Genetic diversity and structure analysis of melon accessions using URP, SRAP, and CDDP markers

Genetic diversity and structure analysis of melon accessions using URP, SRAP, and CDDP 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 Genetic diversity and structure analysis of melon accessions using URP, SRAP, and CDDP markers Rebwar Rafat Aziz, Nawroz Tahir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1640623/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Melon ( Cucumis melo L.) is an ancient and indigenous crop that grows in Iraq's Kurdistan region, and research into its genetic diversity is needed to improve this important culinary plant while also promoting a healthy diet. This study assessed the genetic diversity of 57 melon accessions using three types of molecular markers, namely universal rice primer (URP), sequence-related amplified polymorphism (SRAP), and conserved DNA derived polymorphism (CDDP) markers. The URAP + IRAP, SRAP, and CDDP primers yielded 143, 125, and 131 polymorphic alleles, respectively. The average polymorphic information content (PIC) was 0.29, 0.26, and 0.32 for URAP + IRAP, SRAP, and CDDP, respectively. Based on the average values of polymorphic bands per primer and PIC of three types of markers, the order of the power of discrimination of melon accession was as follows: URP + IRAP > CDDP > SRAP. The URP-4R, Me1-Em2, and MYB-1 primers had the highest Shannon information index values. Based on the Ward method-based cluster and STRUCTURE analysis, URAP + IRAP and SRAP markers grouped 57 accessions into two primary clusters with various sub-clusters, whereas CDDP markers divided 57 accessions into three groups. The genetic distances between accessions ranged from 0.32 to 0.84 for URP + IRAP, 0.20 to 0.78 for SRAP, and 0.25 to 0.73 for CDDP. The analysis of molecular variance revealed an increase in genetic variation within groups, as well as significant gene exchange between populations. In SRAP analysis, substantial genetic distinctiveness was identified among populations. However, in CDDP analysis, high Shannon's information index and predicted heterozygosity were observed among populations. The outcomes of this study indicated a high level of variability in Iraqi melon germplasm, which must be preserved and included in improvement programs for this ancient crop. Cucumis melo DNA-markers Diversity indices Genetic variation Clustering Genetic structure Figures Figure 1 Figure 2 Introduction Cucumis genus belongs to the Cucurbitaceae family. Cucumber ( C. sativus ) and melon ( C. melo ) are two economically valuable species (Maleki et al. 2018 ). Melon is a diploid plant (2n = 2x) with 24 chromosomes (Paris et al. 2012 ). It may have been domesticated primarily for the nutritional value of its seeds, and it then went through a period of great diversity. Because of the species' wide morphological variety, plant taxonomists have proposed intraspecific classifications based mostly on fruit characteristics (Nuñez-Palenius et al. 2008 ). Melon is a highly polymorphic species with a wide range of leaf, flower, and fruit traits. Recently, 19 intraspecific horticultural groups of Cucumis melo was found byPitrat ( 2017 ), including agrestis, kachri, chito, tibish, acidulus, momordica, conomon, makuwa, chinensis, flexuosus, chate, dudaim, chandalak, indicus, ameri, cassaba, ibericus, cantalupensis and inodorus. Plant genetic resource organization entails the establishment, enrichment, appraisal, documenting, and protection of plant collections. Understanding the level of genetic variability and the interactions between different local genotypes is beneficial for both identifying and effectively conserving genetic resources, and improving the effectiveness of breeding programs. The degree of genetic diversity information will assist breeders in selecting favorite parents in the hybrid creation of optimal cultivars while also conserving population diversity (Govindaraj et al. 2015 ; Hill 2001 ; Tian et al. 2015 ). To address this issue, diversity is a primary priority in species. Different morphological, phenological, and physiological approaches(Pandey et al. 2021 ; Saputro et al. 2020 ; Singh et al. 2020 ) have been used to assess genetic variation in melon. Morphological analyses are the most essential criteria in the initial assessment of genetic variation for melon genotype and cultivar categorization and identification. Fruit species' genetic variation is important not just for preserving appropriate genetic diversity for breeding programs, but also for species' long-term survival. To measure genetic diversity, many marker systems, including morphological, biochemical, and molecular (DNA) markers, can be used. Melon genetic diversity has been studied using a diverse of markers, ranging from morphological(Bagheriyan et al. 2015 ; Dantas et al. 2015 ; Merheb et al. 2020 ; Seungbum et al. 2020 ) and physicochemical(Javier Obando et al. 2008; Manchali et al. 2021 ; Pandey et al. 2021 ) to molecular DNA markers, such as amplified fragment length polymorphism (Ibrahim M. Haggag et al. 2021 ), sequence repeat primers (Carvalho et al. 2017 ), single nucleotide polymorphisms (Kishor et al. 2020 ), inter-simple sequence repeat (Maleki et al. 2018 ), and start codon targeted(Ibrahim M. Haggag et al. 2021 ). With recent developments in genomics research, DNA markers have assumed far greater significance. Various marker systems can be used to study the genetic diversity in plants at the species, population, germplasm accessions, and individual genotype levels, such as pure lines or clones. The DNA diversity of most eukaryotic and prokaryotic genomes was investigated using a polymerase chain reaction (PCR) method with universal rice primers (URP), with useful uses in taxonomic and phylogenic research, as well as inter- and intra-species population genotypic screening of individuals (Kang et al. 2003 ). Due to the use of lengthy primers and higher annealing temperatures, URP-PCR offers an advantage over randomly amplified polymorphic DNA and arbitrarily primed polymerase chain reaction approaches. The sequence-related amplified polymorphism (SRAP) markers discovered byLi and Quiros(2001) have been widely used in various genetic research projects due to their simplicity, dependability, productivity, and genome-wide coverage. SRAP markers have been effectively used in a variety of studies on the characterization of plant genetic resources and natural populations throughout the last decade (Zagorcheva et al. 2020 ). The use of CDDP molecular markers is a unique method based on conserved DNA sequences. In this technology, single primers are designed and paired with conserved regions of functional genes to amplify the entire plant genome and generate informative bands (Ahmed et al. 2021 ; Collard and Mackill 2009 ). The northern part of Iraq contains abundant melon genetic resources with different physical traits. Regardless, no research has been conducted to demonstrate the genetic variability of Iraqi native melon accessions. The current study is the first to assess genetic variability among melon accessions using URP + IRAP and gene-targeted (CDDP) markers. In this investigation, fifty-seven C. melo accessions were collected and analyzed for genetic diversity and population structure using three distinct types of molecular markers. The findings may aid in the conservation of genetic variation and encourage its use in breeding programs for genetic improvement. Materials And Methods Plant materials The research was carried out on 57 local melon accessions gathered in northern Iraq (Table 1 ). These accessions have been planted throughout Iraq's northern areas, including Sulaimani, Erbil, and Duhok provinces. The melon accessions belonged to six horticultural groups of Cucumis melo L., based on the botanical classification of Pitrat ( 2017 ) and Raghami et al. ( 2014 ), including cantalupensis, inodorus, Ameri, Dudiam, Charentais, and Chandalak. Table 1 Type, fruit shape, rind color and flesh color of different accessions used in this investigation. Accessions Melon type Fruit shape Rind color Flesh color AN1 Cantalupensis Round Orange with light green lines Orange AN2 Cantalupensis Round Orange with yellow lines Light orange AN3 Inodorus Elliptical Orange-grey-yellow Orange AN4 Ameri Round Brown-orange Light orange AN5 Cantalupensis Oblate Orange-green Light orange AN6 Dudaim Round Orange with light green lines Light green AN7 Ameri Round Green-orange Light green AN8 Ameri Elliptical Orange-brown-green Orange AN9 Inodorus Elongated Orange to brown Light orange AN10 Dudaim Oblate Yellow with some brown lines Light orange AN11 Cantalupensis Oblate Orange-brown with green lines Orange AN12 Cantalupensis Oblate Brown with light green lines Orange AN13 Dudaim Oblate Yellow Orange AN14 Dudaim Elliptical Green-brown with light green lines Orange AN15 Inodorus Round Green-orange Orange AN16 Dudaim Round Orange-green Light green AN17 Cantalupensis Oblate Orange with green lines Light green AN18 Cantalupensis Round Green-brown Orange AN19 Cantalupensis Elongated Green-brown with light green lines Orange AN20 Cantalupensis Oblate Green-yellow Orange AN21 Cantalupensis Oblate Yellow-brown-orange Yellow AN22 Dudaim Oblate Yellow with brown lines Yellow AN23 Dudaim Round Orange Yellow AN24 Charentais Oblate Green-dark orange Light orange AN25 Chandalak Elongated Green-orange-yellow Orange AN26 Dudaim Oblate Green-brown Yellow AN27 Cantalupensis Round Brown-yellow with green lines Orange AN28 Chandalak Round Orange with green lines Orange AN29 Cantalupensis Round Brown-green Light orange AN30 Cantalupensis Oblate Orange to brown and green Orange AN31 Cantalupensis Round Orange to brown-yellow with yellow lines Light orange AN32 Ameri Elliptical Brown-yellow with some brown lines Orange AN33 Dudaim Round Orange-brown with yellow lines Orange AN34 Cantalupensis Elliptical Orange-green Light orange AN35 Inodorus Elliptical Orange-green Orange AN36 Dudaim Round Yellow Orange AN37 Dudaim Round Yellow-green Light orange AN38 Dudaim Oblate Orange with yellow lines Orange AN39 Cantalupensis Round Orange with light green lines Light orange AN40 Cantalupensis Oblate Orange-yellow with green lines Orange AN41 Dudaim Elliptical Orange-green Orange AN42 Cantalupensis Round Orange-green Light orange AN43 Dudaim Elliptical Yellow-orange Orange AN44 Dudaim Oblate Orange-yellow with light green lines Orange AN45 Cantalupensis Round Orange Light orange AN46 Cantalupensis Elliptical Brown-green-yellow Orange AN47 Dudaim Elliptical Orange-yellow Orange AN48 Dudaim Round Orange-yellow Orange AN49 Cantalupensis Oblate Orange Light orange AN50 Chandalak Oblate Orange-yellow with light green lines Orange AN51 Cantalupensis Round Orange with yellow lines Orange AN52 Dudaim Elliptical Orange Orange AN53 Cantalupensis Oblate Orange-brown-green Orange AN54 Cantalupensis Oblate Green-brown Orange AN55 Cantalupensis Oblate Orange Orange AN56 Cantalupensis Round Green-orange Orange AN57 Cantalupensis Round Orange Light orange Sampling and DNA extraction The procedure described by Alaaddin et al. (2022), with some modifications, was used to extract DNA. After eight weeks of sowing, fresh leaves from melon plants in the field were plucked and crushed with liquid nitrogen. A volume of 1.30 mL of lysis buffer [0.50% (w/v) SDS, 8.00% (w/v) PVP, 0.25 M NaCl, 0.025 M EDTA, and 0.2 M Tris-base] and 9 µL RNase (10 mg/mL) was used to lyse powdered leaf material. The tubes were then incubated for 74 min at 64°C and inverted 15 times. For 7 min, the tube samples were cooled at room temperature. A volume of 290 µL of 5 M potassium acetate (pH 6.5) was poured, stirred, and chilled for at least 9 min. For 18 min, the samples were centrifuged at 16200 rpm. The upper phase was collected and relocated Eppendorf tube (2 mL). A volume of 1.10 mL of GTE buffer (2M guanidine thiocyanate dissolved in 75% ethanol) was gently introduced into the upper phase. The mixture (850 µL) was poured on a spin column and incubated for 1 min at 21°C. The solution was centrifuged at 8300 rpm for 6 min, and the flow solution was discarded. A volume of 550 µL of NTEH washing buffer (10 mM NaCl, 10 mM Tris-base pH 6.5, and 80% ethanol) were poured into the spin-column. After centrifuging the column for 6 min at 8200 rpm, the flow solution was withdrawn. The spin column was washed a second time with 550 L of NTE buffer. The flow solution was removed after centrifuging the spin column at 8200 rpm for 5 min. To dry the spin column, it was centrifuged for 6 min at 11100 rpm. The spin column was placed in a new Eppendorf 1.5 mL tube, which was then filled with 108 µL of elution buffer and incubated at room temperature for 5 minutes. The eluted DNA was collected and stored at -20°C by centrifugation at 9500 rpm for 5 min. A 1.1% agarose gel and a nanodrop spectrophotometer (NanoPLUS-MAANLAB AB, Sweden) were used to assess the quality and quantity of isolated DNA. Polymerase chain reaction For genetic diversity analysis in melon accessions, thirteen URAP primers (Kaki et al. 2020 ), 18 SRAP primers (Ahmed et al. 2021 ; Aneja et al. 2013 ), and 12 CDDP primers (Ahmed et al. 2021 ; Collard and Mackill 2009 ) were applied. Five µL of template DNA, 10 liters of PCR master mix (AddStart Taq Master, Addbio, Korea), 3.8 µL of primer (Addbio, Korea), and 6.2 µL of deionized water were used for the PCR amplification reaction. For URAP and CDDP primers (Tables S1), the following conditions were used in an Applied Biosystems PCR machine: initial denaturation at 94°C for 9 min, followed by 38 cycles of denaturation at 94°C for 1 min, annealing at a particular temperature (depending on primer) for 1 min, and extension at 