \u003cb\u003e(\u003c/b\u003eLandjeva et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Soriano, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e In parallel, AFLP coupled with capillary electrophoresis (AFLP-CE) provides high-throughput, fine-scale resolution for identifying polymorphisms, particularly those that are undetected on gel-based platforms \u003cb\u003e(\u003c/b\u003ePapa et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The use of AFLP-CE enhances marker sensitivity and reproducibility, making it especially valuable in complex genomes like wheat. Simple sequence repeats (SSR), inter-simple sequence repeats (ISSR), and amplified fragment length polymorphisms (AFLP) have all proven an outstanding ability in genetic differentiation and genome-wide polymorphism identification \u003cb\u003e(\u003c/b\u003eLandjeva et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Noli et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, a comprehensive analysis using integrative and complementary multi\u003cb\u003e-\u003c/b\u003emarker systems is still required to fill knowledge gaps and gain a precise evaluation of wheat genetic structure, aiding both conservation and breeding efforts.\u003c/p\u003e\u003cp\u003eThe present study aims to evaluate the genetic diversity of 40 wheat genotypes across different agro-climatic zones using SSR, ISSR, and AFLP-CE molecular markers. The inferred genetic relationships, based on similarity matrix analysis and hierarchical clustering, will provide a robust framework for genetic classification. Meanwhile, our findings will contribute to wheat conservation efforts and breeding programs aimed at improving stress tolerance and yield potential under climatic variability.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003ePlant Materials and Field Trials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eField experiments were conducted over two consecutive growing seasons (2021/2022\u0026ndash;2022/2023) at the Shandaweel Agricultural Research Station in Sohag Governorate, Egypt (26\u0026deg;33\u0026prime;N, 31\u0026deg;42\u0026prime;E). As specified in Table 1 and Figure 1, the present study evaluated forty wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) landraces (G1\u0026ndash;G40), collected from diverse agro-climatic regions across Egypt, including Upper Egypt, the New Valley, and North Sinai, by the Egyptian National Gene Bank. These genotypes were assigned accession numbers 111320 to 111370, reflecting their official registration in the national germplasm collection \u003cstrong\u003e(Egyptian Gene Bank, 2024).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate genetic diversity and agronomic performance, the wheat genotypes were regenerated and evaluated under controlled field conditions. A randomized complete block design (RCBD) with three replicates was employed for the experiments to ensure reliable statistical analysis. Standard agronomic practices were implemented to ensure optimal growth conditions throughout both seasons. Each genotype was cultivated in six rows, each measuring 2.5 meters in length, with 20 cm spacing between rows. Individual plants within rows were spaced 20 cm apart. The field trials were conducted under conventional irrigation, with five irrigation cycles per season. Data on morphological and yield component traits were collected from ten randomly selected guarded plants per genotype.\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eHot Arid\u003c/em\u003e: Extremely high temperatures and low precipitation.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eHot Semi-Arid\u003c/em\u003e: High temperatures with slightly higher moisture availability than arid regions.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eArid\u003c/em\u003e: Dry conditions with very low annual precipitation.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eSemi-Arid\u003c/em\u003e: Moderately dry conditions with some seasonal rainfall.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDNA Extraction, Purification, and Quantification:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from the young leaves of each accession using the DNeasy Plant Mini Kit (QIAGEN, Hilden, Germany). The quantity and quality of the extracted DNA were determined using spectrophotometric measurements of UV absorbance at 260 nm and 280 nm on a Thermo Scientific NanoDrop 2000\u0026trade; spectrophotometer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Simple Sequence Repeat Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimple Sequence Repeat (SSR) markers were utilized for genetic diversity assessment and polymorphism detection in the studied wheat genotypes. Ten SSR primers selected for their specificity to key wheat chromosomes and extensive genome coverage, were used for amplification (Table 2). The GrainGenes Database for Triticeae and Avena (http://wheat.pw.usda.gov) included the SSR primer sequences \u003cstrong\u003e(Yao et al. 2022).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePCR reactions were carried out in a 25 \u0026mu;l mixture consisting of 12.5 \u0026mu;l Master Mix (Sigma), 1 \u0026mu;l forward primer, 1 \u0026mu;l reverse primer (10 pmol/\u0026mu;l each), 2.5 \u0026mu;l template DNA (10 ng/\u0026mu;l), and 8 \u0026mu;l dH₂O, following the protocol outlined by \u003cstrong\u003eIbrahim et al. (2016).\u003c/strong\u003e Amplification was performed using a Perkin-Elmer/GeneAmp\u0026reg; PCR System 9700 under the following thermal cycling conditions: initial denaturation at 94\u0026ordm;C for 5 minutes, followed by 40 cycles of denaturation at 94\u0026ordm;C for 30 seconds, annealing at primer-specific temperatures for 30 seconds, and extension at 72\u0026ordm;C for 40 seconds. A final extension at 72\u0026ordm;C for 7 minutes was applied to ensure complete fragment synthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInter Simple Sequence Repeat Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, Inter Simple Sequence Repeat (ISSR) markers were employed to assess genetic diversity and detect polymorphisms among the examined wheat genotypes. Ten ISSR primers, synthesized by Sigma-Aldrich (USA), were used for amplification (Table 3). PCR reactions were conducted in a 25 \u0026mu;l mixture containing 12.5 \u0026mu;l Master Mix (Sigma), 2.5 \u0026mu;l primer (10 pmol/\u0026mu;l), 3 \u0026mu;l template DNA (10 ng/\u0026mu;l), and 7 \u0026mu;l dH₂O, following the protocol described by \u003cstrong\u003eIbrahim et al. (2019).\u0026nbsp;\u003c/strong\u003eAmplification was performed using a Perkin-Elmer/GeneAmp\u0026reg; PCR System 9700 with an initial denaturation at 94\u0026ordm;C for 5 minutes, followed by 40 cycles of denaturation at 94\u0026ordm;C for 1 minute, annealing at 45\u0026ordm;C for 1 minute, and extension at 72\u0026ordm;C for 1.5 minutes. A final extension step at 72\u0026ordm;C for 7 minutes was applied to ensure complete amplification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmplified Fragment Length Polymorphism Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Amplified Fragment Length Polymorphism (AFLP) assay was performed according to the AFLP Plant Mapping protocol \u003cstrong\u003e(Applied Biosystems, 2010).\u003c/strong\u003e Genomic DNA was digested with the restriction enzymes \u003cem\u003eEcoRI\u003c/em\u003e and \u003cem\u003eMseI\u003c/em\u003e to generate modified restricted fragments for the pre-selective amplification step. Adaptors were ligated to the digested fragments using \u003cem\u003eT4 DNA ligase\u003c/em\u003e \u003cstrong\u003e(New England BioLabs, Ipswich, MA, USA),\u003c/strong\u003e according to the manufacturer\u0026apos;s instructions, to ensure efficient ligation. Selective amplification was conducted using fluorescence-labeled EcoRI primers and unlabeled MseI primers. Three primer combinations were utilized: Eco-\u003csub\u003eAAG\u003c/sub\u003e/Mse-\u003csub\u003eCTC\u003c/sub\u003e, Eco-\u003csub\u003eACT\u003c/sub\u003e/Mse-\u003csub\u003eCTT\u003c/sub\u003e, and Eco-\u003csub\u003eAAC\u003c/sub\u003e/Mse-\u003csub\u003eCAG\u003c/sub\u003e (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe amplified fragments were prepared for electrophoresis by mixing 1 \u0026micro;L of the selective product with 12 \u0026micro;L Hi-Di\u0026trade; formamide and 0.5 \u0026micro;L GeneScan\u0026trade; 500 ROX\u0026trade; standard (Applied Biosystems, USA). Prior to analysis, the mixture was denatured and loaded onto the Applied Biosystems 310 Genetic Analyzer, which employs a single-capillary system for high-resolution fragment separation using POP-4\u0026trade; polymer. Fragment analysis was conducted using GeneMapper\u0026reg; software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular data analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the assessment of SSR and ISSR marker results, each gel underwent a duplicate assessment to validate the results, whereas the clearly detectable bands were scored as present (1) or absent (0), while any faint bands were discarded.\u003c/p\u003e\n\u003cp\u003eBinary data obtained from the molecular marker analysis was used to identify key parameters related to the effectiveness of molecular markers, such as polymorphism percentage (P%), polymorphic information content (PIC), marker index (MI), effective multiplex ratio (EMR), and resolving power. These analyses were executed utilizing the Online Marker Efficiency Calculator (https://irscope.shinyapps.io/iMEC/) as developed by \u003cstrong\u003eAmiryousefi et al., (2018).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical and Multivariate Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the results of molecular marker analysis, including SSR, ISSR, and AFLP, were combined to create a comprehensive genetic profile of the studied wheat types. Using Jaccard\u0026rsquo;s coefficient \u003cstrong\u003e(Jaccard, 1908),\u003c/strong\u003e a similarity matrix was created to evaluate the pairwise genetic relationships. Meanwhile, the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) in Multi-Variate Statistical Package (MVSP) software (Version 3.13d; \u003cstrong\u003eKovach, 2002\u003c/strong\u003e) was used to perform cluster analysis, which resulted in a dendrogram that illustrates the genetic relatedness based on polymorphic loci among the studied 40 wheat genotypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis (PCA):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal component analysis (PCA) was performed using PAST software \u003cstrong\u003e(version 4.03; Hammer et al., 2001)\u003c/strong\u003e to reduce dimensionality and capture main variations in the genetic dataset. The amount of variance explained by each main component was calculated to improve genotype differentiation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeat Map Visualization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA heat map analysis was performed using ClustVis, a web-based tool for clustering and visualizing multivariate data \u003cstrong\u003e(Metsalu \u0026amp; Vilo, 2015).