Identification and Genetic Diversity Analysis of Cucurbita Varieties Based on SSR Markers

Zou, Jialong; Wu, Xingting; Liu, Xuejing; Peng, Changcheng; Ma, Yingxue; Zhang, Xiujie; Yang, Xuhong; Luo, Shuailong; Xing, Weigeng; Hong, Hao; Li, Lang; Tan, Bin; Jing, Ruonan; Xu, Zhenjiang

doi:10.3390/agronomy15061420

Open AccessArticle

Identification and Genetic Diversity Analysis of Cucurbita Varieties Based on SSR Markers

by

Jialong Zou

^1,†

,

Xingting Wu

^2,†

,

Xuejing Liu

²,

Changcheng Peng

³,

Yingxue Ma

²,

Xiujie Zhang

²,

Xuhong Yang

²,

Shuailong Luo

¹,

Weigeng Xing

¹

,

Hao Hong

¹,

Lang Li

³,

Bin Tan

¹

,

Ruonan Jing

^2,* and

Zhenjiang Xu

^1,*

¹

College of Agriculture, Guangzhou Sub-Center for New Plant Variety Tests of the Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou 510642, China

²

Development Center of Science and Technology, Ministry of Agriculture and Rural Affairs, Beijing 100176, China

³

Yueyang Academy of Agricultural Sciences, Yueyang 414000, China

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Agronomy 2025, 15(6), 1420; https://doi.org/10.3390/agronomy15061420

Submission received: 14 April 2025 / Revised: 7 June 2025 / Accepted: 8 June 2025 / Published: 10 June 2025

(This article belongs to the Section Horticultural and Floricultural Crops)

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Abstract

Cucurbita L. is a valuable gourd vegetable crop, with high nutritional and economic value. However, the lack of a molecular identification system and population genetic information has impeded the development of proper conservation strategies and marker-assisted genetic breeding for Cucurbita varieties. In this study, we developed a set of simple sequence repeat (SSR) markers for distinguishing the main cultivated Cucurbita cultivars in China and providing technical support for domestic variety preservation, registration, and intellectual property protection. A total of 152 allelic variations and 308 genotypes were identified from 306 Cucurbita cultivars by using 24 SSR markers. Using 24 core markers, we successfully distinguished 300 varieties from 306 Cucurbita varieties, and the identification rate reached 98.36%. The PIC values of the 24 core markers ranged from 0.281 to 0.749, and the average value was 0.643, which was considered high genetic diversity. Based on the results of cluster analysis, principal component analysis, and population genetic structure analysis using 24 pairs of core primers for 306 Cucurbita varieties, the results were basically consistent, all categorized into three genetic clusters, corresponding to the three species: C. moschata, C. pepo, and C. maxima. These results showed a certain correlation with phenotypic traits. DNA fingerprints were constructed for the 306 Cucurbita cultivars based on the core markers. Our research results provide a new tool for population genetic analysis, variety identification, and protection in Cucurbita cultivars with high efficiency, accuracy, and lower costs compared to conventional methods.

Keywords:

Cucurbita; SSR; variety identification; genetic diversity

1. Introduction

Cucurbita belongs to the genus Cucurbita in the gourd family (Cucurbitaceae) in plant taxonomy. It has a long cultivation history and is cultivated worldwide. Currently, there are mainly three species widely cultivated in the world, namely Cucurbita maxima Duch., Cucurbita moschata Duch., and Cucurbita pepo Linn., with a chromosome number of 2n = 2x = 40 [1,2,3]. China is a major country in Cucurbita production and consumption, ranking first in the world in both the cultivation area and yield of Cucurbita. In 2022, the cultivation area of Cucurbita in China was approximately 398,700 hectares, and the yield was 7.3776 million tons, accounting for 26.2% and 32.4% of the world’s total, respectively [4]. As early as 2005, C. pepo was included in “The List of Agricultural Plant Varieties Protected in the People’s Republic of China (Sixth Batch)”; in 2016, C. moschata was included in “The List of Agricultural Plant Varieties Protected in the People’s Republic of China (Tenth Batch)” [5]; C. maxima has not yet been included in the list of new plant variety protection [6]. As of 5 August 2024, the total number of applications for C. pepo and C. moschata varieties reached 727, accounting for approximately 6.17% of the total number of vegetable applications (11,784). The total number of applications ranked 7th among the 41 vegetable genera and species, and the total number of authorized varieties reached 115, accounting for approximately 4.27% of the total number of authorized vegetable varieties (2695), with the total number of authorizations ranking ninth among the vegetable genera and species [7]. China has rich Cucurbita germplasm resources. However, for a long time, the frequent introduction of varieties and self-breeding between different regions have led to the interlacing of strains among the existing Cucurbita varieties and the complexity of their genetic relationships. Phenomena such as “the same name for different varieties” or “different names for the same variety” often occur. In addition, every year, breeders of C. pepo and C. maxima mistakenly apply for variety rights for their varieties as C. moschata. At the same time, the identification process needs to be carried out after the fruit morphology has fully developed, which will inevitably cause a huge waste of human and material resources [8]. Traditional variety identification methods mainly rely on morphological traits to distinguish varieties. Although the identification results are the most reliable, morphological traits are distributed throughout the whole growth period, and the identification cycle is long, making it difficult to meet the demand for rapid variety identification in market supervision. In addition, due to the insufficient genetic diversity of cultivated pumpkin varieties, a set of markers is more urgently needed for variety identification. Therefore, screening a set of SSR primers that meet the requirements for variety identification and are universally applicable to the Cucurbita genus and constructing a molecular identification system and SSR fingerprint database for Cucurbita varieties is of great significance. DNA molecular marker technology that directly detects the differences at the DNA level among varieties is not affected by environmental conditions, does not require field planting, and has the characteristics of being fast, efficient, and reproducible. Currently, research on constructing plant variety identification systems based on SSR markers has been widely reported in crops such as wheat, maize, rice, citrus, lettuce, non-heading Chinese cabbage, and bitter melon [9,10,11,12,13,14,15]. With the rapid development of molecular biology, the completion of the whole-genome sequencing and assembly of C. maxima, C. moschata, and C. pepo has promoted the development of Cucurbita genomics and laid the foundation for the development of molecular markers [16,17]. Molecular marker technology has been widely applied in the identification of Cucurbita varieties. Yun [18] selected two primer combinations, each consisting of 4 primers, and was able to distinguish all 28 tested C. moschata varieties and constructed two ISSR-marker fingerprint maps. Sim [19] used 29 SSR markers to distinguish 160 Cucurbita varieties. Nguyen [20] used 192, 96, and 48 markers to identify 204 (discrimination rate: 91.5%), 190 (85.2%), and 141 (63.2%) out of 223 materials, respectively. Tao [21] used SRAP molecular markers to draw the DNA fingerprint maps of 88 Cucurbita materials, and 5 pairs of SRAP markers could distinguish all 88 Cucurbita materials. Liu [22] used SRAP and RSAP markers to construct the fingerprint map of Jinli Cucurbita, and the markers E1M5 and R1R7 could distinguish Hongli No. 2, Hongli, Jinli No. 2, and Jinli Cucurbita. Zhang [23] used 5 SSR markers to distinguish 48 seed-used C. pepo materials and constructed a DNA fingerprint map. SSR markers have the advantages of high polymorphism, simple operation, good repeatability, high stability, low cost, and co-dominance [24] and are one of the main recommended markers in the agricultural industry standard of China, “General Rules for DNA Molecular Marker Method for Plant Variety Identification” (NY/T 2594-2016) [25]. Establishing a molecular identification system and an SSR fingerprint database based on the fluorescence capillary electrophoresis platform has the advantages of high throughput, high resolution, and accurate data reading [26,27,28]. At present, there are few research reports on the establishment of a molecular identification system and SSR fingerprint database for Cucurbita varieties based on the fluorescence capillary electrophoresis platform. In this study, pumpkin germplasm resources were extensively collected, including cultivated varieties of the Cucurbita genus, varieties applied for variety right protection and their similar varieties, and market-sold or widely promoted varieties. Existing SSR molecular markers were gathered through a literature review, while publicly available genomes were used to develop new SSR molecular markers. A set of primers meeting the requirements for variety identification and universally applicable to the Cucurbita genus was screened using polyacrylamide gel electrophoresis and fluorescent capillary electrophoresis platforms. Based on these primers, an SSR fingerprint database for Cucurbita varieties was constructed, providing technical support for rapid variety identification, genetic diversity analysis, market supervision, prompt resolution of variety disputes, protection of new varieties, and auxiliary screening of similar varieties for DUS (Distinctness, Uniformity, and Stability) testing in the Cucurbita genus.

2. Materials and Methods

The experiment was carried out from 2023 to 2024 at South China Agricultural University, the Science and Technology Development Center of the Ministry of Agriculture and Rural Affairs, Yueyang Academy of Agricultural Sciences, and the Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences.

2.1. Materials

A total of 306 Cucurbita varieties of the genus Cucurbita were included, consisting of 117 C. moschata varieties, 143 C. pepo varieties, and 46 C. maxima varieties. Among them, 252 varieties (No. 1–252) were sourced from the Plant New Variety Preservation Center of the Ministry of Agriculture and Rural Affairs; 32 varieties (No. 253–284) were from Guangdong Helinong Biotechnology Seed Industry Co., Ltd. (Shantou, Guangdong, China); 19 varieties (No. 285–303) were from Anhui Jianghuai Horticulture Co., Ltd. (Hefei, Anhui, China); and 3 varieties (No. 303–306) were from the Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences (Table A1). The collected Cucurbita varieties of the genus Cucurbita have a wide geographical origin, covering most provinces in China, and the variety types include hybrids and inbred lines. Twelve representative varieties (four in each of C. moschata, C. pepo, and C. maxima) (Figure 1) with large phenotypic differences were selected from the collected Cucurbita species for primary screening of SSR markers.

2.2. Source of SSR Primer

A total of 125 pairs of Cucurbita SSR primers were collected from the relevant literature [29,30,31,32,33,34,35,36]. Based on the publicly available whole-genome sequencing data of C. moschata, C. pepo, and C. maxima in NCBI, the MISA v2.1 software was used to search for SSR loci. The search criteria were that the SSRs contained nucleotide repeats of 2, 3, 4, 5, and 6 nucleotides, with the minimum number of repeats being 6, 5, 5, 5, and 5, respectively [37]. The 200 bp sequences before and after the SSR loci were selected, and the software Primer 3.0 was used to design primers. The primer design parameters were as follows: primer length was 18–27 bp; GC content ranged from 40% to 60%; PCR product length was 100–280 bp; and the annealing temperature was 50–60 °C [38]. According to the positions of the primers on the chromosomes, 50 pairs of primers were selected from the designed primers. The above 175 pairs of SSR primers were entrusted to Shanghai Bioengineering Technology Service Co., Ltd. (Shanghai, China) for synthesis (Table A2).

2.3. Field Planting and Traits Investigation

All the tested Cucurbita varieties of the genus Cucurbita were planted in the field at the Testing Sub-center of New Plant Varieties of the Ministry of Agriculture and Rural Affairs (Yueyang) and the Testing Sub-center of New Plant Varieties of the Ministry of Agriculture and Rural Affairs (Beijing). The investigation of traits referred to the “Guidelines for the Test of Distinctness, Uniformity and Stability of New Plant Varieties Cucurbita (C. moschata)” (NY/T 2762-2015) [39], the “Guidelines for the Test of Distinctness, Uniformity and Stability of New Plant Varieties Summer Squash” (NY/T 2343-2013) [40], and the “Guidelines for the Test of Distinctness, Uniformity and Stability of New Plant Varieties Summer Squash” (submitted for approval draft). The analysis was carried out by converting the traits into codes according to the trait classification range. Both Cucurbita moschata and Cucurbita maxima have 35 phenotypic traits, while Cucurbita pepo has 61 phenotypic traits. The specific trait names, types, and methods are detailed in Table A3, Table A4 and Table A5.

