Global Genetic Diversity and Population Structure of Mango (Mangifera indica L.) Germplasm Conserved in Oman Revealed Through SSR Markers

Global Genetic Diversity and Population Structure of Mango (Mangifera indica L.) Germplasm Conserved in Oman Revealed Through SSR Markers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Genetic Diversity and Population Structure of Mango (Mangifera indica L.) Germplasm Conserved in Oman Revealed Through SSR Markers Abdullah Al-Jabri, AL-Ghaliya AL-Mamari, Ali Al-Adawi, Muhammed Al-Jabri, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8255746/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jan, 2026 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 11 You are reading this latest preprint version Abstract Mangifera indica L. (mango) is a major tropical fruit tree valued for its nutritional, economic, and cultural importance. Understanding the genetic diversity within mango germplasm is essential for conservation and for selecting parents in breeding programs. This study assessed the genetic variation of 126 mango accessions (378 samples) maintained in the National Mango GenBank in Oman, originating from 15 geographical regions across the Middle East, Africa, Asia, Australia, and the Americas. A total of 55 polymorphic SSR loci generated 706 alleles, averaging 12.8 alleles per locus, with allele number ranging from 4 (MiSHRS-23, MGDSSR17) to 25 (MiSHRS-18). Polymorphic information content (PIC) values ranged from 0.184 (SSR22) to 0.903 (LMMA01), indicating high marker informativeness. Expected and observed heterozygosity ranged between 0.024–0.496 and 0.048–0.992, respectively. Fixation indices (Fis, Fit, Fst) averaged − 0.978, 0.430, and 0.714, reflecting excess heterozygosity within accessions and strong differentiation among populations. Molecular variance partitioned approximately 87% of diversity among populations and 13% within populations. Cluster, PCoA, and STRUCTURE analyses separated accessions into two major genetic groups, with Oman forming a distinct cluster alongside a small number of foreign cultivars. This study demonstrates extensive genetic diversity in Oman’s mango germplasm and highlights SSR markers as an effective tool for distinguishing genotypes. The findings provide a critical foundation for cultivar preservation, parent selection, and future genome-wide association and marker-assisted breeding in mango. Mangifera indica SSR markers genetic diversity population structure AMOVA germplasm conservation Omani mango accessions PCoA STRUCTURE analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Mango ( Mangifera indica L.) is one of the world’s most important perennial fruit crops, widely cultivated across tropical and subtropical regions. Belonging to the family Anacardiaceae, its primary centre of origin is believed to lie within Southeast Asia, particularly the Malay Archipelago (Viruel et al., 2005 ; Mukherjee and Litz, 2009 ; Pérez et al., 2019 ). The fruit is globally valued for its flavour, nutritional quality, and commercial significance. In Oman, mango is the fourth most cultivated fruit crop after date palm, citrus and banana, with historical records indicating its first introduction between 1568–1575 AD in Wilayat Ibri, Al Dhahirah Governorate (Al Busaidi, 2008 ; Al Salmi, 1997 ). Current estimates suggest that Oman hosts more than half a million mango trees, producing approximately 15,673 tonnes in 2016 with an estimated value exceeding USD 9 million (MAFWR, 2012). Despite its long cultivation history, mango production in Oman has been negatively affected by the emergence of wilt disease caused by Ceratocystis manginecans, frequently vectored by the bark beetle Hypocryphalus mangiferiae (Van Wyk et al., 2007 ; Al Adawi et al., 2006 ; Plotez and Freeman, 2009). The epidemic led to widespread mortality of mature indigenous trees, resulting in the loss of important genetic lines. Local cultivars, commonly seed-derived and genetically diverse due to natural outcrossing, have been shown to be highly susceptible to wilt in both Oman and Pakistan (Panhwar et al., 2008 ; Al Adawi et al., 2006 ). Many existing landraces are characterised by large tree size, irregular bearing, fibrous pulp and acidic flavour profiles (MAF, 1990 ), reinforcing the need for improved disease-tolerant and high-quality genotypes. Understanding the genetic variation preserved in germplasm collections is fundamental for breeding, conservation, and long-term resource management. Population diversity is shaped by mutation, gene flow, breeding system, selection, drift and demographic history (Van Zonneveld et al., 2014 ; Moran, 2002 ; Verde et al., 2013 ). Molecular markers, particularly simple sequence repeats (SSRs), have become powerful tools for such studies due to their high polymorphism, co-dominance, repeatability, and genome-wide distribution (Joop Ouborg et al., 2009 ; Edwards et al., 2012 ). SSRs have previously been employed for mango diversity assessment in India, Australia, Mexico, the Caribbean, and Oman (Duval et al., 2006 ; Schnell et al., 2006 ; Dillon et al., 2013 ; Al-Washahi et al. , 2017). However, existing studies on Omani mango germplasm have been limited to locally collected material and have not integrated a broad comparative dataset representing global origins. Given Oman’s history as a centre of agricultural exchange and maritime trade, imported germplasm may have significantly contributed to the modern varietal pool (Hammer et al., 2009 ). A comprehensive molecular evaluation integrating foreign genetic resources is therefore essential. This study aimed to characterise the genetic diversity of mango germplasm maintained in Oman using informative SSR markers. Specifically, we sought to: Evaluate SSR marker suitability for detecting polymorphism across diverse mango accessions Assess genetic relationships among local and internationally derived cultivars using allelic diversity, heterozygosity and F-statistics. Determine population structure, clustering patterns, and possible introgression between Omani and non-Omani genotypes. The outcomes provide a molecular foundation for cultivar conservation, genetic improvement and selection of elite parental lines for future breeding initiatives in Oman. Materials and Methods Plant Material A total of 126 mango accessions were obtained from the National Mango GenBank at the Sohar Agricultural Research Station in Oman (N 24.3196277, E 56.7155988). Each accession was represented by three independent biological replicates, maintained as separate trees to ensure clonal identity and long-term phenotypic consistency. Young, healthy leaf tissue was collected for DNA extraction (Fig. 1 ). The accessions represented cultivars from 15 geographical regions: Oman (26), Australia (24), India (24), Brazil (17), Thailand (11), Indonesia (3), Indochina (3), Malaysia (3), USA (5), Vietnam (5), Israel (1), Philippines (1), Sri Lanka (1), Tahiti (1), and Kenya (1). Of the total accessions, 58 were monoembryonic and 68 polyembryonic. A full list of accessions, origin and seed type is provided in Supplementary Table 1. DNA Extraction and Quantification Genomic DNA was extracted from fresh leaf tissue using the DNeasy Plant Mini Kit (Qiagen, Germany) following the manufacturer’s protocol with minor optimization. Briefly, 100 mg of leaf powder was incubated in 400 µl of AP1 buffer and 4 µl RNase A at 65°C for 10 min. After addition of 130 µl P3 buffer and ice incubation for 5 min, samples were centrifuged and the supernatant passed through QIAshredder columns. DNA binding, washing, and elution were performed using DNeasy spin columns, with final elution in 50 µl AE buffer. DNA purity was visualized on 1% agarose gel stained with ethidium bromide. DNA concentration was measured using NanoDrop 8000 (Thermo Scientific, USA), and samples were diluted to 30 ng/µl for downstream use. SSR Amplification and Genotyping A total of 106 mango specific SSR primers developed by Duval et al. ( 2005 ); Viruel et al. ( 2005 ); Honsho et al. ( 2005 ); Schnell et al. ( 2005 ); Begum et al. ( 2013 ); and Surapaneni et al. ( 2013 ) were initially tested for amplification. Of these, 55 loci (51.9%) showing clear amplification and polymorphism were selected for final genotyping. Twenty-eight loci were monomorphic and 23 showed no amplification (Supplementary Table 2). PCR amplification was performed in 25 µl reactions containing: 10× PCR buffer with MgCl₂ (Thermo Scientific), 10 mM dNTPs, 10 µM forward and reverse primers, 0.5 U Taq DNA polymerase and 30 ng genomic DNA. Forward primers were fluorescently WellRed-labelled (5′-CACGACGTTGTAAAACGAC-3′). Thermal cycling conditions consisted of: 95°C for 4 min; 35 cycles of 95°C for 30 s, 50–60°C for 1 min (primer-dependent), 72°C for 1 min; and final extension at 72°C for 7 min. PCR products were separated on 2% agarose, and final fragment analysis was conducted on CEQ8000 DNA analyzer (Beckman Coulter, USA). Allele peaks were scored using Beckman Coulter Software v8.0.52. Each sample was run 2–3 times for scoring accuracy and allele size stability. Genetic Diversity and Statistical Analysis Allele scoring, fragment sizing, and internal size calibration were performed with the CEQ8000 platform. PowerMarker v3.25 (Liu and Muse, 2005 ) was used to calculate Polymorphic Information Content (PIC). GenAlEx v6.5 (Peakall and Smouse, 2012 ) was used for Nei’s genetic distance (Nei, 1978 ), percentage of polymorphic loci, and Analysis of Molecular Variance (AMOVA), Total alleles, Effective alleles (Ne), Observed (Hobs) and expected heterozygosity (Hexp), Wright’s fixation indices (Fis, Fst, Fit). Population relationships were visualized using Principal Coordinate Analysis (PCoA) based on Nei’s distance matrix. Phylogenetic clustering was performed using DARwin v6.0 (Perrier and Jacquemoud-Collet, 2006 ) with UPGMA/Neighbor-Joining unrooted trees. Bayesian population structure was inferred using STRUCTURE v2.3.4 (Pritchard et al., 2000 ) with an admixture model, burn-in of 100,000 and 1,000,000 MCMC iterations for K = 1–10, repeated 10 times. Optimal K was determined by using ΔK method (Evanno et al., 2005 ) via STRUCTURE Harvester (Earl and vonHoldt, 2012 ). Results SSR Marker Screening and Polymorphism Out of 106 SSR markers screened across 126 mango accessions, 55 loci (51.9%) amplified successfully and exhibited polymorphism. Twenty-eight loci (26.4%) were monomorphic and excluded, while 23 loci (21.7%) did not amplify reliably (Supplementary Table 2). The selected 55 markers generated a total of 706 alleles, averaging 12.8 alleles per locus, demonstrating high genetic informativeness. Allele number ranged from 4 at MiSHRS-23 and MGDSSR17 to 25 at MiSHRS-18. PIC values varied widely (0.184–0.903) with mean 0.700, indicating that most loci were highly polymorphic. Six loci exhibited PIC < 0.5, while the remaining 49 loci were moderately to highly informative (Table 1). Allele size ranged from 101 bp (MiSHRS-18) to 345 bp (SSR24). Representative electropherograms show clear peak distinction in heterozygous (two-peak) and homozygous (single-peak) genotypes (Figure 2B-C). Table 1. Allelic size range, Number of alleles, expected heterozygosity ( H exp ), observed heterozygosity ( H obs ), Effective number of alleles ( N e ) and the PIC for 55 microsatellite loci calculated with the mean estimate of different parameters for the Mango accessions. Locus Name Allelic size range (bp) Number of alleles H exp H obs N e PIC LMMA01 202-230 17 0.226 0.452 1.452 0.903 LMMA08 274-291 15 0.266 0.532 1.532 0.819 LMMA10 170-200 17 0.242 0.484 1.484 0.824 LMMA11 250-273 14 0.226 0.452 1.452 0.841 LMMA12 215-228 11 0.278 0.556 1.556 0.768 LMMA15 224-242 16 0.238 0.476 1.476 0.832 LMMA02 295-320 10 0.063 0.127 1.127 0.523 LMMA03 121-174 15 0.425 0.849 1.849 0.813 LMMA04 242-269 12 0.266 0.532 1.532 0.672 LMMA07 220-243 14 0.274 0.548 1.548 0.781 LMMA09 187-227 21 0.270 0.540 1.540 0.855 LMMA16 251-262 10 0.226 0.452 1.452 0.726 MIAC-05 135-177 19 0.270 0.540 1.540 0.894 mMiCIR010 295-316 15 0.163 0.325 1.325 0.698 MiSHRS-18 101-133 25 0.417 0.833 1.833 0.901 MiSHRS-32 221-239 9 0.198 0.397 1.397 0.415 MiSHRS-37 130-153 6 0.101 0.148 1.202 0.188 MiSHRS-39 222-390 12 0.258 0.516 1.516 0.749 MiSHRS-01 211-238 11 0.127 0.254 1.254 0.798 MiSHRS-04 142-151 7 0.032 0.063 1.063 0.609 MiSHRS-23 220-225 4 0.052 0.103 1.052 0.561 MiSHRS-29 192-203 9 0.119 0.238 1.238 0.590 MiSHRS-33 253-271 12 0.099 0.198 1.198 0.576 MiSHRS-36 193-211 6 0.095 0.190 1.190 0.668 MiSHRS-48 216-317 19 0.079 0.159 1.159 0.859 SSR-18 103-134 17 0.274 0.548 1.548 0.707 SSR-20 112-133 7 0.286 0.571 1.571 0.537 SSR-28 256-286 6 0.417 0.833 1.833 0.746 SSR-41 149-260 22 0.151 0.302 1.302 0.849 SSR-82 228-282 19 0.167 0.333 1.333 0.819 SSR-85 173-289 20 0.183 0.365 1.365 0.858 SSR-90 204-222 8 0.238 0.476 1.476 0.668 SSR-91 256-268 7 0.079 0.159 1.159 0.451 MngSSR-14 176-193 15 0.143 0.286 1.286 0.655 SSR16 155-188 21 0.119 0.238 1.238 0.835 SSR17 201-218 11 0.131 0.262 1.262 0.580 SSR22 212-218 6 0.024 0.048 1.048 0.184 SSR24 331-345 9 0.175 0.349 1.349 0.669 SSR26 184-206 14 0.226 0.452 1.452 0.777 SSR29 172-188 12 0.202 0.405 1.405 0.689 SSR34 167-187 11 0.246 0.492 1.492 0.621 SSR36 223-254 15 0.143 0.286 1.286 0.843 SSR37 167-288 9 0.194 0.389 1.389 0.808 SSR39 165-203 19 0.274 0.548 1.548 0.880 SSR49 259-284 13 0.250 0.500 1.500 0.735 SSR83 211-241 20 0.345 0.690 1.690 0.887 SSR84 231-268 16 0.226 0.452 1.452 0.843 MNGSSR18 160-165 6 0.071 0.143 1.143 0.697 MGDSSR2 220-281 8 0.198 0.397 1.397 0.564 MGDSSR5 212-247 9 0.167 0.333 1.333 0.681 MGDSSR6 207-246 10 0.103 0.206 1.206 0.705 MGDSSR11 212-228 11 0.234 0.468 1.468 0.784 MGDSSR17 188-202 4 0.496 0.992 1.992 0.404 MGDSSR22 117-201 23 0.375 0.503 1.877 0.840 MGDSSR24 160-179 12 0.083 0.167 1.167 0.434 Mean 12.8 0.204 0.403 1.409 0.700 Figure 2. (A) The percentage of polymorphic loci in mango cultivars was determined using GenAlex. (B) Heterozygous allele showing 2 peaks for Dura accession using MGDSSR5. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs. (C) Homozygous allele shows only 1 peak for Alphonso accession using MiSHRS-33. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs. Genetic diversity Genetic differences in 126 mango accessions were analysed using 55 microsatellite primer pairs, resulting in the detection of 706 alleles. The mean number of alleles per locus was 12.8. The locus MiSHRS-23 and MGDSSR17 had 4 alleles, while the locus MiSHRS-18 had 25 alleles (Table 1). The homozygous alleles were characterized by a single peak, while the heterozygous alleles displayed two peaks (refer to Figure 2 B&C). The allele size for these loci ranged from 101 bp for the locus MiSHRS-18 to 345 bp for the locus SSR24, as showed in Table 1. The number of effective alleles (Ne) varied, with a range of 1.048 for the SSR22 locus to 1.992 for the MGDSSR17 locus, resulting in an average of 1.409 per locus. Calculations for PIC values were based on the frequency and number of alleles at specific loci. The current study found that the average PIC value for the 55 loci evaluated varied from 0.184 for SSR22 to 0.903 for LMMA01, with an overall average of 0.700 across all markers. This indicates that the mango germplasm analysed had a significant degree of polymorphism for most of the loci studied (Table 3). Figure 2. (A) The percentage of polymorphic loci in mango cultivars was determined using GenAlex. (B) Heterozygous allele showing 2 peaks for Dura accession using MGDSSR5. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs. (C) Homozygous allele shows only 1 peak for Alphonso accession using MiSHRS-33. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs. Heterozygosity and fixation index Table 1 shows the presence of moderate levels of observed and expected heterozygosity. The average H exp values of the mango accessions ranged from 0.024 (SSR22) to 0.496 (MGDSSR17), with an average of 0.204 across all loci. The H obs values for all markers varied from 0.048 (SSR22) to 0.992 (MGDSSR17), with an average of 0.403. The cultivars exhibited a range of polymorphic loci percentages, with values ranging from 20% to 67.27%, and an average of 40.59% (Figure 2A). The F is (fixation index or inbreeding coefficient of individuals relative to subpopulations (Wright, 1965) representing the heterozygous deficiency related to the existence of a reproduction within a subpopulation) values for all markers varied between -1.000 (most loci) and -0.341 (MGDSSR22) per primer with an average of -0.978, reflecting heterozygosity excess and low inbreeding. As for the F it , the values were ranged from -0.911 (MGDSSR17) to 0.902 (MiSHRS-04) with an average of 0.430, indicating moderate differentiation when considering entire population. The F st values, which represent the fixation index of subpopulations compared to the whole population (Wright, 1969), ranged from 0.045 (MGDSSR17) to 0.951 (MiSHRS-04), with an average of 0.714, ddemonstrating high genetic divergence among geographic origins (Table 2). These values collectively indicate low inbreeding within accessions, but substantial divergence among populations. Table 1. Allelic size range, Number of alleles, expected heterozygosity (H exp ), observed heterozygosity (H obs ), Effective number of alleles (N e ) and the PIC for 55 microsatellite loci calculated with the mean estimate of different parameters for the Mango accessions. Locus Name Allelic size range (bp) Number of alleles H exp H obs N e PIC LMMA01 202-230 17 0.226 0.452 1.452 0.903 LMMA08 274-291 15 0.266 0.532 1.532 0.819 LMMA10 170-200 17 0.242 0.484 1.484 0.824 LMMA11 250-273 14 0.226 0.452 1.452 0.841 LMMA12 215-228 11 0.278 0.556 1.556 0.768 LMMA15 224-242 16 0.238 0.476 1.476 0.832 LMMA02 295-320 10 0.063 0.127 1.127 0.523 LMMA03 121-174 15 0.425 0.849 1.849 0.813 LMMA04 242-269 12 0.266 0.532 1.532 0.672 LMMA07 220-243 14 0.274 0.548 1.548 0.781 LMMA09 187-227 21 0.270 0.540 1.540 0.855 LMMA16 251-262 10 0.226 0.452 1.452 0.726 MIAC-05 135-177 19 0.270 0.540 1.540 0.894 mMiCIR010 295-316 15 0.163 0.325 1.325 0.698 MiSHRS-18 101-133 25 0.417 0.833 1.833 0.901 MiSHRS-32 221-239 9 0.198 0.397 1.397 0.415 MiSHRS-37 130-153 6 0.101 0.148 1.202 0.188 MiSHRS-39 222-390 12 0.258 0.516 1.516 0.749 MiSHRS-01 211-238 11 0.127 0.254 1.254 0.798 MiSHRS-04 142-151 7 0.032 0.063 1.063 0.609 MiSHRS-23 220-225 4 0.052 0.103 1.052 0.561 MiSHRS-29 192-203 9 0.119 0.238 1.238 0.590 MiSHRS-33 253-271 12 0.099 0.198 1.198 0.576 MiSHRS-36 193-211 6 0.095 0.190 1.190 0.668 MiSHRS-48 216-317 19 0.079 0.159 1.159 0.859 SSR-18 103-134 17 0.274 0.548 1.548 0.707 SSR-20 112-133 7 0.286 0.571 1.571 0.537 SSR-28 256-286 6 0.417 0.833 1.833 0.746 SSR-41 149-260 22 0.151 0.302 1.302 0.849 SSR-82 228-282 19 0.167 0.333 1.333 0.819 SSR-85 173-289 20 0.183 0.365 1.365 0.858 SSR-90 204-222 8 0.238 0.476 1.476 0.668 SSR-91 256-268 7 0.079 0.159 1.159 0.451 MngSSR-14 176-193 15 0.143 0.286 1.286 0.655 SSR16 155-188 21 0.119 0.238 1.238 0.835 SSR17 201-218 11 0.131 0.262 1.262 0.580 SSR22 212-218 6 0.024 0.048 1.048 0.184 SSR24 331-345 9 0.175 0.349 1.349 0.669 SSR26 184-206 14 0.226 0.452 1.452 0.777 SSR29 172-188 12 0.202 0.405 1.405 0.689 SSR34 167-187 11 0.246 0.492 1.492 0.621 SSR36 223-254 15 0.143 0.286 1.286 0.843 SSR37 167-288 9 0.194 0.389 1.389 0.808 SSR39 165-203 19 0.274 0.548 1.548 0.880 SSR49 259-284 13 0.250 0.500 1.500 0.735 SSR83 211-241 20 0.345 0.690 1.690 0.887 SSR84 231-268 16 0.226 0.452 1.452 0.843 MNGSSR18 160-165 6 0.071 0.143 1.143 0.697 MGDSSR2 220-281 8 0.198 0.397 1.397 0.564 MGDSSR5 212-247 9 0.167 0.333 1.333 0.681 MGDSSR6 207-246 10 0.103 0.206 1.206 0.705 MGDSSR11 212-228 11 0.234 0.468 1.468 0.784 MGDSSR17 188-202 4 0.496 0.992 1.992 0.404 MGDSSR22 117-201 23 0.375 0.503 1.877 0.840 MGDSSR24 160-179 12 0.083 0.167 1.167 0.434 Mean 12.8 0.204 0.403 1.409 0.700 Table 2. F -statistics ( F is , F it and F st ) values among all population for each locus with the estimated mean for each parameter. Locus Name F is F it F st LMMA01 -1.000 0.502 0.751 LMMA08 -1.000 0.366 0.683 LMMA10 -1.000 0.424 0.712 LMMA11 -1.000 0.472 0.736 LMMA12 -1.000 0.303 0.652 LMMA15 -1.000 0.439 0.720 LMMA02 -1.000 0.771 0.885 LMMA03 -1.000 -0.021 0.489 LMMA04 -1.000 0.260 0.630 LMMA07 -1.000 0.314 0.657 LMMA09 -1.000 0.378 0.689 LMMA16 -1.000 0.398 0.699 MIAC-05 -1.000 0.402 0.701 mMiCIR010 -1.000 0.547 0.774 MiSHRS-18 -1.000 0.083 0.541 MiSHRS-32 -1.000 0.096 0.548 MiSHRS-37 -0.461 0.242 0.481 MiSHRS-39 -1.000 0.337 0.668 MiSHRS-01 -1.000 0.690 0.845 MiSHRS-04 -1.000 0.902 0.951 MiSHRS-23 -1.000 0.324 0.865 MiSHRS-29 -1.000 0.616 0.808 MiSHRS-33 -1.000 0.682 0.841 MiSHRS-36 -1.000 0.733 0.867 MiSHRS-48 -1.000 0.818 0.909 SSR-18 -1.000 0.266 0.633 SSR-20 -1.000 0.014 0.507 SSR-28 -1.000 -0.071 0.464 SSR-41 -1.000 0.650 0.825 SSR-82 -1.000 0.600 0.800 SSR-85 -1.000 0.581 0.790 SSR-90 -1.000 0.327 0.664 SSR-91 -1.000 0.663 0.831 MngSSR-14 -1.000 0.580 0.790 SSR16 -1.000 0.720 0.860 SSR17 -1.000 0.589 0.795 SSR22 -1.000 0.439 0.719 SSR24 -1.000 0.509 0.754 SSR26 -1.000 0.432 0.716 SSR29 -1.000 0.444 0.722 SSR34 -1.000 0.237 0.619 SSR36 -1.000 0.667 0.834 SSR37 -1.000 0.531 0.766 SSR39 -1.000 0.384 0.692 SSR49 -1.000 0.349 0.674 SSR83 -1.000 0.229 0.614 SSR84 -1.000 0.473 0.736 MNGSSR18 -1.000 0.806 0.903 MGDSSR2 -1.000 0.336 0.668 MGDSSR5 -1.000 0.529 0.764 MGDSSR6 -1.000 0.721 0.861 MGDSSR11 -1.000 0.421 0.710 MGDSSR17 -1.000 -0.911 0.045 MGDSSR22 -0.341 0.411 0.561 MGDSSR24 -1.000 0.651 0.826 Mean -0.978 0.430 0.714 Genetic distance Nei’s genetic distance was used to estimate the genetic relationship between the 126 mango accessions from 15 populations. Nei’s genetic distance showed broad variation among regions. The genetic similarity ranged from 0.096 to 1.191. The lowest genetic distance (Low diversity) was observed between Australia and USA while the highest genetic distance (high diversity) was observed between Sri Lanka and Tahiti, implying unique gene pools. These results highlight clear differentiation among geographic sources (Table 3). Table 3 . The average genetic distance between mango accessions from 15 populations Analysis of Molecular Variance (AMOVA) An AMOVA analysis was achieved to elucidate the distribution of genetic variation among and within different mango accessions at a significance threshold of P < 0.05 (Figure 3). Results indicated that the most (87%) of molecular variation occurred within population, with lesser amount (13%) among populations, , which indicates mixing, gene flow, and shared ancestry rather than strict geographical separation. Figure 3. Proportion of molecular variance among and within mango populations based on 55 SSR markers. Cluster and Principal Coordinate Analysis (PCoA) Principal Coordinate Analysis (PCoA) was performed to visualise allelic relationships across 126 mango accessions and to evaluate whether genetic clustering corresponded to geographic origin. Two independent PCoA plots were generated, one based on population origin, and one based on individual genotype distribution, to increase resolution of diversity patterns. The PCoA constructed at the population level (Figure 5) revealed moderate genetic dispersion among countries, with the first two axes explaining 53.83% of total variance (PC1 = 18.83%, PC2 = 35%). Populations from India, Brazil, Australia and Thailand overlapped extensively within the central coordinate space, suggesting shared genetic ancestry and historical exchange of material. Conversely, populations from Sri Lanka, Kenya and the Philippines appeared more isolated at the plot margins, indicating comparatively distinct genetic backgrounds or reduced introgression with other regions. Interestingly, Oman grouped close to several Asian and South American populations rather than forming an isolated cluster. This supports the notion that Omani mango germplasm has been influenced by historical introduction routes rather than evolving as an independent genetic lineage. When individual genotypes were plotted (Figure 6), a broader distribution pattern emerged, reflecting high intra-population genetic variability, consistent with AMOVA results (87% within population variation). Despite general overlap among global cultivars, Omani accessions exhibited a semi-clustered pattern, reinforcing partial shared ancestry, yet also revealed scattered genotypes intermixing with Indian, Brazilian and Australian accessions. Distinct outlier positioning of cultivars from Sri Lanka, Tahiti, Kenya and the Philippines highlights the presence of geographically unique alleles and possible independent selection histories. These divergent accessions may represent valuable genetic reservoirs for breeding programs that aim to introduce new quality, yield or disease-tolerance traits. Figure 4. Population-based PCoA showing the distribution of accessions according to country of origin. Figure 5. Accession-level PCoA demonstrates individual genetic relationships among all 126 genotypes. Cluster analysis of mango accessions A dendrogram depicting the genetic relationships among different mango accessions was created using the UPGMA method, which assigns equal weight to each pair and calculates the average. Figure 4 displays a dendrogram that was created using a matrix of basic matching coefficients. This dendrogram clearly shows the presence of three main clusters. The first cluster (Cluster I) included cultivar Boribo from Kenya and cultivar Carabao Harbon from Philippines. The second cluster (Cluster II) has 2 subclusters in which one cluster contain accessions from Oman along with two accessions from USA (Haden) and Australia (Phoenix) which were clustered with Omani cultivars, and the second subcluster included 2 cultivars from Vietnam (Coconut and Xoai Tuong) and one cultivar from Indonesia (Kasturi). However, the third main cluster (Cluster III) contains all other mango cultivars from other origins. All clusters showed a mixture of poly-embryonic and mono-embryonic seed-type varieties in their cluster. Figure 6. UPGM with arithmetic mean distance tree based on Nei genetic distances between 126 mango accessions using 55 SSR markers with bootstrap number for each node. Estimated population structure among mangos cultivars Population structure analysis using STRUCTURE v2.3.4 was performed for K = 1–10 under the admixture model. The ΔK method of Evanno et al. (2005) identified a strong peak at K = 2 (Figure 7), indicating that the 126 mango accessions form two major genetic sub-populations. The bar plot for K = 2 clearly separated Omani accessions (Sub-population II) from the majority of non-Omani genotypes (Sub-population I) (Figure 8). Sub-population I (red) contained accessions predominantly from Indonesia (6), Brazil (2), India (3), Kenya (7), Malaysia (8), Sri Lanka (12), Occupied Island (9), Tahiti (13), USA (15), Vietnam (16), Thailand (14), Philippines (11), Indochina (5), and Australia (1). These accessions shared high membership probability within a single ancestry group, suggesting a largely interconnected genetic background. In contrast, Sub-population II (green) consisted mainly of genotypes from Oman (10), forming a distinct genetic cluster with minimal introgression from other regions. The clean separation of Omani accessions suggests the development of a semi-independent genetic lineage, likely shaped by local selection, geographical isolation, or region-specific allele retention. Although most non-Omani accessions grouped together, several genotypes displayed minor admixture signals, indicating historical gene exchange between Omani and foreign populations. This is consistent with PCoA and UPGMA outputs, which also support a shared ancestry between Oman and select Asian and South American germplasm. Figure 7. The Evanno plot, derived from STRUCTURE HARVESTER, is used to detect the number of genetic clusters of mango accessions. Figure 8. Estimated population structure for K=2-4 displayed with population Q-matrix. 10 runs at each K produced nearly similar population membership coefficients. The number in brackets represents the population number. Two sub-populations, or groups, are indicated by color; sub-population one (red) accessions from Indonesia (6), Brazil (2), India (3), Kenya (7), Malaysia (8), Sri Lanka (12), Occupied Island (9), Tahiti (13), USA (15), Vietnam (16), Thailand (14), Philippines (11), Indochina (5) and Australia (1) and sub-population two (green) accessions from Oman (10). Discussion The present study applied highly polymorphic SSR markers to evaluate genetic diversity within a globally sourced mango collection maintained in Oman. The detection of 706 alleles across 55 loci, averaging 12.8 alleles per marker, demonstrates a high level of genome-wide variation. Comparable or lower allele counts have been reported in previous studies from Taiwan, Mexico, India and Brazil (Chiang et al., 2012 ; Gálvez-López et al., 2009 ; Begum et al., 2013 ; Alves et al., 2016 ), whereas higher allelic richness was occasionally observed in elite cultivars or region-specific landrace panels (Vasugi et al., 2012 ; Ravishankar et al., 2017 ). The current dataset therefore represents one of the most diverse mango germplasm collections studied in the Arabian Peninsula. Marker informativeness and heterozygosity PIC values averaged 0.700, confirming the reliability of most markers for diversity assessment. High PIC values are indicative of multi-allelic loci and validate their suitability for genetic differentiation and germplasm fingerprinting. Observed heterozygosity exceeded expected heterozygosity at most loci, suggesting an over-representation of heterozygotes in the population. This pattern is consistent with mango’s predominantly cross-pollinated reproductive biology, historical mass seed propagation, and ongoing orchard intermixing, which together promote allelic reshuffling and prevent inbreeding (Viruel et al., 2005 ; Schnell et al., 2006 ). Genetic variance distribution and population structure AMOVA results revealed that 87% of total variation resides within populations, with only 13% partitioned among populations. This indicates that genetic differences are predominantly found among individual accessions rather than between geographical groups. Such variance patterns are characteristic of long-domesticated, clonally propagated perennial fruit crops where recurrent gene flow and human-mediated movement mask regional separation (Miller and Gross, 2011 ). This intrapopulation variation was further supported by STRUCTURE, UPGMA, and PCoA outputs, which did not strictly cluster accessions according to geographic origin. Instead, accessions from Asia, Australia, and the Americas frequently displayed admixture signals, while most Omani landraces clustered into a shared gene pool with a subset of foreign accessions. The grouping of Haden (USA), Phoenix (Australia), and Kasturi (Indonesia) within the Omani cluster suggests historical exchange of material, likely facilitated by maritime trade routes that linked Oman with mango-producing regions for centuries (Hammer et al., 2009 ). High intrapopulation diversity presents a valuable opportunity for selection and genetic enhancement. The observed heterozygosity excess suggests that desirable traits, such as fruit quality, yield stability, or wilt disease tolerance, may exist in hidden combinations that can be explored through controlled hybridisation. Conversely, the lack of strong regional partitioning means that vegetative lineages have intermixed over time, likely diluting ancestral purity but enriching the available gene pool. Conclusion This study provides the most extensive molecular assessment of mango germplasm maintained in Oman, integrating 126 accessions from 15 global origins using highly informative SSR markers. The detection of 706 alleles, high PIC values, and strong heterozygosity levels reinforce the suitability of SSR markers for diversity estimation, genotype discrimination, and germplasm authentication in mango. Population genetic parameters and AMOVA results indicated that the majority of genetic variation exists within populations rather than among them (87% vs. 13%), reflecting long-term outcrossing, historic germplasm exchange, and human-mediated distribution across regions. STRUCTURE and PCoA analyses confirmed the presence of two main ancestral gene pools, with Omani accessions forming a distinct but partially admixed cluster relative to international material. Accessions from Sri Lanka, Kenya, Philippines, and Tahiti appeared genetically divergent, representing reservoirs of unique alleles that may be valuable for future breeding and trait improvement. Collectively, these findings contribute to a clearer understanding of the molecular diversity landscape of mango in Oman and globally. The results provide strong justification for the targeted conservation of locally adapted cultivars, as well as for the incorporation of genetically distant accessions into breeding programs aimed at improving fruit quality, yield stability, environmental resilience, and wilt-disease tolerance. Future work integrating SNP-based genome scans, phenotypic trait evaluation, and association mapping will further deepen our understanding of trait–gene relationships and accelerate improvement pathways for this economically and culturally significant fruit crop. Declarations Author Contribution Ali Al-Adawi: Writing – review and editing; Funding acquisition; Project administration.AL-Ghaliya Al-Mamari: Writing – review and editing; Data curation; Software and statistical analysis.Muna Al-Jabri: DNA extraction, genotyping, and laboratory analysis.Wafa Al-Shibli: DNA extraction, genotyping, and laboratory analysis.Muhammed