72°C for 2 min. A final extension cycle of 9 min at 72°C was performed. For the SRAP primers (Table S1), PCR conditions included initial denaturation at 94°C for 5 min followed by 5 cycles of denaturation at 94°C for 1 min, primer annealing at 35°C for 1 min, and primer extension at 72°C for 1 min, followed by 35 cycles of denaturation at 94°C for 1 min, primer annealing at 50°C for 1 min, and primer extension at 72°C for 3 min. The amplification was completed with a final extension of 10 min at 72°C. PCR products were separated and stained with ethidium bromide on 1.5% agarose gels. Statistical data analysis The scorable bands were manually assigned to one of two categories: present (1) or absent (0). The dissimilarity was calculated using XLSTAT version 2020.1.3 (Addinsoft, 2020) depending on the Dice coefficient. The dendrogram was created using JMP 16 software based on the Ward method. The polymorphism information content (PIC) was estimated using the PIC = 1 [f 2 + (1 f) 2 ] formula (Jan de Riek et al. 2001; Roldán-Ruiz et al. 2000 ), where f is the marker frequency in the data set. By multiplying the average PIC by the number of polymorphic bands, the marker index (MI) was obtained (Ahmed et al. 2021 ). Using the GenAlEx version 6.5 software, the PhiPT distance and molecular variance (AMOVA) between and within populations were estimated. To identify genetic makeup and explain the number of populations, a model evaluation for population structure was performed using the STRUCTURE version 2.3.4 program (Evanno et al. 2005 ; Pritchard et al. 2000 ). As for ancestry and allele frequency models, the admixture model and correlated allele frequencies were used in this investigation. The number of putative populations (K) was varied between 1 and 9, and the assessment was repeated three times. At 54,000, the burn-in and MCMC concerns were fixed. The run with the highest probability was used to assign accessions to populations. Gene flow was computed via the PhiPT value using the formula: [(1/PhiPT)-1]/4, where PhiPT is the population variation (Mekonnen et al. 2020 ). Results Genetic diversity indices using URP + IRAP, SRAP, and CDDP markers The current work used 41 primers (11 URP + IRAP, 18 SRAP, and 12 CDDP) to predict the molecular genetic diversity of melon accessions. The capacity to amplify melon DNA and the quality of the primer product were criteria in the selection of these primers. Table 2 highlight the informativeness criteria used to compute the informativeness of 41 primers. Following URP + IRAP analysis of the accessions studied, the 11 URP + IRAP primers tested amplified a total of 154 diverse fragments ranging in size from 100 to 3000 bp, with 143 of them revealing polymorphisms. The number of polymorphic bands (TPB) ranged from 8 (URP-2F and LTR6149-3'LTR) to 18 (URP-1F), with an average of 13.00. URP + IRAP makers had a PIC value per primer of 0.29 on average, URP-13R and URP-25F had the highest polymorphism information content (PIC) value (0.34), and the LTR6149-3'LTR combination had the lowest (0.20). The average marker index (MI) score was 3.93, with URP-13R having the maximum (5.79) and LTR6149-3'LTR combination having the least (1.62). Shannon's information index (I) varied between 0.27 (LTR6149-3'LTR) and 0.49 (URP-4R), with an average value of 0.39. Additionally, gene diversity, or expected heterozygosity (He), varied between 0.16 (URP-2F) and 0.33 (URP-4R), with an average of 0.25. The genetic diversity of 57 melon accessions was evaluated using 18 different SRAP primer combinations. The SRAP primer pairs demonstrated good amplification for all accessions and produced polymorphic bands ranging in size from 0.08 to 1.20 kb. By using PCR, the 18 SRAP amplified a total of 143 different and reproducible bands. The Me2-Em11 primers combination obtained the greatest number of amplified bands (16) and polymorphic fragments (15), while the Me1-Em7 primer pair computed the minimum number of amplified (4) and polymorphic bands (3). The PIC value ranged from 0.14 to 0.38, with Me2-Em2 having the highest value of 0.38, followed by Me1-Em12 and Me10-Em2 (0.34), and Me1-Em7 and Me1-Em8 combinations having the lowest value of 0.14. Each primer set produced a marker index in the range of 0.43 (Me1-Em7) to 3.94 (Me2-Em11), with an average of 1.88. Shannon's information index (I) spanned from 0.22 at Me7-Em12 to 0.54 at Me1-Em2, with an average of 0.39. Furthermore, the gene diversity or predicted heterozygosity (He) ranged from 0.12 at Me7-Em12 to 0.36 at Me1-Em2, with an average of 0.25. The 12 CDDP primers generated variable and reliable bands in 57 melon individuals. These CDDP primers amplified 157 reliable bands in total, with the number of reliable bands per primer set ranging from 9 (MADS-1 and WRKY-R3B) to 19 (MADS-4). The size of the scorable band varied from 100 to 1100 base pairs. Polymorphism was found in 131 of the 157 scorable bands. The polymorphic bands formed by 12 CDDP primers ranged from 5 to 18, with a mean of 10.92 per primer. KNOX-2 has the least polymorphic bands, while MADS-4 had the maximum. The PIC and MI values of the CDDP primers showed a high level of variance. The PIC of the 12 CDDP primers ranged from 0.20 to 0.37, with the KNOX-3 primer having the highest value. The MI values ranged from 0.62 (WRKY-R3B) to 5.55 (KNOX-3) per primer, with a mean value of 3.50. The Shannon’s information index (I) ranged from 0.32 at WRKY-R3B to 0.58 at MYB-1 with an average 0.45. Moreover, the gene diversity or expected heterozygosity (He) ranged from 0.18 at WRKY-R3B to 0.40 at MYB-1 with an average of 0.30. Table 2 Description of URP + IRAP, SRAP, and CDDP primers, their amplification, and the degree of polymorphism obtained in melon accessions. URP markers SRAP markers CDDP markers Marker TAB TPB PIC MI I He Marker TAB TPB PIC MI I He Marker TAB TPB PIC MI I He URP-1F 18.00 18.00 0.31 5.52 0.43 0.29 Me1-Em2 9.00 9.00 0.25 2.28 0.54 0.36 ABP1-1 12.00 11.00 0.31 3.41 0.34 0.22 URP-2F 10.00 8.00 0.22 1.77 0.28 0.16 Me1-Em7 4.00 3.00 0.14 0.43 0.23 0.12 ERF-1 12.00 12.00 0.31 3.72 0.48 0.32 URP-4R 13.00 11.00 0.30 3.29 0.49 0.33 Me1-Em8 6.00 5.00 0.14 0.72 0.39 0.26 ERF-2 13.00 10.00 0.34 3.40 0.45 0.30 URP-9F 14.00 13.00 0.33 4.29 0.48 0.32 Me1-Em11 6.00 5.00 0.18 0.91 0.32 0.19 KNOX-2 9.00 5.00 0.20 1.00 0.35 0.23 URP-13R 19.00 17.00 0.34 5.79 0.41 0.27 Me1-Em12 9.00 8.00 0.34 2.70 0.37 0.22 KNOX-3 16.00 15.00 0.37 5.55 0.49 0.33 URP-17R 14.00 13.00 0.32 4.11 0.46 0.31 Me1-Em13 8.00 8.00 0.17 1.33 0.35 0.23 MADS-1 10.00 9.00 0.36 3.24 0.45 0.30 URP-25F 16.00 15.00 0.34 5.16 0.40 0.25 Me2-Em2 9.00 8.00 0.38 3.02 0.46 0.30 MADS-4 19.00 18.00 0.29 5.22 0.44 0.29 URP-30F 12.00 11.00 0.29 3.23 0.35 0.20 Me2-Em11 16.00 15.00 0.26 3.94 0.35 0.23 MYB-1 12.00 9.00 0.34 3.06 0.58 0.40 URP-38F 16.00 16.00 0.32 5.19 0.39 0.24 Me2-Em12 10.00 8.00 0.31 2.45 0.44 0.29 WRKY-F1 13.00 11.00 0.34 3.74 0.53 0.36 LTR6149-3'LTR 9.00 8.00 0.20 1.62 0.27 0.17 Me4-Em7 7.00 6.00 0.28 1.69 0.41 0.27 WRKY-R1 13.00 12.00 0.30 3.60 0.44 0.29 LTR6150-3'LTR 13.00 13.00 0.25 3.24 0.36 0.23 Me5-Em7 12.00 10.00 0.37 3.72 0.46 0.30 WRKY-R3 19.00 17.00 0.32 5.44 0.50 0.34 Mean 14.00 13.00 0.29 3.93 0.39 0.25 Me5-Em12 7.00 6.00 0.21 1.25 0.26 0.15 WRKY-R3B 9.00 2.00 0.31 0.62 0.32 0.18 Total 154.00 143.00 3.22 43.21 4.32 2.77 Me5-Em13 7.00 6.00 0.31 1.86 0.39 0.26 Mean 13.08 10.92 0.32 3.50 0.45 0.30 Me6-Em7 5.00 4.00 0.25 0.98 0.44 0.28 Total 157.00 131.00 3.79 42.00 5.37 3.56 Me6-Em13 9.00 8.00 0.27 2.20 0.52 0.36 Me7-Em12 6.00 5.00 0.21 1.04 0.22 0.12 Me10-Em2 10.00 7.00 0.34 2.36 0.45 0.29 Me10-Em13 6.00 4.00 0.24 0.96 0.34 0.21 Mean 8.11 6.94 0.26 1.88 0.39 0.25 Total 146.00 125.00 4.65 33.84 6.95 4.45 TNB: total number of amplified bands; TPB: total number of polymorphic bands; PIC: polymorphism information content; MI: marker index; ; I: Shannon’s information index; He: expected heterozygosity or gene diversity Clustering and structure analysis of melon accessions The dendrogram built by Ward analysis revealed that URP + IRAP results classified 57 accessions of melon into two primary categories (G-1 and G-2) (Fig. 1 A). Twenty-three accessions were included in the first. This group was subdivided into two subgroups (SG-1 and SG-2). The AN1 accession was included in the first subgroup (SG-1). Twenty-two accessions were included in the second subgroup (SG-2). The second major group was further subdivided into two subgroups, the first of which included 7 accessions and the second of which included 27 accessions. The Jaccard coefficient was used to calculate genetic dissimilarity, which ranged from 0.32 to 0.84. The accessions AN37 and AN38 had the highest genetic dissimilarity (84%), whereas the accessions AN13 and AN26 had the lowest genetic distance (32%). The STRUCTURE software was used to assess the marker information with a Bayesian-based model in order to better understand the association between the analyzed accessions. The proportions of membership ranged from K = 1 to K = 9. According to Evanno's approach, Delta K had the largest ad hoc value at K = 2, as shown in Fig. 2 A, demonstrating that the 57 accessions are better separated into two populations using URP + IRAP data. Population 1 consisted of 27 accessions, while population 2 included 30 accessions. Assuming that accessions with a membership coefficient (Q value) of 0.79 or higher were considered pure (Ahmed et al. 2021 ), 49.12% of the accessions tested belonged to corresponding pure groups, while the remaining 50.88% belonged to an admixed group (Fig. 2 B). Based on the SRAP data, the Ward method was used to perform hierarchical clustering, which divided 57 melon accessions into two primary groups (SG-1 and SG-2) (Fig. 1 B). The initial group (G-1) included twenty-one different accessions. This was divided into two subgroups (SG-1 and SG-2) with the first (SG-1) having one accession (AN50) and the second (SG-2) having twenty accessions. The second largest group (G-2) consisted of 36 accessions. The genetic distance was calculated using the Jaccard coefficient, which ranged from 0.20 to 0.78. The accessions AN6 and AN46 had the smallest genetic dissimilarity (22%), whereas AN12 and AN36 had the largest genetic difference (78%). To infer population structure (K > 1) based on SRAP data, the STRUCTURE software employed a model-based Bayesian approach. The ad hoc statistic K was used to calculate the actual number of clusters (K) based on the log probability of data with regard to K values. STRUCTURE analysis of the 18 SRAP primer pairs suggests that K = 2 is the greatest value (Fig. 2 C). This number denotes the presence of two informative populations among all melon accessions. Population 1 included 29 accessions, while population 2 comprised 28 accessions. Thirty-one of the accessions investigated corresponded to pure populations, whereas the other 26 belonged to an admixed population (Fig. 2 D). The CDDP dendrogram classified the accessions into three principal groupings (G-1, G-2, and G-3) (Fig. 1 C). The largest cluster (G-1) has 42 accessions, which were further subdivided into two sub-clusters. There were nine accessions in the first sub-cluster (SG-1). The second sub-cluster included thirty-three accessions. The second (G-2) and third (G-3) main CDDP groups were made up of eleven and four accessions, respectively. The genetic disparity was determined using the Jaccard coefficient, which varied from 0.25 to 0.73. The accessions AN8 and AN24 exhibited the least genetic distance (25%), while AN4 and AN20 had the highest genetic variation (73%). Using the STRUCTURE software, the CDDP genotyping findings were utilized to perform population structure analysis on 57 accessions under an admixture model. The Evano method determined K = 3 as the best number of clusters (Fig. 2 E). An accession was deemed a pure member of a cluster if the likelihood of membership in that cluster was greater than 79%. For K = 3, the resulting clusters had 15, 18, and 24 accessions, respectively, for clusters 1, 2, and 3. Thirty accessions were classified as pure, while the remaining accessions were designated as admixed (Fig. 2 F). Analysis of molecular variance and diversity indices Based on the results of STRUCTURE clustering, the analysis of molecular variance (AMOVA) approach approximates population divergence directly from the three types of markers. AMOVA revealed 5.64, 9.80, and 7.55% variation among populations for the URP + IRAP, SRAP, and CDDP markers, respectively, as well as significant AMOVA variance within populations of 94.36, 91.20, and 92.45% for the URP + IRAP, SRAP, and CDDP markers, respectively. Phi-statistics provides a summary of the degree of differentiation between clusters. According to the phi-statistics, there is little differentiation between STRUCTURE clusters (0.056, 0.098, and 0.076 for URP + IRAP, SRAP, and CDDP markers, respectively), but there were high and significant variance (p < 0.001) within STRUCTURE groupings for three types of markers (Table 3 ). Heterozygosity can be estimated using expected heterozygosity (He), which provides information about the likelihood of an individual's fraction of heterozygosity for all studied loci. Using URP + IRAP primers, the number of effective allele (Ne), Shannon’s information index (I), expected heterozygosity (He), and polymorphic loci percentage (PP) ranged from 1.42–1.43, 0.40–0.41, 0.25–0.26, and 90.21–95.80, respectively (Table 4 ). The highest fixation index (Fst) value was recorded by population 1. The gene flow (GF), based on the PhiPT value, between both populations was 4.2. Based on SRAP primers, the Ne, I, He, and PP were between 1.40–1.46, 0.38–0.42, 0.24–0.28, and 92.89–94.40, respectively. The highest Ne, I, He, and PP