2.4. DNA Extraction

The collected Cucurbita seeds were soaked in water to promote germination. After germination, they were planted in seedling trays. After the true leaves emerged, in order to fully represent the genotypes of the population and eliminate the influence of individual mixed plants, 20–30 tender leaves were taken from each variety, and DNA was extracted using the improved CTAB method [41,42,43].

2.5. PCR Amplification

A 20 µL PCR reaction system was adopted, which included 2 µL of DNA template (with a concentration of 50 ng/µL), 0.5 µL of each of the upstream and downstream SSR primers (with a concentration of 0.25 µmol/L), 10 µL of 2 × Taq Plus Master Mix, and 7 µL of ultrapure water.

The PCR amplification program was as follows: pre-denaturation at 94 °C for 5 min; denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, and extension at 72 °C for 45 s, for a total of 35 cycles; extension at 72 °C for 10 min’ and preservation at 4 °C for later use.

2.6. Polyacrylamide Gel Electrophoresis (PAGE)

2.6.1. Gel Making

First, we washed the glass plates with a detergent and then rinsed them twice with distilled water. We wiped the glass plates clean with a cloth. After the glass plates were dry, we applied 0.8 mL of the affinity silane working solution to the inner side of the long glass plate and 0.8 mL of the stripping silane working solution to the inner side of the short glass plate. After the glass plates were completely dry, we placed 0.4 mm-thick plastic strips on both sides of the long glass plate, covered it with the short glass plate, fixed both sides with clips, and then placed it horizontally using a spirit level. In the preparation of the gel solution, we took 109.38 mL of distilled water, 46.725 mL of the PAGE solution, 12.50 mL of 10 × TBE, 687.50 µL of ammonium persulfate (APS), and 62.50 µL of tetramethylethylenediamine (TEMED) and gently shook them to mix them evenly. We then poured the gel solution into the glass gel chamber along the glass groove at a uniform speed to avoid generating air bubbles. The shark tooth comb was gently inserted between the two glass plates, at about 4–6 mm, and left to stand at room temperature for 1–2 h. After the gel solution polymerized, we cleaned the gel solution overflowing on the surface of the glass plates, pulled out the comb, and rinsed it with clean water for later use.

2.6.2. Electrophoresis

The gel plate was installed on the electrophoresis tank and 1 × TBE buffer solution was added to both the positive and negative electrode tanks of the electrophoresis tank, ensuring that the electrode wires were submerged. Then, we performed pre-electrophoresis at a constant power of 80 W for 15 to 20 min. After that, we carried out the sample loading. We then added 2 µL of the DNA Marker and the PCR amplification products successively and used a pipette tip to load 1 to 1.5 µL of the PCR amplification product into each sample well.

2.6.3. Silver Staining

After the electrophoresis was completed, we placed the long glass plate with the attached gel into distilled water for washing. We gently shook the shaker to separate the gel plate from the glass plate. For the preparation of the staining solution and the developing solution, the staining solution consisted of 2 g of silver nitrate and 2 mL of formaldehyde, and the developing solution consisted of 12 g of sodium hydroxide and 2 mL of formaldehyde. We added them respectively to distilled water and to make up a volume of 1 L. The gel plate was placed into the staining solution and onto the shaker, where it was shaken slowly for 8 to 10 min. When the gel plate became translucent, we rinsed it with distilled water 2 to 3 times. After that, the gel plate was placed into the developing solution and gently shaken on the shaker for 8 to 10 min. When the bands on the gel plate were clear, we took out the gel plate and washed it with distilled water. After the gel plate was dried, we placed it on the gel plate observation lamp, observed and recorded the results, and took photos for preservation [44,45,46].

2.7. Fluorescent Capillary Electrophoresis

According to the lengths of the amplified fragments of each primer in the polyacrylamide gel electrophoresis, one of the fluorescent dyes, including 6-FAM (blue), ROX (red), TAMRA (black), and HEX (green), is selected to label the 5′ end of the forward primer, and the fluorescent primer is synthesized. We diluted the PCR products 100 to 180 times. We used a 10 µL capillary electrophoresis system, which contains 1 µL of the PCR product, 0.1 µL of the molecular weight internal standard, and 8.9 µL of deionized formamide, which were mixed evenly, shaken, and then centrifuged. We then denatured the mixture at 94 °C for 5 min. After cooling, it was briefly centrifuged. Genotyping was performed using a genetic analyzer (ABI3730), and the results were calibrated on the SSR fingerprint analyzer according to the molecular weight internal standard, allowing us to read the data and save it [47,48,49].

2.8. Data Analysis

The SSR Analyser (V1.2.6) was used to read the data exported from the genetic analyzer [10]. For homozygous loci in the genotype data, they were represented as X/X, and for heterozygous loci, they were represented as X/Y, where X and Y were the two allelic variations at that locus, respectively, with the data of the smaller fragment in front and that of the larger fragment behind. The genotype data of the missing loci were recorded as 0/0. The software GenALEx 6.5 was used to calculate relevant genetic parameters such as the effective number of alleles (Ne), the number of alleles (Na), the expected heterozygosity (He), the observed heterozygosity (Ho), and Shannon’s diversity index (I) for each primer [50]. The software PowerMarker V3.25 was used to calculate the major allele frequency, the number of genotypes (GTs), the polymorphism information content (PIC) of each primer, and the genetic distances among the varieties [51]. The software MEGA 4.0 was used to construct an UPGMA clustering diagram based on the Nei’s genetic distances among the varieties [52,53]. The software RStudio v2024.12 was used to construct a two-dimensional principal component analysis (PCA) diagram for the 306 Cucurbita varieties of the genus Cucurbita (the input data format is a “01 matrix” converted from genotype data, the PCA function used is dudi.pca, and the data preprocessing includes centering and scaling) [54]. The software Structure 2.3.4 was used to conduct a population genetic structure analysis of the 306 Cucurbita varieties of the genus Cucurbita (Length of Burnin Period: 10,000; Number of MCMC Reps after Burnin: 100,000; Use Admixture Model, Initial Value of ALPHA (Dirichlet Parameter for Degree of Admixture): 1.0; Frequency Model Info: Allele Frequencies are Correlated among Pops; Set K from 1 to 10; Number of Iterations:15) [55].

3. Results

3.1. Primer Screening

A total of 175 pairs of SSR primers were used to perform PCR amplification on 12 representative varieties. Subsequently, electrophoresis was carried out on a 6% denaturing polyacrylamide gel. As a result, 66 pairs of SSR primers with stable amplification, high polymorphism, and clear band patterns were preliminarily screened. Figure 2 shows the electrophoresis detection results of the amplified products of 3 pairs of primers in 12 Cucurbita varieties of the genus Cucurbita. According to the lengths of the amplified fragments, one of the fluorescent dyes, namely 6-FAM, ROX, TAMRA, or HEX, was selected to label the 5′ end of the forward primer, and fluorescent primers were synthesized.

A subset of 86 varieties was selected from the 306 Cucurbita varieties of the genus Cucurbita from different sources. Capillary electrophoresis detection was carried out using 66 pairs of fluorescent primers. Considering factors such as the ease of reading the peak patterns, high polymorphism, amplification stability, the discriminatory power of the primers, their distribution on the chromosomes, and the presence or absence of linkage effects among the primers, 24 pairs of core primers were finally determined for the identification of Cucurbita varieties of the genus Cucurbita (Table 1). To improve the detection efficiency, based on the sizes of the amplified fragments and the fluorescent colors of these 24 pairs of core primers, they were divided into 5 groups (Table 2). The primers in each group could be mixed for multiplex fluorescent capillary electrophoresis. Figure 3 shows the results of multiplex capillary electrophoresis of the five pairs of primers in Group 1 for the variety “Huangbeibei”. The interference among different primers within the group was minimal, with no abnormal peaks, and the peak patterns were easy to read.

3.2. Analysis of Core Primers

The 24 pairs of core primers were distributed across all 20 chromosomes (Figure 4), and there were no cases of equivalent identification. The primer sequences are shown in Table 2. These 24 pairs of core primers amplified a total of 152 allelic variations and 308 genotypes in 306 varieties. On average, each pair of primers produced 6.3 allelic variations and 12.8 genotypes. Among them, the primer NW56 had the largest number of allelic variations and genotypes, with 11 allelic variations and 22 genotypes, respectively, and a PIC value of 0.721 (Table 3). The PIC values of the core primers ranged from 0.281 to 0.749, with an average of 0.643. The 24 pairs of core primers exhibited rich polymorphism, which could effectively detect the genetic diversity among Cucurbita varieties of the genus Cucurbita and were suitable for variety identification.

To ensure the validity and reliability of the results based on microsatellites, this study conducted microsatellite quality control tests on 24 pairs of SSR primers. Microsatellite allele dropout and null allele detection were performed using Micro-Checker [56]. The results showed that the allele dropout rates of the 24 pairs of SSR primers ranged from 0 to 1.31%, and most primers had no dropout (Table A6). Our analysis revealed that all 24 pairs of SSR primers contained null alleles, which may be attributed to the presence of multiple alleles at SSR loci, and individual alleles might be affected by factors such as sample size, population genetic evolution, and narrow genetic background. Linkage disequilibrium (LD) tests were conducted using Genepop [57], and LD analysis was performed on all pairwise combinations (276 pairs in total) of the 24 SSR marker loci. Using r² > 0.8 as the criterion for strong linkage, the results (Table A7) showed that only four marker pairs exhibited weak linkage (r²: 0.2–0.5), while the remaining marker pairs showed no significant linkage and had high independence. These findings indicate that the 24 SSR markers screened in this study have strong application capabilities, and the results based on SSR markers are valid and reliable.

3.3. Construction of DNA Fingerprint Database

Based on the original data of capillary electrophoresis, the different amplified allelic variations were named by rounding. The fingerprint data of 306 Cucurbita varieties of the genus Cucurbita at 24 SSR core loci were collected to construct a DNA fingerprint database for Cucurbita varieties of the genus Cucurbita. Figure 5 shows the DNA fingerprint of the variety “Nongren 91”. In addition, the fingerprint data of 306 Cucurbita varieties were converted into digital codes, and information such as their corresponding variety types and provenance was integrated. For specific information, see the website: https://kdocs.cn/l/cmkIiUYKH5Ng (accessed on 7 June 2025).

3.4. Selection of Reference Varieties

To correct the systematic errors between different test batches or detection platforms, this study selected corresponding reference varieties for the main allelic variations detected by the core markers. Finally, 20 reference varieties were selected. Among them, 15 were from the Plant New Variety Preservation Center of the Ministry of Agriculture and Rural Affairs, 3 were from Anhui Jianghuai Horticulture Co., Ltd., (Hefei, Anhui, China) and 2 were from Guangdong Helinong Biotechnology Seed Industry Co., Ltd. (Shantou, Guangdong, China) (Table 4).