\u003c/strong\u003e Genetic similarity indices derived from SSR, ISSR, and AFLP markers were used to generate a standardized data matrix.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eGenotypic Differentiation Using SSR Marker Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive evaluation of genetic diversity among wheat genotypes was conducted using Simple Sequence Repeat (SSR) markers, revealing genetic diversity and genotype-specific correlations across nine chromosomes: 1A, 1B, 3A, 5A, 5B, 6B, and 7A (Table 5, Figure 2). 823 bands were amplified from 41 SSR amplicons with 10 SSR primers. These 41 alleles comprise 35 polymorphic and 6 monomorphic alleles., \u003c/p\u003e\n\u003cp\u003eFragment sizes varied widely, from 153-105 bp for Xgwm99 (SSR10, 1AL) to 1000-175 bp Xgwm369 (SSR12, 3A), indicating amplification across loci with different size ranges. This diversity in product size improves allele resolution and confirms the extensive genomic coverage of the used primer set. Five primers, WMS273 (SSR2, 1B), Xgwm371 (SSR4, 5B), Xgwm219 (SSR5, 6B), Xgwm99 (SSR10, 1AL), and wmc422 (SSR16, 7A) exhibited full polymorphism among the tested genotypes, confirming their high discriminatory potential. In contrast, Xgwm205a (SSR14, 5A) and Xgwm124 (SSR17, 1B) showed the lowest polymorphism levels (50%), limiting their utility in diversity analysis. \u003c/p\u003e\n\u003cp\u003ePolymorphic alleles with band frequencies (B.F.) ranging from 0.25 to 0.9 were categorized as rare (BF \u0026le; 0.2), low (0.2 \u0026lt; BF \u0026le; 0.5), moderate (0.5 \u0026lt; BF \u0026le; 0.8), and high (BF \u0026gt; 0.8), in addition to 2 unique bands.\u003c/p\u003e\n\u003cp\u003eThe highly informative loci included Xwmc596 (SSR9, 7A) (149 bp) and wmc422 (SSR16, 7A) (150 bp), each amplified in 90% of genotypes (B.F. = 0.9), making them suitable for diversity analysis, population structure assessment, and marker-assisted selection. Moderately frequent loci such as Xgwm234 (SSR13, 5B) (256 bp) (B.F. = 0.775), Xgwm99 (SSR10, 1AL) (113 bp) (B.F. = 0.725), and Xgwm371 (SSR4, 5B) (168 bp) (B.F. = 0.725) were also effective in discriminating among accessions. Conversely, low-frequency loci, including Xgwm371 (SSR4, 5B) (208 bp) (B.F. = 0.25) and Xgwm219 (SSR5, 6B) (176 bp) (B.F. = 0.275), suggest allele restriction to a subset of genotypes, indicating population-specific variations or alleles under selection pressure (\u003cstrong\u003eHan et al., 2015).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTen rare alleles (B.F. \u0026lt; 0.2) were found; for instance, WMS273 (SSR2, 1B) at 161 bp was found in only three genotypes (G13, G19, and G22), with a B.F. of 0.075, indicating that it is a rare allele with significant discriminating power. Other sorts of bands were found in just one to three genotypes, including WMS273 (SSR2, 1B) at 119 bp (B.F. = 0.05) and 161 bp (B.F. = 0.075); Xgwm369 (SSR12, 3A) at 155 bp (B.F. = 0.05); and Xgwm124 (SSR17, 1B) at 210 bp, demonstrating great discriminatory power. Similar alleles are critical for distinguishing distinct genotypes, such as Xgwm234 (SSR13, 5B) at 263 bp (B.F. = 0.2), Xgwm371 (SSR4, 5B) at 235 bp (B.F. = 0.15), Xgwm99 (SSR10, 1AL) at 105 bp (B.F. = 0.01), and wmc422 (SSR16, 7A) at 160 bp (B.F. = 0.075), and they may represent novel genetic changes important to marker-assisted selection or gene mapping.\u003c/p\u003e\n\u003cp\u003eAdditionally, two markers, Xgwm234 (SSR13, 5B) and wmc422 (SSR16, 7A), produced unique bands, indicating potential for genotype-specific allele identification. \u003c/p\u003e\n\u003cp\u003eThe identified monomorphic markers, each showing a band frequency of 1.0, indicating uniform allele presence across all 40 genotypes, including SSRs targeting Xwmc596 (SSR9) at (279 bp and 237 bp), Xgwm369 (SSR12) at 1000 bp, Xgwm234 (SSR13) at 217 bp, Xgwm205a (SSR14) at 145 bp, and Xgwm124 (SSR17) at 200 bp. While these markers do not contribute to genetic differentiation, they confirm conserved regions in the wheat genome \u003cstrong\u003e(Han et al., 2015).\u003c/strong\u003e Their presence suggests strong sequence conservation across tested lines, potentially representing core genome regions or housekeeping loci \u003cstrong\u003e(Mullan et al., 2005).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding markers performance, Xgwm371 (SSR4, 5B), Xgwm219 (SSR5, 6B), Xwmc596 (SSR9, 7A), Xgwm99 (SSR10, 1AL), and Xgwm369 (SSR12, 3A), emerged as the most reliable, exhibiting high Polymorphism Information Content (PIC), Effective Multiplex Ratio (EMR), Marker Index (MI), and Discriminating Power (D).\u003c/p\u003e\n\u003cp\u003ePolymorphic information content (PIC) values ranged from 0.1948 (SSR14) to 0.3750 (SSR13), with an average around 0.35-0.37, categorizing these markers as moderately informative \u003cstrong\u003e(Qian et al., 2012).\u003c/strong\u003e Higher PIC values observed in SSR4, SSR12, and SSR17 reflect their strong allelic diversity, enhancing their ability to differentiate genotypes. The observed heterozygosity (H.) values were consistent with the polymorphism levels, with SSR17 displaying the highest average heterozygosity (0.0062), suggesting a greater allelic diversity at this locus.\u003c/p\u003e\n\u003cp\u003eEffective Multiplex Ratio (EMR) values were highest for SSR9 (3.875) and SSR12 (3.325), suggesting efficient polymorphic detection across multiple loci. Corresponding Marker Index (MI) values for these primers (0.0074 and 0.0059, respectively) further emphasize their robustness, combining both informativeness and multiplexing efficiency \u003cstrong\u003e(Jahnke et al., 2022). \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiscriminating power (D) and resolving power (Rp) analyses further reinforced the utility of SSR5, SSR12, and SSR16, which showed high D values (\u0026ge; 0.77) and Rp values (\u0026gt; 2.0), confirming their strong capacity to distinguish among individuals. Conversely, SSR14 and SSR17 exhibited low D (0.2358 and 0.7275) and very low Rp (0.5 and 0.1), indicating limited effectiveness in resolving genotypic differences.\u003c/p\u003e\n\u003cp\u003eOverally, SSR4, SSR5, SSR9, SSR10, and SSR12 emerged as the most effective markers, demonstrating high polymorphism, PIC, EMR, MI, and resolving power. These findings recommend their application in future wheat genetic diversity assessments and marker-assisted selection programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXu et al. (2014)\u003c/strong\u003e employed 34 SSR markers to find polymorphisms in wheat genotypes. Eight markers, including cfa2040, wmc273, and barc32, were discovered to be polymorphic between resistant and susceptible cultivars, allowing the discovery of the stripe rust resistance gene YrC591 for marker-assisted selection in wheat breeding projects. \u003cstrong\u003eWu et al. (2018)\u003c/strong\u003e employed SSR and SNP markers to detect wheat genotype differences, identifying a chromosomal area associated with stripe rust resistance on chromosome 1B. The combination of SSR and KASP markers found the resistance gene YrH62, which is critical for wheat breeding selection. \u003cstrong\u003eMohi-Ud-Din et al. (2022)\u003c/strong\u003e found 458 alleles in 56 wheat genotypes, with an average of 18.32 per locus, using 30 SSR markers. The polymorphism information content values varied between 0.434 and 0.932, with wmc177 and wms292 having 24 alleles apiece. This genetic diversity is critical for developing drought-tolerant wheat cultivars. Despite their advantages, SSR markers face certain limitations, including time-consuming primer development and relatively high costs, which may hinder broader application \u003cstrong\u003e(Nicot et al., 2004).\u003c/strong\u003e Nonetheless, their co-dominant inheritance, capacity to distinguish homozygous and heterozygous loci, and overall reliability make them ideal for genetic mapping, cultivar identification, and marker-assisted breeding\u003cstrong\u003e (Kalia et al., 2011; Han et al., 2015). \u003c/strong\u003eAs demonstrated in this study and supported by \u003cstrong\u003eMohamed et al. (2023),\u003c/strong\u003e SSR markers offer higher resolution than morphological traits in evaluating genetic diversity among Egyptian wheat genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of Genome-Wide Variation by ISSR Marker analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ISSR marker system also demonstrated high utility for assessing genetic variation among the studied wheat genotypes, as illustrated in Table 6 and Figure 3. A total of 3,883 bands (from 124 amplicons) were amplified using ten ISSR primers. Of these, 84 bands were polymorphic, resulting in a mean polymorphism rate of 67.74%, indicating effective allelic diversity detection.\u003c/p\u003e\n\u003cp\u003eAmong the primers, ISSR-7 (92%), ISSR-14 (83%), H24 (83%), and ISSR-9 (80%) recorded the highest polymorphism percentages, highlighting their suitability for distinguishing genotypes. Conversely, H28 and ISSR-8 displayed lower polymorphism levels (27% and 36%, respectively), which limits their effectiveness in diversity studies.