were registered by population 1. Population 1 had the greatest Fst value (0.44). The gene flow (GF) between the two populations was 2.30 based on the PhiPT value (Table 4 ). According to the CDDP data, population 3 had the highest Ne (1.55), I (0.48), He (0.32), and PP (93.89%) values. These populations likewise had a high Fst value (0.30), which was followed by population 1. Based on the PhiPT value, the gene flow (GF) between the two populations was 3.04 (Table 4 ). Table 3 Analysis of molecular variance (AMOVA) in melon populations using URP + IRAP, SRAP, and CDDP data. URP + IRAP markers Source df SS MS Est. Var. Var (%) Among Pops 1.00 59.37 59.37 1.32 5.64 Within Pops 55.00 1209.46 21.99 21.99 94.36 Total 56.00 1268.82 23.31 100.00 PhiPT 0.056** P-value 0.001 SRAP markers Source df SS MS Est. Var. Var (%) Among Pops 1.00 69.74 69.74 1.85 9.80 Within Pops 55.00 936.59 17.03 17.03 90.20 Total 56.00 1006.33 18.88 100.00 PhiPT 0.098** P-value 0.001 CDDP markers Source df SS MS Est. Var. Var (%) Among Pops 2.00 102.91 51.46 1.67 7.55 Within Pops 54.00 1101.95 20.41 20.41 92.45 Total 56.00 1204.86 22.07 100.00 PhiPT 0.076** P-value 0.001 Df: degree of freedom; SS: sum of squared observations; MS: mean of the squared observations; Est Var: estimated variance; Var: variance; PhiPT: proportion of the total genetic variance among the individuals within a population; p-value: probability value. Table 4 Diversity indices, genetic differentiation, and gene flow detected in melon populations based on the data of URP + IRAP, SRAP, and CDDP data. URP + IRAP markers Population N Ne I He PP Fst GF Pop-1 27.00 1.43 0.40 0.26 90.21 0.26 4.21 Pop-2 30.00 1.42 0.41 0.25 95.80 0.25 Mean 28.50 1.42 0.40 0.26 93.01 0.26 SRAP marker Population N Ne I He PP Fst GF Pop-1 29.00 1.46 0.42 0.28 94.40 0.44 2.30 Pop-2 28.00 1.40 0.38 0.24 92.80 0.28 Mean 28.50 1.43 0.40 0.26 93.60 0.36 CDDP marker Population N Ne I He PP Fst GF Pop-1 15.00 1.53 0.46 0.31 88.55 0.30 3.04 Pop-2 18.00 1.51 0.45 0.30 88.55 0.25 Pop-3 24.00 1.55 0.48 0.32 93.89 0.30 Mean 19.00 1.53 0.46 0.31 90.33 0.28 N: number of accessions; Ne: number of effective alleles; I: Shannon’s information index; He: expected heterozygosity or gene diversity; PP: percentage of polymorphism, populations; Fst: fixation index; GF: gene flow; Pop: population. Discussion Genetic diversity research is a mechanism that characterizes species or accessions using specific statistical methods or a mix of approaches based on molecular characteristics of individuals. The assessment of genetic variation in plant germplasm is a powerful method to investigate superior breeding resources and improving breeding efficiency. In response to environmental stresses, plant populations require the evaluation of genetic diversity. The frequency of high genetic diversity within populations has been shown in numerous plant species, and the outcrossing nature of these species contributes to variation (Sheidai et al. 2014 ). Although the relationship between fitness and sustainability is unknown, genetic variation is related to both (Woodruff 2001 ). Thus, a population's evolutionary potential is governed by its gene pool, the size of which determines whether natural selection and genetic drift can function. Once obtained, conservation geneticists employ genetic data to measure within- and between-population diversity. The allelic richness of plant accessions is a measure of genetic diversity enrichment, which is widely used by informative molecular markers to define populations for selection, breeding, and conservation. As the number of markers and genome coverage increases, the data's dependability should improve. In many plant species, URP, SRAP, and CDDP have been shown to be more informative than other prominent DNA marker types for detecting genetic diversity. In this work, diverse molecular markers and primers produced different amplification products, showing genomic polymorphism. The main reason for these differences is related to the amplification of the genome by different marker types. SRAP markers are gene-targeted markers that target the coding region of a gene, whereas CDDP markers target conserved parts of functional genes and URP markers are designed from a repetitive DNA fragment and target non-coding regions of the genome. To the best of our knowledge, this is the first investigation to use URP and CDDP markers to describe the genetic structure and variability of melon accessions. Using these three types of markers, a reasonably high percentage of polymorphic bands were found in the current study. As a result, three separate types of molecular markers produced 398 bands. In our study, a large number of polymorphisms, combined with a high number of polymorphic alleles derived per primer, could be explained by both the wide range of genetic diversity, as well as the performance of URP, SRAP, and CDDP markers in achieving adequate polymorphism in targeted regions of the melon genome accessions. The large percentages of polymorphism also indicate the heterozygous nature of melon accessions' genomic makeup, suggesting their utility in genetic variability studies in melon accessions. Because the targeted genes (CDDP and SRAP) and URP markers create such a wide range of variation, they might be utilized to identify plant components that are related across accessions. The largest mean number of polymorphisms in each marker revealed the use of each locus for measuring genetic variety; as a result, primers with more alleles are better for genetic variation testing since they cover more of the genome. A PIC is a marker of genetic variation among population accessions. This focuses on the evolutionary pressure on alleles as well as the mutation rate that a locus may have experienced over time. A PIC value is also a critical predictor of marker effectiveness for linkage analysis when determining the inheritance between offspring and parental genotypes. Some primer efficiency indices, like MI, show the overall utility of a specific primer for description and discriminating across a large number of accessions. The higher their levels, the more efficient and informative the primers will be. In the current study, the PIC mean values of three markers were larger than 0.25, indicating the presence of considerable genetic diversity among the melon accessions. The order of discrimination power of the three types of markers was as follows: URP + IRAP > CDDP > SRAP based on TPB, PIC, and MI values. Diversity indices are statistics that are used to characterize the variation of a population in which each individual belongs to a distinct group. Indices with lower values imply less diversity, whereas indices with higher values suggest greater diversity. The mean values of expected heterozygosity (He) for URP + IRAP, SRAP, and CDDP were 0.26, 0.26. and 0.31, respectively, showing a moderate degree of genetic diversity between accessions in a population. The average value of Shannon’s information index (I) acquired by CDDP markers was higher than that obtained by URP + IRAP and SRAP markers, indicating that the CDDP genome contains a significant degree of variation among the accessions. The analysis of melon accessions' diversity has provided the framework to understand population structure. The population differentiating analysis can help you understand genetic diversity and improve the accuracy of genome-wide association study (GWAS). As a consequence, a great deal of effort is put into thoroughly investigating the underlying population structure of any population that will be utilized to determine marker-trait correlations. As a result, the first step in conducting a GWAS for real marker-trait relationships is to investigate population structure. Without any prior knowledge, Bayesian model-based analysis could ascribe each accession to a putative ancestral group (s), as well as reveal non-obvious mixing via distance-based clustering methods. The results of two distance-based clustering studies (Ward clustering and structure analyses) are highly comparable, with investigated accessions divided into two groups for URP + IRAP and SRAP markers and three groups for CDDP markers. Ancestral mixing was thought to be linked to plant germplasm interchange and hybridization. In addition, genetic differences in this case may be attributable in part to gene flow, as genetic drift has a considerable impact on the populations of this species. The largest gene flow between groups corroborated the AMOVA findings, indicating that intra-population variance was greater than inter-population variation, The structure analysis results of three marker types mainly agreed with the groups revealed in the cluster analysis, which split the 57 melon accessions into two or three genetic groups based on the delta K value. The two clusters of URP and SRAP markers and the three clusters of the CDDP method were heavily admixed, showing that the majority of variation exists within the groupings among the accessions. The fact that melon is a powerful cross-pollinating plant with primarily heterozygous offspring produced through selfing may explain the large proportion of accessions of mixed ancestry. Two or three populations may be eligible for our panel based on population size and the variation in the number of accessions representing the six provinces from which they were taken. A population's genetic differentiation represents the interactions of numerous evolutionary processes such as dispersion shifts, habitat change, and population separation, mutation, genetic drift, mating system, gene flow, and natural selection. Geographic isolation, community fragmentation, breeding systems, and genetic drift are all major sources of large population diversity. The fixation index (Fst) is a genetic divergence metric that uses a scale of 0 to 1 to evaluate genetic distance caused by population structure, with 0 indicating total genetic material sharing and 1 indicating no sharing. A Fst value larger than 0.15 is as regarded significant in distinguishing populations (Frankham 1995 ). When the Fst values of the three types of molecular markers were compared, the results revealed that the highest Fst mean value (0.36) was found in the populations created by SRAP markers, confirming the existence of significant genetic variation within the individuals of their populations. The predicted heterozygosity values of melon within a population were high, as revealed by our findings of three markers, indicating that they contain relatively significant amounts of genetic variation. Previously, multiple studies investigated the genetic diversity of melon accessions using various types of molecular markers, with diverse results shown by different authors (Baudracco-Arnas and Pitrat 1996 ; Karimi et al. 2016 ; Maleki et al. 2018 ; Trimech et al. 2015 ), but our study was the first to use URP, SRAP, and CDDP for the study of melon genetic diversity. Conclusions In essence, combining field results based on URP + IRAP, SRAP, and CDDP analysis may be more useful in defining genetic variation among melon accessions. Based on URP + IRAP, SRAP, and CDDP markers, the melon accessions exhibited a wide range of variability that might be used for genetic studies and breeding programs. Based on the data, Ward dendrograms revealed distinct distribution patterns of genetic variation among melon accessions. Some accessions were placed in the same cluster in the dendrograms created by URP + IRAP, SRAP, and CDDP. Based on the average values of polymorphic bands per primer and PIC of three types of markers, the order of the power of discrimination of melon accession was as follows: URP + IRAP > CDDP > SRAP. URP markers, particularly URP-1F and URP-13R, can be used to initiate the differentiation of melon accessions. Model-based STRUCTURE, on the other hand, identified three groups based on CDDP data, while the two other markers showed only two populations. The melon accessions investigated here offer a valuable gene pool that should be further evaluated for agronomic features and performance in different conditions. These analyses can help to find the diversity of germplasm collections and select accessions for future breeding. Within the expanding consideration of agrobiodiversity and its important relevance in feeding communities, attention should be paid to the protection and sustainable usage of melon genetic resources. Declarations Acknowledgments The authors would like to express their gratitude to the Department of Horticulture at the College of Agricultural Engineering Sciences for their help and support. Credit author statement Nawroz Abdul-razzak Tahir: Conceived and designed the experiments. Rebwar Rafat Aziz: Carried out the experiments and analyzed the data. Nawroz Abdul-razzak Tahir and Rebwar Rafat Aziz: Writing- Reviewing and editing, Nawroz Abdul-razzak Tahir: Visualization, investigation, and supervision this manuscript. Declaration of competing interest The authors note that they have no known competing financial interests or personal affiliations that could appear to have impacted the work presented in this paper. References Ahmed DA, Tahir NA-r, Salih SH, Talebi R (2021) Genome diversity and population structure analysis of Iranian landrace and improved barley ( Hordeum vulgare L.) genotypes using arbitrary functional gene-based molecular markers. Genet Resour Crop Evol 68:1045–1060 Aneja B, Yadav NR, Yadav RC, Kumar R (2013) Sequence related amplified polymorphism (SRAP) analysis for genetic diversity and micronutrient content among gene pools in mungbean ( Vigna radiata L.) Wilczek. Physiol Mol Biol Plants 19:399–407 Bagheriyan S, Karimi HR, Esmaelizadeh M (2015) Evaluation of genetic relationships