3.5. Analysis of Genetic Diversity of Cucurbita Varieties

Nei’s genetic distances among 306 Cucurbita varieties of the genus Cucurbita were calculated using PowerMarker V3.25, and a clustering diagram (Figure 6) was drawn using the unweighted pair-group method with arithmetic means (UPGMA). The 306 varieties were divided into three main groups, namely Pop1, Pop2, and Pop3. Pop1 included 102 C. moschata varieties, 4 C. pepo varieties, and 1 C. maxima variety. Pop2 consisted entirely of C. pepo varieties. Pop3 included 45 C. maxima varieties, 15 C. moschata varieties, and 2 C. pepo varieties. This clustering could clearly distinguish most of the C. moschata, C. pepo, and C. maxima varieties. The fact that 4 C. pepo varieties and 1 C. maxima variety were mixed in the C. moschata cluster, and 15 C. moschata varieties and 2 C. pepo varieties were mixed in the C. maxima cluster might be because these varieties were bred through hybridization between different cultivated Cucurbita species, resulting in their close genetic relationships with the cultivated species of one of their parents, thus clustering together. ‘Jinghu 36’ numbers 122 and 126 are varieties with the same name from different sources, and their genetic similarity coefficient is 1. Based on the traceability of parental relationships, it is speculated that they may be the same variety. ‘HC203’ and ‘PK No. 2’ numbers 146 and 152, as well as ‘Zhongtai 268’, ‘Jiarui 570’, and ‘Jiuyuan Jinfeng 118’ numbers 147, 154, and 170, are suspected to be the same variety or similar varieties. Further phenotypic identification is required to determine whether there are obvious differences among these varieties.

3.6. Principal Component Analysis

Dimensionality reduction analysis can intuitively reveal the genetic structure within the population. Based on the results of 24 SSR markers, the RSudio v2024.12 software was used to conduct a principal component analysis (PCA) on 306 Cucurbita varieties of the genus Cucurbita, and a two-dimensional PCA plot for these 306 varieties was constructed (Figure 7). According to the principle that the distance between points in the two-dimensional PCA plot corresponds to the genetic relationship, it can be seen that the varieties are mainly divided into three groups, which is consistent with the clustering analysis. The upper-left part of the plot mainly consists of the C. moschata group, the lower-left part mainly consists of the C. maxima group, and the right part mainly consists of the C. pepo group. Some varieties are cross-distributed among the three major groups, indicating that these varieties have complex genetic backgrounds. They may be bred through hybridization between different cultivated Cucurbita species, resulting in their close genetic relationships with the cultivated species of one of their parents, thus clustering together. It is also possible that they contain the same gene fragments or phenotypes as other different types of Cucurbita.

3.7. Analysis of Population Genetic Structure

In order to analyze the genetic structure present in the 306 Cucurbita materials in this study, the Structure 2.3.4 software was used to conduct a population genetic structure analysis of the 306 Cucurbita varieties of the genus Cucurbita. When the value of K ranged from 1 to 10, the ln P(K) value continuously increased as the value of K increased (Figure 8a). Referring to the research method of Evanno [58], the optimal number of groups K was determined according to the ∆K value. As can be seen in Figure 8b, when K = 3, the likelihood value was the largest, and the 306 Cucurbita varieties of the genus Cucurbita were genetically divided into three groups in terms of genetic structure (Figure 8c). Group I, the red part, was mainly the C. pepo group; Group II, the green part, was mainly the C. moschata group; and Group III, the blue part, was mainly the C. maxima group. The results of the population genetic structure analysis were basically consistent with those of the clustering analysis and the principal component analysis. Using the Q value analysis [59], the Q value of most varieties was greater than 0.6, indicating that the genetic relationships of most varieties in the large population were relatively simple. The genetic relationships of a very small number of varieties were complex, and there were hybridization situations among varieties from different regions.

3.8. Analysis of Diversity of Cucurbita Varieties in Different Regions

According to the statistical analysis of the variety source regions in Appendix A, the 306 Cucurbita germplasm materials in this study are mainly distributed in regions such as East China, North China, South China, and Northwest China, while fewer varieties are found in Northeast China, Central China, Southwest China, and other regions. To better evaluate the diversity of pumpkin varieties in each region, Shannon’s Information Index and Expected Heterozygosity were calculated for varieties in each region (Table 5). The results show that pumpkin varieties in the Northwest region exhibit the highest Shannon’s Information Index and Expected Heterozygosity, indicating the richest genetic diversity and the largest intraspecific genetic variation in this region. In contrast, pumpkin varieties in the South China region have lower Shannon’s Information Index and Expected Heterozygosity values, suggesting relatively scarce genetic diversity and smaller intraspecific genetic variation in this region.

3.9. Phenotypic Identification of Molecular-Undifferentiated Varieties

Phenotypic identification is the most direct method for identifying the identity of plant varieties. When the results of molecular identification are inconsistent with those of phenotypic identification, the results of phenotypic identification shall prevail. A total of three groups, involving 7 varieties, which could not be distinguished by the 24 pairs of SSR core primers, all belonged to C. pepo varieties. Phenotypic identification was carried out for these varieties. Referring to the “Guidelines for the Test of Distinctness, Uniformity and Stability of New Plant Varieties Summer Squash” (NY/T 2343-2013), data from 61 basic traits were investigated, and the analysis was conducted by converting them into notes according to the trait classification range. The results were as follows: For the first group, the trait descriptions of the variety ‘Jinghu 36’ (122) and ‘Jinghu 36’ (126) were very similar, with no obvious differences, and they were determined to be the same variety. In the other two groups, there were some traits with significant differences among the varieties (Table 6), and comparison photos of the mature fruits were taken (Figure 9). In the second group, there were obvious differences in the secondary color (stripes) of the fruit surface and the stripe type of the mature fruits between the variety ‘HC203’ (146) and ‘PK No. 2’ (152). In the third group, there were obvious differences in the shape of the mature fruits and the secondary color (stripes) of the fruit surface between ‘Zhongtai 268’ (147) and ‘Jiarui 570’ (154). There were obvious differences in the shape of the mature fruits between ‘Zhongtai 268’ (147) and ‘Jiuyuan Jinfeng 118’ (170), and there were obvious differences in the secondary color (stripes) of the fruit surface of the mature fruits between ‘Jiarui 570’ (154) and ‘Jiuyuan Jinfeng 118’ (170). Among the three groups of varieties, the varieties within the second to third groups only had obvious differences in one to two traits, and they were very similar in phenotype, indicating that this molecular identification system can be used to assist in the screening of DUS test similar varieties.

4. Discussion

4.1. Evaluation of SSR Core Primer Polymorphism and Variety Identification Ability

The quality of molecular markers plays a decisive role, to a certain extent, in the establishment of a variety identification system. In this study, 24 pairs of core primers were screened out from 175 pairs of SSR primers. The PIC values of these core primers ranged from 0.281 to 0.749, with an average PIC value of 0.643, showing moderately high polymorphism. This value is higher than Zhang et al.’s 0.63 [23] and slightly lower than Sim et al.’s 0.674 [19]. Using these 24 pairs of core primers in this study, a total of 152 allelic variation sites were amplified from 306 collected varieties, which included C. moschata, C. pepo, and C. maxima. On average, each pair of primers produced 6.3 allelic variation sites, which is higher than Zhang et al.’s 5.65 [23], Wang et al.’s 4.12 [60], and Liu et al.’s 2.09 [61] and slightly lower than Sim et al.’s 0.674 [19]. The test materials in Sim et al.’s study [19] included varieties of Cucurbita ficifolia type and some varieties bred through interspecific hybridization, while the test materials in this study were mainly the major cultivated C. moschata, C. pepo, and C. maxima varieties in China. The genetic diversity of the materials in this study is relatively narrow, which may be the reason why the average number of allelic variations and the average PIC value of the core primers in this study are slightly lower.

The establishment of a variety identification system should use the optimal primer combination to distinguish the largest number of varieties, so as to achieve the best identification results. Su et al. [15] used 20 pairs of SSR core primers to distinguish 202 out of 208 bitter gourd varieties, with a distinguishing rate of 97.11%. Yin et al. [14] used 23 pairs of SSR core primers to distinguish 418 out of 423 non-heading Chinese cabbage varieties, with a distinguishing rate of 99.99%. Ling et al. [13] used 22 pairs of core primers to distinguish 221 out of 233 lettuce varieties, with a distinguishing rate of 96.09%. Among the 306 Cucurbita varieties of the genus Cucurbita in this study, there was a group of varieties with no obvious differences in field phenotypic identification, which were determined to be the same variety. Therefore, the actual number of varieties was 305. The 24 pairs of core primers can distinguish 300 out of 305 Cucurbita varieties. The actual discrimination rate reaches 98.36%, which is higher than the 97.11% reported by Su et al. [15] and 96.09% reported by Ling et al. [13]. It meets the requirement of a 95% variety discrimination rate stipulated in the “General Principles for DNA Molecular Marker Methods in Plant Variety Identification” [25]. These primers have a strong ability to distinguish varieties and can be effectively used for the rapid identification of Cucurbita varieties, authenticity testing, and the protection of variety rights.

4.2. Identification of Germplasm Resources and Varieties Combined with Molecular Markers and Phenotypes

In this study, 24 pairs of SSR core primers were used to cluster the potential varieties with the same name from different sources together and distinguish two groups of “varieties with the same name but different identities” within the genus Cucurbita. Applying this set of core primers to the collection and identification of Cucurbita germplasm resources can, on the one hand, avoid the repeated collection of Cucurbita germplasm resources, and on the other hand, prevent the omission of some excellent germplasm resources, especially the local varieties with the same name, so as to improve the efficiency of the collection, preservation, and identification of germplasm resources. In this study, 24 pairs of SSR core primers were screened based on the fluorescent capillary electrophoresis platform. According to the fragment sizes of the amplified products of each core primer and the colors of the fluorescent markers, these 24 pairs of core primers were divided into 5 groups for electrophoresis, which greatly improved the detection efficiency and reduced the detection cost. It is suitable for the large-scale identification of Cucurbita germplasm resources and varieties.

SSR markers are considered “neutral” markers, which do not have obvious biological functions and are usually located in the non-transcribed regions of the genome [62]. Therefore, SSR markers are very suitable for variety identification. However, not all varieties can be identified by molecular markers. Especially for some varieties with a very narrow genetic background, it is sometimes difficult to distinguish them by molecular markers. Among the 306 varieties in this study, 7 varieties could not be distinguished by molecular markers, among which 2 belonged to the same variety, and the remaining 5 varieties could be distinguished by field phenotypic identification. This indicates that in variety identification, molecular markers still cannot completely replace morphological markers. Combining the two methods can effectively improve the accuracy of identification. The preliminarily screened varieties in this study are highly representative, and the screened molecular markers can better reflect the genetic diversity of the population. In general, varieties with a close molecular genetic distance also have very similar descriptions of their phenotypic traits. Among the three groups of seven varieties with a molecular genetic distance of zero in this study, there is one group of two varieties whose phenotypic traits are very similar and are judged to be the same variety. For varieties with obvious phenotypic differences, the number of different traits is small and the degree of difference in trait expression is generally small. This shows that the set of molecular markers in this study can be used as a technical means to assist the screening of DUS to test similar varieties.

5. Conclusions

In this study, 24 pairs of core primers screened using the polyacrylamide gel electrophoresis and fluorescent capillary electrophoresis platforms were employed to construct a fingerprint database for 306 varieties of the genus Cucurbita. A technical system for variety identification that is applicable to C. moschata, C. pepo, and C. maxima was established. The 24 pairs of core primers can distinguish 300 out of 305 varieties of the genus Cucurbita, with a discrimination rate of 98.36%, which meets the requirements for variety identification specified in the “General Principles for DNA Molecular Marker Methods in Plant Variety Identification”. This system can be used for the rapid identification of Cucurbita varieties, analysis of genetic diversity, market supervision, rapid identification in variety disputes arbitration, protection of new varieties, and auxiliary screening of similar varieties for DUS testing.