\u003c/p\u003e\n\u003cp\u003eThe polymorphism analysis of ISSR markers on wheat genotypes revealed a wide range of allelic diversity, with band frequencies ranging from 0.05 to 0.975. from these polymorphic alleles, 27 high-frequency alleles shared by the majority of wheat genotypes remained polymorphic as a result of continual amplification and varying molecular weight. ISSR5 at 1100 bp, ISSR9 at 1500 bp, ISSR15 at 600 bp, H24 at 970 bp and 1500 bp, H26 at 570 bp, and H28 at 1350 bp (each BF = 0.975) were mainly fixed in the population while staying different from monomorphic loci due to minor but significant genetic exclusions. In addition, 32 moderate-frequency alleles, such as ISSR5 at 830 bp, ISSR14 at 350 bp, and ISSR15 at 1500 bp, have frequencies ranging from 0.55 to 0.6, making them useful for differentiating subpopulations and marker-assisted breeding, as they offer a larger allele distribution with intermediate informativeness. Among the polymorphic alleles, 25 low-frequency alleles, including ISSR5-400 bp and H26-1800 bp, are markers of intermediate variation, useful for analyzing population structure and selection inspections. Alleles like ISSR6 at 530 bp, ISSR7 at 2400 bp, and H26 at 320 bp show variations between accessions. Eight rare alleles were identified, indicating highly discriminative loci in a few genotypes. These include ISSR14 at 460 bp (BF = 0.1), which is found in only two genotypes; ISSR9 at 250 bp (BF = 0.05), 500 bp (BF = 0.2), and 2300 bp (BF = 0.175); ISSR8 at 1000 bp (BF = 0.175); H24 at 650 bp and 350 bp (BF = 0.15 and 0.175, respectively); and H26 at 1650 bp (BF = 0.15), highlighting specific variability, potentially indicating adaptive or unique genotypic traits relevant for breeding.\u003c/p\u003e\n\u003cp\u003eIn contrast, the present investigation identified 40 monomorphic alleles in the 40 wheat genotypes using the ISSR primers (Table 3). including ISSR6, ISSR7, ISSR8, ISSR9, ISSR14, ISSR15, H24, H26, and H28 (Figure 3). Each allele has a band frequency of 1.0, indicating uniformity across all genotypes. These loci, such as ISSR6 at 1500 bp, ISSR8 at 1300 bp, ISSR14 at 940 bp, and H28 at 1900 bp, represent genomic regions with no detectable polymorphism in the population. The identified monomorphic bands, possibly from conserved genomic regions or housekeeping genes, confirm sequence conservation and low mutation rates in regions like ISSR15 at 2000 bp and H24 at 1250 bp, respectively. However, certain primers generate multiple monomorphic bands, suggesting potential primer affinity for tandem repeats or conserved SSR-rich regions \u003cstrong\u003e(Kumar et al., 2020).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePIC values ranged from 0.1364 (ISSR-5) to 0.5739 (ISSR-9). Based on criteria from \u003cstrong\u003eQian et al. (2012)\u003c/strong\u003e and \u003cstrong\u003eChen et al. (2021),\u003c/strong\u003e four primers, ISSR-9, H24, ISSR-8, and ISSR-14, were classified as highly informative (PIC \u0026gt; 0.5), underscoring their utility in wheat genetic research.\u003c/p\u003e\n\u003cp\u003eEffective Multiplex Ratio (EMR) was highest in ISSR-6 (12.775), ISSR-H26 (11.775), and ISSR-15 (11.375), reflecting their ability to simultaneously detect multiple polymorphic loci. These values led to a high genotyping resolution, valuable for cultivar differentiation and breeding program support \u003cstrong\u003e(Najaphy et al., 2011).\u003c/strong\u003e Marker Index (MI) peaked at H26 (0.0074), followed by ISSR-15 (0.0062), and H24 (0.5606), reinforcing the high efficiency of these primers in capturing genomic variation.\u003c/p\u003e\n\u003cp\u003eISSR-9 and H24 also recorded the highest heterozygosity values (0.6467 and 0.5606), indicating rich allelic diversity. Discriminating power (D) was greatest for H26 (0.5206), ISSR-15 (0.3401), and H24 (0.2628), while resolving power (Rp), a key parameter in assessing marker resolution, was highest for H26 (4.75), ISSR-9 (4.65), and ISSR-15 (4.55). These results collectively affirm the utility of these primers for differentiating closely related genotypes \u003cstrong\u003e(Xiao et al., 2006).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerally, ISSR primers ISSR-9, H24, H26, and ISSR-15 demonstrated superior performance across most of the estimated diversity parameters. These markers are particularly suitable for genetic diversity assessments, cultivar identification, and marker-assisted selection in wheat breeding. \u003c/p\u003e\n\u003cp\u003eUsing ISSR markers, \u003cstrong\u003eVaillancourt et al. (2008)\u003c/strong\u003e found 41 polymorphic loci in wheat genotypes and amplified 196 bands from wheat, rye, and triticale DNA samples to demonstrate genetic heterogeneity in the examined accessions. \u003cstrong\u003eNajaphy et al. (2011) \u003c/strong\u003erevealed high genetic diversity by 86 bands from thirty wheat genotypes, with 69 bands (80.2%) being polymorphic, showing genetic variation among the examined cultivars and breeding lines. \u003cstrong\u003eMohamed et al. (2023)\u003c/strong\u003e detected 153 ISSR markers, including 85 polymorphic markers, demonstrating high genetic variation among wheat genotypes, with an average polymorphism band per primer of 7.7. However, a notable limitation of ISSR markers is their dominant inheritance pattern, which prevents differentiation between homozygous and heterozygous loci, reducing their utility in fine-scale genetic mapping \u003cstrong\u003e(Grover \u0026amp; Sharma, 2016).\u003c/strong\u003e Despite this, their capacity to target both coding and non-coding regions makes them a valuable complement to co-dominant markers like SSRs and AFLPs, contributing to a more comprehensive evaluation of genetic diversity \u003cstrong\u003e(Reddy et al., 2002).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-Resolution Fingerprinting with AFLP-CE Marker Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 7, the Amplified Fragment Length Polymorphism) technique, coupled with the capillary electrophoresis system (AFLP-CE), and three selected primer combinations, was highly effective in identifying genetic diversity among 40 wheat genotypes. Each primer pair produced a large number of amplicons (264 to 425), almost all of which were polymorphic. This demonstrates the excellent ability of the AFLP-CE technique to capture the genome-wide variation at high resolution.\u003c/p\u003e\n\u003cp\u003eThe E\u003csub\u003eAAC\u003c/sub\u003e\u0026ndash;M\u003csub\u003eCAG\u003c/sub\u003e combination produced the highest total number of amplicons (425), all of which were polymorphic, including 65 unique amplicons. It also had the highest resolving power (Rp = 179.65) and discriminating power (D = 0.9273), indicating a greater capacity to detect uncommon alleles and distinguish between genotypes. Although it had the lowest average heterozygosity (2.32 \u0026times; 10⁻⁵), this might be due to marker saturation or allelic fixation within certain regions. E\u003csub\u003eAAG\u003c/sub\u003e\u0026ndash;M\u003csub\u003eCTC\u003c/sub\u003e followed closely, producing 391 total amplicons and 33 unique ones, with a high effective multiplex ratio (EMR = 129.90) and Rp = 159.30, indicating its broad genome coverage and informative value. The high polymorphism rate (99.49%) reinforces its use in assessing inter-genotypic variation. E\u003csub\u003eACT\u003c/sub\u003e\u0026ndash;M\u003csub\u003eCTT\u003c/sub\u003e, while yielding fewer total amplicons (264), had the highest average heterozygosity (3.99 \u0026times; 10⁻⁵) and a PIC value of 0.3323, making it valuable for detecting minor allelic differences. Its strong discriminating power (D = 0.9093) suggests its usefulness in genotypic differentiation even with fewer loci.\u003c/p\u003e\n\u003cp\u003eOverall, the integration of AFLP-CE with capillary electrophoresis enabled precise detection of amplified loci with high polymorphic potential. The combination of high PIC, EMR, and resolving power across primer sets confirms the reliability of this platform in molecular characterization of wheat germplasm. These results support the adoption of AFLP-CE in marker-assisted selection, genetic diversity studies, and pre-breeding programs targeting complex traits \u003cstrong\u003e(Papa et al., 2005).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AFLP (Amplified Fragment Length Polymorphism) approach, when paired with capillary electrophoresis technology (AFLP-CE), provides an enhanced platform for high-resolution fragment analysis, improved reproducibility, and multiplexing efficiency \u003cstrong\u003e(Papa et al., 2005; Blignaut et al., 2013).\u003c/strong\u003e Unlike traditional gel-based AFLP technique, which can introduce electrophoretic variability, the use of the Applied Biosystems 310 Genetic Analyzer in this study allowed for precise peak separation using single-capillary electrophoresis and POP-4 polymer technology \u003cstrong\u003e(Wenz et al., 1998; Blignaut et al., 2013).\u003c/strong\u003e This technique guarantees precise and uniform DNA movement, decreasing signal variability that often affects manual polyacrylamide gel electrophoresis \u003cstrong\u003e(Papa et al., 2005; Blignaut et al., 2013).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe incorporation of GeneMapper software improved the AFLP procedure by automating peak identification, fragment size, and allele binning, reducing subjective bias in AFLP scoring and enhancing data uniformity \u003cstrong\u003e(Holland et al., 2008). \u003c/strong\u003eFurthermore, fluorescence-based detection increased the sensitivity and multiplexing capability of the AFLP approach, allowing for simultaneous examination of numerous AFLP peaks \u003cstrong\u003e(Schwarz et al., 2000). \u003c/strong\u003eThe absence of post-PCR gel handling significantly reduced contamination hazards, boosting overall data integrity and increasing the dependability of the AFLP technique in high-throughput genetic screening and genome-wide diversity studies \u003cstrong\u003e(Papa et al., 2005; Blignaut et al., 2013).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAFLP markers also provided valuable insights into genotypic differentiation across contrasting agro-climatic zones. Genotypes from Semi-Arid regions (Sohag, Assiut, and Beni Suef) exhibited the highest number of unique AFLP peaks, suggesting the presence of selection-driven genomic variations. Additionally, wheat genotypes from North Sinai (G40) displayed distinct AFLP patterns, potentially reflecting genetic adaptations to salinity stress and fluctuating moisture availability \u003cstrong\u003e(Hazen et al., 2002).\u003c/strong\u003e The combination of AFLP with capillary electrophoresis enabled the detection of exceptionally high polymorphism levels (99.49%\u0026ndash;100%), confirming its effectiveness in wheat molecular breeding and conservation programs \u003cstrong\u003e(Papa et al., 2005).