Al-Jabri: Methodology and experimental work.All authors have read and approved the final manuscript. Acknowledgment The authors gratefully acknowledge the Ministry of Agriculture, Fisheries and Water Resources, Sultanate of Oman, for funding this research. Experimental work and laboratory analyses were conducted at the General Directorate of Agricultural and Livestock Research – Tissue Culture and Biotechnology Research Laboratories, whose support, facilities, and technical assistance are deeply appreciated. References A.B. Addisalem, Jérôme Duminil, Wouters, D., Bongers, F. and M.J.M. Smulders (2016). Fine-scale spatial genetic structure in the frankincense tree Boswellia papyrifera (Del.) Hochst. and implications for conservation. Tree Genetics & Genomes , 12(5). Al Adawi, A.O., Deadman, M.L., Al Rawahi, A.K., Al Maqbali, Y.M., Al Jahwari, A.A., Al Saadi, B.A., Al Amri, I.S. and Wingfield, M.J. (2006). Aetiology and causal agents of mango sudden decline disease in the Sultanate of Oman. European Journal of Plant Pathology , 116, pp.247–254. Al Busaidi, S.S. (2008). A raiea fe tarkeh al omani (Remarkable in history of Oman). Anfal library. Vol.1, p.420. Al Salmi, A.H. (1997). Tohfat al aiaean beserat ahel Oman (Masterpiece of biographical objects the people of Oman). Volume 1. Muscat library press. P.415. Alves, E., Neto, F., Santos, C., Ribeiro, I., de Melo, C., Holanda, I., de Souza, A. and Corrêa, R. (2016). Genetic diversity of mango accessions ( Mangifera indica ) using new microsatellite markers and morphological descriptors. Australian Journal of Crop Science , 10(9), pp.1281–1287. Al Washahi, L., Al Shamsi, H., Dillon, N., Sharma, N., Al Qamashoui, B. and Al Saadi, A. (2017). Molecular characterization of local Mango ( Mangifera indica L.) germplasm in Oman. Acta Horticulturae , (1183), pp.105–112. Azmat, M., Khan, A., Khan, I., Rajwana, I., Cheema, H. and Khan, A. (2016). Morphological characterization and SSR based DNA fingerprinting of elite commercial mango cultivars. Pakistan Journal of Agricultural Sciences , 53(2), pp.321–330. Bajpai, A., Sharma, N., Srivastava, N., Rajan, S. and M, M. (2018). Intra- Cultivar Variability Endorsed by SSR Markers in Mango. Biosciences, Biotechnology Research Asia , 15(1), pp.181–186. Bally, I.S.E. (2006). Mangifera indica (mango), ver. 3.1. In: Elevitch, C.R. (ed.). Species Profiles for Pacific Island Agroforestry. Permanent Agriculture Resources (PAR), Hōlualoa, Hawai‘i. http://www.traditionaltree.org . Begum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2013). Molecular analysis of intracultivar polymorphism in ‘Peddarasam’ Mango ( Mangifera indica L.) using microsatellite markers. Asia Pacific Journal of Molecular Biology and Biotechnology , 21(3), pp.97–113. Begum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2013). Molecular Analysis of Intracultivar Polymorphism of (Panchadarakalasa) Mango by Microsatellite Markers. Jordan Journal of Biological Sciences , 6(2), pp.127–136. Begum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2013). Microsatellite analysis of Intra-cultivar diversity in 'Chinnarasam' Mango from Andhra Pradesh, India. African Crop Science Journal , 21(2), pp.109–117. Begum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2014). Morphological and Microsatellite Analysis of Intravarietal Heterogeneity in ‘Beneshan’ Mango ( Mangifera indica L.). International Journal of Agricultural and Food Research , 3(2), pp.16–33. Begum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2014). Morphological and Microsatellite Analysis of Intravarietal Heterogeneity in ‘Beneshan’ Mango ( Mangifera indica L.). International Journal of Agricultural and Food Research , 3(2), pp.452–472. Begum, H., Reddy, M.T., Malathi, S., Reddy, B.P., Arcahk, S., Nagaraju, J. and Siddiq, E.A. (2012). Molecular Analysis for Genetic Distinctiveness and Relationships of Indigenous Landraces with Popular Cultivars of Mango ( Mangifera indica L.) in Andhra Pradesh, India. The Asian and Australasian Journal of Plant Science and Biotechnology , 6(1), pp.24–37. Booy, G., Hendriks, R.J.J., Smulders, M.J.M., Groenendael, J.M. and Vosman, B. (2000). Genetic Diversity and the Survival of Populations. Plant Biology , 2(4), pp.379–395. Botstein, D., White, R. L., Skolnick, M., and Davis, R. W. (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics , 32 (3), 314–331. Charlesworth, B. (2009). Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics , 10(3), pp.195–205. Chiang, Y., Tsai, C., Chen, Y., Lee, S., Chen, C., Lin, Y. and Tsai, C. (2012). Development and characterization of 20 new polymorphic microsatellite markers from Mangifera indica (Anacardiaceae). American Journal of Botany , 99(3), pp.e117-e119. Chunwongse, C., Phumichai, C., Tongyoo, P., Juejun, N. and Chunwongse, J. (2015). Development of di-nucleotide microsatellite markers and construction of genetic linkage map in Mango ( Mangifera indica L.). Songklanakarin Journal of Science and Technology , 37(2), pp.119–127. Coppi, A., Cecchi, L., Selvi, F. and Raffaelli, M. (2010). The Frankincense tree (Boswellia sacra, Burseraceae) from Oman: ITS and ISSR analyses of genetic diversity and implications for conservation. Genetic Resources and Crop Evolution , 57(7), pp.1041–1052. Dillon, N., Bally, I., Hucks, L., Wright, C., Innes, D. and Dietzgen, R. (2013). Implementation of SSR markers in mango breeding in Australia. Acta Horticulturae , (992), pp.259–267. Dillon, N., Bally, I., Wright, C., Hucks, L., Innes, D. and Dietzgen, R. (2013). Genetic diversity of the Australian National Mango Genebank. Scientia Horticulturae , 150, pp.213–226. Dos Santos Ribeiro, I., Lima Neto, F. and Santos, C. (2012). Allelic database and accession divergence of a Brazilian mango collection based on microsatellite markers. Genetics and Molecular Research , 11(4), pp.4564–4574. Duval, M., Bunel, J., Sitbon, C. and Risterucci, A. (2005). Development of microsatellite markers for Mango ( Mangifera indica L.). Molecular Ecology Notes , 5(4), pp.824–826. Duval, M., Risterucci, A., Calabre, C., Le Bellec, F., Bunel, J. and Sitbon, C. (2006). Genetic diversity of Caribbean mangoes ( Mangifera indica L.) using microsatellite markers. Acta Horticulturae , (820), pp.183–188. Earl, D.A. and vonHoldt, B.M. (2012). STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources , 4, pp.359–361. Edwards, C.E., Parchman, T.L. and Weekley, C.W. (2012). Assembly, Gene Annotation and Marker Development Using 454 Floral Transcriptome Sequences in Ziziphus Celata (Rhamnaceae), a Highly Endangered, Florida Endemic Plant. DNA Research , 19(1), pp.1–9. Evanno, G., Regnaut, S. and Goudet, J. (2005). Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology , 14, pp.2611–2620. Fatimah, F., Husni, A., Kosmiatin, M., Karsinah, K. and Baroya, M. (2016). Characterization of zygotic and nucellar embryo of six Indonesian mango cultivars using molecular markers. Annales Bogorienses , 20(2), pp.69–77. González-Martínez, S.C., Krutovsky, K.V. and Neale, D.B. (2006). Forest-tree population genomics and adaptive evolution. New Phytologist , 170(2), pp.227–238. Gora, J.S., Kumar, R. and Chet Ram, S.V. (2018). Biochemical responses of monoembryonic and polyembryonic seedlings of mango rootstocks under salt stress conditions. International Journal of Chemical Studies , 6(6), pp.2199–2203. Gálvez-López, D., Hernández-Delgado, S., González-Paz, M., Becerra-Leor, E., Salvador-Figueroa, M. and Mayek-Pérez, N. (2009). Genetic analysis of mango landraces from Mexico based on molecular markers. Plant Genetic Resources , 7(3), pp.244–251. Hammer, K., Gebauer, J., Al Khanjari, S. and Buerkert, A. (2009). Oman at the cross-roads of inter-regional exchange of cultivated plants. Genetic Resources Crop Evolution , 56, pp.547–560. Honsho, C., Nishiyama, K., Eiadthong, W. and Yonemori, K. (2005). Isolation and characterization of new microsatellite markers in Mango ( Mangifera indica ). Molecular Ecology Notes , 5(1), pp.152–154. Htway, H., Soe, A., Myint, M., Yi, K., Chan, N., Kyaing, M., Hlaing, N., Aung, S., Phyo, S., Htwe, Y., Maung, C. and Yu, S. (2018). Genetic Diversity and Genetic Uniqueness of Indigenous Myanmar Mango (Sein Ta Lone) Cultivar in Kyaukse District. International Journal of Plant Biology & Research , 6(3), p.1089. Joop Ouborg, N., Angeloni, F. and Vergeer, P. (2009). An essay on the necessity and feasibility of conservation genomics. Conservation Genetics , 11(2), pp.643–653. Karihaloo, J.L., Dwivedi, Y.K., Archak, S. and Galkwab, A.B. (2003). Analysis of genetic diversity of Indian Mango cultivars using RAPD markers. Journal of Horticultural Science and Biotechnology , 78, pp.285–289 Kumar, M., Ponnuswami, V., Nagarajan, P., Jeyakumar, P. and Senthil, N. (2013). Molecular characterization of ten mango cultivars using simple sequences repeat (SSR) markers. African Journal of Biotechnology , 12(47), pp.6568–6573. Lamy, T., Laroche, F., David, P., Massol, F. and Jarne, P. (2017). The contribution of species-genetic diversity correlations to the understanding of community assembly rules. Oikos , 126(6), pp.759–771. Lawton-Rauh, A. (2008). Demographic processes shaping genetic variation. Current Opinion in Plant Biology , 11(2), pp.103–109. Levin, D.A. and Kerster, H.W. (1974). Gene flow in seed plants. Evolutionary Biology , 7, pp.139–220. Litt, M. and Luty, J. (1989). A Hypervariable Microsatellite Revealed by In Vitro Amplification of a Dinucleotide Repeat within the Cardiac Muscle Actin Gene. American Journal of Human Genetics , 44(3), pp.397–401. Liu, K. and Muse, S. (2005). PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics , 21(9), pp.2128–2129. MAF. (1990). Annual report of Agriculture research 1989. Ministry of Agriculture & Fisheries. Sultanate of Oman. p.189. MAF, (2014). Agricultural census 2012/13. Ministry of Agriculture and Fisheries, Sultanate of Oman. Available at: http://www.moa.gov.om/arabic/index.asp . MAF. (2018). Data base of agriculture sector: Agricultural production 2017. Ministry of Agriculture and Fisheries, Sultanate of Oman. Available at: http://www.moa.gov.om/arabic/index.asp . Messmer, M., Melchinger, A., Boppenmaier, J., Brunklaus-Jung, E. and Herrmann, R. (1992). Relationships among Early European Maize Inbreds: I. Genetic Diversity among Flint and Dent Lines Revealed by RFLPs. Crop Science , 32(6), p.1301. Miles, S.B. (1896). Journal of an Excursion in Oman, in South-East Arabia. The Geographical Journal , 7, pp.522–537. Miller, A.J. and Gross, B.L. (2011). From forest to field: Perennial fruit crop domestication. American Journal of Botany , 98(9), pp.1389–1414. Moran, P. (2002). Current conservation genetics: building an ecological approach to the synthesis of molecular and quantitative genetic methods. Ecology of Freshwater Fish , 11(1), pp.30–55. Mukherjee, S.K. and Litz, R.E. (2009). Introduction: Botany and Importance. In: Litz RE. (ed.) The Mango: Botany, Production and Uses. 2nd edition. CAB International, Wallingford, UK, pp. 1–18. Nazish, T., Shabbir, G., Ali, A., Sami-ul-Allah, S., Naeem, M., Javed, M., Batool, S., Arshad, H., Hussain, S., Aslam, K., Seher, R., Tahir, M. and Baber, M. (2017). Molecular diversity of Pakistani Mango ( Mangifera indica L.) varieties based on microsatellite markers. Genetics and Molecular Research , 16(2), pp.1–8. Nei, M. (1978). Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics , 89(3), pp.583 – 90. Pandit, S.S., Mitra, S., Giri, A.P., Pujari, K.H., Patil, B.P. and Jambhale, N.D. (2007) Genetic diversity analysis of mango cultivars using inter simple sequence repeat markers. Current Science , 93, pp.1135–1141. Panhwar, A., Nizamani, S.M., Abbasi, Q.D., Jiskani, M.M. and Khuhro, R.D. (2008). Response of different varieties to sudden mango decline disease in Sindh, Pakistan. P.17. Peakall, R. and Smouse, P. (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics , 28(19), pp.2537–2539. Pérez, V., Herrero, M. and Hormaza, J.I., (2019). Pollen performance in Mango ( Mangifera indica L., Anacardiaceae): Andromonoecy and effect of temperature. Scientia Horticulturae , 253, pp.439–446. Perrier X., Jacquemoud-Collet J.P. (2006) DARwin software http://darwin.cirad.fr/ . Ploetz, R.C. and Freeman, S. (2009). Foliar, floral and soil-borne diseases. In: Litz RE. (ed.) The Mango: Botany, Production and Uses. 2nd edition. CAB International, Wallingford, UK, pp.281–325. Powell, W., Machray, G.C. and Provan, J. (1996). Polymorphism revealed by simple sequence repeats. Trends in Plant Science , 1(7), pp.215–222. Pritchard, J., Stephens, M. and Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics , 155(2), pp.945–959. Ravishankar, K., Mani, B., Anand, L. and Dinesh, M. (2011). Development of new microsatellite markers from Mango ( Mangifera indica ) and cross-species amplification. American Journal of Botany , 98(4), pp.e96-e99. Ravishankar, K., Padmakar, B., Lavanya, B., Mani, B. and Dinesh, M. (2017). Development and characterization of microsatellite loci from Mango ( Mangifera indica L.). Indian Journal of Biotechnology , 16(2), pp.250–253. Ravishankar, K.V., Anand, L. and Dinesh, M.R. (2000). Assessment of genetic relatedness among mango cultivars of India using RAPD markers. J ournal of Horticultural Science & Biotechnology , 75(2), pp.198–201. Ratnam, W., Rajora, O.P., Finkeldey, R., Aravanopoulos, F., Bouvet, J.-M., Vaillancourt, R.E., Kanashiro, M., Fady, B., Tomita, M. and Vinson, C. (2014). Genetic effects of forest management practices: Global synthesis and perspectives. Forest Ecology and Management , 333, pp.52–65. Rocha, A., Luiz, C.S., Tania, F.S., Cosme, D.C. and de Dalmo, L.S. (2012). Genetic diversity of ‘Uba’ mango tree using ISSR markers. Molecular Biotechnology , 50(2), pp.108–113. Rosenberg, N. (2003). Distruct: a program for the graphical display of population structure. Molecular Ecology Notes , 4(1), pp.137–138. Sales, E. and Butardo, N. (2017). SSR markers for Mango ( Mangifera indica L.) cultivar identification and genetic characterization. Philippine Journal of Crop Science , 42(3), pp.30–38. Schnell, R., Brown, S., Olano, C., Meerow, A., Campbell, R. and Kuhn, D. (2006). Mango Genetic Diversity Analysis and Pedigree Inferences for Florida Cultivars using Microsatellite Markers. American Society for Horticultural Science , 131(2), pp.214–224. Schnell, R., Olano, C., Quintanilla, W. and Meerow, A. (2005). Isolation and characterization of 15 microsatellite loci from mango ( Mangifera indica L.) and cross-species amplification in closely related taxa. Molecular Ecology Notes , 5(3), pp.625–627. Shamili, M., Fatahi, R. and Hormaza, J. (2012). Characterization and evaluation of genetic diversity of Iranian Mango ( Mangifera indica L., Anacardiaceae) genotypes using microsatellites. Scientia Horticulturae , 148, pp.230–234. Shyam Sundar Sharma, Islam, A., Vivek Kumar Singh, Madan Singh Negi and Shashi Bhushan Tripathi (2017). Genetic diversity, population structure and association study using TE-AFLP markers in Pongamia pinnata (L.) Pierre germplasm. Tree Genetics & Genomes , 13(1). Singh, S. and Bhat, K.V. (2009). Molecular characterization and analysis of geographical differentiation of Indian Mango (Mangifera Indica L.) germplasm. Acta Horticulturae , 839(839), pp.599–606. Surapaneni, M., Vemireddy, L., Begum, H., Purushotham Reddy, B., Neetasri, C., Nagaraju, J., Anwar, S. and Siddiq, E. (2013). Population structure and genetic analysis of different utility types of Mango ( Mangifera indica L.) germplasm of Andhra Pradesh state of India using microsatellite markers. Plant Systematics and Evolution , 299(7), pp.1215–1229. Takezaki, N., and Nei, M. (1996). Genetic Distance and reconstruction of Phylogenetic trees from microsatellite DNA. Genetics , 144(1), pp.389–399. Tsai, C., Chen, Y., Chen, C., Weng, I., Tsai, C., Lee, S., Lin, Y. and Chiang, Y. (2013). Cultivar identification and genetic relationship of Mango ( Mangifera indica ) in Taiwan using 37 SSR markers. Scientia Horticulturae , 164, pp.196–201. Van Wyk M., Al Adawi, A.O., Khan, I.A., Deadman, M.L., Al Jahwari, A.A., Wingfield, B.D., Ploetz, R. and Wingfield, M.J. (2007). Ceratocystis manginecans sp. Nov., causal agent of a destructive mango wilt disease in Oman and Pakistan. Fungal Diversity , 27, pp.213–230. Van Zonneveld, M., I. Dawson, E. Thomas, X. Scheldeman, J. van Etten, J. Loo and J. I. Hormaza (2014). Application of molecular markers in spatial analysis to optimize in situ conservation of plant genetic resources. Genomics of plant genetic resources, Springer : 67–91. Vasugi, C., Dinesh, M., Sekar, K., Shivashankara, K., Padmakar, B. and Ravishankar, K. (2012). Genetic diversity of indigenous mango accessions (Appemidi) of the Western Ghats for certain fruit characteristics. Current Science , 103(2), pp.199–207. Vavilov, N.I. (1992). The phyto-geographical basis for plant