Author Contributions

Conceptualization, J.Z. and X.W.; methodology, Y.M.; software, S.L., W.X., H.H., and L.L.; validation, J.Z., X.L., and Y.M.; formal analysis, J.Z.; investigation, J.Z., C.P., L.L., and B.T.; resources, J.Z. and Z.X.; data curation, J.Z., X.L., S.L., W.X., and H.H.; writing—original draft, J.Z., X.W., X.L., B.T., R.J., and Z.X.; writing—review and editing, J.Z., X.W., C.P., X.Z., X.Y., B.T., and Z.X.; visualization, J.Z.; supervision, R.J.; project administration, X.W., X.L., X.Z., and X.Y.; funding acquisition, R.J. and Z.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Project for the Conservation of Species and Varietal Resources (h20230603); National Science and Technology Major Project for Agricultural Biological Breeding (2022ZD04019, 2022ZD040190203).

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Information on 306 Cucurbita varieties.

NO.	Variety Name	Variety Type	Region	NO.	Variety Name	Variety Type	Region	NO.	Variety Name	Variety Type	Region
1	Panlong204 ^A	C. moschata	Anhui	103	Xuelong ^A	C. maxima	Xinjiang	205	ZT17429 ^B	C. pepo	Gansu
2	Xinjiangmi2hao ^A	C. moschata	Anhui	104	Meiyaqiantu ^A	C. maxima	Anhui	206	ZT5816 ^B	C. pepo	Gansu
3	Jiangyimopan16005 ^A	C. moschata	Anhui	105	RCH2521 ^B	C. moschata	Shandong	207	ZT5818 ^B	C. pepo	Gansu
4	RWP93102 ^B	C. moschata	Shandong	106	RTCU9267 ^B	C. moschata	Shandong	208	ZT5886 ^B	C. pepo	Gansu
5	Jinyoumi1hao ^A	C. moschata	Hunan	107	Weiermixiaobei ^A	C. maxima	Shandong	209	Dingyan2202 ^A	C. pepo	Henan
6	Heliqihao ^A	C. moschata	Shandong	108	RCA2203 ^B	C. moschata	Shandong	210	Xinlü4306 ^A	C. pepo	Shandong
7	Youmi1hao ^A	C. moschata	Guangdong	109	Weiermihuangfei ^A	C. moschata	Shandong	211	Zhongtai16 ^A	C. pepo	Gansu
8	Tianmi ^A	C. moschata	Guangdong	110	Weiermijinling ^A	C. maxima	Shandong	212	Xiuyu916 ^A	C. pepo	Shandong
9	C128 ^B	C. moschata	Guangdong	111	Weiermiyihao ^A	C. maxima	Shandong	213	TBB9083 ^B	C. pepo	Neimenggu
10	Wawamiyihao ^A	C. moschata	Hunan	112	Jinpingguo33 ^A	C. maxima	Gansu	214	Dingyan2201 ^A	C. pepo	Henan
11	Xiaohei ^A	C. moschata	Beijing	113	Jinpingguo77 ^A	C. pepo	Gansu	215	Yuhu816 ^A	C. pepo	Hainan
12	Xingshudaguomiben ^A	C. moschata	Hunan	114	RCP21423 ^B	C. moschata	Shandong	216	Luhu3hao ^A	C. pepo	Shandong
13	Heliyihao ^A	C. moschata	Shandong	115	RCP21442 ^B	C. moschata	Shandong	217	Zifeng119 ^A	C. pepo	Neimenggu
14	Helisanhao ^A	C. moschata	Shandong	116	RCP1767 ^B	C. moschata	Shandong	218	ZT9092 ^B	C. pepo	Gansu
15	Tianli ^A	C. moschata	Sichuan	117	PK1582 ^B	C. maxima	Jiangsu	219	Zifeng49 ^A	C. pepo	Neimenggu
16	Jinbaoliyihao ^A	C. moschata	Shandong	118	Chunyu60 ^A	C. pepo	Shandong	220	Yuyan16 ^A	C. pepo	Gansu
17	Fulu333 ^A	C. moschata	Guangdong	119	Boshou410 ^A	C. pepo	Beijing	221	JH15835 ^B	C. pepo	Beijing
18	Xingshudaguo ^A	C. moschata	Hunan	120	Boshou607 ^A	C. pepo	Beijing	222	JH865 ^B	C. pepo	Beijing
19	Yunnan2hao-1-1 ^A	C. moschata	yunnan	121	Weikeduo ^A	C. pepo	Beijing	223	Bofeng ^A	C. pepo	Xinjiang
20	Panlong203 ^A	C. moschata	Guangdong	122	Jinghu36 ^A	C. pepo	Beijing	224	Baijiale ^A	C. pepo	Xinjiang
21	Youmi1hao ^A	C. moschata	Guangdong	123	Jinlierhao	C. pepo	Shanxi	225	Haojie9hao ^A	C. pepo	Xinjiang
22	RCP1730 ^B	C. moschata	Shandong	124	Kuaihulu ^A	C. pepo	Beijing	226	Suchengyihao ^A	C. pepo	Shandong
23	Guangzhoujinling ^A	C. moschata	Guangdong	125	9794Xihulu ^A	C. pepo	Beijing	227	HS513 ^B	C. pepo	Xinjiang
24	Mitianxing ^A	C. moschata	Guangdong	126	Jinghu36 ^A	C. pepo	Beijing	228	Kairuite ^A	C. pepo	Xinjiang
25	Jinchuanmixiang ^A	C. moschata	Guangdong	127	Jiuyuanjinfeng68 ^A	C. pepo	Neimenggu	229	HS524 ^B	C. pepo	Xinjiang
26	Jinchuangaoming ^A	C. moschata	Guangdong	128	Jinfengerhao ^A	C. pepo	Neimenggu	230	HS512 ^B	C. pepo	Xinjiang
27	Jinchuanjinxiang ^A	C. moschata	Guangdong	129	Zhongzhongrekang5hao ^A	C. pepo	Beijing	231	Yuyan17 ^A	C. pepo	Gansu
28	Panlong211 ^A	C. moschata	Shandong	130	Zhongzhongrekang1hao ^A	C. pepo	Beijing	232	Yuyan112 ^A	C. pepo	Gansu
29	Nanmiyihao ^A	C. moschata	Tianjin	131	Zhongzhongzi2hao ^A	C. pepo	Beijing	233	HCA2 ^B	C. pepo	Neimenggu
30	Jibei ^A	C. moschata	Tianjin	132	Zhongzhongzi1hao ^A	C. pepo	Beijing	234	Farui ^A	C. pepo	Xinjiang
31	Runbei ^A	C. moschata	Tianjin	133	Cuiying108 ^A	C. pepo	Beijing	235	JP2102 ^B	C. pepo	Gansu
32	Fubei ^A	C. moschata	Tianjin	134	Zhongzhongz10 ^A	C. pepo	Beijing	236	JP2203 ^B	C. pepo	Gansu
33	Yinjueerhao ^A	C. moschata	Tianjin	135	P7 ^A	C. pepo	Beijing	237	TEQ3 ^B	C. pepo	Gansu
34	Yinzhu ^A	C. moschata	Tianjin	136	Lüfu95 ^A	C. pepo	Jiangsu	238	X066 ^B	C. pepo	Gansu
35	Guifeimi ^A	C. moschata	Hunan	137	Jinhui5hao ^A	C. pepo	Heilongjiang	239	Zhangfengerhao ^A	C. pepo	Gansu
36	Jinyuan ^A	C. moschata	Hunan	138	Younite928 ^A	C. pepo	Henan	240	HC0024 ^B	C. pepo	Neimenggu
37	Jiuyuanjinfeng108 ^A	C. moschata	Neimenggu	139	8M03 ^B	C. pepo	Gansu	241	HCE674 ^B	C. pepo	Neimenggu
38	Panlong212 ^A	C. moschata	Anhui	140	Yuncui ^A	C. pepo	Shandong	242	Nongren22	C. pepo	Shandong
39	PJB0012 ^B	C. moschata	Fujian	141	S736 ^B	C. pepo	Henan	243	Yuyan109 ^A	C. pepo	Gansu
40	Sizhuang17 ^A	C. moschata	Zhejiang	142	Shengrun817 ^A	C. pepo	Henan	244	HCA5 ^B	C. pepo	Neimenggu
41	Beiluli2hao ^A	C. moschata	Shandong	143	HC208 ^B	C. pepo	Neimenggu	245	Hongchangzhangfeng ^A	C. pepo	Neimenggu
42	Beiluli1hao ^A	C. moschata	Shandong	144	Hongchang212 ^A	C. pepo	Neimenggu	246	HCE671 ^B	C. pepo	Neimenggu
43	Madun2hao ^A	C. moschata	Shandong	145	Chuanqihongchang211 ^A	C. pepo	Neimenggu	247	HCE682 ^B	C. pepo	Neimenggu
44	Xiaojinbei ^A	C. moschata	Xinjiang	146	HC203 ^B	C. pepo	Neimenggu	248	Hongchangyihao ^A	C. pepo	Neimenggu
45	Zhengyuan3hao ^A	C. moschata	Guangdong	147	Zhongtai268 ^A	C. pepo	Gansu	249	Nongren91 ^B	C. pepo	Shandong
46	Sizhuang21 ^A	C. moschata	Zhejiang	148	Nongren518 ^A	C. pepo	Hebei	250	Nongren2230 ^B	C. pepo	Shandong
47	Zhengyuan1hao ^A	C. moschata	Guangdong	149	H020 ^B	C. pepo	Gansu	251	Donghu19hao ^A	C. pepo	Shanxi
48	PJB0092 ^B	C. moschata	Fujian	150	Donghu32hao ^A	C. pepo	Shanxi	252	Dingyan2205 ^A	C. pepo	Henan
49	Zhengyuan2hao ^A	C. moschata	Guangdong	151	Donghu No.16 ^A	C. pepo	Shanxi	253	Linong1haoxiangyunangua ^A	C. moschata	Guangdong
50	Sizhuang19 ^A	C. moschata	Zhejiang	152	PK No.2 ^B	C. pepo	Gansu	254	Zhengyuan1haomibennangua ^A	C. moschata	Guangdong
51	Deli327 ^A	C. moschata	Shandong	153	Jiarui600 ^A	C. pepo	Gansu	255	Chengxingmibennangua ^A	C. moschata	Guangdong
52	Dinglibahao ^A	C. moschata	Shandong	154	Jiarui570 ^A	C. pepo	Gansu	256	Shanhaimibennangua ^A	C. moschata	Guangdong
53	Deli902 ^A	C. moschata	Shandong	155	Huangbeibei ^A	C. pepo	Shanghai	257	Xiangxiangnangua ^A	C. moschata	Guangdong
54	Lüxiuer ^A	C. moschata	Xinjiang	156	Baibeibei ^A	C. pepo	Shanghai	258	Linonghuashengnangua ^A	C. moschata	Guangdong
55	Xinhuali ^A	C. moschata	Zhejiang	157	Nongren968 ^A	C. pepo	Shandong	259	Zajiaofaxinangua ^A	C. moschata	Guangdong
56	Fangfang ^A	C. moschata	Guangdong	158	Meihuicheng216 ^A	C. pepo	Gansu	260	V201 ^B	C. moschata	Guangdong
57	Meimei ^A	C. moschata	Guangdong	159	Bulanding ^A	C. pepo	Shandong	261	Linongmopannangua ^A	C. moschata	Guangdong
58	Shilengmi1hao ^A	C. moschata	Anhui	160	Meixiu ^A	C. pepo	Xinjiang	262	Xinxilanbanli ^A	C. maxima	Guangdong
59	Jinpingguo203 ^A	C. maxima	Gansu	161	Yuanbaosanhao ^A	C. pepo	Xinjiang	263	Linongnennangua ^A	C. moschata	Guangdong
60	Shengshengmi2hao ^A	C. moschata	Anhui	162	Qiusheng ^A	C. pepo	Xinjiang	264	Linonglüguizunangua ^A	C. maxima	Guangdong
61	Jifeng77 ^A	C. moschata	Guangxi	163	Jialing ^A	C. pepo	Xinjiang	265	Ganlanguizunangua ^A	C. maxima	Guangdong
62	Hongjianguizu ^A	C. maxima	Guangdong	164	Badao ^A	C. pepo	Xinjiang	266	Linonghuipiduancuguizu2haonangua ^A	C. maxima	Guangdong
63	Lüyuan28hao ^A	C. maxima	Zhejiang	165	JR003CM ^B	C. pepo	Gansu	267	Linong3haobanlinangua ^A	C. maxima	Guangdong
64	Chengzhang42hao ^A	C. maxima	Zhejiang	166	JR19D ^B	C. pepo	Gansu	268	Linong9haobanlinangua ^A	C. maxima	Guangdong
65	Jifei ^A	C. moschata	Guangdong	167	ZTPK04 ^B	C. pepo	Gansu	269	Aibisibanlinangua ^A	C. maxima	Guangdong
66	Jixianfeng ^A	C. moschata	Hebei	168	ZT2H ^B	C. pepo	Gansu	270	Yinlinangua ^A	C. maxima	Guangdong
67	Fuwaxianfeng ^A	C. pepo	Gansu	169	Xiuyu160 ^A	C. pepo	Shandong	271	Beilishihaonangua ^A	C. maxima	Guangdong
68	Hongfenjiaren ^A	C. maxima	Guangdong	170	Jiuyuanjinfeng118 ^A	C. pepo	Neimenggu	272	Dabeinangua ^A	C. maxima	Guangdong
69	RCP1257 ^B	C. moschata	Shandong	171	Zhenhu9hao ^A	C. pepo	Shandong	273	Huiyou1haobeibeinangua ^A	C. maxima	Guangdong
70	RWP9402 ^B	C. moschata	Shandong	172	Shenghu11 ^A	C. pepo	Shandong	274	Huiyou2haobeibeinangua ^A	C. maxima	Guangdong
71	RCP6321 ^B	C. moschata	Shandong	173	Luhu2hao ^A	C. pepo	Shandong	275	Huiyou3haobeibeinangua ^A	C. maxima	Guangdong
72	RWP281 ^B	C. moschata	Shandong	174	Gangqinuo ^A	C. pepo	Shandong	276	Linong3haobeibeinangua ^A	C. maxima	Guangdong
73	RWP297 ^B	C. moschata	Shandong	175	Dongdiou ^A	C. pepo	Shandong	277	Linong23haobeibeinangua ^A	C. maxima	Guangdong
74	RCB2263 ^B	C. moschata	Shandong	176	Donghu688 ^A	C. pepo	Henan	278	G163 ^B	C. maxima	Guangdong
75	RCB8N25 ^B	C. moschata	Shandong	177	Xiaboke ^A	C. pepo	Shandong	279	Q1174 ^B	C. moschata	Guangdong
76	RWP286 ^B	C. moschata	Shandong	178	Maodun ^A	C. pepo	Shandong	280	Q1177 ^B	C. moschata	Guangdong
77	Sizhuang11 ^A	C. moschata	Zhejiang	179	Jinyu1hao ^A	C. pepo	Gansu	281	V222 ^B	C. moschata	Guangdong
78	Weiermiheizhenzhu ^A	C. maxima	Shandong	180	9M13 ^B	C. pepo	Gansu	282	V224 ^B	C. moschata	Guangdong
79	Linongjinxiang ^A	C. moschata	Guangdong	181	Zhangfengyihao ^A	C. pepo	Gansu	283	V264 ^B	C. maxima	Guangdong
80	RTSY671 ^B	C. moschata	Shandong	182	Ziguan12hao ^A	C. pepo	Shanxi	284	V379 ^B	C. maxima	Guangdong
81	Beili4Hao ^A	C. maxima	Anhui	183	Ziguan No.8 ^A	C. pepo	Shanxi	285	Xiangyunangua ^A	C. moschata	Anhui
82	Fuwa808 ^A	C. maxima	Gansu	184	Sanruishuoguo ^A	C. pepo	Xinjiang	286	Youmi No.4 ^A	C. moschata	Anhui
83	RCP1734 ^B	C. moschata	Shandong	185	Haojie5hao ^A	C. pepo	Xinjiang	287	Naiyounangua ^A	C. moschata	Anhui
84	RTCU2534 ^B	C. moschata	Shandong	186	Sanruiyuguo ^A	C. pepo	Xinjiang	288	Shengsheng1hao ^A	C. moschata	Anhui
85	RTCU2530 ^B	C. moschata	Shandong	187	Gongxi518 ^A	C. pepo	Shandong	289	Shengsheng4hao ^A	C. moschata	Anhui
86	RTDL6802 ^B	C. moschata	Shandong	188	Gongxi568 ^A	C. pepo	Shandong	290	Shilengmi3hao ^A	C. moschata	Anhui
87	PK201 ^B	C. maxima	Neimenggu	189	SF2080D2 ^B	C. pepo	Xinjiang	291	Youmi18hao ^A	C. moschata	Anhui
88	PK529 ^B	C. moschata	Neimenggu	190	Haojie4hao ^A	C. pepo	Xinjiang	292	Yinbei No.1 ^A	C. maxima	Anhui
89	Sizhuang33 ^A	C. maxima	Zhejiang	191	Haojie1hao ^A	C. pepo	Xinjiang	293	Ganlanbeibei ^A	C. maxima	Anhui
90	Sizhuang31 ^A	C. moschata	Zhejiang	192	SF2021XT ^B	C. pepo	Xinjiang	294	Xiyangyang No.3 ^A	C. maxima	Anhui
91	Tianmi1hao ^A	C. moschata	Beijing	193	SF2025dapian ^B	C. pepo	Xinjiang	295	Beili4hao ^A	C. maxima	Anhui
92	Jinpinyinbei2hao ^A	C. maxima	Fujian	194	9X218 ^B	C. pepo	Gansu	296	Jiabei6hao ^A	C. maxima	Anhui
93	RTCU8401 ^B	C. moschata	Shandong	195	9M34 ^B	C. pepo	Gansu	297	Jinbei No.5 ^A	C. maxima	Anhui
94	Z10 ^B	C. moschata	Beijing	196	ZT5133 ^B	C. pepo	Gansu	298	Yinbei2hao ^A	C. maxima	Anhui
95	RTCU4767 ^B	C. moschata	Shandong	197	Jindianjiuhao ^A	C. pepo	Tianjin	299	Jiangli5hao ^A	C. maxima	Anhui
96	RTCU6730 ^B	C. moschata	Shandong	198	Zhongtai266 ^A	C. pepo	Gansu	300	Yinli6hao ^A	C. maxima	Anhui
97	RTCU6457 ^B	C. moschata	Shandong	199	Zhongtai13 ^A	C. pepo	Gansu	301	JSNO.7 ^A	C. pepo	Anhui
98	RWP9604 ^B	C. moschata	Shandong	200	ZT3H ^B	C. pepo	Gansu	302	Mingzhu1hao ^A	C. pepo	Anhui
99	RCH3063 ^B	C. moschata	Shandong	201	Nongren818 ^A	C. pepo	Shandong	303	Mingzhu6hao ^A	C. pepo	Anhui
100	Meiyasihao ^A	C. maxima	Anhui	202	Nongren778 ^A	C. pepo	Shandong	304	KN-11 ^A	C. pepo	Beijing
101	Jindian ^A	C. moschata	Xinjiang	203	Youhulü058 ^A	C. pepo	Shandong	305	Defeng No.4 ^A	C. pepo	Beijing
102	Lüfeicui ^A	C. moschata	Xinjiang	204	ZT17428 ^B	C. pepo	Gansu	306	Heibaoshi No.4 ^A	C. pepo	Beijing