\u003c/strong\u003e These results underscore the superior capacity of AFLP markers for high-resolution genetic fingerprinting, making them a powerful tool for assessing genetic variability and guiding marker-assisted selection (MAS) strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of SSR, ISSR, and AFLP Markers for Sustainable Wheat Breeding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe integration of SSR, ISSR, and AFLP markers in this study provided an intensive and complementary approach to analyzing wheat biodiversity. This resulted in higher accuracy for detecting genome-wide variations and locus-specific polymorphisms \u003cstrong\u003e(Noli et al., 2008; Malik et al., 2012; Costa et al., 2016). \u003c/strong\u003eMeanwhile, SSR markers, due to their co-dominant nature and trait association potential, are widely applied in trait-specific selection, such as drought tolerance, disease resistance, and grain quality \u003cstrong\u003e(Han et al., 2015).\u003c/strong\u003e ISSR markers facilitate the detection of genome-wide polymorphisms, which is valuable in identifying genotypic responses to abiotic stress \u003cstrong\u003e(Reddy et al., 2002; Mohamed et al., 2023). \u003c/strong\u003eAFLP markers, despite their complexity, contribute significantly to mapping multigenic traits, like resistance to biotic diseases and yield stability \u003cstrong\u003e(Bai et al., 1999; Guo et al., 2003; Liu et al., 2019).\u003c/strong\u003e The combined marker analysis is particularly advantageous for marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping,which in turn leads to the identification of climate-resilient genotypes and the preservation of genetic diversity, which are important aspects of sustainable wheat improvement. However, while the integration of these markers enhances genetic resolution, it also presents practical challenges such as data consistency, higher operating costs, and technical demands \u003cstrong\u003e(Agarwal et al., 2008; Soriano, 2020).\u003c/strong\u003e Along with the integration of molecular marker techniques, future investigations should benefit from the availability of single-nucleotide polymorphism (SNP) arrays and genotyping-by-sequencing (GBS). Combining these high-throughput technologies with marker-assisted selection (MAS) and genome-wide association studies (GWAS) is critical for enhancing breeding efficiency and accelerating genetic gains \u003cstrong\u003e(Jiangfeng et al., 2014; Rasheed \u0026amp; Xia, 2019).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Similarity Matrix: Assessing Relationships Among Wheat Genotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study of wheat genotypes using molecular marker techniques revealed significant genetic variations, highlighting the importance of multi-locus fingerprinting in understanding population structure and diversity in wheat landraces. The pairwise similarity utilizing Jaccard coefficients ranged from 0.253 to 0.827 (table 8), indicating a broad spectrum of genetic distances and the presence of closely related and genetically divergent members. This variability can be attributed to the rich evolutionary history, local adaptation, and complex seed exchange networks in Egyptian wheat germplasm \u003cstrong\u003e(Balfourier et al., 2019).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to earlier research, closely related wheat varieties cluster together based on molecular markers \u003cstrong\u003e(Peng et al., 2000; Fufa et al., 2005).\u003c/strong\u003e The highest similarity (J = 0.827) was found between G28 and G29, which were collected from surrounding villages in Sohag Governorate. This strongly indicates that similarly adapted regional landraces have almost identical genetic histories, most likely due to localized seed circulation, shared environmental pressures, and limited external genetic inflow, as previously reported for closely adapted regional landraces \u003cstrong\u003e(Ijaz \u0026amp; Khan, 2009).\u003c/strong\u003e Another relatively high similarity (J = 0.698) was found between G38 (Assiut) and G39 (Beni Suef), two landraces from Upper Egypt but different governorates. This inter-regional similarity could reflect a common ancestral lineage or recent seed exchange between the regions, emphasizing the dynamic nature of traditional wheat cultivation systems in Upper Egypt.\u003c/p\u003e\n\u003cp\u003eBy contrast, G19 and G22, both from Upper Egypt, showed only moderate similarity (J = 0.443). This finding contradicts the assumption of regional homogeneity within Upper Egypt and indicates the presence of sub-regional genetic differentiation. Such divergence may arise from distinct evolutionary pathways, segregated seed populations, or variation in farming practices across villages, as is commonly observed in traditional farming systems. The lowest similarity values were recorded between G3 and G35 (J = 0.253) and between G1 and G34 (J = 0.254), indicating substantial genetic divergence. These genotypes may differ in ecogeographic origin, genetic background, or selection pressure, possibly due to genetic drift, historical isolation, or introgression from different breeding pools \u003cstrong\u003e(Zhou et al., 2018).\u003c/strong\u003e Their genetic distinctiveness enhances their potential as unique sources of alleles for crop improvement, particularly for adaptive traits like drought tolerance or pest resistance \u003cstrong\u003e(Mondal et al., 2016).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs depicted in figure 4, the UPGMA dendrogram constructed from the Jaccard similarity matrix, reveals notable genetic diversity among the 40 wheat genotypes collected from a wide range of agro-climatic regions in Egypt. The similarity ranged approximately from 0.25 to 0.83, reflecting a broad spectrum of genetic divergence and highlighting the rich heritage of wheat germplasm preserved across different environmental and geographic zones\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe dendrogram delineates the genotypes into two main clusters, each consisting of several sub-clusters, with distinguishable correlations to ecogeographic origin. The first cluster, which is genetically distinct from genotypes from the semi-arid and arid regions, includes several genotypes from Sohag Governorate, particularly from Al Monshaah, Sohag city, and Akhmim districts (e.g., G16\u0026ndash;G29), along with entries from the New Valley region (e.g., G11\u0026ndash;G15). Notably,\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eG22 (Kombedar, Al Monshaah), G19 (Al Salaah, Sohag), and G17 (Shandawil, Sohag) represent some of the most genetically distinct genotypes, clustering separately at low similarity coefficients. Their divergence suggests that these local landraces, collected from isolated rural areas, have preserved unique alleles that may be attuned to specific drought conditions, salinity levels, or selective cultural influences. Genotypes such as G14 and G15 (Farafrah, New Valley) and G26\u0026ndash;G29 (Al Monshaah villages) form tight sub-clusters, suggesting shared ancestry and localized adaptation within the oases of the Western Desert and the central semi-arid locations of Upper Egypt. Interestingly, G27 (Awlaad Harron) and G28 (Nagaa Abd El Azeem Salem) are closely grouped, suggesting genetic homogeneity among samples from proximate villages within Al Monshaah\u003cspan dir=\"RTL\"\u003e. \u003c/span\u003eThese findings reinforce the importance of semi-arid and arid zones, especially Sohag and New Valley, as central locations for genetic uniqueness and conservation priorities\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe second cluster, which is the larger cluster, has genetically similar genotypes from hot arid and hot semi-arid regions. This second cluster encompasses most of the genotypes from Aswan, Luxor, Qena, Assiut, Beni Suef, and North Sinai, many of which likely underwent modern selection, informal seed exchange, or adaptation to similar agro-ecological niches. Genotypes from Aswan (G1\u0026ndash;G3) and Luxor (G4\u0026ndash;G6), both categorized under the Hot Arid Region, formed a sub-cluster with moderate similarity, indicating shared traits potentially shaped by high temperature and low precipitation stress conditions\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e Entries from Qena (G7\u0026ndash;G10) and New Valley (G11\u0026ndash;G13) grouped relatively closely, underscoring the genetic proximity between genotypes from adjacent semi-arid governorates\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e G33 (Al Sawamaa Sharq, Akhmim) and G2 (Aswan) clustered tightly with other Upper Egyptian entries like G1 and G3, suggesting gene flow or varietal dissemination across the Nile Valley corridor\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e A tight sub-cluster including G36 (Bany Aysh, Gerga), G37 (Al Zaraby, Abo Teeg, Assiut), G38 (Assiut city), and G39 (Sayan Abu Ali, Beni Suef) displayed the highest Jaccard similarity values, indicating minimal genetic distance. These may represent improved cultivars or genetically uniform varieties that have diffused widely through national seed systems or farmer networks\u003cspan dir=\"RTL\"\u003e. \u003c/span\u003eNotably, G40 (Rafah, North Sinai) clustered within this group, suggesting that even distant North Sinai shares a genetic affinity with Upper Egyptian materials, potentially due to varietal introductions or shared breeding stock.\u003c/p\u003e\n\u003cp\u003eThe observed clustering patterns, shaped by both geographic isolation and shared environmental pressures, underscore the importance of preserving region-specific germplasm, particularly from underrepresented areas such as Sohag, New Valley, and North Sinai. Genotypes like G22 (Kombedar) and G19 (Al Salaah), which lie at the borders of the dendrogram, should be prioritized for in situ conservation and GenBank safeguarding, as they harbor rare or divergent alleles valuable for climate resilience\u003cspan dir=\"RTL\"\u003e. \u003c/span\u003eFrom a breeding perspective, the high divergence among genotypes from different governorates presents an excellent opportunity to exploit heterosis by crossing distant pairs (e.g., G22 \u0026times; G39 or G17 \u0026times; G36). Moreover, the tight clustering of certain genotypes highlights redundancy risks, emphasizing the need to avoid overrepresentation of genetically similar lines in breeding pools\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe geographic structuring of genetic diversity revealed in this study holds critical value for conservation planning, germplasm management, and sustainable breeding strategies aimed at enhancing wheat productivity and resilience in arid and semi-arid environments\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA Biplot Analysis of Genetic Similarity:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal component analysis (PCA) biplot (Figure 5) provides a comprehensive overview of the genetic structure of wheat genotypes. The PCA results complement molecular results by reducing the genetic data into key dimensions that explain the observed variation \u003cstrong\u003e(Chou et al., 2022).