breeding. In: Origin and geography of cultivated plants . Cambridge: Cambridge University Press, pp.316–366. Verde, I., Abbott, A.G., Scalabrin, S., Jung, S., Shu, S., Marroni, F., Zhebentyayeva, T., Dettori, M.T., Grimwood, J., Cattonaro, F., Zuccolo, A., Rossini, L., Jenkins, J., Vendramin, E., Meisel, L.A., Decroocq, V., Sosinski, B., Prochnik, S., Mitros, T. and Policriti, A. (2013). The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication, and genome evolution. Nature Genetics , [online] 45(5), pp.487–494. Viruel, M., Escribano, P., Barbieri, M., Ferri, M. and Hormaza, J. (2005). Fingerprinting, embryo type and geographic differentiation in Mango ( Mangifera indica L., Anacardiaceae) with microsatellites. Molecular Breeding , 15(4), pp.383–393. Wahdan, M., Abdelsalam, A., El-Naggar, A. and Hussein, M. (2011). Preliminary Horticultural Studies to Describe and Identify Of Two New Egyptian Mango Strains Using DNA Fingerprint. Journal of American Science , 7(2), pp.641–650. Wright, S. (1969). Evolution and the Genetics of Populations: The Theory of Gene Frequencies. The University of Chicago Press, Chicago, Illinois. Wright, S. (1965). The Interpretation of Population Structure by F-Statistics with Special Regard to Systems of Mating. Evolution , 19(3), p.395. Zwemer, S.M. (1902). Three Journeys in Northern Oman. The Geographical Journal , 19, pp.54- Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Jan, 2026 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Revision requested 17 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 02 Dec, 2025 Submission checks completed at journal 02 Dec, 2025 First submitted to journal 01 Dec, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8255746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559653248,"identity":"221c7960-c5aa-4aeb-a3fb-ad7c9158c80f","order_by":0,"name":"Abdullah Al-Jabri","email":"data:image/png;base64,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","orcid":"","institution":"Ministry of Agriculture, Fisheries and Water Resources","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Al-Jabri","suffix":""},{"id":559653249,"identity":"d91fda50-a818-4f1e-b0cf-b9c0af3ad18a","order_by":1,"name":"AL-Ghaliya AL-Mamari","email":"","orcid":"","institution":"Ministry of Agriculture, Fisheries and Water Resources","correspondingAuthor":false,"prefix":"","firstName":"AL-Ghaliya","middleName":"","lastName":"AL-Mamari","suffix":""},{"id":559653250,"identity":"95731aad-f7a6-4792-9101-3ab8dfad524d","order_by":2,"name":"Ali Al-Adawi","email":"","orcid":"","institution":"Ministry of Agriculture, Fisheries and Water Resources","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Al-Adawi","suffix":""},{"id":559653251,"identity":"6b72bf95-3e07-4e41-8760-8c7f77204336","order_by":3,"name":"Muhammed Al-Jabri","email":"","orcid":"","institution":"Ministry of Agriculture, Fisheries and Water Resources","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"","lastName":"Al-Jabri","suffix":""},{"id":559653252,"identity":"7d8cbcdb-e187-4402-a18e-6c7a515b76fd","order_by":4,"name":"Wafa Al-Shibli","email":"","orcid":"","institution":"Ministry of Agriculture, Fisheries and Water Resources","correspondingAuthor":false,"prefix":"","firstName":"Wafa","middleName":"","lastName":"Al-Shibli","suffix":""},{"id":559653253,"identity":"2bba0ab3-51a7-4efc-a8ae-60d068b1c4a4","order_by":5,"name":"Muna Al-Jabri","email":"","orcid":"","institution":"Ministry of Agriculture, Fisheries and Water Resources","correspondingAuthor":false,"prefix":"","firstName":"Muna","middleName":"","lastName":"Al-Jabri","suffix":""}],"badges":[],"createdAt":"2025-12-02 04:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8255746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8255746/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-026-02730-x","type":"published","date":"2026-01-21T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98433549,"identity":"dcf4fdc1-d713-4933-9543-ee93bf50f97e","added_by":"auto","created_at":"2025-12-17 16:50:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1909001,"visible":true,"origin":"","legend":"","description":"","filename":"MangoSSRManuscript2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/246e9695ad786bda78fba808.docx"},{"id":98214941,"identity":"efd9d323-e4ee-4d3c-b48a-160fc7fe51b8","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7573,"visible":true,"origin":"","legend":"","description":"","filename":"da25d6b4c1254b758a8c17634b089925.json","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/e2ccec28e3f1dccdc1bbb117.json"},{"id":98214950,"identity":"9f08b856-f1e8-41f0-9701-102c59c0af7d","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":258078,"visible":true,"origin":"","legend":"","description":"","filename":"da25d6b4c1254b758a8c17634b0899251enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/c77a2e2f898f446fabc01a95.xml"},{"id":98214966,"identity":"27af8dd1-496b-426b-b557-7ccbc8ce3f20","added_by":"auto","created_at":"2025-12-15 10:22:04","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109969,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/ff03efabb9b7a8d120c21788.png"},{"id":98433546,"identity":"7a9ae57e-1164-400f-ba10-214e07bc2fe6","added_by":"auto","created_at":"2025-12-17 16:50:53","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4945190,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/988ed52d07d0f02720116824.jpeg"},{"id":98214968,"identity":"ddcd2f90-9a0a-4893-b82a-0af61492fac4","added_by":"auto","created_at":"2025-12-15 10:22:09","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4945190,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/c33e07f895f597b8be330abb.jpeg"},{"id":98214967,"identity":"ffde6c21-e5b0-4255-adbb-aa977649ca2e","added_by":"auto","created_at":"2025-12-15 10:22:09","extension":"emf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24112,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.emf","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/2c0a12088261fa7f408605d6.emf"},{"id":98433594,"identity":"31395cba-d762-4833-a306-d6e2a668b55b","added_by":"auto","created_at":"2025-12-17 16:50:56","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44104,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/a7f3d5c0c8b7a0268f31675e.png"},{"id":98432309,"identity":"216c39e6-833e-4360-9955-bc96e47cc03d","added_by":"auto","created_at":"2025-12-17 16:49:22","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70014,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/02830dad767886d7f3ae6d55.png"},{"id":98433302,"identity":"79cf6abe-d607-4568-b11e-ec003b746a48","added_by":"auto","created_at":"2025-12-17 16:50:35","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1287216,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/6ba0d20cf0fe41b9aa01e79b.jpeg"},{"id":98214952,"identity":"f2fcca79-7c47-4bec-92c3-6a1c4385127e","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16701,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/4412dcda2227635eda7668b8.png"},{"id":98214953,"identity":"ee7a479d-32a2-48f0-b07e-10e28ec71703","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":463954,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/d1005a05a711f2df4d5cd601.png"},{"id":98432203,"identity":"2c0090ba-399c-4d4a-83eb-fd69aa2e4e4f","added_by":"auto","created_at":"2025-12-17 16:49:13","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31667,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/3f428054f3838936e0cb5c74.png"},{"id":98214955,"identity":"d3de0325-e2fe-40aa-9c72-2449670ef017","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47977,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/0feef34b335e406f2c010e31.png"},{"id":98433454,"identity":"89e82fdb-bf64-4df8-8738-228efa8fae42","added_by":"auto","created_at":"2025-12-17 16:50:47","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47977,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/51d2507f95868688e3990554.png"},{"id":98431201,"identity":"fb5a8c22-e223-479c-97a0-edeb92c0e0e5","added_by":"auto","created_at":"2025-12-17 16:47:15","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15478,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/a44f3f725409afee43c4fcfc.png"},{"id":98214959,"identity":"b647a389-b6ec-4fd6-b35f-248f1df2d6fb","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40350,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/7b283c1789bdcce09c84afe5.png"},{"id":98433337,"identity":"b9d882ce-d84c-41d1-9440-4b96b4404db0","added_by":"auto","created_at":"2025-12-17 16:50:39","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23592,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/8e1ed14206853d2cdb7e49c2.png"},{"id":98214960,"identity":"5c5280e2-469f-4f53-9dfd-d85e7745f046","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49957,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/98ed1e65ba7ec533faeb5df4.png"},{"id":98214958,"identity":"032b9f2c-80bc-4b2e-908d-60c9ab8ba1e7","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5325,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/185f90137770a5628d04cec0.png"},{"id":98431687,"identity":"26a7aad0-428c-47a5-a2cd-9f388235376e","added_by":"auto","created_at":"2025-12-17 16:48:09","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69893,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/6f6a9e696e249690e27d16b0.png"},{"id":98214961,"identity":"707ea183-b65d-4ece-8359-10afa8e3da69","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"xml","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":250076,"visible":true,"origin":"","legend":"","description":"","filename":"da25d6b4c1254b758a8c17634b0899251structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/9a55966d4d9f3f4de7d6707f.xml"},{"id":98214962,"identity":"eda6713a-b0cd-40ab-94ac-0e331d12502c","added_by":"auto","created_at":"2025-12-15 10:21:32","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267341,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/b1668847d091ce629ede633b.html"},{"id":98214937,"identity":"70025476-5038-416a-bcbe-9ed29c0dc6bd","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59528,"visible":true,"origin":"","legend":"\u003cp\u003eMango growing in Oman and their accessions with code, Geographic origin, and embryo type for current study.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/4e89feb60c836a401c08eb42.jpg"},{"id":98214938,"identity":"7fe37d25-125e-4c06-a316-a2a1912d9208","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92346,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The percentage of polymorphic loci in mango cultivars was determined using GenAlex. (B) Heterozygous allele showing 2 peaks for Dura accession using MGDSSR5. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs. (C) Homozygous allele shows only 1 peak for Alphonso accession using MiSHRS-33. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/dd418d65a1c73df0d2bd74da.jpg"},{"id":98433488,"identity":"9196c636-7be3-4da9-a133-ed36b555fe65","added_by":"auto","created_at":"2025-12-17 16:50:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64006,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of molecular variance among and within mango populations based on 55 SSR markers.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/d0f041213c23d7f7741676db.jpg"},{"id":98214939,"identity":"2d87537d-b2bd-4d0e-8a48-445dc1d2d509","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113539,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation-based PCoA showing the distribution of accessions according to country of origin.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/99fdf9c5274654c58519b2c4.jpg"},{"id":98432905,"identity":"8ea407c6-d731-48af-b620-2736ee9e3cd2","added_by":"auto","created_at":"2025-12-17 16:50:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96214,"visible":true,"origin":"","legend":"\u003cp\u003eAccession-level PCoA demonstrates individual genetic relationships among all 126 genotypes.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/b8c5dcebb17a7f1b0c85646a.jpg"},{"id":98214944,"identity":"d336b88c-0dcd-4bb3-bfcf-5565a787bd6c","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":215408,"visible":true,"origin":"","legend":"\u003cp\u003eUPGM with arithmetic mean distance tree based on Nei genetic distances between 126 mango accessions using 55 SSR markers with bootstrap number for each node.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/700f434273308376f166a459.jpg"},{"id":98214946,"identity":"21ef51bf-9554-482b-a79c-7d6624bd349e","added_by":"auto","created_at":"2025-12-15 10:21:31","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5232,"visible":true,"origin":"","legend":"\u003cp\u003eThe Evanno plot, derived from STRUCTURE HARVESTER, is used to detect the number of genetic clusters of mango accessions.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/6fcdef67039b2cc70a2daad9.jpg"},{"id":98431718,"identity":"7544db30-a041-45fe-a378-fab706cc30e7","added_by":"auto","created_at":"2025-12-17 16:48:13","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":140091,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated population structure for K=2-4 displayed with population Q-matrix. 10 runs at each K produced nearly similar population membership coefficients. The number in brackets represents the population number. Two sub-populations, or groups, are indicated by color; sub-population one (red) accessions from Indonesia (6), Brazil (2), India (3), Kenya (7), Malaysia (8), Sri Lanka (12), Occupied Island (9), Tahiti (13), USA (15), Vietnam (16), Thailand (14), Philippines (11), Indochina (5) and Australia (1) and sub-population two (green) accessions from Oman (10).\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/5b661873af46c6070a613f7f.jpg"},{"id":101152869,"identity":"2f88bdf8-f8cb-4ce8-92df-3d377e7d0c41","added_by":"auto","created_at":"2026-01-26 16:13:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2701392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8255746/v1/770c1b4b-01b5-4aec-8027-08d2baac27e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Genetic Diversity and Population Structure of Mango (Mangifera indica L.) Germplasm Conserved in Oman Revealed Through SSR Markers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) is one of the world\u0026rsquo;s most important perennial fruit crops, widely cultivated across tropical and subtropical regions. Belonging to the family Anacardiaceae, its primary centre of origin is believed to lie within Southeast Asia, particularly the Malay Archipelago (Viruel et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Mukherjee and Litz, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; P\u0026eacute;rez et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The fruit is globally valued for its flavour, nutritional quality, and commercial significance. In Oman, mango is the fourth most cultivated fruit crop after date palm, citrus and banana, with historical records indicating its first introduction between 1568\u0026ndash;1575 AD in Wilayat Ibri, Al Dhahirah Governorate (Al Busaidi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Al Salmi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Current estimates suggest that Oman hosts more than half a million mango trees, producing approximately 15,673 tonnes in 2016 with an estimated value exceeding USD 9\u0026nbsp;million (MAFWR, 2012).\u003c/p\u003e\u003cp\u003eDespite its long cultivation history, mango production in Oman has been negatively affected by the emergence of wilt disease caused by Ceratocystis manginecans, frequently vectored by the bark beetle Hypocryphalus mangiferiae (Van Wyk et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Al Adawi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Plotez and Freeman, 2009). The epidemic led to widespread mortality of mature indigenous trees, resulting in the loss of important genetic lines. Local cultivars, commonly seed-derived and genetically diverse due to natural outcrossing, have been shown to be highly susceptible to wilt in both Oman and Pakistan (Panhwar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Al Adawi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Many existing landraces are characterised by large tree size, irregular bearing, fibrous pulp and acidic flavour profiles (MAF, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), reinforcing the need for improved disease-tolerant and high-quality genotypes.