Note: ^A: Hybrid; ^B: Inbred line.

Table A2. Information on 175 pairs of SSR primers.

Primer No.	Forward Primer Sequence (5′→3′)	Reverse Primer Sequence (5′→3′)	Primer Source
NG1	CACCAAAATGGCCGCTCAAA	AGCTGCGACGAATGTGAAGA	Independent development
NG2	CAGCTTCTTCAATCTCGCGC	CCTCCACAACAACAAGCAGC	Independent development
NG3	GTGACCCAACTGACAGAGGG	TCGACTTCGAACGCAACAGA	Independent development
NG4	GTGCCTTGGTTCTGTCGGTA	GCGCGCGTAAATATGTGTGT	Independent development
NG5	CTCGTCGGGTCTTCGATCAG	AACAGTTCGCGTGCAACTTC	Independent development
NG6	TGTGAAACGCCTCCAGTACC	GAGATCCGGTAAGCCACGAG	Independent development
NG7	ACCTACCCCAGCCATACCAT	GATCGGCTGACGTCAATTGC	Independent development
NG8	CGCGGTTGATCATTGTCGTC	CCATGCTGTGTGTGTGTGTG	Independent development
NG9	GAATGAGCTTCGTCGAACGC	GCTCTGATCGGGGCTAGTTC	Independent development
NG10	TGCATAGGTATCGGCAGCTG	AGTGGGTTCCAAGTGCAACA	Independent development
NG11	GGCTGAGCCCCTCTTCAATT	CATCGTCGCCATTGCTTCTG	Independent development
NG12	CAGAGGCTTGTTGTGTTGGC	CCACACTCGGAATTCCACGA	Independent development
NG13	CCCTGGTTTTGGCTCCCTAG	CATGATGCGCACGAACTGAG	Independent development
NG14	GATCAACGCAATACACCGGC	CTAGGTCGTCTTCGTCGTCG	Independent development
NG15	CGGAATCGCCTGCAATTCAG	GGGAATTGGCGCGAAGAATC	Independent development
NG16	TCATCCCAGGCATTTGGACC	GAATTTGACTCCGCCGCATC	Independent development
NG17	GAGGCTAACGACGATGCGTA	CGTTACGTATCGCCGTCAGA	Independent development
NG18	CGTTTATCTGTTGCTCGGCG	GAGGGATTCCAGGAGAGGGT	Independent development
NG19	AGACCAAGCTCTATCCGGGT	GTTCGACGCTGCATCCATTC	Independent development
NG20	GCCGACGGATCTTTTTACGC	CTGCAAACACGTCGTTTCGT	Independent development
NG21	CGCCGAGTTTGAGTAATGCG	CCAAACTCCTCGACAACGGA	Independent development
NG22	GAAGACGCCGATTTCGTTCG	CCATCATACGAGTGAGCGCT	Independent development
NG23	CGTAACGTGCGAATCGTTCC	GACCGTCCAACCACCATGAT	Independent development
NG24	GCCATAACTTGCTCTTCGCG	TGTTCCATAGCCGCCATTGT	Independent development
NG25	CGGACTCTAGCGTCGAGTTC	CAACTTCCGAGAACGTGGGA	Independent development
NG26	CGGCGTATGGCTCAGTACAT	ACCACAGCGTTGAAATTGGC	Independent development
NG27	GATTTGCATCGGAACTCGGC	TCTGTTTCTCCAGCGGACAC	Independent development
NG28	CATAGTCGCCTGATTCCGCT	CTTCGATTTCGCAGTCGCAG	Independent development
NG29	GTTGTGCCATGACGGTCCTA	GAAAGCGGCGTTGATAGCAG	Independent development
NG30	GTGGCCTCTGTTTGCACTTG	GACGTGTGATGAACGATGCG	Independent development
NG31	GGAGGAAGAGAAGATCGCCG	GAGGCGAAAATGGCGGATTC	Independent development
NG32	CTAAGCAGTAGTCGGCCTCG	CGTGAACTTTTGCAGCGTCA	Independent development
NG33	GCCGTGTGAAAGGTTGTGAC	CCGCGTTTCGTTTGACTTGT	Independent development
NG34	TTCGCGACTCCTAATCGACG	CGCACTTGTCGCTAATCGTG	Independent development
NG35	GCCGCAGAAAAAGCACTTCA	CCCTAGCCCCTCTTCTTCCT	Independent development
NG36	CCAATGGCGTTGCTAATCCG	CGTTAATCGGTTCGACGCAC	Independent development
NG37	TCGCTGTTGGAGCTGTGAAT	GGCAACGTGTCGATCGATTG	Independent development
NG38	ATGCCGCGTCATTCAAATCG	CTCATTGCTCGATGGCGTTG	Independent development
NG39	CGCTTTCTCGTGCTCAATCG	GATTTGAACCGTATGCGCCC	Independent development
NG40	GAAGCGACGGATTCAATGGC	TCACGTCGTGTTTTCAACGC	Independent development
NG41	CCATCAGCAATGTCGAAGCG	GTGTTGTCGGCACAGTTGAC	Independent development
NG42	TGGGATAAGGGCAGCCAAAG	GCGACTAGCGTTCGGGAATA	Independent development
NG43	ACGTCGAACAAGCAAACGTG	CAATTGCCGATCAAGCCTCG	Independent development
NG44	CCTCTTGATTTGCATCGCCG	CTACCAAGAAAATCGCCGCG	Independent development
NG45	CACAGCCCAACTCAACAACG	TTCATGTGTTTGCTGCAGGC	Independent development
NG46	GCCGCGTTTTGATGAGATCC	GCTCCATATGCCAGCAAACG	Independent development
NG47	CACCAAAATGGCCGCTCAAA	CCTCTGCAACTTCGTCTCGT	Independent development
NG48	GCAGCTTCTTCAATCTCGCG	CCTCCACAACAACAAGCAGC	Independent development
NG49	GGTGACCCAACTGACAGAGG	TCGACTTCGAACGCAACAGA	Independent development
NG50	ACCAACTGGAGCTCGAAAGG	GCTCTCGCAAATACCGCTTG	Independent development
NW1	GCTTGAACAGAGATGGAGGG	AAAGTCGCTGAGAGCTGGAG	[29]
NW2	GGTGCATTGTCCAAACACAA	CCGCATCCATGAAAGAAAGT	[29]
NW3	ATTTGCTTACCAAACAGCCG	GTTCAGAGGAGCTGGGTACG	[29]
NW4	GGACTTGAGATGGAGGTGGA	TTTGTACGTTGTTCGTTGCC	[29]
NW5	TCGCTTCACCGGTAATTTTC	GCGCTGAAGAATCCATGTTT	[29]
NW6	ACAACGAAGCCTCAAAGGAA	GATGCAAAGGATGGAAAGGA	[29]
NW7	CTCAGTGGAGGGACAAGCTC	CCGACTCCACCATGTCCTAT	[29]
NW8	ACCTCTGCATTTCAACCCAC	ATACCCACCAAGCCCTTTCT	[29]
NW9	CTGTTGCTGTTGTTGCTGGT	GCGCTTCTCTCAATGCTTCT	[29]
NW10	AGCTACGCATGCCTGAATCT	TGCACCTGCTGTCATAGCTC	[29]
NW11	CATACCCACCGTCGACTCCT	GGGCGAAGTGGAGGTTATGA	[30]
NW12	GTTGCTCCAACTCGATCTTCA	TTTCAAACGAGCACAAGCAC	[30]
NW13	TAGTCGAGAAGGCCGAGAAG	AAATTCGACGACCGCTTG	[30]
NW14	AAATTCGACGACCGCTTG	TTTTTAAAGGGCTGAAAATAATTG	[30]
NW15	AAAATTGCTAGGCTGTAGTGGTG	AAAATTGCTAGGCTGTAGTGGTG	[30]
NW16	TGTCAGCTTCCTCAGTAGGG	TGAACTGGGAGAGAGGTTTG	[30]
NW17	TCCCATCCTCTACTGTTGCAC	TAAGTTGTGGGTGGGGAAAC	[30]
NW18	ACCCCACCAAATTAATGCAG	AGAGCCCACTGTGATGACCT	[30]
NW19	TGATTTGCGCACAAACAAAC	GCCAAAGGTTCCAAATGACA	[30]
NW20	GCCAAAGGTTCCAAATGACA	TGATCGAATTGTGGCTGGT	[30]
NW21	TGATCGAATTGTGGCTGGT	GTGGCCGTAGGTTTGTCAGT	[30]
NW22	CACCTGGCTGTTTTGTCTGA	ACATGGGCATACCTCGAATC	[30]
NW23	TGAAATGAACGCAGAATTGC	ACTTGCGGACTTTCACACCT	[30]
NW24	TTTTTGAAATTCTTTGCATCACT	AAGCCAAAGCCCCTTATCT	[30]
NW25	GCCAAAGGTTCCAAATGAC	GCAACAAATTGTAGTTGCAAAG	[30]
NW26	CACGAAGATTTGATGGCCTTA	GGATTGGGATGGTGAAGATG	[31]
NW27	GCAGAGGAGAAGTGGGTTTG	CTTTATCCGACCAAGCGTTC	[31]
NW28	AGCCTTTCAGAAGAACCAAG	GGCTTCAAACAAATACTAACCA	[31]
NW29	ATTAAATGCTCCTCCCCACC	GGAGAGAGAGGGAAAAACGG	[32]