\u003c/strong\u003e The provided biplots illustrate distributions of genotypes and marker contributions to enable the detection of clusters, outliers, and high-contribution markers. The spatial distribution of genotypes in the PCA scatter plot aligns with the dendrogram, reinforcing the robustness of the clustering analysis. The first two components cumulatively explained a substantial portion of the variance, with Component 1 distinguishing genotypes along a gradient likely related to regional adaptation, while Component 2 captured orthogonal variation patterns. \u003c/p\u003e\n\u003cp\u003eA prominent cluster along Component 1 comprised genotypes from the hot arid and semi-arid regions of Upper Egypt, such as G1, G2 (Aswan), G3 (Al Sayala), G4, G6 (Luxor), G7, and G10 (Qena). Their tight grouping suggests a high degree of genetic similarity, potentially reflecting convergent selection pressure in similarly hot environments. Genotypes G11 and G13 (Dakhla Oasis, New Valley Governorate), from the arid zone, are also shown to be close to this cluster, indicating shared allelic characteristics adapted to harsh desert climates.\u003c/p\u003e\n\u003cp\u003eIn contrast, genotypes from Sohag, especially those from the Al Monshaah district (e.g., G22, G23, G24, G25, G26, G27, G28, and G29), formed a separate sub-cluster oriented more toward Component 2. Their separation implies intra-regional variability, likely due to micro-environmental conditions or farmer selection practices within villages like Al Kawamel Kebly, Al Zook Al Gharbyea, and Kombedar. Genotypes G34 (Al Zook Al Sharkhya, Sohag) and G39 (Sayan Abu Ali, Beni Suef) were positioned in the lower-left quadrant of the biplot, distinctly apart from the central mass. This indicates their unique genetic background, possibly due to geographic isolation or limited germplasm exchange. G40, from Rafah in North Sinai, also displayed considerable divergence, suggesting an adaptation to the cooler and possibly more Mediterranean-influenced coastal climate. \u003c/p\u003e\n\u003cp\u003eThe vectors overlaid on the biplot represent trait or marker loadings, pointing to the traits driving genotype dispersion. Genotypes like G28 (Nagaa Abd El Azeem Salem) and G20 (Al Shoryfat, Sohag) appeared aligned with longer vectors, suggesting strong associations with the underlying variables, potentially representing adaptive or agronomic traits. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeatmap Analysis of Genetic Similarity:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe heatmap clustering (Figure 6) provides a visual synthesis of pairwise genetic similarity among the 40 wheat genotypes based on the Jaccard coefficient matrix. The hierarchical clustering dendrograms aligned along both axes corroborate the patterns detected in PCA and UPGMA analyses but further emphasize subgroup structures and levels of intragroup homogeneity.\u003c/p\u003e\n\u003cp\u003eDistinct blocks of high similarity are indicated by warm color gradients (red to orange), while cool tones (light to dark blue) denote dissimilarity. A prominent diagonal line represents perfect self-similarity, as expected, while square clusters along the diagonal highlight groups of closely related genotypes.\u003c/p\u003e\n\u003cp\u003eOne such cluster is observed among genotypes from Sohag (Al Monshaah district), including G22 (Kombedar), G23 (Al Zook Al Gharbyea), G24, G27 (Al Kawamel Kebly to Awlaad Harron), and G28 (Nagaa Abd El Azeem Salem). These genotypes exhibited strong within-group similarity (bright red-orange blocks) and minimal divergence, which likely reflects localized farmer selection and seed exchange within the same ecological and cultural context.\u003c/p\u003e\n\u003cp\u003eSimilarly, G1 and G3 (Aswan and Al Sayala) formed another compact, high-similarity block, reinforcing their close genetic relationship previously seen in PCA space. Their proximity, both geographically and genetically, suggests either a common ancestral landrace or shared selection pressures due to the extreme hot, arid climate of Aswan. The New Valley genotypes, G11 (Bashandy), G12, G13 (El Qasr), G14, and G15 (Farafrah), though slightly more dispersed, still formed a moderately cohesive cluster, consistent with their adaptation to the arid Western Desert conditions.\u003c/p\u003e\n\u003cp\u003eOutlier genotypes again stood apart in this heatmap, G34 (Al Zook Al Sharkhya, Sohag) and G39 (Sayan Abu Ali, Beni Suef) clustered weakly with others and displayed mostly cool-toned interactions, emphasizing their unique allelic profiles. G40 (Rafah, North Sinai) formed a distinct branch with limited similarity to other genotypes, which supports its divergent adaptation to the Mediterranean coastal agro-ecology and potential genetic isolation from Upper Egyptian wheat pools.\u003c/p\u003e\n\u003cp\u003eThe heatmap also exposed internal differentiation within broader governorates. For instance, Sohag genotypes such as G16 (Sohag City), G19 (Al Salaah), and G33 (Al Sawamaa Sharq) showed lower similarity with other Sohag landraces, implying that intra-governorate environmental variation and localized seed systems contribute significantly to genetic structuring.\u003c/p\u003e\n\u003cp\u003eCollectively, the heatmap enriches the understanding of genotype relationships by illustrating fine-scale clustering and suggesting candidate genotypes for breeding crosses. Specifically, crossing highly divergent genotypes such as G40 (Rafah) or G34 (Sohag) with cluster-bound landraces (e.g., G22 and G28) could maximize heterosis and trait recombination potential.\u003c/p\u003e\n\u003cp\u003eIn summary, the integration of similarity matrix, dendrogram, PCA, and heatmap analysis provides a comprehensive assessment of genetic diversity among wheat genotypes. The observed clustering patterns highlight genetic subpopulations essential for wheat breeding and conservation strategies. The identification of genetically diverse and outlier genotypes offers valuable insights into potential parental lines for hybridization, ensuring the sustainable enhancement of wheat biodiversity and resilience in future breeding programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors Shaping Genetic Diversity in Wheat Genotypes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genetic biodiversity observed among the 40 wheat genotypes analyzed in this study reflects a complex interplay of several factors, such as environmental selection pressures, geographical origins, agronomic practices, historical gene flow, genetic drift, and molecular evolution. Over centuries, these components have contributed to wheat adapting to a wide range of environments and cropping strategies \u003cstrong\u003e(Sahri et al., 2014). \u003c/strong\u003eUnderstanding these aspects is crucial for enhancing genetic conservation, promoting sustainable breeding practices, and enhancing wheat resistance to changing climate conditions. The integration of SSR, ISSR, and AFLP markers in this study provided a comprehensive framework for assessing genetic diversity, achieving correct genotypic differentiation and genome-wide polymorphism identification, which are required for marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Selection Pressures and Genetic Adaptation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStrengthening the link between molecular data and agronomic performance is essential for translating genetic diversity into sustainable yield and food security outcomes. The adaptation of wheat genotypes to diverse agro-climatic zones in Egypt has been a major driver of genetic diversity \u003cstrong\u003e(Hamada et al., 2022).\u003c/strong\u003e The genotypes analyzed in this study were sourced from Hot Arid, Hot Semi-Arid, Arid, and Semi-Arid regions, each imposing distinct selective pressures that shape genotypic differentiation \u003cstrong\u003e(He et al., 2019; Terwisscha van Scheltinga et al., 2021; Hamed et al., 2022).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWheat populations in the Hot Arid and Hot Semi-Arid zones (e.g., Aswan, Luxor, and Qena) are subjected to high temperatures and prolonged drought periods, leading to overexpression of the genes involved in water-use efficiency, root system development, and heat tolerance \u003cstrong\u003e(Tricker et al., 2018).\u003c/strong\u003e The existence of unique SSR alleles and AFLP polymorphism peaks in these genotypes indicates that they include adaptive genomic changes associated with abiotic stress tolerance \u003cstrong\u003e(Huang et al., 2016; Du et al., 2023).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, wheat genotypes from the Arid region (New Valley: G11\u0026ndash;G15) demonstrated distinct ISSR banding patterns, indicating genetic divergence caused by high temperature variations and low soil fertility. The comprehensive genome coverage of ISSR markers allowed for the identification of stress-responsive loci, which are crucial for increasing wheat performance in difficult desert settings\u003cstrong\u003e (Kumar et al., 2024).\u003c/strong\u003e In contrast, genotypes from Semi-Arid regions (e.g., Sohag, Assiut, and Beni Suef) experience seasonal temperature variations and moderate water availability, leading to the selection of genotypes with increased photosynthetic efficiency, enhanced nutrient uptake, and greater yield stability \u003cstrong\u003e(Wasaya et al., 2021; Koua et al., 2022).\u003c/strong\u003e The AFLP analysis of these genotypes revealed higher levels of genetic differentiation, confirming that environmental adaptation plays a major role in shaping genetic variation \u003cstrong\u003e(Sasanuma et al., 2002).\u003c/strong\u003e These findings highlight the necessity of implementing breeding strategies to develop resilient wheat varieties to climate change while maintaining high productivity \u003cstrong\u003e(Mondal et al., 2016; Yadav et al., 2022).