\u003c/p\u003e\u003cp\u003eUnderstanding the genetic variation preserved in germplasm collections is fundamental for breeding, conservation, and long-term resource management. Population diversity is shaped by mutation, gene flow, breeding system, selection, drift and demographic history (Van Zonneveld et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moran, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Verde et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Molecular markers, particularly simple sequence repeats (SSRs), have become powerful tools for such studies due to their high polymorphism, co-dominance, repeatability, and genome-wide distribution (Joop Ouborg et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Edwards et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). SSRs have previously been employed for mango diversity assessment in India, Australia, Mexico, the Caribbean, and Oman (Duval et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schnell et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dillon et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Al-Washahi \u003cem\u003eet al.\u003c/em\u003e, 2017).\u003c/p\u003e\u003cp\u003eHowever, existing studies on Omani mango germplasm have been limited to locally collected material and have not integrated a broad comparative dataset representing global origins. Given Oman\u0026rsquo;s history as a centre of agricultural exchange and maritime trade, imported germplasm may have significantly contributed to the modern varietal pool (Hammer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A comprehensive molecular evaluation integrating foreign genetic resources is therefore essential.\u003c/p\u003e\u003cp\u003eThis study aimed to characterise the genetic diversity of mango germplasm maintained in Oman using informative SSR markers. Specifically, we sought to:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEvaluate SSR marker suitability for detecting polymorphism across diverse mango accessions\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAssess genetic relationships among local and internationally derived cultivars using allelic diversity, heterozygosity and F-statistics.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDetermine population structure, clustering patterns, and possible introgression between Omani and non-Omani genotypes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe outcomes provide a molecular foundation for cultivar conservation, genetic improvement and selection of elite parental lines for future breeding initiatives in Oman.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant Material\u003c/h2\u003e\u003cp\u003eA total of 126 mango accessions were obtained from the National Mango GenBank at the Sohar Agricultural Research Station in Oman (N 24.3196277, E 56.7155988). Each accession was represented by three independent biological replicates, maintained as separate trees to ensure clonal identity and long-term phenotypic consistency. Young, healthy leaf tissue was collected for DNA extraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The accessions represented cultivars from 15 geographical regions: Oman (26), Australia (24), India (24), Brazil (17), Thailand (11), Indonesia (3), Indochina (3), Malaysia (3), USA (5), Vietnam (5), Israel (1), Philippines (1), Sri Lanka (1), Tahiti (1), and Kenya (1). Of the total accessions, 58 were monoembryonic and 68 polyembryonic. A full list of accessions, origin and seed type is provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA Extraction and Quantification\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from fresh leaf tissue using the DNeasy Plant Mini Kit (Qiagen, Germany) following the manufacturer\u0026rsquo;s protocol with minor optimization. Briefly, 100 mg of leaf powder was incubated in 400 \u0026micro;l of AP1 buffer and 4 \u0026micro;l RNase A at 65\u0026deg;C for 10 min. After addition of 130 \u0026micro;l P3 buffer and ice incubation for 5 min, samples were centrifuged and the supernatant passed through QIAshredder columns. DNA binding, washing, and elution were performed using DNeasy spin columns, with final elution in 50 \u0026micro;l AE buffer. DNA purity was visualized on 1% agarose gel stained with ethidium bromide. DNA concentration was measured using NanoDrop 8000 (Thermo Scientific, USA), and samples were diluted to 30 ng/\u0026micro;l for downstream use.\u003c/p\u003e\n\u003ch3\u003eSSR Amplification and Genotyping\u003c/h3\u003e\n\u003cp\u003eA total of 106 mango specific SSR primers developed by Duval et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Viruel et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Honsho et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Schnell et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Begum et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); and Surapaneni et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) were initially tested for amplification. Of these, 55 loci (51.9%) showing clear amplification and polymorphism were selected for final genotyping. Twenty-eight loci were monomorphic and 23 showed no amplification (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003ePCR amplification was performed in 25 \u0026micro;l reactions containing: 10\u0026times; PCR buffer with MgCl₂ (Thermo Scientific), 10 mM dNTPs, 10 \u0026micro;M forward and reverse primers, 0.5 U Taq DNA polymerase and 30 ng genomic DNA. Forward primers were fluorescently WellRed-labelled (5\u0026prime;-CACGACGTTGTAAAACGAC-3\u0026prime;). Thermal cycling conditions consisted of: 95\u0026deg;C for 4 min; 35 cycles of 95\u0026deg;C for 30 s, 50\u0026ndash;60\u0026deg;C for 1 min (primer-dependent), 72\u0026deg;C for 1 min; and final extension at 72\u0026deg;C for 7 min. PCR products were separated on 2% agarose, and final fragment analysis was conducted on CEQ8000 DNA analyzer (Beckman Coulter, USA). Allele peaks were scored using Beckman Coulter Software v8.0.52. Each sample was run 2\u0026ndash;3 times for scoring accuracy and allele size stability.\u003c/p\u003e\n\u003ch3\u003eGenetic Diversity and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAllele scoring, fragment sizing, and internal size calibration were performed with the CEQ8000 platform. PowerMarker v3.25 (Liu and Muse, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) was used to calculate Polymorphic Information Content (PIC). GenAlEx v6.5 (Peakall and Smouse, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) was used for Nei\u0026rsquo;s genetic distance (Nei, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), percentage of polymorphic loci, and Analysis of Molecular Variance (AMOVA), Total alleles, Effective alleles (Ne), Observed (Hobs) and expected heterozygosity (Hexp), Wright\u0026rsquo;s fixation indices (Fis, Fst, Fit). Population relationships were visualized using Principal Coordinate Analysis (PCoA) based on Nei\u0026rsquo;s distance matrix.\u003c/p\u003e\u003cp\u003ePhylogenetic clustering was performed using DARwin v6.0 (Perrier and Jacquemoud-Collet, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) with UPGMA/Neighbor-Joining unrooted trees. Bayesian population structure was inferred using STRUCTURE v2.3.4 (Pritchard et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) with an admixture model, burn-in of 100,000 and 1,000,000 MCMC iterations for K\u0026thinsp;=\u0026thinsp;1\u0026ndash;10, repeated 10 times. Optimal K was determined by using ΔK method (Evanno et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) via STRUCTURE Harvester (Earl and vonHoldt, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSSR Marker Screening and Polymorphism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 106 SSR markers screened across 126 mango accessions, 55 loci (51.9%) amplified successfully and exhibited polymorphism. Twenty-eight loci (26.4%) were monomorphic and excluded, while 23 loci (21.7%) did not amplify reliably (Supplementary Table 2). The selected 55 markers generated a total of 706 alleles, averaging 12.8 alleles per locus, demonstrating high genetic informativeness.\u003c/p\u003e\n\u003cp\u003eAllele number ranged from 4 at MiSHRS-23 and MGDSSR17 to 25 at MiSHRS-18. PIC values varied widely (0.184\u0026ndash;0.903) with mean 0.700, indicating that most loci were highly polymorphic. Six loci exhibited PIC \u0026lt; 0.5, while the remaining 49 loci were moderately to highly informative (Table 1).\u003c/p\u003e\n\u003cp\u003eAllele size ranged from 101 bp (MiSHRS-18) to 345 bp (SSR24). Representative electropherograms show clear peak distinction in heterozygous (two-peak) and homozygous (single-peak) genotypes (Figure 2B-C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Allelic size range, Number of alleles, expected heterozygosity (\u003cem\u003eH\u003csub\u003eexp\u003c/sub\u003e\u003c/em\u003e), observed heterozygosity (\u003cem\u003eH\u003csub\u003eobs\u003c/sub\u003e\u003c/em\u003e), Effective number of alleles (\u003cem\u003eN\u003csub\u003ee\u003c/sub\u003e\u003c/em\u003e) and the PIC for 55 microsatellite loci calculated with the mean estimate of different parameters for the Mango accessions.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocus Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllelic size range (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of alleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003csub\u003eexp\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003csub\u003eobs\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eN\u003csub\u003ee\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e202-230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e274-291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e170-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e250-273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e215-228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e224-242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e295-320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e121-174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e242-269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e220-243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e187-227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e251-262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIAC-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e135-177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003emMiCIR010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e295-316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e101-133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e221-239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e130-153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e222-390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e211-238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e142-151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e220-225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e192-203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e253-271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e193-211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e216-317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e103-134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e112-133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e256-286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e149-260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e228-282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e173-289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e204-222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e256-268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMngSSR-14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e176-193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e155-188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e201-218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e212-218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e331-345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e184-206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e172-188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e167-187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e223-254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e167-288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e165-203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e259-284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e211-241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e231-268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNGSSR18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e160-165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e220-281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e212-247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e207-246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e212-228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e188-202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e117-201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e160-179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFigure 2.\u003c/strong\u003e (A) The percentage of polymorphic loci in mango cultivars was determined using GenAlex. (B) Heterozygous allele showing 2 peaks for Dura accession using MGDSSR5. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs. (C) Homozygous allele shows only 1 peak for Alphonso accession using MiSHRS-33. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic differences in 126 mango accessions were analysed using 55 microsatellite primer pairs, resulting in the detection of 706 alleles. The mean number of alleles per locus was 12.8. The locus MiSHRS-23 and MGDSSR17 had 4 alleles, while the locus MiSHRS-18 had 25 alleles (Table 1). \u0026nbsp;The homozygous alleles were characterized by a single peak, while the heterozygous alleles displayed two peaks (refer to Figure 2 B\u0026amp;C). The allele size for these loci ranged from 101 bp for the locus MiSHRS-18 to 345 bp for the locus SSR24, as showed in Table 1. The number of effective alleles (Ne) varied, with a range of 1.048 for the SSR22 locus to 1.992 for the MGDSSR17 locus, resulting in an average of 1.409 per locus. Calculations for PIC values were based on the frequency and number of alleles at specific loci. The current study found that the average PIC value for the 55 loci evaluated varied from 0.184 for SSR22 to 0.903 for LMMA01, with an overall average of 0.700 across all markers. This indicates that the mango germplasm analysed had a significant degree of polymorphism for most of the loci studied (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e (A) The percentage of polymorphic loci in mango cultivars was determined using GenAlex. (B) Heterozygous allele showing 2 peaks for Dura accession using MGDSSR5. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs. (C) Homozygous allele shows only 1 peak for Alphonso accession using MiSHRS-33. Scoring was performed using Beckman coulter software. The y-axis represents the maximum height of the peak, while the x-axis represents the size of the peak in terms of base pairs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterozygosity and fixation index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the presence of moderate levels of observed and expected heterozygosity. The average H\u003csub\u003eexp\u003c/sub\u003e values of the mango accessions ranged from 0.024 (SSR22) to 0.496 (MGDSSR17), with an average of 0.204 across all loci. The H\u003csub\u003eobs\u003c/sub\u003e values for all markers varied from 0.048 (SSR22) to 0.992 (MGDSSR17), with an average of 0.403. The cultivars exhibited a range of polymorphic loci percentages, with values ranging from 20% to 67.27%, and an average of 40.59% (Figure 2A). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe F\u003csub\u003eis\u003c/sub\u003e (fixation index or inbreeding coefficient of individuals relative to subpopulations (Wright, 1965) representing the heterozygous deficiency related to the existence of a reproduction within a subpopulation) values for all markers varied between -1.000 (most loci) and -0.341 (MGDSSR22) per primer with an average of -0.978, reflecting heterozygosity excess and low inbreeding. As for the F\u003csub\u003eit\u003c/sub\u003e, the values were ranged from -0.911 (MGDSSR17) to 0.902 (MiSHRS-04) with an average of 0.430, indicating moderate differentiation when considering entire population. The F\u003csub\u003est\u003c/sub\u003e values, which represent the fixation index of subpopulations compared to the whole population (Wright, 1969), ranged from 0.045 (MGDSSR17) to 0.951 (MiSHRS-04), with an average of 0.714, ddemonstrating high genetic divergence among geographic origins (Table 2). These values collectively indicate low inbreeding within accessions, but substantial divergence among populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Allelic size range, Number of alleles, expected heterozygosity (H\u003csub\u003eexp\u003c/sub\u003e), observed heterozygosity (H\u003csub\u003eobs\u003c/sub\u003e), Effective number of alleles (N\u003csub\u003ee\u003c/sub\u003e) and the PIC for 55 microsatellite loci calculated with the mean estimate of different parameters for the Mango accessions.