NW30	AGTCCGACGAAGCTCAGGTA	TACATGTCTCTGCGAGCGTC	[32]
NW31	TCAATGGATCTGCCTTTTCC	AGGGAAGGATGCTAAGGAGC	[32]
NW32	GGTGGGTATGGAGGAGGTG	ACCGCCACGTGGATAACTAA	[32]
NW33	CCTTTCAAAATGGCTTCCAA	TCTTCTTCCCAAGCTGCCTA	[32]
NW34	CAGACGGCTTTTGAAGGAAG	TCGAAGAGCTCTGTTGGTGA	[33]
NW35	TTCTCAGGTGCTGTTGATGC	TCCTTTCCTTCGCTTCCTCT	[33]
NW36	GCTTTTGAAGATGAGGCGAG	CACTCAAGCAGATTGCCAAA	[33]
NW37	CGGTCGTGAATACATCATGG	GCTCCACCAATGGGAAACTA	[33]
NW38	CGATATGATGGAGCTGCTGA	CCAGCTCCCGAGCTTCTAAT	[33]
NW39	GAAGAGGAAGAAGCAGCACG	TGTCCACGATCTCTGCTTTG	[33]
NW40	AGAGACGAGAATGGGGGAGT	GCGAAATCGGTGCAATAAAT	[33]
NW41	TTTCTTTTCTCCTCTGCCCA	AATCACACCTTGGGCACTTC	[33]
NW42	ATCATAGTCGTCGTCGGGTC	GCCGATTCTTGAGGAACAGA	[33]
NW43	TTCAAAGCTTCTCTGGTAAGGC	ACATCGCCCAAGAGAAGTTG	[33]
NW44	GAAGGCGAGGTTTTGAGTTG	GGCGGAACCCTAAGAAATGT	[34]
NW45	TCGGACCAAAGTACCCTCCA	TCATCGCCGGTTGTGATCT	[34]
NW46	GCACTTGAATCTTCGTCAAC	CGAGAAAGAATTAACGAGCA	[34]
NW47	ATGGCTTCCAAGCTCCTCTT	GTCGGCCATGAGCTTGAG	[34]
NW48	GAAGGACCGTGAGTGAAAGG	ATCTTGTGCCAAAGCTCCAT	[34]
NW49	CAGGCTATTCGCACCCTCTA	CCTCATGCATTTTGCGTGTA	[34]
NW50	CGTGAACATTCGTTTGTTGG	TCATCCGTTTCCTTTTCAGC	[34]
NW51	TCACTTTACAACCAGAAGCTGA	CACTTTGCTGCTCATCCAC	[34]
NW52	GGCTGGCTCATAAAGAAAGC	GGGGTTTTTGAAGATGCTTG	[34]
NW53	ATGATGGAGTCCCAGTCGAG	ACCCACACACCCTCTCCTC	[34]
NW54	ATCCACAAACAAGGCACCTC	GTAGTGGAGGCTCGGGTGTA	[34]
NW55	CACAAGCCATCACACAAACC	AGGTGGAGCTGACCGTAGTG	[34]
NW56	TCTCACTCATCCACCACACC	CGTTTCGAGTCATTTGTTCG	[34]
NW57	CCTCTCATTCTTCCCCATCTC	TGATCCGGTAGGGGTCTACTC	[34]
NW58	GCCCAGAAGACAAAAGTTCG	TTTTTGTGTGCGTGTGTGG	[34]
NW59	ATTGGTGCCGAAGCTATCAC	CCCACGTTATGGAGCAGAAT	[34]
NW60	ATGCTCAGACATCCATGCAC	GCGAAAGATTACCGATGCTC	[34]
NW61	AACACTCGGCCACAACATC	CTCCTTGTAAAACGGGTTGC	[34]
NW62	CTCCATTCCCATGGCTTC	CCATGAGCTTGAGAGAGGTG	[34]
NW63	CAAATTCAGACGCTTCTTTTGG	AGAATTGAGCAAAAAGGAGATGG	[34]
NW64	TTAAGATAGTTTCAGGATTCCATGT	AGGAGTTTGAAACAAATGAAGG	[34]
NW65	GAGTGATGTTTTGAGTAAACAG	CTTGTTCATCATCATCTGTG	[34]
NW66	AGGTGGCATCTGTACACTGAG	TGAACAAACTCCACACCAATAG	[34]
NW67	GACAGGACAGGTCAACACCTC	AACCCAATTGCACAGCTTCT	[34]
NW68	TGTTCAGTAGCCATTGATCTATCC	TGGATGACTTCTGGGTTGGT	[34]
NW69	TTATAAGAATGATGTTACTCGAT	CATCATCATACATCTTACATTG	[34]
NW70	GAACTCTAATCCAGCCGTTG	TTAAAATAAATGGAGCAAATAATGAG	[34]
NW71	GGTCAAATTCAAGGGCTTACC	AGGAGCATCCTTGTCGTTGT	[34]
NW72	CTGGTTTTTCCACACAGAATCA	CCCAGAAGGGAATTGAAACA	[34]
NW73	CCGCCACCACTGAACAACT	GGCTGTGGCCCTCCATAGT	[34]
NW74	TCACAAAGTCAAAACAGACAAACA	TGTTTCAGGGAAATGAAGAGG	[34]
NW75	GGCGAAAAGGAAGAACGAAT	TTTTTCTCCCCCTTCCACAT	[34]
NW76	ACCTACCGTCACACCCACAT	CCACCTGAAAACAGGGCTAA	[34]
NW77	GAACTTCGTGTGTGCGTGTC	TTGCTGGAACTTCCTCTCGT	[34]
NW78	CTCTCCCCCTCCTCCACTC	GCGTTCTGCATTGTGGAAGT	[34]
NW79	CAAATTTTCTTCTACAATTTGGT	GTGAATGAACATGCGTCTC	[34]
NW80	ATGGAGACGCGCAAGTAGAT	ACCTCGAGGAAGCAAAAATG	[34]
NW81	TATGTAGCGCTCTGCACAAT	CCTCAATGTAATGTTTTGAATCC	[34]
NW82	TGAAACTACACTACATGACCTTGG	TGGGTTGGTAGACTTGTAGTTGA	[34]
NW83	CATCAGGTTATTGAGTTTTACTCAGAC	TCGTCTGCCCCAATAATTC	[34]
NW84	CGTGTTTGTGTTGGAAAAGC	AAGCAAACCCACAGAAGGAG	[34]
NW85	ACGTTCTTTCTGCCTCCAT	ATGCCCATTATGCAATTCTC	[34]
NW86	ACAATGGTTCTTTTGATCCTTGA	TGCAAACAATGTGTGTGTGTG	[34]
NW87	TGTGGGGTTTTGCTTTTAGG	ATCCAAAATGGTGGTGCATT	[35]
NW88	GCAAGCCCTAGCTGATTTTT	GGGCGAAAACAGAGTGAGAG	[35]
NW89	AACAGAGAATCTGGTGCTGGA	GCCAATTCCTCCTTTTCTCC	[35]
NW90	GGATTGCCTTGCTGGAGAG	CAAATCCAGGTGGAAGGCTA	[35]
NW91	TCACCAACTTGCCATAGACG	CGCGCAACTGATAAGGATTT	[35]
NW92	TGATCTGACAGCAACCGAAG	CCATTCCCTTAGTTTCTAAACCACT	[35]
NW93	TGATGACAAAGAAGCCATCG	ATCTTATGCCGGAGCAGATG	[35]
NW94	TTTCGTTGCAGAGAATGGTG	CCCATTTCTTTCTCGCTCAA	[35]
NW95	AATTCTAACCGTTGGGGGTT	CCCAGAAACGAAAGAAGCAG	[35]
NW96	ATGGCATAATCAGCCCTCAC	TTTGAAGAAGGGAAGAGGGG	[35]
NW97	TCAAACTCTCAACTGGGTCG	TCCGATTGAGAGCTGGAGTT	[35]
NW98	TCCTCGTCGGAACAGAACTT	TCTAACTTGGAGCCTGGAGC	[35]
NW99	GAGCTTCTGGATGATAGCGG	GCTTCGAACTTTCGTTTGCT	[35]
NW100	TGTAATTTGAAGATGAGATTATGAAGA	GGGTCTTGTTTCTCGAGCTG	[35]
NW101	TTTCAAATTCGCTCGTTTCC	GAGTCGTCGATGTCGTCAAA	[35]
NW102	TCTCGAATCCGAAGAAATCG	TGCGTTGCAGAATATCAAGC	[35]
NW103	GGGTGAAGCTGTGGATTCAT	TCTCTAGTGCCACACTCAAGTACA	[35]
NW104	GTGGGCTAAGTTCAAATCGTTC	GAACCCAATCTTCTCATTTCCA	[36]
NW105	CAAGCCTTTACCAAAACCAAAC	ACGATCTTCCTCCTCCTCTTCT	[36]
NW106	GAACCTCGTTGCTGGTTCTTCT	TCTAGCATCTTACGCACGCTTC	[36]
NW107	TGTCAAATTCGCTTCCATCATC	ACGACTGTGAGAACGGTGAAAA	[36]
NW108	TGGAATCCAGAGACTGATGAAG	CACGATTCCGATAACAACAAGA	[36]
NW109	TGGAGGTATCCTTCCGTATGTT	CATACGGGAGTTTCGTTTTTCT	[36]
NW110	ATCCCAGGTCCCAATTTTCTTC	CCATACCTGAGGGACCTGAAAC	[36]
NW111	CGAATTTCTCCGAAAAGAAACC	CCTCGATCAATCCTCCTACACC	[36]
NW112	CGACTACAGCTCAACAAAGACC	GAAGTCTATTACGCCCAATTCC	[36]
NW113	CGAGTACGTTTACAACCATCCA	CAACTCTCAACAAGAACCCAAG	[36]
NW114	TCTTCTCGGAGTTTGATGTCCA	TTTACCCTCATCGGAACCTCAT	[36]
NW115	GACACTCACGAGCAAAATGACC	CGTGTGGAGCTTTCTCACAACT	[36]
NW116	GCAGCACACTCGATTCCTCTTT	TAGGAAAAATGGCGGTGAAGAA	[36]
NW117	AGACTTTGGGTTTGTAGGAAGG	GCTAAGCATTGTTAGGGCTTCT	[36]
NW118	GACGAATACTTTCGCAATCAAG	CATGTGGAAGTTGTTGTTGTTG	[36]
NW119	CTAATCAGGCTGGCCAAGAAGA	CGACGTTACCGACAAGATTTCC	[36]
NW120	AGCACTGGGTTAACAGAGGAAA	ATGATACAGTAATCGGCGTCCT	[36]
NW121	TGTGATTTGTTGGGCTCTCTGT	ACATGGAAATCCACCTCTTCGT	[36]
NW122	TCCACACCAGTGGTTCTTTCGT	TTTCACCATCCGGACAATCATC	[36]
NW123	GCCGGTGATTCTGGATGAAGTA	CAGGTAGAAGGTGGGGAGATGG	[36]
NW124	TTGGACAATCTGAGGAAGTTGG	AAGGTTTGTCCTTCGACAAGGT	[36]
NW125	CCCTCGGTAGCAATTTTGTAGT	TTTGGTGGGAGTGATACTGATG	[36]