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeographic Isolation, Gene Flow, and Historical Breeding Influences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the environmental selection pressures and the adaptation of wheat cultivars, geographic isolation and gene flow impact the genetic structure of wheat populations, since they lead to determining the extent of allelic exchange \u003cstrong\u003e(Luo et al., 2007; Rufo et al., 2019).\u003c/strong\u003e Genotypes from distantly isolated regions (e.g., Dakhla and Farafrah in New Valley) exhibited higher genetic differentiation due to restricted cross-pollination and localized selection pressures \u003cstrong\u003e(Bhuri, 2013; Li et al., 2000). \u003c/strong\u003eLimited seed exchange and farmer selection in these regions may have contributed to higher genetic uniqueness, as observed in the presence of specific ISSR and AFLP polymorphisms \u003cstrong\u003e(Bhuri \u0026amp; Upadhyay, 2013; Balfourier et al., 2019).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConversely, some genotypes showed genetic similarities across different regions, indicating possible historical gene flow through seed trade, farmer migration, or cross-regional breeding efforts \u003cstrong\u003e(Balfourier et al., 2019; Scott et al., 2019).\u003c/strong\u003e For example, wheat genotypes from Sohag (Semi-Arid) and Luxor (Hot Semi-Arid) shared common AFLP markers, suggesting historical introgression of adaptive alleles between these populations. Understanding these patterns of gene flow is critical for the conservation of genetic resources and breeding programs that aim for the optimization of trait introgression for sustainable wheat production \u003cstrong\u003e(Royo et al., 2021).\u003c/strong\u003e\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe present study provides a comprehensive assessment of the genetic diversity of 40 wheat genotypes using SSR, ISSR, and AFLP molecular markers and provides insights regarding their genetic structure, phylogenetic relationships, and their potential for breeding applications. The genetic similarity matrix revealed considerable variability, highlighting the impact of environmental selection pressures, geographic origin, and historical breeding practices on wheat populations. The adaptation of genotypes to agro-climatic zones, especially in Hot Arid and Semi-Arid environments, has enabled stress tolerance selection, crucial for developing climate-resilient wheat cultivars. Geographic isolation and gene flow also play a role in detecting genetic diversity, emphasizing the need to conserve both elite and underutilized germplasm for breeding programs. Future research should incorporate next-generation sequencing and genome-wide association studies to improve breeding accuracy and genetic improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Deanship of Graduate Studies and Scientific Research, Taif University for funding this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003eShafik D. Ibrahim (SDI), Ahmed F. Abd El-Hakim (AFA),Mohammed Alqurashi (MA), Salman Aloufi (SA), Doha A. Albalawi (DAA), Amenah S. Al-Otaibi (ASA), Basmah M. Alharbi (BMA) Fahad Mohammed Alzuaibr (FMA), Hanan Khalaf Anazi (HKA), Siham M. Al-Balawi (SMA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollection and Data Evaluation:\u0026nbsp;\u003c/strong\u003e SDI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology \u0026ndash; Laboratory and Greenhouse Experiments:\u0026nbsp;\u003c/strong\u003eSDI, AFA.\u003cbr\u003e\u003cstrong\u003eData Analysis:\u003c/strong\u003e AFA, SDI.\u003cbr\u003e\u003cstrong\u003eManuscript Preparation (Drafting):\u0026nbsp;\u003c/strong\u003eAFA, SDI.\u003cbr\u003e\u003cstrong\u003eReview and Editing:\u0026nbsp;\u003c/strong\u003eMA, SA, DAA, ASA, BMA, FMA, HKA, SMA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall guidance:\u003c/strong\u003e AFA.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Deanship of Graduate Studies and Scientific Research, Taif University for funding this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollege of Science, Taif University- Saudi Arabia, Agricultural Genetic Engineering Research Institute (AGERI), Agriculture Research Center (ARC)-Egypt, Faculty of Science, University of Tabuk-Saudi Arabia, Faculty of Agriculture, Ain Shams University- Egypt, Faculty of Agriculture, Al-Azhar University, Egypt.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eAgarwal, M., Shrivastava, N., \u0026amp; Padh, H. 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K., Kumar, R., Yadav, A. K., \u0026amp; Prasad, P. V. V. (2022).\u003c/strong\u003e Impacts, Tolerance, Adaptation, and Mitigation of Heat Stress on Wheat under Changing Climates. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(5), 2838. https://doi.org/10.3390/ijms23052838\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYao E., Blake V.C, Cooper L., Wight C.P., Michel S., Cagirici H.B., Lazo G.R., Birkett C.L., Waring D.J., Jannink J.-L., Holmes, I., Waters A.J., Eickholt D.P., Sen, T.Z. (2022).\u003c/strong\u003e GrainGenes: a data-rich repository for small grains genetics and genomics. \u003cem\u003eDatabase\u003c/em\u003e, \u003cem\u003e2022\u003c/em\u003e, baac034. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhou, Y., Chen, Z., Cheng, M., Chen, J., Zhu, T., Wang, R., Liu, Y., Qi, P., Chen, G., Jiang, Q., Wei, Y., Luo, M.-C., Nevo, E., Allaby, R. G., Liu, D., Wang, J., Dvorak, J., \u0026amp; Zheng, Y.-L. (2018).\u003c/strong\u003e Uncovering the dispersion history, adaptive evolution and selection of wheat in China. \u003cem\u003ePlant Biotechnology Journal\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 280\u0026ndash;291. https://doi.org/10.1111/PBI.12770\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Classification of the studied wheat genotypes based on climatic regions in Egypt.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarcoding No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource in Egypt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernorate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistrict\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVillage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003e\u0026nbsp;Hot Arid Region:\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAswan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAswan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAswan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAswan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAswan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAswan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAswan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Noba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Sayala\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLuxor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEsna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTuffnice\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLuxor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEsna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDanffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLuxor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEsna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDanffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eHot Semi-Arid Region:\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQeft\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNag HamadI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl HalfayaKebly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNag HamadI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Rahmanya\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDandrah\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003e\u0026nbsp;Arid Region:\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNew Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDakhla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBashandy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNew Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDakhla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEl Qasr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNew Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDakhla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEl Qasr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNew valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarafrah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarafrah\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNew valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarafrah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFarafrah\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eSemi-Arid Region:\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShandawil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJazirat-Shandawil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Salaah\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Shoryfat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAwlaadNaseer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKombedar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Zook Al Gharbyea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Kawamel Kebly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Kawamel Kebly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Horyzat Al Gharbeya\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAwlaadHarron\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNagaaAbd El Azeem Salem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKombedar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIdfa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIdfa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAkhmim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl deyabat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAkhmim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArab Al Atawla\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAkhmim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl SawamaaSharq\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Monshaah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Zook Al sharkhya\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGerga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGerga\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSohag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGerga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBanyAysh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAssiut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbo teeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAl Zaraby\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAssiut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAssiut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAssiut\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeniSwif\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeniSwif\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSayan Abu Ali\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorth Sinai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRafah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRafah\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: SSR primer details, including marker name, chromosomal location, annealing temperature, and primer sequences.