among melon genotypes based on morphological markers. Int J Veg Sci 21:36–52 Baudracco-Arnas S, Pitrat M (1996) A genetic map of melon ( Cucumis melo L.) with RFLP, RAPD, isozyme, disease resistance and morphological markers. Theor Appl Genet 93:57–64 Carvalho N, Canela FM, Leite PHS, Ferreira MA, Oliveira VR, Santos MF, Souza NOS, Buso GSC (2017) Analysis of genetic variability of commercial melon cultivars using SSR molecular markers. Genet Mol Res 16:1–8 Collard BCY, Mackill DJ (2009) Start codon targeted (SCoT) polymorphism: A Ssmple, novel DNA marker technique for generating gene-targeted markers in plants. Plant Mol Biol Rep 27:86–93 Dantas ACA, Holanda ISA, Esteras C, Nunes GHS, Picó. (2015) MB Diversity of melon accessions from northeastern Brazil and their relationships with germplasms of diverse origins. J Am Soc Hortic Sci 140:504–517 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611–2620 Frankham R (1995) Conservation genetics. Annu Rev Genet 29:305–327 Govindaraj M, Vetriventhan M, Srinivasan M, Rogozin IB (2015) Importance of genetic diversity assessment in crop plants and its recent advances: An overview of its analytical perspectives. Genet Res Int 2015:431487 Hill WG (2001) Selective Breeding. In: Brenner S, Miller JH (eds) Encyclopedia of Genetics. Academic Press, New York, pp 1796–1799 Ibrahim M, Haggag NA, Hassan, Haggag S, Zein NA, Abdallah, Nagwa I, Elatabi (2021) Molecular markers assessment of genetic diversity of ten Egyptian melon ( cucumis melo l.) accessions cultivated. Plant Cell Biotechnol Mol Biol 22:103–123 de Jan E, Calsyn IG, Everaert E, Van Bockstaele, Marc de Loose (2001) AFLP based alternatives for the assessment of distinctness, uniformity and stability of sugar beet varieties. Theor Appl Genet 103:1254–1265 Javier Obando (2008) Juan Pablo Fernández-Trujillo, Juan Antonio Martínez, Antonio Luis Alarcón, Iban Eduardo, Pere Arús, Antonio José Monforte Identification of melon fruit quality quantitative trait loci using near-isogenic lines. J Am Soc Hortic Sci 133:139–151 Kaki A, Vafaee Y, Khadivi A (2020) Genetic variation of Anacamptis coriophora , Dactylorhiza umbrosa, Himantoglossum affine, Orchis mascula , and Ophrys schulzei in the western parts of Iran. Ind Crops Prod 156:112854 Kang HW, Kwon SW, Go SJ (2003) PCR-based specific and sensitive detection of Pectobacterium carotovorum ssp. carotovorum by primers generated from a URP‐PCR fingerprinting‐derived polymorphic band. Plant Pathol 52:127–133 Karimi HR, Bagheriyan S, Esmaelizadeh M, Estaji A (2016) Genetic relationships among melons using RAPD markers. Int J Veg Sci 22:200–208 Kishor DS, Song W-H, Noh Y, Lee GP, Park Y, Jung J-K, Shim E-J, Chung S-M (2020) Development of SNP markers and validation assays in commercial Korean melon cultivars, using Genotyping-by-sequencing and Fluidigm analyses. Sci Hortic 263:109113 Li G, Quiros CF (2001) Sequence-related amplified polymorphism (SRAP), a new marker system based on a simple PCR reaction: its application to mapping and gene tagging in Brassica. Theor Appl Genet 103:455–461 Maleki M, Shojaeiyan A, Rashidi Monfared S (2018) Population structure, morphological and genetic diversity within and among melon ( Cucumis melo L.) landraces in Iran. J Genet Eng Biotechnol 16:599–606 Manchali S, Chidambara Murthy KN, Vishnuvardana, Patil BS (2021) Nutritional composition and health benefits of various botanical types of melon ( Cucumis melo L.). Plants 10:1755 Mekonnen T, Haileselassie T, Goodwin SB, Tesfayea K (2020) Genetic diversity and population structure of Zymoseptoria tritici in Ethiopia as revealed by microsatellite markers. Fungal Genet Biol 141:103413 Merheb J, Pawełkowicz M, Branca F, Bolibok-Brągoszewska H, Skarzyńska A, Pląder W, Chalak L (2020) Characterization of Lebanese germplasm of snake melon ( Cucumis melo subsp. melo var. flexuosus) using morphological traits and SSR markers. Agronomy 10:1293 Nuñez-Palenius HG, Gomez-Lim M, Ochoa-Alejo N, Grumet R, Lester G, Cantliffe DJ (2008) Melon fruits: genetic diversity, physiology, and biotechnology features. Crit Rev Biotechnol 28:13–55 Pandey A, Ranjan P, Ahlawat SP, Bhardwaj R, Dhariwal OP, Singh PK, Malav PK, Harish GD, Prabhu P, Agrawal A (2021) Studies on fruit morphology, nutritional and floral diversity in less-known melons ( Cucumis melo L.) of India. Genet Resour Crop Evol 68:1453–1470 Paris HS, Amar Z, Lev E (2012) Medieval emergence of sweet melons, Cucumis melo (Cucurbitaceae). Ann Bot 110:23–33 Pitrat M (2017) Melon Genetic Resources: Phenotypic Diversity and Horticultural Taxonomy. In: Grumet R, Katzir N, Garcia-Mas J (eds) Genetics and genomics of cucurbitaceae, vol 20. Springer, Cham, pp 25–60 Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959 Raghami M, López-Sesé AI, Hasandokht MR, Zamani Z, Moghadam MRF, Kashi A (2014) Genetic diversity among melon accessions from Iran and their relationships with melon germplasm of diverse origins using microsatellite markers. Plant Syst Evol 300:139–151 Roldán-Ruiz I, Dendauw J, van Bockstaele E, Depicker A, de Loose M (2000) AFLP markers reveal high polymorphic rates in ryegrasses ( Lolium spp.). Mol Breed 6:125–134 Saputro NW, Hidayat T, Bayfurqon FM, Khamid MBR (2020) Evaluation of morpho-agronomic characterization Apple cucumber: a new variety of melon from Indonesia. IOP Conf Ser : Earth Environ Sci 457:12061 Seungbum L, Ik J, Do Yoon H, Jung-Ro L, Seong-Hoon K, Eunae Y, Sookyeong L, Gyu-Taek C, Kyung JL (2020) Evaluation of morphological traits and genetic composition in melon germplasm. Korean J Crop Sci 65:485–498 Sheidai M, Afshar F, Keshavarzi M, Talebi S-M, Noormohammadi Z, Shafaf T (2014) Genetic diversity and genome size variability in Linum austriacum (Lineaceae) populations. Biochem Syst Ecol 57:20–26 Singh D, Di Leskovar, Sharma SP, Sarao NK, Vashisht VK (2020) Genetic diversity and interrelationship among Indian and exotic melons based on fruit morphology, quality components and microsatellite markers. Physiol Mol Biol Plants 26:985–1002 Tian J, Chen J, Chen G, Wu P, Zhang H, Zhao Y (2015) Genetic analyses of wheat and molecular marker-assisted breeding. Conditional QTL analysis and MAS, vol 2. Science Press, Dordrecht: Springer, Beijing Trimech R, Afif M, Boussaid M (2015) Genetic diversity of Tunisian melon ( Cucumis melo . L) landraces and their relationships with introduced varieties as assessed by simple-sequence repeat (SSR) markers. Afr J Biotechnol 14:86–95 Woodruff DS (2001) Populations, Species, and Conservation Genetics.Encyclopedia of Biodiversity:811–829 Zagorcheva T, Stanev S, Rusanov K, Atanassov I (2020) SRAP markers for genetic diversity assessment of lavender ( Lavandula angustifolia mill.) varieties and breeding lines. Biotechnol Biotechnol Equip 34:303–308 Supplementary Files Supplemntaryfile.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Jun, 2022 Reviewers invited by journal 02 Jun, 2022 Editor assigned by journal 10 May, 2022 First submitted to journal 09 May, 2022 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-1640623","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":110654663,"identity":"21c8fca2-c9e8-4f8e-8a0c-5425d50c664f","order_by":0,"name":"Rebwar Rafat Aziz","email":"","orcid":"","institution":"University of Sulaimani","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rebwar","middleName":"Rafat","lastName":"Aziz","suffix":""},{"id":110654664,"identity":"faaeabef-a265-48ed-bcd2-da95323787bb","order_by":1,"name":"Nawroz Tahir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYFADCcYGhg8MDDxAJuMBBgZmMMKrAaSFcQZECwOxWoBqeKC8A/jUm7OfMfvw4Q9DHf/s5rbPtm13ZAzOnzE4wFBhndjAzrwBmxbLnhzjmTN4GCQk7hxsnp3b9ozH4EYOUMuZ9MQGZrYCbFoMDuRuZuYBOetGYjNzbtthHskZPAYHGNsOA7XwGGDVcv7tZpCUhDxIiyVISz/QYYz/8Gi5AbIlgUHCAKQFaDgPPwPQYYwN+LS8/8w444CE5EagFsaec0AtEmkFBxKOpRu34fLL+bRkhg9/bPjlbqQ/ZvhRdtiejf/wxgcfaqxl+/kPYw0xKJBA4ycAMRvQQDxacAAytIyCUTAKRsEwBACCP1yFpFGWyAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8478-7127","institution":"University of Sulaimani","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Nawroz","middleName":"","lastName":"Tahir","suffix":""}],"badges":[],"createdAt":"2022-05-10 06:44:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1640623/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1640623/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22465008,"identity":"0cb9a0d4-03a9-4e55-8d98-6f186f4e6185","added_by":"auto","created_at":"2022-06-09 15:48:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":509110,"visible":true,"origin":"","legend":"\u003cp\u003eWard dendrograms of 57 accessions based on URP+IRAP (A), SRAP (B), and CDDP (C) data. The colors of the branches reflect to the clustering groups. G-1, G-2, and G-3 represent the groups 1, 2, and 3, respectively. SG-1 and SG-2 correspond to the subgroup-1 and subgroup-2, respectively.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1640623/v1/eab9d58e5caadcd50e111b29.jpeg"},{"id":22465007,"identity":"546381b8-8aa4-448a-94c9-3791e032f550","added_by":"auto","created_at":"2022-06-09 15:48:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":427821,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure of 57 melon accessions using URP+IRAP (A and B), SRAP (C and D), and CDDP (E and F) markers derived by the STRUCTURE software. A, C, and E: The number of subpopulations indicated by the highest K.\u0026nbsp;Each color symbolizes a different population. B, D, and F: Represent the proportion of individuals grouping into populations. Pop-1, -2, and -3 are the population numbers 1, 2, and 3, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1640623/v1/3bb4ad4a5647ca01812c5974.jpeg"},{"id":22465777,"identity":"729fc405-4ed1-479c-ae28-375495976b55","added_by":"auto","created_at":"2022-06-09 15:53:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":640307,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1640623/v1/e00a49c0-36a0-44c7-8e94-c6d1efc5a662.pdf"},{"id":22465776,"identity":"493e30a6-79db-4650-87b4-86f0a599a84a","added_by":"auto","created_at":"2022-06-09 15:53:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18571,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemntaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-1640623/v1/1c9f407072eacd2f74b00403.docx"}],"financialInterests":"","formattedTitle":"Genetic diversity and structure analysis of melon accessions using URP, SRAP, and CDDP markers","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cem\u003eCucumis\u003c/em\u003e genus belongs to the Cucurbitaceae family. Cucumber (\u003cem\u003eC. sativus\u003c/em\u003e) and melon (\u003cem\u003eC. melo\u003c/em\u003e) are two economically valuable species (Maleki et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Melon is a diploid plant (2n\u0026thinsp;=\u0026thinsp;2x) with 24 chromosomes (Paris et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It may have been domesticated primarily for the nutritional value of its seeds, and it then went through a period of great diversity. Because of the species' wide morphological variety, plant taxonomists have proposed intraspecific classifications based mostly on fruit characteristics (Nu\u0026ntilde;ez-Palenius et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Melon is a highly polymorphic species with a wide range of leaf, flower, and fruit traits. Recently, 19 intraspecific horticultural groups of \u003cem\u003eCucumis melo\u003c/em\u003e was found byPitrat (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), including agrestis, kachri, chito, tibish, acidulus, momordica, conomon, makuwa, chinensis, flexuosus, chate, dudaim, chandalak, indicus, ameri, cassaba, ibericus, cantalupensis and inodorus. Plant genetic resource organization entails the establishment, enrichment, appraisal, documenting, and protection of plant collections. Understanding the level of genetic variability and the interactions between different local genotypes is beneficial for both identifying and effectively conserving genetic resources, and improving the effectiveness of breeding programs. The degree of genetic diversity information will assist breeders in selecting favorite parents in the hybrid creation of optimal cultivars while also conserving population diversity (Govindaraj et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hill \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Tian et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To address this issue, diversity is a primary priority in species. Different morphological, phenological, and physiological approaches(Pandey et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saputro et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have been used to assess genetic variation in melon. Morphological analyses are the most essential criteria in the initial assessment of genetic variation for melon genotype and cultivar categorization and identification. Fruit species' genetic variation is important not just for preserving appropriate genetic diversity for breeding programs, but also for species' long-term survival. To measure genetic diversity, many marker systems, including morphological, biochemical, and molecular (DNA) markers, can be used. Melon genetic diversity has been studied using a diverse of markers, ranging from morphological(Bagheriyan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dantas et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Merheb et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Seungbum et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and physicochemical(Javier Obando et al. 2008; Manchali et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pandey et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to molecular DNA markers, such as amplified fragment