Table A3. Detailed information on the phenotypic traits of Cucurbita moschata.

No.	Trait Name	Observation Method	Trait Type
1	Cotyledon Shape	VG	PQ
2	Main Vine Length	MS	QN
3	Leaf Size	MS	QN
4	Degree of Leaf Margin Incision	VG	QN
5	Greenness of the Front Leaf Surface	VG	QN
6	Presence or Absence of White Spots on the Front Leaf Surface	VG	QL
7	Petiole Length	MS	QN
8	Petiole Thickness	MS	QN
9	Greenness of the Melon Rind	VG	QN
10	Fruit Longitudinal Diameter	MS/VG	QN
11	Fruit Transverse Diameter	MS/VG	QN
12	Ratio of Fruit Longitudinal Diameter to Transverse Diameter	MS/VG	QN
13	Position of the Maximum Transverse Diameter of the Fruit	VG	QN
14	Fruit Shape	VG	PQ
15	Prominence of the Melon Neck	VG	QN
16	Melon Neck Length	VG	QN
17	Degree of Fruit Curvature	VG	QN
18	Shape of the Fruit Stalk	VG	PQ
19	Shape of the Fruit Navel	VG	QN
20	Presence or Absence of Fruit Furrows	VG	QL
21	Spacing of Fruit Furrows	VG	QN
22	Depth of Fruit Furrows	VG	QN
23	Patterns on the Fruit Surface	VG	QN
24	Maturity Period	VG	QN
25	Main Color of the Fruit Peel	VG	PQ
26	Depth of the Main Color of the Fruit Peel	VG	QN
27	Presence or Absence of Wax Powder on the Fruit Surface	VG	QL
28	Presence or Absence of Fruit Nodules	VG	QL
29	Main Color of the Fruit Pulp	VG	PQ
30	Thickness of the Fruit Pulp	VG	QN
31	Diameter of the Fruit Navel	VG	QN
32	Seed Length	VG	QN
33	Ratio of Seed Width to Length	VG	QN
34	Color of the Outer Seed Coat	VG	PQ
35	Presence or Absence of the Outer Seed Coat	VG	QL

Note: VG: visual assessment of a group; MS: measurement of individual; PQ: pseudo-qualitative characteristics; QN: quantitative characteristics; QL: qualitative characteristics.

Table A4. Detailed information on the phenotypic traits of Cucurbita pepo.

No.	Trait Name	Observation Method	Trait Type
1	Cotyledon Shape	VG	PQ
2	Cotyledon Cross-Section Shape	VG	PQ
3	Presence of Inner Corolla Wreath in Female Flowers	VG	QL
4	Color of Inner Corolla Wreath in Female Flowers	VG	PQ
5	Color Intensity of Inner Corolla Wreath in Female Flowers	VG	QN
6	Presence of Inner Corolla Wreath in Male Flowers	VG	QL
7	Color of Inner Corolla Wreath in Male Flowers	VG	PQ
8	Color Intensity of Inner Corolla Wreath in Male Flowers	VG	QN
9	Color of Male Flower Corolla	VG	PQ
10	Shape of Male Flower Tube	VG	PQ
11	Apex Shape of Male Flower Petals	VG	PQ
12	Shape of Male Flower Buds	VG	PQ
13	Length of Male Flower Sepals	VG	QN
14	Plant Growth Habit	VG	PQ
15	Presence of Plant Branches	VG	QL
16	Number of Plant Branches	MS	QN
17	Stem Color	VG	PQ
18	Greenness Intensity of Stem	VG	QN
19	Presence of Stem Patterns	VG	QL
20	Presence of Stem Tendrils	VG	QL
21	Leaf Shape	VG	PQ
22	Leaf Margin Shape	VG	PQ
23	Leaf Size	MS	QN
24	Degree of Leaf Sinus	VG	QN
25	Greenness Intensity of Leaf Adaxial Surface	VG	QN
26	Presence of White Spots on Leaf Adaxial Surface	VG	QL
27	Area of White Spots on Leaves	VG	QN
28	Size of Commercial Fruit	MS	QN
29	Presence of Fruit Neck in Commercial Fruit	VG	QL
30	Curvature of Fruit Neck in Commercial Fruit	VG	QL
31	Number of Fruit Surface Colors in Commercial Fruit	VG	QL
32	Primary Color of Commercial Fruit Surface	VG	PQ
33	Color Intensity of Primary Fruit Surface Color	VG	QN
34	Glossiness of Commercial Fruit Surface	VG	QN
35	Type of Fruit Surface Patterns	VG	PQ
36	Color of Primary Fruit Surface Patterns	VG	PQ
37	Size of Flower Scar on Commercial Fruit	VG	QN
38	Morphology of Flower Scar End	VG	QN
39	Length of Fruit Stalk	MS	QN
40	Color of Fruit Stalk	VG	PQ
41	Overall Shape of Mature Fruit	VG	PQ
42	Length of Mature Fruit	MS	QN
43	Maximum Diameter of Mature Fruit	MS	QN
44	Fruit Shape Index (Length/Diameter Ratio)	MS	QN
45	Primary Color of Mature Fruit Surface	VG	PQ
46	Color Intensity of Primary Mature Fruit Surface Color	VG	QN
47	Secondary Color of Mature Fruit Surface	VG	PQ
48	Type of Mature Fruit Surface Patterns	VG	PQ
49	Presence of Fruit Furrows	VG	QL
50	Depth of Fruit Furrows	VG	QN
51	Presence of Fruit Ridges	VG	QL
52	Color of Fruit Ridges	VG	QL
53	Presence of Fruit Nodules	VG	QL
54	Number of Fruit Nodules	VG	QN
55	Color of Fruit Pulp	VG	PQ
56	Structure of Fruit Pulp	VG	PQ
57	Seed Size	VG	QN
58	Seed Shape	VG	PQ
59	Presence of Outer Seed Coat	VG	QL
60	Color of Outer Seed Coat	VG	PQ
61	Color of Inner Seed Coat	VG	PQ

Table A5. Detailed information on the phenotypic traits of Cucurbita maxima.