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocus Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllelic size range (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of alleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003eexp\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003eobs\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e202-230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e274-291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e170-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e250-273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e215-228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e224-242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e295-320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e121-174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e242-269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e220-243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e187-227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e251-262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIAC-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e135-177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003emMiCIR010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e295-316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e101-133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e221-239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e130-153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e222-390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e211-238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e142-151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e220-225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e192-203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e253-271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e193-211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e216-317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e103-134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e112-133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e256-286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e149-260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e228-282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e173-289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e204-222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e256-268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMngSSR-14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e176-193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e155-188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e201-218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e212-218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e331-345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e184-206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e172-188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e167-187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e223-254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e167-288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e165-203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e259-284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e211-241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e231-268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNGSSR18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e160-165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e220-281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e212-247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e207-246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e212-228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e188-202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e117-201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e160-179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e0.700\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.\u003c/strong\u003e \u003cem\u003eF\u003c/em\u003e-statistics (\u003cem\u003eF\u003csub\u003eis\u003c/sub\u003e, F\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e and \u003cem\u003eF\u003csub\u003est\u003c/sub\u003e\u003c/em\u003e) values among all population for each locus with the estimated mean for each parameter.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocus Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003csub\u003eis\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003csub\u003eit\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003csub\u003est\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMMA16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIAC-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003emMiCIR010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiSHRS-48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR-91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMngSSR-14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSR84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNGSSR18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e-0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMGDSSR24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.2121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3232%;\"\u003e\n \u003cp\u003e-0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1717%;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.2929%;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic distance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNei\u0026rsquo;s genetic distance was used to estimate the genetic relationship between the 126 mango accessions from 15 populations. Nei\u0026rsquo;s genetic distance showed broad variation among regions. The genetic similarity ranged from 0.096 to 1.191. The lowest genetic distance (Low diversity) was observed between Australia and USA while the highest genetic distance (high diversity) was observed between Sri Lanka and Tahiti, implying unique gene pools. These results highlight clear differentiation among geographic sources (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. The average genetic distance between mango accessions from 15 populations\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1765793865.png\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Molecular Variance (AMOVA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn AMOVA analysis was achieved to elucidate the distribution of genetic variation among and within different mango accessions at a significance threshold of P \u0026lt; 0.05 (Figure 3). \u0026nbsp;Results indicated that the most (87%) of molecular variation occurred within population, with lesser amount (13%) among populations, , which indicates mixing, gene flow, and shared ancestry rather than strict geographical separation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3.\u003c/strong\u003e Proportion of molecular variance among and within mango populations based on 55 SSR markers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster and Principal Coordinate Analysis (PCoA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Coordinate Analysis (PCoA) was performed to visualise allelic relationships across 126 mango accessions and to evaluate whether genetic clustering corresponded to geographic origin. Two independent PCoA plots were generated, one based on population origin, and one based on individual genotype distribution, to increase resolution of diversity patterns.\u003c/p\u003e\n\u003cp\u003eThe PCoA constructed at the population level (Figure 5) revealed moderate genetic dispersion among countries, with the first two axes explaining 53.83% of total variance (PC1 = 18.83%, PC2 = 35%). Populations from India, Brazil, Australia and Thailand overlapped extensively within the central coordinate space, suggesting shared genetic ancestry and historical exchange of material. Conversely, populations from Sri Lanka, Kenya and the Philippines appeared more isolated at the plot margins, indicating comparatively distinct genetic backgrounds or reduced introgression with other regions. Interestingly, Oman grouped close to several Asian and South American populations rather than forming an isolated cluster. This supports the notion that Omani mango germplasm has been influenced by historical introduction routes rather than evolving as an independent genetic lineage.\u003c/p\u003e\n\u003cp\u003eWhen individual genotypes were plotted (Figure 6), a broader distribution pattern emerged, reflecting high intra-population genetic variability, consistent with AMOVA results (87% within population variation). Despite general overlap among global cultivars, Omani accessions exhibited a semi-clustered pattern, reinforcing partial shared ancestry, yet also revealed scattered genotypes intermixing with Indian, Brazilian and Australian accessions. \u0026nbsp;Distinct outlier positioning of cultivars from Sri Lanka, Tahiti, Kenya and the Philippines highlights the presence of geographically unique alleles and possible independent selection histories. These divergent accessions may represent valuable genetic reservoirs for breeding programs that aim to introduce new quality, yield or disease-tolerance traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4.\u0026nbsp;\u003c/strong\u003ePopulation-based PCoA showing the distribution of accessions according to country of origin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5.\u0026nbsp;\u003c/strong\u003eAccession-level PCoA demonstrates individual genetic relationships among all 126 genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster analysis of mango accessions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA dendrogram depicting the genetic relationships among different mango accessions was created using the UPGMA method, which assigns equal weight to each pair and calculates the average. \u0026nbsp; \u0026nbsp;Figure 4 displays a dendrogram that was created using a matrix of basic matching coefficients. This dendrogram clearly shows the presence of three main clusters. \u0026nbsp;The first cluster (Cluster I) included cultivar Boribo from Kenya and cultivar Carabao Harbon from Philippines. The second cluster (Cluster II) has 2 subclusters in which one cluster contain accessions from Oman along with two accessions from USA (Haden) and Australia (Phoenix) which were clustered with Omani cultivars, and the second subcluster included 2 cultivars from Vietnam (Coconut and Xoai Tuong) and one cultivar from Indonesia (Kasturi). However, the third main cluster (Cluster III) contains all other mango cultivars from other origins. All clusters showed a mixture of poly-embryonic and mono-embryonic seed-type varieties in their cluster.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 6.\u003c/strong\u003e UPGM with arithmetic mean distance tree based on Nei genetic distances between 126 mango accessions using 55 SSR markers with bootstrap number for each node.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimated population structure among mangos cultivars\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation structure analysis using STRUCTURE v2.3.4 was performed for K = 1\u0026ndash;10 under the admixture model. The \u0026Delta;K method of Evanno et al. (2005) identified a strong peak at K = 2 (Figure 7), indicating that the 126 mango accessions form two major genetic sub-populations. The bar plot for K = 2 clearly separated Omani accessions (Sub-population II) from the majority of non-Omani genotypes (Sub-population I) (Figure 8).\u003c/p\u003e\n\u003cp\u003eSub-population I (red) contained accessions predominantly from Indonesia (6), Brazil (2), India (3), Kenya (7), Malaysia (8), Sri Lanka (12), Occupied Island (9), Tahiti (13), USA (15), Vietnam (16), Thailand (14), Philippines (11), Indochina (5), and Australia (1). These accessions shared high membership probability within a single ancestry group, suggesting a largely interconnected genetic background. In contrast, Sub-population II (green) consisted mainly of genotypes from Oman (10), forming a distinct genetic cluster with minimal introgression from other regions. The clean separation of Omani accessions suggests the development of a semi-independent genetic lineage, likely shaped by local selection, geographical isolation, or region-specific allele retention. Although most non-Omani accessions grouped together, several genotypes displayed minor admixture signals, indicating historical gene exchange between Omani and foreign populations. This is consistent with PCoA and UPGMA outputs, which also support a shared ancestry between Oman and select Asian and South American germplasm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 7.\u0026nbsp;\u003c/strong\u003eThe Evanno plot, derived from STRUCTURE HARVESTER, is used to detect the number of genetic clusters of mango accessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 8.\u003c/strong\u003e Estimated population structure for K=2-4 displayed with population Q-matrix. 10 runs at each K produced nearly similar population membership coefficients. The number in brackets represents the population number. Two sub-populations, or groups, are indicated by color; sub-population one (red) accessions from Indonesia (6), Brazil (2), India (3), Kenya (7), Malaysia (8), Sri Lanka (12), Occupied Island (9), Tahiti (13), USA (15), Vietnam (16), Thailand (14), Philippines (11), Indochina (5) and Australia (1) and sub-population two (green) accessions from Oman (10).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study applied highly polymorphic SSR markers to evaluate genetic diversity within a globally sourced mango collection maintained in Oman. The detection of 706 alleles across 55 loci, averaging 12.8 alleles per marker, demonstrates a high level of genome-wide variation. Comparable or lower allele counts have been reported in previous studies from Taiwan, Mexico, India and Brazil (Chiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; G\u0026aacute;lvez-L\u0026oacute;pez et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Begum et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Alves et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), whereas higher allelic richness was occasionally observed in elite cultivars or region-specific landrace panels (Vasugi et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ravishankar et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The current dataset therefore represents one of the most diverse mango germplasm collections studied in the Arabian Peninsula.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMarker informativeness and heterozygosity\u003c/h2\u003e\u003cp\u003ePIC values averaged 0.700, confirming the reliability of most markers for diversity assessment. High PIC values are indicative of multi-allelic loci and validate their suitability for genetic differentiation and germplasm fingerprinting. Observed heterozygosity exceeded expected heterozygosity at most loci, suggesting an over-representation of heterozygotes in the population. This pattern is consistent with mango\u0026rsquo;s predominantly cross-pollinated reproductive biology, historical mass seed propagation, and ongoing orchard intermixing, which together promote allelic reshuffling and prevent inbreeding (Viruel et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Schnell et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eGenetic variance distribution and population structure\u003c/h2\u003e\u003cp\u003eAMOVA results revealed that 87% of total variation resides within populations, with only 13% partitioned among populations. This indicates that genetic differences are predominantly found among individual accessions rather than between geographical groups. Such variance patterns are characteristic of long-domesticated, clonally propagated perennial fruit crops where recurrent gene flow and human-mediated movement mask regional separation (Miller and Gross, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis intrapopulation variation was further supported by STRUCTURE, UPGMA, and PCoA outputs, which did not strictly cluster accessions according to geographic origin. Instead, accessions from Asia, Australia, and the Americas frequently displayed admixture signals, while most Omani landraces clustered into a shared gene pool with a subset of foreign accessions. The grouping of Haden (USA), Phoenix (Australia), and Kasturi (Indonesia) within the Omani cluster suggests historical exchange of material, likely facilitated by maritime trade routes that linked Oman with mango-producing regions for centuries (Hammer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHigh intrapopulation diversity presents a valuable opportunity for selection and genetic enhancement. The observed heterozygosity excess suggests that desirable traits, such as fruit quality, yield stability, or wilt disease tolerance, may exist in hidden combinations that can be explored through controlled hybridisation. Conversely, the lack of strong regional partitioning means that vegetative lineages have intermixed over time, likely diluting ancestral purity but enriching the available gene pool.