length polymorphism (Ibrahim M. Haggag et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), sequence repeat primers (Carvalho et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), single nucleotide polymorphisms (Kishor et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), inter-simple sequence repeat (Maleki et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and start codon targeted(Ibrahim M. Haggag et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With recent developments in genomics research, DNA markers have assumed far greater significance. Various marker systems can be used to study the genetic diversity in plants at the species, population, germplasm accessions, and individual genotype levels, such as pure lines or clones. The DNA diversity of most eukaryotic and prokaryotic genomes was investigated using a polymerase chain reaction (PCR) method with universal rice primers (URP), with useful uses in taxonomic and phylogenic research, as well as inter- and intra-species population genotypic screening of individuals (Kang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Due to the use of lengthy primers and higher annealing temperatures, URP-PCR offers an advantage over randomly amplified polymorphic DNA and arbitrarily primed polymerase chain reaction approaches. The sequence-related amplified polymorphism (SRAP) markers discovered byLi and Quiros(2001) have been widely used in various genetic research projects due to their simplicity, dependability, productivity, and genome-wide coverage. SRAP markers have been effectively used in a variety of studies on the characterization of plant genetic resources and natural populations throughout the last decade (Zagorcheva et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The use of CDDP molecular markers is a unique method based on conserved DNA sequences. In this technology, single primers are designed and paired with conserved regions of functional genes to amplify the entire plant genome and generate informative bands (Ahmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Collard and Mackill \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The northern part of Iraq contains abundant melon genetic resources with different physical traits. Regardless, no research has been conducted to demonstrate the genetic variability of Iraqi native melon accessions. The current study is the first to assess genetic variability among melon accessions using URP\u0026thinsp;+\u0026thinsp;IRAP and gene-targeted (CDDP) markers. In this investigation, fifty-seven C. \u003cem\u003emelo\u003c/em\u003e accessions were collected and analyzed for genetic diversity and population structure using three distinct types of molecular markers. The findings may aid in the conservation of genetic variation and encourage its use in breeding programs for genetic improvement.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003ePlant materials\u003c/p\u003e \u003cp\u003eThe research was carried out on 57 local melon accessions gathered in northern Iraq (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These accessions have been planted throughout Iraq's northern areas, including Sulaimani, Erbil, and Duhok provinces. The melon accessions belonged to six horticultural groups of \u003cem\u003eCucumis melo\u003c/em\u003e L., based on the botanical classification of Pitrat (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Raghami et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), including cantalupensis, inodorus, Ameri, Dudiam, Charentais, and Chandalak.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eType, fruit shape, rind color and flesh color of different accessions used in this investigation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelon type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFruit shape\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRind color\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFlesh color\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with yellow lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInodorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-grey-yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmeri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrown-orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight green\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmeri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight green\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmeri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-brown-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInodorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange to brown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellow with some brown lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-brown with green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrown with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-brown with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInodorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight green\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight green\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-brown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-brown with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellow-brown-orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellow with brown lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharentais\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-dark orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChandalak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElongated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-orange-yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-brown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrown-yellow with green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChandalak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrown-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange to brown and green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange to brown-yellow with yellow lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmeri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrown-yellow with some brown lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-brown with yellow lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInodorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellow-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with yellow lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-yellow with green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYellow-orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-yellow with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrown-green-yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChandalak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-yellow with light green lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange with yellow lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDudaim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange-brown-green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-brown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOblate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen-orange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAN57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCantalupensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLight orange\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSampling and DNA extraction\u003c/p\u003e \u003cp\u003eThe procedure described by Alaaddin et al. (2022), with some modifications, was used to extract DNA. After eight weeks of sowing, fresh leaves from melon plants in the field were plucked and crushed with liquid nitrogen. A volume of 1.30 mL of lysis buffer [0.50% (w/v) SDS, 8.00% (w/v) PVP, 0.25 M NaCl, 0.025 M EDTA, and 0.2 M Tris-base] and 9 \u0026micro;L RNase (10 mg/mL) was used to lyse powdered leaf material. The tubes were then incubated for 74 min at 64\u0026deg;C and inverted 15 times. For 7 min, the tube samples were cooled at room temperature. A volume of 290 \u0026micro;L of 5 M potassium acetate (pH 6.5) was poured, stirred, and chilled for at least 9 min. For 18 min, the samples were centrifuged at 16200 rpm. The upper phase was collected and relocated Eppendorf tube (2 mL). A volume of 1.10 mL of GTE buffer (2M guanidine thiocyanate dissolved in 75% ethanol) was gently introduced into the upper phase. The mixture (850 \u0026micro;L) was poured on a spin column and incubated for 1 min at 21\u0026deg;C. The solution was centrifuged at 8300 rpm for 6 min, and the flow solution was discarded. A volume of 550 \u0026micro;L of NTEH washing buffer (10 mM NaCl, 10 mM Tris-base pH 6.5, and 80% ethanol) were poured into the spin-column. After centrifuging the column for 6 min at 8200 rpm, the flow solution was withdrawn. The spin column was washed a second time with 550 L of NTE buffer. The flow solution was removed after centrifuging the spin column at 8200 rpm for 5 min. To dry the spin column, it was centrifuged for 6 min at 11100 rpm. The spin column was placed in a new Eppendorf 1.5 mL tube, which was then filled with 108 \u0026micro;L of elution buffer and incubated at room temperature for 5 minutes. The eluted DNA was collected and stored at -20\u0026deg;C by centrifugation at 9500 rpm for 5 min. A 1.1% agarose gel and a nanodrop spectrophotometer (NanoPLUS-MAANLAB AB, Sweden) were used to assess the quality and quantity of isolated DNA.\u003c/p\u003e \u003cp\u003ePolymerase chain reaction\u003c/p\u003e \u003cp\u003eFor genetic diversity analysis in melon accessions, thirteen URAP primers (Kaki et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), 18 SRAP primers (Ahmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aneja et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and 12 CDDP primers (Ahmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Collard and Mackill \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) were applied. Five \u0026micro;L of template DNA, 10 liters of PCR master mix (AddStart Taq Master, Addbio, Korea), 3.8 \u0026micro;L of primer (Addbio, Korea), and 6.2 \u0026micro;L of deionized water were used for the PCR amplification reaction. For URAP and CDDP primers (Tables S1), the following conditions were used in an Applied Biosystems PCR machine: initial denaturation at 94\u0026deg;C for 9 min, followed by 38 cycles of denaturation at 94\u0026deg;C for 1 min, annealing at a particular temperature (depending on primer) for 1 min, and extension at 72\u0026deg;C for 2 min. A final extension cycle of 9 min at 72\u0026deg;C was performed. For the SRAP primers (Table S1), PCR conditions included initial denaturation at 94\u0026deg;C for 5 min followed by 5 cycles of denaturation at 94\u0026deg;C for 1 min, primer annealing at 35\u0026deg;C for 1 min, and primer extension at 72\u0026deg;C for 1 min, followed by 35 cycles of denaturation at 94\u0026deg;C for 1 min, primer annealing at 50\u0026deg;C for 1 min, and primer extension at 72\u0026deg;C for 3 min. The amplification was completed with a final extension of 10 min at 72\u0026deg;C. PCR products were separated and stained with ethidium bromide on 1.5% agarose gels.\u003c/p\u003e \u003cp\u003eStatistical data analysis\u003c/p\u003e \u003cp\u003eThe scorable bands were manually assigned to one of two categories: present (1) or absent (0). The dissimilarity was calculated using XLSTAT version 2020.1.3 (Addinsoft, 2020) depending on the Dice coefficient. The dendrogram was created using JMP 16 software based on the Ward method. The polymorphism information content (PIC) was estimated using the PIC\u0026thinsp;=\u0026thinsp;1 [f\u003csup\u003e2\u003c/sup\u003e + (1 f)\u003csup\u003e2\u003c/sup\u003e] formula (Jan de Riek et al. 2001; Rold\u0026aacute;n-Ruiz et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), where f is the marker frequency in the data set. By multiplying the average PIC by the number of polymorphic bands, the marker index (MI) was obtained (Ahmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using the GenAlEx version 6.5 software, the PhiPT distance and molecular variance (AMOVA) between and within populations were estimated. To identify genetic makeup and explain the number of populations, a model evaluation for population structure was performed using the STRUCTURE version 2.3.4 program (Evanno et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pritchard et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). As for ancestry and allele frequency models, the admixture model and correlated allele frequencies were used in this investigation. The number of putative populations (K) was varied between 1 and 9, and the assessment was repeated three times. At 54,000, the burn-in and MCMC concerns were fixed. The run with the highest probability was used to assign accessions to populations. Gene flow was computed via the PhiPT value using the formula: [(1/PhiPT)-1]/4, where PhiPT is the population variation (Mekonnen et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGenetic diversity indices using URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP markers\u003c/p\u003e \u003cp\u003eThe current work used 41 primers (11 URP\u0026thinsp;+\u0026thinsp;IRAP, 18 SRAP, and 12 CDDP) to predict the molecular genetic diversity of melon accessions. The capacity to amplify melon DNA and the quality of the primer product were criteria in the selection of these primers. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlight the informativeness criteria used to compute the informativeness of 41 primers. Following URP\u0026thinsp;+\u0026thinsp;IRAP analysis of the accessions studied, the 11 URP\u0026thinsp;+\u0026thinsp;IRAP primers tested amplified a total of 154 diverse fragments ranging in size from 100 to 3000 bp, with 143 of them revealing polymorphisms. The number of polymorphic bands (TPB) ranged from 8 (URP-2F and LTR6149-3'LTR) to 18 (URP-1F), with an average of 13.00. URP\u0026thinsp;+\u0026thinsp;IRAP makers had a PIC value per primer of 0.29 on average, URP-13R and URP-25F had the highest polymorphism information content (PIC) value (0.34), and the LTR6149-3'LTR combination had the lowest (0.20). The average marker index (MI) score was 3.93, with URP-13R having the maximum (5.79) and LTR6149-3'LTR combination having the least (1.62). Shannon's information index (I) varied between 0.27 (LTR6149-3'LTR) and 0.49 (URP-4R), with an average value of 0.39. Additionally, gene diversity, or expected heterozygosity (He), varied between 0.16 (URP-2F) and 0.33 (URP-4R), with an average of 0.25.\u003c/p\u003e \u003cp\u003eThe genetic diversity of 57 melon accessions was evaluated using 18 different SRAP primer combinations. The SRAP primer pairs demonstrated good amplification for all accessions and produced polymorphic bands ranging in size from 0.08 to 1.20 kb. By using PCR, the 18 SRAP amplified a total of 143 different and reproducible bands. The Me2-Em11 primers combination obtained the greatest number of amplified bands (16) and polymorphic fragments (15), while the Me1-Em7 primer pair computed the minimum number of amplified (4) and polymorphic bands (3). The PIC value ranged from 0.14 to 0.38, with Me2-Em2 having the highest value of 0.38, followed by Me1-Em12 and Me10-Em2 (0.34), and Me1-Em7 and Me1-Em8 combinations having the lowest value of 0.14. Each primer set produced a marker index in the range of 0.43 (Me1-Em7) to 3.94 (Me2-Em11), with an average of 1.88. Shannon's information index (I) spanned from 0.22 at Me7-Em12 to 0.54 at Me1-Em2, with an average of 0.39. Furthermore, the gene diversity or predicted heterozygosity (He) ranged from 0.12 at Me7-Em12 to 0.36 at Me1-Em2, with an average of 0.25. The 12 CDDP primers generated variable and reliable bands in 57 melon individuals. These CDDP primers amplified 157 reliable bands in total, with the number of reliable bands per primer set ranging from 9 (MADS-1 and WRKY-R3B) to 19 (MADS-4). The size of the scorable band varied from 100 to 1100 base pairs. Polymorphism was found in 131 of the 157 scorable bands. The polymorphic bands formed by 12 CDDP primers ranged from 5 to 18, with a mean of 10.92 per primer. KNOX-2 has the least polymorphic bands, while MADS-4 had the maximum. The PIC and MI values of the CDDP primers showed a high level of variance. The PIC of the 12 CDDP primers ranged from 0.20 to 0.37, with the KNOX-3 primer having the highest value. The MI values ranged from 0.62 (WRKY-R3B) to 5.55 (KNOX-3) per primer, with a mean value of 3.50. The Shannon\u0026rsquo;s information index (I) ranged from 0.32 at WRKY-R3B to 0.58 at MYB-1 with an average 0.45. Moreover, the gene diversity or expected heterozygosity (He) ranged from 0.18 at WRKY-R3B to 0.40 at MYB-1 with an average of 0.30.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP primers, their amplification, and the degree of polymorphism obtained in melon accessions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"21\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eURP markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eSRAP markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c19\" namest=\"c16\"\u003e \u003cp\u003eCDDP markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eTAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-1F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe1-Em2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eABP1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-2F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe1-Em7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eERF-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe1-Em8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eERF-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-9F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe1-Em11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eKNOX-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-13R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe1-Em12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eKNOX-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-17R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe1-Em13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMADS-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-25F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe2-Em2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMADS-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-30F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe2-Em11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMYB-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURP-38F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe2-Em12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eWRKY-F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTR6149-3'LTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe4-Em7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eWRKY-R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTR6150-3'LTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe5-Em7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eWRKY-R3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe5-Em12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eWRKY-R3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe5-Em13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e13.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe6-Em7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e157.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e131.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe6-Em13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe7-Em12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe10-Em2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMe10-Em13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e146.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e125.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e33.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTNB: total number of amplified bands; TPB: total number of polymorphic bands; PIC: polymorphism information content; MI: marker index; ; I: Shannon\u0026rsquo;s information index; He: expected heterozygosity or gene diversity\u003c/p\u003e \u003cp\u003eClustering and structure analysis of melon accessions\u003c/p\u003e \u003cp\u003eThe dendrogram built by Ward analysis revealed that URP\u0026thinsp;+\u0026thinsp;IRAP results classified 57 accessions of melon into two primary categories (G-1 and G-2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Twenty-three accessions were included in the first. This group was subdivided into two subgroups (SG-1 and SG-2). The AN1 accession was included in the first subgroup (SG-1). Twenty-two accessions were included in the second subgroup (SG-2). The second major group was further subdivided into two subgroups, the first of which included 7 accessions and the second of which included 27 accessions. The Jaccard coefficient was used to calculate genetic dissimilarity, which ranged from 0.32 to 0.84. The accessions AN37 and AN38 had the highest genetic dissimilarity (84%), whereas the accessions AN13 and AN26 had the lowest genetic distance (32%). The STRUCTURE software was used to assess the marker information with a Bayesian-based model in order to better understand the association between the analyzed accessions. The proportions of membership ranged from K\u0026thinsp;=\u0026thinsp;1 to K\u0026thinsp;=\u0026thinsp;9. According to Evanno's approach, Delta K had the largest ad hoc value at K\u0026thinsp;=\u0026thinsp;2, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, demonstrating that the 57 accessions are better separated into two populations using URP\u0026thinsp;+\u0026thinsp;IRAP data. Population 1 consisted of 27 accessions, while population 2 included 30 accessions. Assuming that accessions with a membership coefficient (Q value) of 0.79 or higher were considered pure (Ahmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), 49.12% of the accessions tested belonged to corresponding pure groups, while the remaining 50.88% belonged to an admixed group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eBased on the SRAP data, the Ward method was used to perform hierarchical clustering, which divided 57 melon accessions into two primary groups (SG-1 and SG-2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The initial group (G-1) included twenty-one different accessions. This was divided into two subgroups (SG-1 and SG-2) with the first (SG-1) having one accession (AN50) and the second (SG-2) having twenty accessions. The second largest group (G-2) consisted of 36 accessions. The genetic distance was calculated using the Jaccard coefficient, which ranged from 0.20 to 0.78. The accessions AN6 and AN46 had the smallest genetic dissimilarity (22%), whereas AN12 and AN36 had the largest genetic difference (78%). To infer population structure (K\u0026thinsp;\u0026gt;\u0026thinsp;1) based on SRAP data, the STRUCTURE software employed a model-based Bayesian approach. The ad hoc statistic K was used to calculate the actual number of clusters (K) based on the log probability of data with regard to K values. STRUCTURE analysis of the 18 SRAP primer pairs suggests that K\u0026thinsp;=\u0026thinsp;2 is the greatest value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This number denotes the presence of two informative populations among all melon accessions. Population 1 included 29 accessions, while population 2 comprised 28 accessions. Thirty-one of the accessions investigated corresponded to pure populations, whereas the other 26 belonged to an admixed population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThe CDDP dendrogram classified the accessions into three principal groupings (G-1, G-2, and G-3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The largest cluster (G-1) has 42 accessions, which were further subdivided into two sub-clusters. There were nine accessions in the first sub-cluster (SG-1). The second sub-cluster included thirty-three accessions. The second (G-2) and third (G-3) main CDDP groups were made up of eleven and four accessions, respectively. The genetic disparity was determined using the Jaccard coefficient, which varied from 0.25 to 0.73. The accessions AN8 and AN24 exhibited the least genetic distance (25%), while AN4 and AN20 had the highest genetic variation (73%). Using the STRUCTURE software, the CDDP genotyping findings were utilized to perform population structure analysis on 57 accessions under an admixture model. The Evano method determined K\u0026thinsp;=\u0026thinsp;3 as the best number of clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). An accession was deemed a pure member of a cluster if the likelihood of membership in that cluster was greater than 79%. For K\u0026thinsp;=\u0026thinsp;3, the resulting clusters had 15, 18, and 24 accessions, respectively, for clusters 1, 2, and 3. Thirty accessions were classified as pure, while the remaining accessions were designated as admixed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eAnalysis of molecular variance and diversity indices\u003c/p\u003e \u003cp\u003eBased on the results of STRUCTURE clustering, the analysis of molecular variance (AMOVA) approach approximates population divergence directly from the three types of markers. AMOVA revealed 5.64, 9.80, and 7.55% variation among populations for the URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP markers, respectively, as well as significant AMOVA variance within populations of 94.36, 91.20, and 92.45% for the URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP markers, respectively. Phi-statistics provides a summary of the degree of differentiation between clusters. According to the phi-statistics, there is little differentiation between STRUCTURE clusters (0.056, 0.098, and 0.076 for URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP markers, respectively), but there were high and significant variance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) within STRUCTURE groupings for three types of markers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Heterozygosity can be estimated using expected heterozygosity (He), which provides information about the likelihood of an individual's fraction of heterozygosity for all studied loci. Using URP\u0026thinsp;+\u0026thinsp;IRAP primers, the number of effective allele (Ne), Shannon\u0026rsquo;s information index (I), expected heterozygosity (He), and polymorphic loci percentage (PP) ranged from 1.42\u0026ndash;1.43, 0.40\u0026ndash;0.41, 0.25\u0026ndash;0.26, and 90.21\u0026ndash;95.80, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest fixation index (Fst) value was recorded by population 1. The gene flow (GF), based on the PhiPT value, between both populations was 4.2. Based on SRAP primers, the Ne, I, He, and PP were between 1.40\u0026ndash;1.46, 0.38\u0026ndash;0.42, 0.24\u0026ndash;0.28, and 92.89\u0026ndash;94.40, respectively. The highest Ne, I, He, and PP were registered by population 1. Population 1 had the greatest Fst value (0.44). The gene flow (GF) between the two populations was 2.30 based on the PhiPT value (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the CDDP data, population 3 had the highest Ne (1.55), I (0.48), He (0.32), and PP (93.89%) values. These populations likewise had a high Fst value (0.30), which was followed by population 1. Based on the PhiPT value, the gene flow (GF) between the two populations was 3.04 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular variance (AMOVA) in melon populations using URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eURP\u0026thinsp;+\u0026thinsp;IRAP markers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEst. Var.