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarker\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr.*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTa (\u0026deg;C)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFwd (5\u0026prime;\u0026ndash;3\u0026prime;)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRev (5\u0026prime;\u0026ndash;3\u0026prime;)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWMS273\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eATTGGACGGACAGATGCTTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAGCAGTGAGGAAGGGGATC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXgwm371\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGACCAAGATATTCAAACTGGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAGCTCAGCTTGCTTGGTACC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXgwm219\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGATGAGCGACACCTAGCCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGGGGTCCGAGTCCACAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXwmc596\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTCAGCAACAAACATGCTCGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCCCGTGTAGGCGGTAGCTCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXgwm99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1AL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAAGATGGACGTATGCATCACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCCATATTTGATGACGCATA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXgwm369\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCTGCAGGCCATGATGATG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACCGTGGGTGTTGTGAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXgwm234\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGAGTCCTGATGTGAAGCTGTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCTCATTGGGGTGTGTACGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXgwm205a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCGACCCGGTTCACTTCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAGTCGCCGTTGTATAGTGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ewmc422\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGACTACTGAACTGGAGAGTGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCATTAGAATTTGGAGTTTGGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXgwm124\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCCATGGCTATCACCCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACTGTTCGGTGCAATTTGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Chr.*:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eChromosome;\u003cem\u003e\u0026nbsp;Ta (\u0026deg;C)*\u003c/em\u003e; Annealing Temperature (\u0026deg;C); \u003cem\u003eFwd (5\u0026prime;\u0026ndash;3\u0026prime;)*;\u0026nbsp;\u003c/em\u003eForward Sequence (5\u0026rsquo; - 3\u0026rsquo;); \u003cem\u003eRev (5\u0026prime;\u0026ndash;3\u0026prime;)*\u003c/em\u003e; Reverse Sequence (5\u0026rsquo; - 3\u0026rsquo;).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: ISSR primer sequences and details.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-GTGTGTGTGTGTGTGTG-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-CGCGATAGATAGATAGATA-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-GACGATAGATAGATAGATA-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-AGACAGACAGACAGACGC-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-GATAGATAGATAGATAGC-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-CTCCTCCTCCTCCTCTT-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-CTCTCTCTCTCTCTCTRG-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-AGAGAGAGAGAGAGAGYC-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-AGAGAGAGAGAGAGAGYG-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u0026apos;-HVHTGTGTGTGTGTGTGT-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: AFLP Adapter and Primer Sequences.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence (5\u0026rsquo;-3\u0026rsquo;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdapters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcoRI Adapter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-CTCGTAGACTGCGTACC-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3\u0026rsquo;-CATCTGACGCATGGTTAA-5\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMseI Adapter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5\u0026rsquo;-GACGATGAGTCCTGAG-3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3\u0026rsquo;-TACTCAGGACTCAT-5\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMseI Primer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGATGAGTCCTGAGTAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMseI + CTC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGATGAGTCCTGAGTAACTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMseI + CTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGATGAGTCCTGAGTAACTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMseI + CAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGATGAGTCCTGAGTAACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcoRI Primer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGACTGCGTACCAATTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcoRI + AAG (Ned)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGACTGCGTACCAATTCAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcoRI + ACT (Fam)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGACTGCGTACCAATTCACT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcoRI + AAC (Fam)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eGACTGCGTACCAATTCAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable (5): performance parameters of SSR primers used for genetic diversity analysis of the studied wheat genotypes.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFragment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT. AB\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonomorphic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(PB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnique\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBands\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(UB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH. Avg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e161-106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.8331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e235-119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e2.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.7360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e201-176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.8735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e279-149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e3.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.5839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e153-105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.7818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1000-175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e85.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e3.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.7753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e263-217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.7516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e160-145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.1948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.2188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.2358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e160-140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.8908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e210-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.3744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.4988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e0.7275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e823\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAmplicon:\u0026nbsp;\u003c/strong\u003eA DNA fragment amplified and detected across the 40 genotypes via SSR marker technique.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e**Abbreviations:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePIC*\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Polymorphic Information Content, \u003cstrong\u003e\u003cem\u003eEMR*\u003c/em\u003e:\u003c/strong\u003e Effective Multiplex Ratio, \u003cstrong\u003e\u003cem\u003eH*\u003c/em\u003e:\u003c/strong\u003e Heterozygosity \u003cstrong\u003e\u003cem\u003eH. Avg*\u003c/em\u003e:\u003c/strong\u003e Average Heterozygosity, \u003cstrong\u003e\u003cem\u003eMI*\u003c/em\u003e:\u003c/strong\u003e Marker Index, \u003cstrong\u003e\u003cem\u003eD*\u003c/em\u003e:\u003c/strong\u003e Discriminatory Power, \u003cstrong\u003e\u003cem\u003eRp*\u003c/em\u003e:\u003c/strong\u003e Resolving Power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (6): performance parameters of ISSR primers used for genetic diversity analysis of the studied wheat genotypes.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer Code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFragment Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT. AB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonomorphic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(PB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnique\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBands\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(UB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH. Avg.