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the most extensive molecular assessment of mango germplasm maintained in Oman, integrating 126 accessions from 15 global origins using highly informative SSR markers. The detection of 706 alleles, high PIC values, and strong heterozygosity levels reinforce the suitability of SSR markers for diversity estimation, genotype discrimination, and germplasm authentication in mango.\u003c/p\u003e\u003cp\u003ePopulation genetic parameters and AMOVA results indicated that the majority of genetic variation exists within populations rather than among them (87% vs. 13%), reflecting long-term outcrossing, historic germplasm exchange, and human-mediated distribution across regions. STRUCTURE and PCoA analyses confirmed the presence of two main ancestral gene pools, with Omani accessions forming a distinct but partially admixed cluster relative to international material. Accessions from Sri Lanka, Kenya, Philippines, and Tahiti appeared genetically divergent, representing reservoirs of unique alleles that may be valuable for future breeding and trait improvement.\u003c/p\u003e\u003cp\u003eCollectively, these findings contribute to a clearer understanding of the molecular diversity landscape of mango in Oman and globally. The results provide strong justification for the targeted conservation of locally adapted cultivars, as well as for the incorporation of genetically distant accessions into breeding programs aimed at improving fruit quality, yield stability, environmental resilience, and wilt-disease tolerance. Future work integrating SNP-based genome scans, phenotypic trait evaluation, and association mapping will further deepen our understanding of trait\u0026ndash;gene relationships and accelerate improvement pathways for this economically and culturally significant fruit crop.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAli Al-Adawi: Writing \u0026ndash; review and editing; Funding acquisition; Project administration.AL-Ghaliya Al-Mamari: Writing \u0026ndash; review and editing; Data curation; Software and statistical analysis.Muna Al-Jabri: DNA extraction, genotyping, and laboratory analysis.Wafa Al-Shibli: DNA extraction, genotyping, and laboratory analysis.Muhammed Al-Jabri: Methodology and experimental work.All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the Ministry of Agriculture, Fisheries and Water Resources, Sultanate of Oman, for funding this research. Experimental work and laboratory analyses were conducted at the General Directorate of Agricultural and Livestock Research \u0026ndash; Tissue Culture and Biotechnology Research Laboratories, whose support, facilities, and technical assistance are deeply appreciated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eA.B. Addisalem, J\u0026eacute;r\u0026ocirc;me Duminil, Wouters, D., Bongers, F. and M.J.M. Smulders (2016). Fine-scale spatial genetic structure in the frankincense tree Boswellia papyrifera (Del.) Hochst. and implications for conservation. \u003cem\u003eTree Genetics \u0026amp; Genomes\u003c/em\u003e, 12(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Adawi, A.O., Deadman, M.L., Al Rawahi, A.K., Al Maqbali, Y.M., Al Jahwari, A.A., Al Saadi, B.A., Al Amri, I.S. and Wingfield, M.J. (2006). Aetiology and causal agents of mango sudden decline disease in the Sultanate of Oman. \u003cem\u003eEuropean Journal of Plant Pathology\u003c/em\u003e, 116, pp.247\u0026ndash;254.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Busaidi, S.S. (2008). A raiea fe tarkeh al omani (Remarkable in history of Oman). Anfal library. Vol.1, p.420.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Salmi, A.H. (1997). Tohfat al aiaean beserat ahel Oman (Masterpiece of biographical objects the people of Oman). Volume 1. Muscat library press. P.415.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlves, E., Neto, F., Santos, C., Ribeiro, I., de Melo, C., Holanda, I., de Souza, A. and Corr\u0026ecirc;a, R. (2016). Genetic diversity of mango accessions (\u003cem\u003eMangifera indica\u003c/em\u003e) using new microsatellite markers and morphological descriptors. \u003cem\u003eAustralian Journal of Crop Science\u003c/em\u003e, 10(9), pp.1281\u0026ndash;1287.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Washahi, L., Al Shamsi, H., Dillon, N., Sharma, N., Al Qamashoui, B. and Al Saadi, A. (2017). Molecular characterization of local Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) germplasm in Oman. \u003cem\u003eActa Horticulturae\u003c/em\u003e, (1183), pp.105\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzmat, M., Khan, A., Khan, I., Rajwana, I., Cheema, H. and Khan, A. (2016). Morphological characterization and SSR based DNA fingerprinting of elite commercial mango cultivars. \u003cem\u003ePakistan Journal of Agricultural Sciences\u003c/em\u003e, 53(2), pp.321\u0026ndash;330.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBajpai, A., Sharma, N., Srivastava, N., Rajan, S. and M, M. (2018). Intra- Cultivar Variability Endorsed by SSR Markers in Mango. \u003cem\u003eBiosciences, Biotechnology Research Asia\u003c/em\u003e, 15(1), pp.181\u0026ndash;186.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBally, I.S.E. (2006). \u003cem\u003eMangifera indica\u003c/em\u003e (mango), ver. 3.1. In: Elevitch, C.R. (ed.). Species Profiles for Pacific Island Agroforestry. Permanent Agriculture Resources (PAR), Hōlualoa, Hawai\u0026lsquo;i. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.traditionaltree.org\u003c/span\u003e\u003cspan address=\"http://www.traditionaltree.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBegum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2013). Molecular analysis of intracultivar polymorphism in \u0026lsquo;Peddarasam\u0026rsquo; Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) using microsatellite markers. \u003cem\u003eAsia Pacific Journal of Molecular Biology and Biotechnology\u003c/em\u003e, 21(3), pp.97\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBegum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2013). Molecular Analysis of Intracultivar Polymorphism of (Panchadarakalasa) Mango by Microsatellite Markers. \u003cem\u003eJordan Journal of Biological Sciences\u003c/em\u003e, 6(2), pp.127\u0026ndash;136.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBegum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2013). Microsatellite analysis of Intra-cultivar diversity in 'Chinnarasam' Mango from Andhra Pradesh, India. \u003cem\u003eAfrican Crop Science Journal\u003c/em\u003e, 21(2), pp.109\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBegum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2014). Morphological and Microsatellite Analysis of Intravarietal Heterogeneity in \u0026lsquo;Beneshan\u0026rsquo; Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.). \u003cem\u003eInternational Journal of Agricultural and Food Research\u003c/em\u003e, 3(2), pp.16\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBegum, H., Reddy, M., Malathi, S., Reddy, B., Narshimulu, G., Nagaraju, J. and Siddiq, E. (2014). Morphological and Microsatellite Analysis of Intravarietal Heterogeneity in \u0026lsquo;Beneshan\u0026rsquo; Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.). \u003cem\u003eInternational Journal of Agricultural and Food Research\u003c/em\u003e, 3(2), pp.452\u0026ndash;472.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBegum, H., Reddy, M.T., Malathi, S., Reddy, B.P., Arcahk, S., Nagaraju, J. and Siddiq, E.A. (2012). Molecular Analysis for Genetic Distinctiveness and Relationships of Indigenous Landraces with Popular Cultivars of Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) in Andhra Pradesh, India. \u003cem\u003eThe Asian and Australasian Journal of Plant Science and Biotechnology\u003c/em\u003e, 6(1), pp.24\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBooy, G., Hendriks, R.J.J., Smulders, M.J.M., Groenendael, J.M. and Vosman, B. (2000). Genetic Diversity and the Survival of Populations. \u003cem\u003ePlant Biology\u003c/em\u003e, 2(4), pp.379\u0026ndash;395.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBotstein, D., White, R. L., Skolnick, M., and Davis, R. W. (1980). Construction of a genetic linkage map in man using restriction fragment length polymorphisms. \u003cem\u003eAmerican Journal of Human Genetics\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(3), 314\u0026ndash;331.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharlesworth, B. (2009). Effective population size and patterns of molecular evolution and variation. \u003cem\u003eNature Reviews Genetics\u003c/em\u003e, 10(3), pp.195\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChiang, Y., Tsai, C., Chen, Y., Lee, S., Chen, C., Lin, Y. and Tsai, C. (2012). Development and characterization of 20 new polymorphic microsatellite markers from \u003cem\u003eMangifera indica\u003c/em\u003e (Anacardiaceae). \u003cem\u003eAmerican Journal of Botany\u003c/em\u003e, 99(3), pp.e117-e119.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChunwongse, C., Phumichai, C., Tongyoo, P., Juejun, N. and Chunwongse, J. (2015). Development of di-nucleotide microsatellite markers and construction of genetic linkage map in Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.). \u003cem\u003eSongklanakarin Journal of Science and Technology\u003c/em\u003e, 37(2), pp.119\u0026ndash;127.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoppi, A., Cecchi, L., Selvi, F. and Raffaelli, M. (2010). The Frankincense tree (Boswellia sacra, Burseraceae) from Oman: ITS and ISSR analyses of genetic diversity and implications for conservation. \u003cem\u003eGenetic Resources and Crop Evolution\u003c/em\u003e, 57(7), pp.1041\u0026ndash;1052.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDillon, N., Bally, I., Hucks, L., Wright, C., Innes, D. and Dietzgen, R. (2013). Implementation of SSR markers in mango breeding in Australia. \u003cem\u003eActa Horticulturae\u003c/em\u003e, (992), pp.259\u0026ndash;267.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDillon, N., Bally, I., Wright, C., Hucks, L., Innes, D. and Dietzgen, R. (2013). Genetic diversity of the Australian National Mango Genebank. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, 150, pp.213\u0026ndash;226.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDos Santos Ribeiro, I., Lima Neto, F. and Santos, C. (2012). Allelic database and accession divergence of a Brazilian mango collection based on microsatellite markers. \u003cem\u003eGenetics and Molecular Research\u003c/em\u003e, 11(4), pp.4564\u0026ndash;4574.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuval, M., Bunel, J., Sitbon, C. and Risterucci, A. (2005). Development of microsatellite markers for Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.). \u003cem\u003eMolecular Ecology Notes\u003c/em\u003e, 5(4), pp.824\u0026ndash;826.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuval, M., Risterucci, A., Calabre, C., Le Bellec, F., Bunel, J. and Sitbon, C. (2006). Genetic diversity of Caribbean mangoes (\u003cem\u003eMangifera indica\u003c/em\u003e L.) using microsatellite markers. \u003cem\u003eActa Horticulturae\u003c/em\u003e, (820), pp.183\u0026ndash;188.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEarl, D.A. and vonHoldt, B.M. (2012). STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. \u003cem\u003eConservation Genetics Resources\u003c/em\u003e, 4, pp.359\u0026ndash;361.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdwards, C.E., Parchman, T.L. and Weekley, C.W. (2012). Assembly, Gene Annotation and Marker Development Using 454 Floral Transcriptome Sequences in Ziziphus Celata (Rhamnaceae), a Highly Endangered, Florida Endemic Plant. \u003cem\u003eDNA Research\u003c/em\u003e, 19(1), pp.1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvanno, G., Regnaut, S. and Goudet, J. (2005). Detecting the number of clusters of individuals using the software structure: a simulation study. \u003cem\u003eMolecular Ecology\u003c/em\u003e, 14, pp.2611\u0026ndash;2620.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFatimah, F., Husni, A., Kosmiatin, M., Karsinah, K. and Baroya, M. (2016). Characterization of zygotic and nucellar embryo of six Indonesian mango cultivars using molecular markers. \u003cem\u003eAnnales Bogorienses\u003c/em\u003e, 20(2), pp.69\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Mart\u0026iacute;nez, S.C., Krutovsky, K.V. and Neale, D.B. (2006). Forest-tree population genomics and adaptive evolution. \u003cem\u003eNew Phytologist\u003c/em\u003e, 170(2), pp.227\u0026ndash;238.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGora, J.S., Kumar, R. and Chet Ram, S.V. (2018). Biochemical responses of monoembryonic and polyembryonic seedlings of mango rootstocks under salt stress conditions. \u003cem\u003eInternational Journal of Chemical Studies\u003c/em\u003e, 6(6), pp.2199\u0026ndash;2203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026aacute;lvez-L\u0026oacute;pez, D., Hern\u0026aacute;ndez-Delgado, S., Gonz\u0026aacute;lez-Paz, M., Becerra-Leor, E., Salvador-Figueroa, M. and Mayek-P\u0026eacute;rez, N. (2009). Genetic analysis of mango landraces from Mexico based on molecular markers. \u003cem\u003ePlant Genetic Resources\u003c/em\u003e, 7(3), pp.244\u0026ndash;251.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHammer, K., Gebauer, J., Al Khanjari, S. and Buerkert, A. (2009). Oman at the cross-roads of inter-regional exchange of cultivated plants. \u003cem\u003eGenetic Resources Crop Evolution\u003c/em\u003e, 56, pp.547\u0026ndash;560.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHonsho, C., Nishiyama, K., Eiadthong, W. and Yonemori, K. (2005). Isolation and characterization of new microsatellite markers in Mango (\u003cem\u003eMangifera indica\u003c/em\u003e). \u003cem\u003eMolecular Ecology Notes\u003c/em\u003e, 5(1), pp.152\u0026ndash;154.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHtway, H., Soe, A., Myint, M., Yi, K., Chan, N., Kyaing, M., Hlaing, N., Aung, S., Phyo, S., Htwe, Y., Maung, C. and Yu, S. (2018). Genetic Diversity and Genetic Uniqueness of Indigenous Myanmar Mango (Sein Ta Lone) Cultivar in Kyaukse District. \u003cem\u003eInternational Journal of Plant Biology \u0026amp; Research\u003c/em\u003e, 6(3), p.1089.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoop Ouborg, N., Angeloni, F. and Vergeer, P. (2009). An essay on the necessity and feasibility of conservation genomics. \u003cem\u003eConservation Genetics\u003c/em\u003e, 11(2), pp.643\u0026ndash;653.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarihaloo, J.L., Dwivedi, Y.K., Archak, S. and Galkwab, A.B. (2003). Analysis of genetic diversity of Indian Mango cultivars using RAPD markers. \u003cem\u003eJournal of Horticultural Science and Biotechnology\u003c/em\u003e, 78, pp.285\u0026ndash;289\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar, M., Ponnuswami, V., Nagarajan, P., Jeyakumar, P. and Senthil, N. (2013). Molecular characterization of ten mango cultivars using simple sequences repeat (SSR) markers. \u003cem\u003eAfrican Journal of Biotechnology\u003c/em\u003e, 12(47), pp.6568\u0026ndash;6573.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamy, T., Laroche, F., David, P., Massol, F. and Jarne, P. (2017). The contribution of species-genetic diversity correlations to the understanding of community assembly rules. \u003cem\u003eOikos\u003c/em\u003e, 126(6), pp.759\u0026ndash;771.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawton-Rauh, A. (2008). Demographic processes shaping genetic variation. \u003cem\u003eCurrent Opinion in Plant Biology\u003c/em\u003e, 11(2), pp.103\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevin, D.A. and Kerster, H.W. (1974). Gene flow in seed plants. \u003cem\u003eEvolutionary Biology\u003c/em\u003e, 7, pp.139\u0026ndash;220.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLitt, M. and Luty, J. (1989). A Hypervariable Microsatellite Revealed by In Vitro Amplification of a Dinucleotide Repeat within the Cardiac Muscle Actin Gene. \u003cem\u003eAmerican Journal of Human Genetics\u003c/em\u003e, 44(3), pp.397\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, K. and Muse, S. (2005). PowerMarker: an integrated analysis environment for genetic marker analysis. \u003cem\u003eBioinformatics\u003c/em\u003e, 21(9), pp.2128\u0026ndash;2129.