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVar (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1209.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1268.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhiPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eSRAP markers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEst. Var.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVar (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e936.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1006.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhiPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eCDDP markers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEst. Var.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVar (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1101.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1204.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhiPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDf: degree of freedom; SS: sum of squared observations; MS: mean of the squared observations; Est Var: estimated variance; Var: variance; PhiPT: proportion of the total genetic variance among the individuals within a population; p-value: probability value.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiversity indices, genetic differentiation, and gene flow detected in melon populations based on the data of URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eURP\u0026thinsp;+\u0026thinsp;IRAP markers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eSRAP marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eCDDP marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eN: number of accessions; Ne: number of effective alleles; I: Shannon\u0026rsquo;s information index; He: expected heterozygosity or gene diversity; PP: percentage of polymorphism, populations; Fst: fixation index; GF: gene flow; Pop: population.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGenetic diversity research is a mechanism that characterizes species or accessions using specific statistical methods or a mix of approaches based on molecular characteristics of individuals. The assessment of genetic variation in plant germplasm is a powerful method to investigate superior breeding resources and improving breeding efficiency. In response to environmental stresses, plant populations require the evaluation of genetic diversity. The frequency of high genetic diversity within populations has been shown in numerous plant species, and the outcrossing nature of these species contributes to variation (Sheidai et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although the relationship between fitness and sustainability is unknown, genetic variation is related to both (Woodruff \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Thus, a population's evolutionary potential is governed by its gene pool, the size of which determines whether natural selection and genetic drift can function. Once obtained, conservation geneticists employ genetic data to measure within- and between-population diversity.\u003c/p\u003e \u003cp\u003eThe allelic richness of plant accessions is a measure of genetic diversity enrichment, which is widely used by informative molecular markers to define populations for selection, breeding, and conservation. As the number of markers and genome coverage increases, the data's dependability should improve. In many plant species, URP, SRAP, and CDDP have been shown to be more informative than other prominent DNA marker types for detecting genetic diversity. In this work, diverse molecular markers and primers produced different amplification products, showing genomic polymorphism. The main reason for these differences is related to the amplification of the genome by different marker types. SRAP markers are gene-targeted markers that target the coding region of a gene, whereas CDDP markers target conserved parts of functional genes and URP markers are designed from a repetitive DNA fragment and target non-coding regions of the genome.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first investigation to use URP and CDDP markers to describe the genetic structure and variability of melon accessions. Using these three types of markers, a reasonably high percentage of polymorphic bands were found in the current study. As a result, three separate types of molecular markers produced 398 bands. In our study, a large number of polymorphisms, combined with a high number of polymorphic alleles derived per primer, could be explained by both the wide range of genetic diversity, as well as the performance of URP, SRAP, and CDDP markers in achieving adequate polymorphism in targeted regions of the melon genome accessions. The large percentages of polymorphism also indicate the heterozygous nature of melon accessions' genomic makeup, suggesting their utility in genetic variability studies in melon accessions. Because the targeted genes (CDDP and SRAP) and URP markers create such a wide range of variation, they might be utilized to identify plant components that are related across accessions. The largest mean number of polymorphisms in each marker revealed the use of each locus for measuring genetic variety; as a result, primers with more alleles are better for genetic variation testing since they cover more of the genome. A PIC is a marker of genetic variation among population accessions. This focuses on the evolutionary pressure on alleles as well as the mutation rate that a locus may have experienced over time. A PIC value is also a critical predictor of marker effectiveness for linkage analysis when determining the inheritance between offspring and parental genotypes. Some primer efficiency indices, like MI, show the overall utility of a specific primer for description and discriminating across a large number of accessions. The higher their levels, the more efficient and informative the primers will be. In the current study, the PIC mean values of three markers were larger than 0.25, indicating the presence of considerable genetic diversity among the melon accessions. The order of discrimination power of the three types of markers was as follows: URP\u0026thinsp;+\u0026thinsp;IRAP\u0026thinsp;\u0026gt;\u0026thinsp;CDDP\u0026thinsp;\u0026gt;\u0026thinsp;SRAP based on TPB, PIC, and MI values.\u003c/p\u003e \u003cp\u003eDiversity indices are statistics that are used to characterize the variation of a population in which each individual belongs to a distinct group. Indices with lower values imply less diversity, whereas indices with higher values suggest greater diversity. The mean values of expected heterozygosity (He) for URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP were 0.26, 0.26. and 0.31, respectively, showing a moderate degree of genetic diversity between accessions in a population. The average value of Shannon\u0026rsquo;s information index (I) acquired by CDDP markers was higher than that obtained by URP\u0026thinsp;+\u0026thinsp;IRAP and SRAP markers, indicating that the CDDP genome contains a significant degree of variation among the accessions.\u003c/p\u003e \u003cp\u003eThe analysis of melon accessions' diversity has provided the framework to understand population structure. The population differentiating analysis can help you understand genetic diversity and improve the accuracy of genome-wide association study (GWAS). As a consequence, a great deal of effort is put into thoroughly investigating the underlying population structure of any population that will be utilized to determine marker-trait correlations. As a result, the first step in conducting a GWAS for real marker-trait relationships is to investigate population structure. Without any prior knowledge, Bayesian model-based analysis could ascribe each accession to a putative ancestral group (s), as well as reveal non-obvious mixing via distance-based clustering methods. The results of two distance-based clustering studies (Ward clustering and structure analyses) are highly comparable, with investigated accessions divided into two groups for URP\u0026thinsp;+\u0026thinsp;IRAP and SRAP markers and three groups for CDDP markers. Ancestral mixing was thought to be linked to plant germplasm interchange and hybridization. In addition, genetic differences in this case may be attributable in part to gene flow, as genetic drift has a considerable impact on the populations of this species. The largest gene flow between groups corroborated the AMOVA findings, indicating that intra-population variance was greater than inter-population variation, The structure analysis results of three marker types mainly agreed with the groups revealed in the cluster analysis, which split the 57 melon accessions into two or three genetic groups based on the delta K value. The two clusters of URP and SRAP markers and the three clusters of the CDDP method were heavily admixed, showing that the majority of variation exists within the groupings among the accessions. The fact that melon is a powerful cross-pollinating plant with primarily heterozygous offspring produced through selfing may explain the large proportion of accessions of mixed ancestry. Two or three populations may be eligible for our panel based on population size and the variation in the number of accessions representing the six provinces from which they were taken. A population's genetic differentiation represents the interactions of numerous evolutionary processes such as dispersion shifts, habitat change, and population separation, mutation, genetic drift, mating system, gene flow, and natural selection. Geographic isolation, community fragmentation, breeding systems, and genetic drift are all major sources of large population diversity. The fixation index (Fst) is a genetic divergence metric that uses a scale of 0 to 1 to evaluate genetic distance caused by population structure, with 0 indicating total genetic material sharing and 1 indicating no sharing. A Fst value larger than 0.15 is as regarded significant in distinguishing populations (Frankham \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). When the Fst values of the three types of molecular markers were compared, the results revealed that the highest Fst mean value (0.36) was found in the populations created by SRAP markers, confirming the existence of significant genetic variation within the individuals of their populations. The predicted heterozygosity values of melon within a population were high, as revealed by our findings of three markers, indicating that they contain relatively significant amounts of genetic variation. Previously, multiple studies investigated the genetic diversity of melon accessions using various types of molecular markers, with diverse results shown by different authors (Baudracco-Arnas and Pitrat \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Karimi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Maleki et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trimech et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), but our study was the first to use URP, SRAP, and CDDP for the study of melon genetic diversity.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn essence, combining field results based on URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP analysis may be more useful in defining genetic variation among melon accessions. Based on URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP markers, the melon accessions exhibited a wide range of variability that might be used for genetic studies and breeding programs. Based on the data, Ward dendrograms revealed distinct distribution patterns of genetic variation among melon accessions. Some accessions were placed in the same cluster in the dendrograms created by URP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP. Based on the average values of polymorphic bands per primer and PIC of three types of markers, the order of the power of discrimination of melon accession was as follows: URP\u0026thinsp;+\u0026thinsp;IRAP\u0026thinsp;\u0026gt;\u0026thinsp;CDDP\u0026thinsp;\u0026gt;\u0026thinsp;SRAP. URP markers, particularly URP-1F and URP-13R, can be used to initiate the differentiation of melon accessions. Model-based STRUCTURE, on the other hand, identified three groups based on CDDP data, while the two other markers showed only two populations. The melon accessions investigated here offer a valuable gene pool that should be further evaluated for agronomic features and performance in different conditions. These analyses can help to find the diversity of germplasm collections and select accessions for future breeding. Within the expanding consideration of agrobiodiversity and its important relevance in feeding communities, attention should be paid to the protection and sustainable usage of melon genetic resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the Department of Horticulture at the College of Agricultural Engineering Sciences for their help and support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit author statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNawroz Abdul-razzak Tahir: Conceived and designed the experiments. Rebwar Rafat Aziz: Carried out the experiments and analyzed the data. Nawroz