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e423-1903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e371-1541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e12.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e266-2684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.2538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e364-2526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e282-1581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.6467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.6467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.6467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e310-1961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.1949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISSR-15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e218-1319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.2583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e11.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.3047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.3401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e219-1493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.2628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e226-2184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.3351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e11.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.5206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e302-1806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.3799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.4790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e124\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3883\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAmplicon:\u0026nbsp;\u003c/strong\u003eA DNA fragment amplified and detected across the 40 genotypes via ISSR marker technique.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e**Abbreviations:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePIC*\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Polymorphic Information Content, \u003cstrong\u003e\u003cem\u003eEMR*\u003c/em\u003e:\u003c/strong\u003e Effective Multiplex Ratio, \u003cstrong\u003e\u003cem\u003eH*\u003c/em\u003e:\u003c/strong\u003e Heterozygosity \u003cstrong\u003e\u003cem\u003eH. Avg*\u003c/em\u003e:\u003c/strong\u003e Average Heterozygosity, \u003cstrong\u003e\u003cem\u003eMI*\u003c/em\u003e:\u003c/strong\u003e Marker Index, \u003cstrong\u003e\u003cem\u003eD*\u003c/em\u003e:\u003c/strong\u003e Discriminatory Power, \u003cstrong\u003e\u003cem\u003eRp*\u003c/em\u003e:\u003c/strong\u003e Resolving Power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (7): Efficiency Parameters of CE-AFLP Primer Combinations for Assessing Genetic Diversity Among 40 Wheat Genotypes.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"985\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCombination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonomorphic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MF)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(PF)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnique\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicons\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(UF)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphism (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeterozygosity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(H)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvg.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHeterozygosity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(H Avg.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003eAAG\u003c/sub\u003e-M\u003csub\u003eCTC\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.83698E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e129.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e159.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003eACT\u003c/sub\u003e-M\u003csub\u003eCTT\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.98586E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003eAAC\u003c/sub\u003e-M\u003csub\u003eCAG\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.31723E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e179.6500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAmplicon:\u0026nbsp;\u003c/strong\u003eA DNA fragment amplified and detected across the 40 genotypes via CE-AFLP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e**Abbreviations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003ePIC\u003c/em\u003e:\u003c/strong\u003e Polymorphic Information Content, \u003cstrong\u003e\u003cem\u003eEMR\u003c/em\u003e:\u003c/strong\u003e Effective Multiplex Ratio, \u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e:\u003c/strong\u003e Heterozygosity \u003cstrong\u003e\u003cem\u003eH. Avg\u003c/em\u003e:\u003c/strong\u003e Average Heterozygosity, \u003cstrong\u003e\u003cem\u003eMI\u003c/em\u003e:\u003c/strong\u003e Marker Index, \u003cstrong\u003e\u003cem\u003eD\u003c/em\u003e:\u003c/strong\u003e Discriminatory Power, \u003cstrong\u003e\u003cem\u003eRp\u003c/em\u003e:\u003c/strong\u003e Resolving Power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8:\u003c/strong\u003e \u003cstrong\u003eSimilarity matrix of the examined 40 wheat genotypes based on combined ISSR, SSR, and AFLP marker data.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG4\u003c/strong\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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2px;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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2px;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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2px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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2px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 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style=\"width: 2px;\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n 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style=\"width: 2px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 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Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6959182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6959182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is a vital cereal crop, providing a substantial share of dietary calories and protein. However, climate variability, including heat stress, drought, and irregular rainfall, poses serious challenges to wheat production, particularly in regions already experiencing food insecurity. Enhancing genetic diversity and stress tolerance is crucial for sustaining wheat yields under changing environmental conditions. However, intensive breeding for high-yielding cultivars has led to a reduced genetic base, limiting adaptability and resilience.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aims to assess the genetic diversity of 40 wheat genotypes collected from diverse agro-climatic zones using a combination of molecular marker systems: simple sequence repeats (SSR), inter-simple sequence repeats (ISSR), and amplified fragment length polymorphism coupled with capillary electrophoresis (AFLP-CE). The goal is to characterize genetic relationships, detect polymorphisms, and provide insights to support conservation and sustainable breeding strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenotypic differentiation was analyzed using a combination of SSR, ISSR markers, and high-resolution fragment analysis of AFLP-CE to ensure comprehensive genome-wide polymorphism detection. The genetic relationships among wheat genotypes were inferred through similarity matrix analysis and hierarchical clustering, facilitating a robust framework for classification and selection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe integrated marker approach revealed substantial genetic variability among the studied wheat genotypes, reflecting diverse ecological adaptations. AFLP-CE enhanced the resolution of rare and informative polymorphisms, improving genotype discrimination. These results highlight the richness of genetic resources that can be harnessed to improve stress tolerance and yield stability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study underscores the effectiveness of combining SSR, ISSR, and AFLP-CE for the complete evaluation of wheat biodiversity. The findings support conservation and targeted breeding strategies for improved resilience and sustainable yield under climate variability.\u003c/p\u003e","manuscriptTitle":"Molecular Marker Based Assessment of Wheat Biodiversity for Conservation and Sustainable Breeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 08:11:58","doi":"10.21203/rs.3.rs-6959182/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-14T23:58:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T11:34:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T07:26:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328899701640504777699074775996838794768","date":"2025-10-10T07:55:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178131494608099192258614769051271635360","date":"2025-10-07T12:11:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208363708294775654223690612167097914473","date":"2025-07-30T06:48:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334500488788802619027187597722917186336","date":"2025-07-28T15:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T15:01:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T15:00:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T06:41:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2025-06-23T17:56:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e9919730-ed98-4177-b3ac-ca47664ba431","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:04:58+00:00","versionOfRecord":{"articleIdentity":"rs-6959182","link":"https://doi.org/10.1007/s10531-026-03271-6","journal":{"identity":"biodiversity-and-conservation","isVorOnly":false,"title":"Biodiversity and Conservation"},"publishedOn":"2026-03-05 15:59:19","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2025-07-16 08:11:58","video":"","vorDoi":"10.1007/s10531-026-03271-6","vorDoiUrl":"https://doi.org/10.1007/s10531-026-03271-6","workflowStages":[]},"version":"v1","identity":"rs-6959182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6959182","identity":"rs-6959182","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}