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMAF. (1990). Annual report of Agriculture research 1989. Ministry of Agriculture \u0026amp; Fisheries. Sultanate of Oman. p.189.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMAF, (2014). Agricultural census 2012/13. Ministry of Agriculture and Fisheries, Sultanate of Oman. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.moa.gov.om/arabic/index.asp\u003c/span\u003e\u003cspan address=\"http://www.moa.gov.om/arabic/index.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMAF. (2018). Data base of agriculture sector: Agricultural production 2017. Ministry of Agriculture and Fisheries, Sultanate of Oman. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.moa.gov.om/arabic/index.asp\u003c/span\u003e\u003cspan address=\"http://www.moa.gov.om/arabic/index.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMessmer, M., Melchinger, A., Boppenmaier, J., Brunklaus-Jung, E. and Herrmann, R. (1992). Relationships among Early European Maize Inbreds: I. Genetic Diversity among Flint and Dent Lines Revealed by RFLPs. \u003cem\u003eCrop Science\u003c/em\u003e, 32(6), p.1301.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiles, S.B. (1896). Journal of an Excursion in Oman, in South-East Arabia. \u003cem\u003eThe Geographical Journal\u003c/em\u003e, 7, pp.522\u0026ndash;537.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller, A.J. and Gross, B.L. (2011). From forest to field: Perennial fruit crop domestication. \u003cem\u003eAmerican Journal of Botany\u003c/em\u003e, 98(9), pp.1389\u0026ndash;1414.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoran, P. (2002). Current conservation genetics: building an ecological approach to the synthesis of molecular and quantitative genetic methods. \u003cem\u003eEcology of Freshwater Fish\u003c/em\u003e, 11(1), pp.30\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMukherjee, S.K. and Litz, R.E. (2009). Introduction: Botany and Importance. In: Litz RE. (ed.) The Mango: Botany, Production and Uses. 2nd edition. CAB International, Wallingford, UK, pp. 1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNazish, T., Shabbir, G., Ali, A., Sami-ul-Allah, S., Naeem, M., Javed, M., Batool, S., Arshad, H., Hussain, S., Aslam, K., Seher, R., Tahir, M. and Baber, M. (2017). Molecular diversity of Pakistani Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) varieties based on microsatellite markers. \u003cem\u003eGenetics and Molecular Research\u003c/em\u003e, 16(2), pp.1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNei, M. (1978). Estimation of average heterozygosity and genetic distance from a small number of individuals. \u003cem\u003eGenetics\u003c/em\u003e, 89(3), pp.583\u0026thinsp;\u0026ndash;\u0026thinsp;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandit, S.S., Mitra, S., Giri, A.P., Pujari, K.H., Patil, B.P. and Jambhale, N.D. (2007) Genetic diversity analysis of mango cultivars using inter simple sequence repeat markers. \u003cem\u003eCurrent Science\u003c/em\u003e, 93, pp.1135\u0026ndash;1141.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanhwar, A., Nizamani, S.M., Abbasi, Q.D., Jiskani, M.M. and Khuhro, R.D. (2008). Response of different varieties to sudden mango decline disease in Sindh, Pakistan. P.17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeakall, R. and Smouse, P. (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research\u0026ndash;an update. \u003cem\u003eBioinformatics\u003c/em\u003e, 28(19), pp.2537\u0026ndash;2539.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eP\u0026eacute;rez, V., Herrero, M. and Hormaza, J.I., (2019). Pollen performance in Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L., Anacardiaceae): Andromonoecy and effect of temperature. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, 253, pp.439\u0026ndash;446.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerrier X., Jacquemoud-Collet J.P. (2006) DARwin software \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://darwin.cirad.fr/\u003c/span\u003e\u003cspan address=\"http://darwin.cirad.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePloetz, R.C. and Freeman, S. (2009). Foliar, floral and soil-borne diseases. In: Litz RE. (ed.) The Mango: Botany, Production and Uses. 2nd edition. CAB International, Wallingford, UK, pp.281\u0026ndash;325.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePowell, W., Machray, G.C. and Provan, J. (1996). Polymorphism revealed by simple sequence repeats. \u003cem\u003eTrends in Plant Science\u003c/em\u003e, 1(7), pp.215\u0026ndash;222.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePritchard, J., Stephens, M. and Donnelly, P. (2000). Inference of population structure using multilocus genotype data. \u003cem\u003eGenetics\u003c/em\u003e, 155(2), pp.945\u0026ndash;959.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRavishankar, K., Mani, B., Anand, L. and Dinesh, M. (2011). Development of new microsatellite markers from Mango (\u003cem\u003eMangifera indica\u003c/em\u003e) and cross-species amplification. \u003cem\u003eAmerican Journal of Botany\u003c/em\u003e, 98(4), pp.e96-e99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRavishankar, K., Padmakar, B., Lavanya, B., Mani, B. and Dinesh, M. (2017). Development and characterization of microsatellite loci from Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.). \u003cem\u003eIndian Journal of Biotechnology\u003c/em\u003e, 16(2), pp.250\u0026ndash;253.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRavishankar, K.V., Anand, L. and Dinesh, M.R. (2000). Assessment of genetic relatedness among mango cultivars of India using RAPD markers. J\u003cem\u003eournal of Horticultural Science \u0026amp; Biotechnology\u003c/em\u003e, 75(2), pp.198\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRatnam, W., Rajora, O.P., Finkeldey, R., Aravanopoulos, F., Bouvet, J.-M., Vaillancourt, R.E., Kanashiro, M., Fady, B., Tomita, M. and Vinson, C. (2014). Genetic effects of forest management practices: Global synthesis and perspectives. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, 333, pp.52\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRocha, A., Luiz, C.S., Tania, F.S., Cosme, D.C. and de Dalmo, L.S. (2012). Genetic diversity of \u0026lsquo;Uba\u0026rsquo; mango tree using ISSR markers. \u003cem\u003eMolecular Biotechnology\u003c/em\u003e, 50(2), pp.108\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenberg, N. (2003). Distruct: a program for the graphical display of population structure. \u003cem\u003eMolecular Ecology Notes\u003c/em\u003e, 4(1), pp.137\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSales, E. and Butardo, N. (2017). SSR markers for Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) cultivar identification and genetic characterization. \u003cem\u003ePhilippine Journal of Crop Science\u003c/em\u003e, 42(3), pp.30\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchnell, R., Brown, S., Olano, C., Meerow, A., Campbell, R. and Kuhn, D. (2006). Mango Genetic Diversity Analysis and Pedigree Inferences for Florida Cultivars using Microsatellite Markers. \u003cem\u003eAmerican Society for Horticultural Science\u003c/em\u003e, 131(2), pp.214\u0026ndash;224.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchnell, R., Olano, C., Quintanilla, W. and Meerow, A. (2005). Isolation and characterization of 15 microsatellite loci from mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) and cross-species amplification in closely related taxa. \u003cem\u003eMolecular Ecology Notes\u003c/em\u003e, 5(3), pp.625\u0026ndash;627.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShamili, M., Fatahi, R. and Hormaza, J. (2012). Characterization and evaluation of genetic diversity of Iranian Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L., Anacardiaceae) genotypes using microsatellites. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, 148, pp.230\u0026ndash;234.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShyam Sundar Sharma, Islam, A., Vivek Kumar Singh, Madan Singh Negi and Shashi Bhushan Tripathi (2017). Genetic diversity, population structure and association study using TE-AFLP markers in Pongamia pinnata (L.) Pierre germplasm. \u003cem\u003eTree Genetics \u0026amp; Genomes\u003c/em\u003e, 13(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh, S. and Bhat, K.V. (2009). Molecular characterization and analysis of geographical differentiation of Indian Mango (Mangifera Indica L.) germplasm. \u003cem\u003eActa Horticulturae\u003c/em\u003e, 839(839), pp.599\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSurapaneni, M., Vemireddy, L., Begum, H., Purushotham Reddy, B., Neetasri, C., Nagaraju, J., Anwar, S. and Siddiq, E. (2013). Population structure and genetic analysis of different utility types of Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L.) germplasm of Andhra Pradesh state of India using microsatellite markers. \u003cem\u003ePlant Systematics and Evolution\u003c/em\u003e, 299(7), pp.1215\u0026ndash;1229.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakezaki, N., and Nei, M. (1996). Genetic Distance and reconstruction of Phylogenetic trees from microsatellite DNA. \u003cem\u003eGenetics\u003c/em\u003e, 144(1), pp.389\u0026ndash;399.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsai, C., Chen, Y., Chen, C., Weng, I., Tsai, C., Lee, S., Lin, Y. and Chiang, Y. (2013). Cultivar identification and genetic relationship of Mango (\u003cem\u003eMangifera indica\u003c/em\u003e) in Taiwan using 37 SSR markers. \u003cem\u003eScientia Horticulturae\u003c/em\u003e, 164, pp.196\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Wyk M., Al Adawi, A.O., Khan, I.A., Deadman, M.L., Al Jahwari, A.A., Wingfield, B.D., Ploetz, R. and Wingfield, M.J. (2007). \u003cem\u003eCeratocystis manginecans\u003c/em\u003e sp. Nov., causal agent of a destructive mango wilt disease in Oman and Pakistan. \u003cem\u003eFungal Diversity\u003c/em\u003e, 27, pp.213\u0026ndash;230.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Zonneveld, M., I. Dawson, E. Thomas, X. Scheldeman, J. van Etten, J. Loo and J. I. Hormaza (2014). Application of molecular markers in spatial analysis to optimize in situ conservation of plant genetic resources. Genomics of plant genetic resources, \u003cem\u003eSpringer\u003c/em\u003e: 67\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVasugi, C., Dinesh, M., Sekar, K., Shivashankara, K., Padmakar, B. and Ravishankar, K. (2012). Genetic diversity of indigenous mango accessions (Appemidi) of the Western Ghats for certain fruit characteristics. \u003cem\u003eCurrent Science\u003c/em\u003e, 103(2), pp.199\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVavilov, N.I. (1992). The phyto-geographical basis for plant breeding. In: \u003cem\u003eOrigin and geography of cultivated plants\u003c/em\u003e. Cambridge: Cambridge University Press, pp.316\u0026ndash;366.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerde, I., Abbott, A.G., Scalabrin, S., Jung, S., Shu, S., Marroni, F., Zhebentyayeva, T., Dettori, M.T., Grimwood, J., Cattonaro, F., Zuccolo, A., Rossini, L., Jenkins, J., Vendramin, E., Meisel, L.A., Decroocq, V., Sosinski, B., Prochnik, S., Mitros, T. and Policriti, A. (2013). The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication, and genome evolution. \u003cem\u003eNature Genetics\u003c/em\u003e, [online] 45(5), pp.487\u0026ndash;494.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eViruel, M., Escribano, P., Barbieri, M., Ferri, M. and Hormaza, J. (2005). Fingerprinting, embryo type and geographic differentiation in Mango (\u003cem\u003eMangifera indica\u003c/em\u003e L., Anacardiaceae) with microsatellites. \u003cem\u003eMolecular Breeding\u003c/em\u003e, 15(4), pp.383\u0026ndash;393.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWahdan, M., Abdelsalam, A., El-Naggar, A. and Hussein, M. (2011). Preliminary Horticultural Studies to Describe and Identify Of Two New Egyptian Mango Strains Using DNA Fingerprint. \u003cem\u003eJournal of American Science\u003c/em\u003e, 7(2), pp.641\u0026ndash;650.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright, S. (1969). Evolution and the Genetics of Populations: The Theory of Gene Frequencies. The University of Chicago Press, Chicago, Illinois.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright, S. (1965). The Interpretation of Population Structure by F-Statistics with Special Regard to Systems of Mating. \u003cem\u003eEvolution\u003c/em\u003e, 19(3), p.395.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZwemer, S.M. (1902). Three Journeys in Northern Oman. \u003cem\u003eThe Geographical Journal\u003c/em\u003e, 19, pp.54-\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mangifera indica, SSR markers, genetic diversity, population structure, AMOVA, germplasm conservation, Omani mango accessions, PCoA, STRUCTURE analysis","lastPublishedDoi":"10.21203/rs.3.rs-8255746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8255746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eMangifera indica\u003c/em\u003e L. (mango) is a major tropical fruit tree valued for its nutritional, economic, and cultural importance. Understanding the genetic diversity within mango germplasm is essential for conservation and for selecting parents in breeding programs. This study assessed the genetic variation of 126 mango accessions (378 samples) maintained in the National Mango GenBank in Oman, originating from 15 geographical regions across the Middle East, Africa, Asia, Australia, and the Americas. A total of 55 polymorphic SSR loci generated 706 alleles, averaging 12.8 alleles per locus, with allele number ranging from 4 (MiSHRS-23, MGDSSR17) to 25 (MiSHRS-18). Polymorphic information content (PIC) values ranged from 0.184 (SSR22) to 0.903 (LMMA01), indicating high marker informativeness. Expected and observed heterozygosity ranged between 0.024\u0026ndash;0.496 and 0.048\u0026ndash;0.992, respectively. Fixation indices (Fis, Fit, Fst) averaged \u0026minus;\u0026thinsp;0.978, 0.430, and 0.714, reflecting excess heterozygosity within accessions and strong differentiation among populations. Molecular variance partitioned approximately 87% of diversity among populations and 13% within populations. Cluster, PCoA, and STRUCTURE analyses separated accessions into two major genetic groups, with Oman forming a distinct cluster alongside a small number of foreign cultivars. This study demonstrates extensive genetic diversity in Oman\u0026rsquo;s mango germplasm and highlights SSR markers as an effective tool for distinguishing genotypes. The findings provide a critical foundation for cultivar preservation, parent selection, and future genome-wide association and marker-assisted breeding in mango.\u003c/p\u003e","manuscriptTitle":"Global Genetic Diversity and Population Structure of Mango (Mangifera indica L.) Germplasm Conserved in Oman Revealed Through SSR Markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 10:21:26","doi":"10.21203/rs.3.rs-8255746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-17T09:28:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T23:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61169521669042324835000024047769261393","date":"2025-12-15T17:52:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28659169343181356656345741487362127754","date":"2025-12-13T03:05:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-11T09:04:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34440355495941130738144838607206576168","date":"2025-12-11T08:51:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7973472128804334157404400767004506661","date":"2025-12-10T05:35:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T17:18:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T04:31:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-03T04:29:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2025-12-02T04:14:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a802536d-785d-4041-b9c9-f1a0623ec3e5","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:10:31+00:00","versionOfRecord":{"articleIdentity":"rs-8255746","link":"https://doi.org/10.1007/s10722-026-02730-x","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2026-01-21 15:57:33","publishedOnDateReadable":"January 21st, 2026"},"versionCreatedAt":"2025-12-15 10:21:26","video":"","vorDoi":"10.1007/s10722-026-02730-x","vorDoiUrl":"https://doi.org/10.1007/s10722-026-02730-x","workflowStages":[]},"version":"v1","identity":"rs-8255746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8255746","identity":"rs-8255746","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}