Abdul-razzak Tahir and Rebwar Rafat Aziz: Writing- Reviewing and editing, Nawroz Abdul-razzak Tahir: Visualization, investigation, and supervision this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors note that they have no known competing financial interests or personal affiliations that could appear to have impacted the work presented in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed DA, Tahir NA-r, Salih SH, Talebi R (2021) Genome diversity and population structure analysis of Iranian landrace and improved barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) genotypes using arbitrary functional gene-based molecular markers. Genet Resour Crop Evol 68:1045\u0026ndash;1060\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAneja B, Yadav NR, Yadav RC, Kumar R (2013) Sequence related amplified polymorphism (SRAP) analysis for genetic diversity and micronutrient content among gene pools in mungbean (\u003cem\u003eVigna radiata\u003c/em\u003e L.) Wilczek. Physiol Mol Biol Plants 19:399\u0026ndash;407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagheriyan S, Karimi HR, Esmaelizadeh M (2015) Evaluation of genetic relationships among melon genotypes based on morphological markers. Int J Veg Sci 21:36\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaudracco-Arnas S, Pitrat M (1996) A genetic map of melon (\u003cem\u003eCucumis melo\u003c/em\u003e L.) with RFLP, RAPD, isozyme, disease resistance and morphological markers. Theor Appl Genet 93:57\u0026ndash;64\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho N, Canela FM, Leite PHS, Ferreira MA, Oliveira VR, Santos MF, Souza NOS, Buso GSC (2017) Analysis of genetic variability of commercial melon cultivars using SSR molecular markers. Genet Mol Res 16:1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollard BCY, Mackill DJ (2009) Start codon targeted (SCoT) polymorphism: A Ssmple, novel DNA marker technique for generating gene-targeted markers in plants. Plant Mol Biol Rep 27:86\u0026ndash;93\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDantas ACA, Holanda ISA, Esteras C, Nunes GHS, Pic\u0026oacute;. (2015) MB Diversity of melon accessions from northeastern Brazil and their relationships with germplasms of diverse origins. J Am Soc Hortic Sci 140:504\u0026ndash;517\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol Ecol 14:2611\u0026ndash;2620\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrankham R (1995) Conservation genetics. Annu Rev Genet 29:305\u0026ndash;327\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGovindaraj M, Vetriventhan M, Srinivasan M, Rogozin IB (2015) Importance of genetic diversity assessment in crop plants and its recent advances: An overview of its analytical perspectives. Genet Res Int 2015:431487\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill WG (2001) Selective Breeding. In: Brenner S, Miller JH (eds) Encyclopedia of Genetics. Academic Press, New York, pp 1796\u0026ndash;1799\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbrahim M, Haggag NA, Hassan, Haggag S, Zein NA, Abdallah, Nagwa I, Elatabi (2021) Molecular markers assessment of genetic diversity of ten Egyptian melon (\u003cem\u003ecucumis melo\u003c/em\u003e l.) accessions cultivated. Plant Cell Biotechnol Mol Biol 22:103\u0026ndash;123\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jan E, Calsyn IG, Everaert E, Van Bockstaele, Marc de Loose (2001) AFLP based alternatives for the assessment of distinctness, uniformity and stability of sugar beet varieties. Theor Appl Genet 103:1254\u0026ndash;1265\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJavier Obando (2008) Juan Pablo Fern\u0026aacute;ndez-Trujillo, Juan Antonio Mart\u0026iacute;nez, Antonio Luis Alarc\u0026oacute;n, Iban Eduardo, Pere Ar\u0026uacute;s, Antonio Jos\u0026eacute; Monforte Identification of melon fruit quality quantitative trait loci using near-isogenic lines. J Am Soc Hortic Sci 133:139\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaki A, Vafaee Y, Khadivi A (2020) Genetic variation of \u003cem\u003eAnacamptis coriophora\u003c/em\u003e, Dactylorhiza umbrosa, Himantoglossum affine, \u003cem\u003eOrchis mascula\u003c/em\u003e, and \u003cem\u003eOphrys schulzei\u003c/em\u003e in the western parts of Iran. Ind Crops Prod 156:112854\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang HW, Kwon SW, Go SJ (2003) PCR-based specific and sensitive detection of \u003cem\u003ePectobacterium carotovorum\u003c/em\u003e ssp. carotovorum by primers generated from a URP‐PCR fingerprinting‐derived polymorphic band. Plant Pathol 52:127\u0026ndash;133\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimi HR, Bagheriyan S, Esmaelizadeh M, Estaji A (2016) Genetic relationships among melons using RAPD markers. Int J Veg Sci 22:200\u0026ndash;208\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKishor DS, Song W-H, Noh Y, Lee GP, Park Y, Jung J-K, Shim E-J, Chung S-M (2020) Development of SNP markers and validation assays in commercial Korean melon cultivars, using Genotyping-by-sequencing and Fluidigm analyses. Sci Hortic 263:109113\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Quiros CF (2001) Sequence-related amplified polymorphism (SRAP), a new marker system based on a simple PCR reaction: its application to mapping and gene tagging in Brassica. Theor Appl Genet 103:455\u0026ndash;461\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaleki M, Shojaeiyan A, Rashidi Monfared S (2018) Population structure, morphological and genetic diversity within and among melon (\u003cem\u003eCucumis melo\u003c/em\u003e L.) landraces in Iran. J Genet Eng Biotechnol 16:599\u0026ndash;606\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManchali S, Chidambara Murthy KN, Vishnuvardana, Patil BS (2021) Nutritional composition and health benefits of various botanical types of melon (\u003cem\u003eCucumis melo\u003c/em\u003e L.). Plants 10:1755\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekonnen T, Haileselassie T, Goodwin SB, Tesfayea K (2020) Genetic diversity and population structure of \u003cem\u003eZymoseptoria tritici\u003c/em\u003e in Ethiopia as revealed by microsatellite markers. Fungal Genet Biol 141:103413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerheb J, Pawełkowicz M, Branca F, Bolibok-Brągoszewska H, Skarzyńska A, Pląder W, Chalak L (2020) Characterization of Lebanese germplasm of snake melon (\u003cem\u003eCucumis melo\u003c/em\u003e subsp. melo var. flexuosus) using morphological traits and SSR markers. Agronomy 10:1293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNu\u0026ntilde;ez-Palenius HG, Gomez-Lim M, Ochoa-Alejo N, Grumet R, Lester G, Cantliffe DJ (2008) Melon fruits: genetic diversity, physiology, and biotechnology features. Crit Rev Biotechnol 28:13\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey A, Ranjan P, Ahlawat SP, Bhardwaj R, Dhariwal OP, Singh PK, Malav PK, Harish GD, Prabhu P, Agrawal A (2021) Studies on fruit morphology, nutritional and floral diversity in less-known melons (\u003cem\u003eCucumis melo\u003c/em\u003e L.) of India. Genet Resour Crop Evol 68:1453\u0026ndash;1470\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParis HS, Amar Z, Lev E (2012) Medieval emergence of sweet melons, \u003cem\u003eCucumis melo\u003c/em\u003e (Cucurbitaceae). Ann Bot 110:23\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePitrat M (2017) Melon Genetic Resources: Phenotypic Diversity and Horticultural Taxonomy. In: Grumet R, Katzir N, Garcia-Mas J (eds) Genetics and genomics of cucurbitaceae, vol 20. Springer, Cham, pp 25\u0026ndash;60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945\u0026ndash;959\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghami M, L\u0026oacute;pez-Ses\u0026eacute; AI, Hasandokht MR, Zamani Z, Moghadam MRF, Kashi A (2014) Genetic diversity among melon accessions from Iran and their relationships with melon germplasm of diverse origins using microsatellite markers. Plant Syst Evol 300:139\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRold\u0026aacute;n-Ruiz I, Dendauw J, van Bockstaele E, Depicker A, de Loose M (2000) AFLP markers reveal high polymorphic rates in ryegrasses (\u003cem\u003eLolium\u003c/em\u003e spp.). Mol Breed 6:125\u0026ndash;134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaputro NW, Hidayat T, Bayfurqon FM, Khamid MBR (2020) Evaluation of morpho-agronomic characterization Apple cucumber: a new variety of melon from Indonesia. IOP Conf Ser : Earth Environ Sci 457:12061\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeungbum L, Ik J, Do Yoon H, Jung-Ro L, Seong-Hoon K, Eunae Y, Sookyeong L, Gyu-Taek C, Kyung JL (2020) Evaluation of morphological traits and genetic composition in melon germplasm. Korean J Crop Sci 65:485\u0026ndash;498\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheidai M, Afshar F, Keshavarzi M, Talebi S-M, Noormohammadi Z, Shafaf T (2014) Genetic diversity and genome size variability in \u003cem\u003eLinum austriacum\u003c/em\u003e (Lineaceae) populations. Biochem Syst Ecol 57:20\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh D, Di Leskovar, Sharma SP, Sarao NK, Vashisht VK (2020) Genetic diversity and interrelationship among Indian and exotic melons based on fruit morphology, quality components and microsatellite markers. Physiol Mol Biol Plants 26:985\u0026ndash;1002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian J, Chen J, Chen G, Wu P, Zhang H, Zhao Y (2015) Genetic analyses of wheat and molecular marker-assisted breeding. Conditional QTL analysis and MAS, vol 2. Science Press, Dordrecht: Springer, Beijing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrimech R, Afif M, Boussaid M (2015) Genetic diversity of Tunisian melon (\u003cem\u003eCucumis melo\u003c/em\u003e. L) landraces and their relationships with introduced varieties as assessed by simple-sequence repeat (SSR) markers. Afr J Biotechnol 14:86\u0026ndash;95\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoodruff DS (2001) Populations, Species, and Conservation Genetics.Encyclopedia of Biodiversity:811\u0026ndash;829\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZagorcheva T, Stanev S, Rusanov K, Atanassov I (2020) SRAP markers for genetic diversity assessment of lavender (\u003cem\u003eLavandula angustifolia\u003c/em\u003e mill.) varieties and breeding lines. Biotechnol Biotechnol Equip 34:303\u0026ndash;308\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":"Cucumis melo, DNA-markers, Diversity indices, Genetic variation, Clustering, Genetic structure","lastPublishedDoi":"10.21203/rs.3.rs-1640623/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1640623/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMelon (\u003cem\u003eCucumis melo\u003c/em\u003e L.) is an ancient and indigenous crop that grows in Iraq's Kurdistan region, and research into its genetic diversity is needed to improve this important culinary plant while also promoting a healthy diet. This study assessed the genetic diversity of 57 melon accessions using three types of molecular markers, namely universal rice primer (URP), sequence-related amplified polymorphism (SRAP), and conserved DNA derived polymorphism (CDDP) markers. The URAP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP primers yielded 143, 125, and 131 polymorphic alleles, respectively. The average polymorphic information content (PIC) was 0.29, 0.26, and 0.32 for URAP\u0026thinsp;+\u0026thinsp;IRAP, SRAP, and CDDP, respectively. Based on the average values of polymorphic bands per primer and PIC of three types of markers, the order of the power of discrimination of melon accession was as follows: URP\u0026thinsp;+\u0026thinsp;IRAP\u0026thinsp;\u0026gt;\u0026thinsp;CDDP\u0026thinsp;\u0026gt;\u0026thinsp;SRAP. The URP-4R, Me1-Em2, and MYB-1 primers had the highest Shannon information index values. Based on the Ward method-based cluster and STRUCTURE analysis, URAP\u0026thinsp;+\u0026thinsp;IRAP and SRAP markers grouped 57 accessions into two primary clusters with various sub-clusters, whereas CDDP markers divided 57 accessions into three groups. The genetic distances between accessions ranged from 0.32 to 0.84 for URP\u0026thinsp;+\u0026thinsp;IRAP, 0.20 to 0.78 for SRAP, and 0.25 to 0.73 for CDDP. The analysis of molecular variance revealed an increase in genetic variation within groups, as well as significant gene exchange between populations. In SRAP analysis, substantial genetic distinctiveness was identified among populations. However, in CDDP analysis, high Shannon's information index and predicted heterozygosity were observed among populations. The outcomes of this study indicated a high level of variability in Iraqi melon germplasm, which must be preserved and included in improvement programs for this ancient crop.\u003c/p\u003e","manuscriptTitle":"Genetic diversity and structure analysis of melon accessions using URP, SRAP, and CDDP markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-09 15:48:34","doi":"10.21203/rs.3.rs-1640623/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-06-05T11:12:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-06-02T11:40:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-10T13:18:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2022-05-10T02:43:07+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":"40aec7bd-5ad1-405e-84b0-d5f3341d55fb","owner":[],"postedDate":"June 9th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-16T03:23:45+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-09 15:48:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1640623","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1640623","identity":"rs-1640623","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}