No.	Trait Name	Observation Method	Trait Type
1	Cotyledon Shape	VG	PQ
2	Main Vine Length	MS	QN
3	Main Vine Color	VG	PQ
4	Number of Lateral Branches	MS	QN
5	Leaf Size	MS	QN
6	Degree of Leaf Margin Incision	VG	QN
7	Greenness of Adaxial Leaf Surface	VG	QN
8	Density of White Spots on Adaxial Leaf Surface	VG	QN
9	Initial Flowering Stage	VG	QN
10	Yellowness of Female Flower Corolla	VG	QN
11	Length of Female Flower Sepals	MS	QN
12	Length of Male Flower Sepals	MS	QN
13	Fruit Longitudinal Diameter	MS/VG	QN
14	Fruit Transverse Diameter	MS/VG	QN
15	Ratio of Fruit Longitudinal to Transverse Diameter	MS/VG	QN
16	Fruit Shape	VG	PQ
17	Position of Maximum Fruit Transverse Diameter	VG	QN
18	Outline of Fruit Stalk End	VG	PQ
19	Outline of Flower Stalk End	VG	PQ
20	Presence/Absence of Fruit Furrows	VG	QL
21	Spacing of Fruit Furrows	VG	QN
22	Depth of Fruit Furrows	VG	QN
23	Number of Fruit Peel Colors	VG	QN
24	Primary Color of Fruit Peel	VG	PQ
25	Intensity of Primary Fruit Peel Color	VG	QN
26	Secondary Color of Fruit Peel	VG	PQ
27	Intensity of Secondary Fruit Peel Color	VG	QN
28	Distribution Pattern of Secondary Fruit Color	VG	PQ
29	Lignification Status of Fruit Peel	VG	PQ
30	Diameter of Flower Stalk Base	VG	QN
31	Primary Color of Fruit Pulp	VG	PQ
32	Seed Size	VG	QN
33	Seed Shape	VG	PQ
34	Seed Coat Color	VG	PQ
35	Surface Texture of Seed Coat	VG	PQ

Table A6. Allele dropout detection and null allele detection for 24 pairs of SSR primers.

Marker	Missing Rate	Null Alleles Detection
NG2	0.00%	Existence
NW1	1.31%	Existence
NW112	1.31%	Existence
NW121	0.00%	Existence
NW124	0.00%	Existence
NW125	0.00%	Existence
NW18	0.00%	Existence
NW22	0.00%	Existence
NW23	0.33%	Existence
NW26	0.33%	Existence
NW37	0.00%	Existence
NW42	0.00%	Existence
NW44	0.00%	Existence
NW5	0.00%	Existence
NW51	0.00%	Existence
NW56	0.00%	Existence
NW57	0.98%	Existence
NW59	0.00%	Existence
NW63	0.00%	Existence
NW66	0.33%	Existence
NW84	0.98%	Existence
NW85	0.98%	Existence
NW87	0.33%	Existence
NW98	0.33%	Existence

Table A7. Detailed information on the four marker combinations with weak linkage relationships.

Marker Pairs	r² Value	Linkage Strength
NW1 & NW121	0.46	Weak linkage
NW5 & NW51	0.44	Weak linkage
NW18 & NW22	0.42	Weak linkage
NW37 & NW42	0.41	Weak linkage

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Figure 1. Twelve representative varieties: (a–d) C. moschata; (e–h) C. maxima; (i–l) C. pepo.

Figure 2. Polymorphism of NW106, NW18, and NG2 primers in 12 representative varieties. Note: (a–c) were primers NW106, NW18, and NG2; 1–12 are Xiangyunangua, Youmi No.4, Panlong204, Naiyounnagua, Xinxilanbanlinangua, Yinbei No.1, Ganlanbeibei, Xiyangyang No.3, KN—11, Defeng No.4, Heibaoshi No.4, and JSNO.7; M: DNA Marker; bp: Size of amplified fragment.

Figure 3. Multiple capillary electrophoresis peak diagram of five pairs of primers in the first group on the variety “Huangbeibei”.

Figure 4. Chromosome mapping of 24 SSR core primers.

Figure 5. DNA fingerprint database of the variety “Nongren91”.

Figure 6. Cluster analysis of 306 Cucurbita varieties by 24 SSR markers based on Nei’s distance. Note: ● Represents a variety combination with a difference point of 0 between varieties.

Figure 7. Two-dimensional scatter plot of principal component analysis of 306 Cucurbita varieties.

Figure 8. Population genetic structure analysis of 306 Cucurbita varieties based on 24 SSR markers. Note: (a) Line chart of Ln P(K) values varying with K values; (b) Line chart of ∆K values varying with K values; (c) Genetic structure of 306 Cucurbita genus varieties at K = 3; In (c), the red area represents the Cucurbita pepo group, the green area represents the Cucurbita moschata group, and the blue area represents the Cucurbita maxima group.

Figure 9. The comparison photographs of ripe fruits among varieties exhibiting zero locus difference in groups 2 to 3. Note: (a) comparison between varieties ‘HC203’ and ‘PK No. 2’; (b) comparison among ‘Zhongtai268’, ‘Jiarui570’, and ‘Jiuyuanjinfeng118’.

Table 1. Information of 24 pairs of SSR core primers.

Primer No.	Primer Name	Chromosome	Forward Primer Sequence (5′→3′)	Reverse Primer Sequence (5′→3′)	Primer Source
NW56	CMTp252	1	TCTCACTCATCCACCACACC	CGTTTCGAGTCATTTGTTCG	[34]
NG2	NG2	2	CAGCTTCTTCAATCTCGCGC	CCTCCACAACAACAAGCAGC	Independent development
NW18	S231	3	ACCCCACCAAATTAATGCAG	AGAGCCCACTGTGATGACCT	[30]
NW1	ZN1	4	GCTTGAACAGAGATGGAGGG	AAAGTCGCTGAGAGCTGGAG	[29]
NW121	CUTC017708	4	TGTGATTTGTTGGGCTCTCTGT	ACATGGAAATCCACCTCTTCGT	[36]
NW5	ZN8	5	TCGCTTCACCGGTAATTTTC	GCGCTGAAGAATCCATGTTT	[29]
NW85	CMTm137	6	ACGTTCTTTCTGCCTCCAT	ATGCCCATTATGCAATTCTC	[34]
NW66	CMTm178	7	AGGTGGCATCTGTACACTGAG	TGAACAAACTCCACACCAATAG	[34]
NW98	Unigene0001517	8	TCCTCGTCGGAACAGAACTT	TCTAACTTGGAGCCTGGAGC	[35]
NW59	CMTm42	9	ATTGGTGCCGAAGCTATCAC	CCCACGTTATGGAGCAGAAT	[34]
NW44	CMTp34	10	GAAGGCGAGGTTTTGAGTTG	GGCGGAACCCTAAGAAATGT	[34]
NW84	CMTm37	11	CGTGTTTGTGTTGGAAAAGC	AAGCAAACCCACAGAAGGAG	[34]
NW26	CMTp257	12	CACGAAGATTTGATGGCCTTA	GGATTGGGATGGTGAAGATG	[31]
NW125	CUTC046645	13	CCCTCGGTAGCAATTTTGTAGT	TTTGGTGGGAGTGATACTGATG	[36]
NW42	S65	14	ATCATAGTCGTCGTCGGGTC	GCCGATTCTTGAGGAACAGA	[33]
NW22	S254	15	CACCTGGCTGTTTTGTCTGA	ACATGGGCATACCTCGAATC	[30]
NW87	Unigene0000190	15	TGTGGGGTTTTGCTTTTAGG	ATCCAAAATGGTGGTGCATT	[35]
NW23	S263	16	TGAAATGAACGCAGAATTGC	ACTTGCGGACTTTCACACCT	[30]
NW57	CMTp265	16	CCTCTCATTCTTCCCCATCTC	TGATCCGGTAGGGGTCTACTC	[34]
NW51	CMTp209	17	TCACTTTACAACCAGAAGCTGA	CACTTTGCTGCTCATCCAC	[34]
NW37	S35	18	CGGTCGTGAATACATCATGG	GCTCCACCAATGGGAAACTA	[33]
NW63	CMTm127	18	CAAATTCAGACGCTTCTTTTGG	AGAATTGAGCAAAAAGGAGATGG	[34]
NW112	CUTC006703	19	CGACTACAGCTCAACAAAGACC	GAAGTCTATTACGCCCAATTCC	[36]
NW124	CUTC022867	20	TTGGACAATCTGAGGAAGTTGG	AAGGTTTGTCCTTCGACAAGGT	[36]

Table 2. Grouping of 24 pairs of core primers.

Group	FAM	TAMRA	ROX	HEX
1	NG2 (152–175)	NW26 (105–150)	NW98 (190–225)	NW57 (76–100)
1	NW37 (235–275)	NW26 (105–150)	NW98 (190–225)	NW57 (76–100)
2	NW18 (125–155)	NW23 (115–155)	NW56 (158–200)	NW63 (85–115)
2	NW18 (125–155)	NW23 (115–155)	NW56 (158–200)	NW59 (202–233)
3	NW44 (116–134)	NW85 (80–105)	NW51 (105–128)	NW121 (205–235)
3	NW44 (116–134)	NW22 (135–175)	NW51 (105–128)	NW121 (205–235)
4	NW66 (110–134)	NW84 (134–160)	NW112 (80–134)	NW125 (170–200)
4	NW5 (168–184)	NW84 (134–160)	NW112 (80–134)	NW125 (170–200)
5	NW1 (190–235)		NW87 (105–155)	NW124 (114–137)
5	NW1 (190–235)		NW87 (105–155)	NW42 (139–166)

Table 3. Genetic diversity parameters of 24 SSR core markers in 306 Cucurbita varieties.

Primer No.	MAF	GT	Na	Ne	Ho	He	PIC	I
NG2	0.35	19	7	4.362	0.268	0.771	0.737	1.621
NW1	0.394	17	9	3.939	0.321	0.746	0.709	1.548
NW112	0.664	6	4	1.99	0.265	0.497	0.442	0.907
NW121	0.407	11	5	3.529	0.333	0.717	0.67	1.393
NW124	0.609	8	4	2.292	0.137	0.564	0.514	1.037
NW125	0.345	7	4	3.036	0.15	0.671	0.598	1.13
NW18	0.43	18	7	3.824	0.395	0.739	0.706	1.565
NW22	0.386	20	10	4.25	0.297	0.765	0.734	1.703
NW23	0.307	16	8	4.26	0.105	0.765	0.728	1.613
NW26	0.339	16	7	3.647	0.151	0.726	0.677	1.441
NW37	0.314	16	8	4.47	0.212	0.776	0.741	1.63
NW42	0.513	15	8	2.786	0.206	0.641	0.586	1.231
NW44	0.786	4	3	1.509	0.016	0.337	0.281	0.529
NW5	0.324	10	5	4.009	0.225	0.751	0.708	1.472
NW51	0.528	11	5	2.589	0.16	0.614	0.55	1.098
NW56	0.32	22	11	4.14	0.203	0.758	0.721	1.621
NW57	0.389	13	5	3.423	0.182	0.708	0.658	1.368
NW59	0.418	13	7	3.446	0.111	0.71	0.662	1.396
NW63	0.265	11	6	4.625	0.297	0.784	0.749	1.6
NW66	0.511	9	4	2.408	0.148	0.585	0.502	1.002
NW84	0.396	5	3	2.947	0.086	0.661	0.587	1.09
NW85	0.31	8	5	4.196	0.297	0.762	0.722	1.501
NW87	0.336	17	8	4.448	0.292	0.775	0.741	1.614
NW98	0.4	16	9	3.882	0.187	0.742	0.706	1.584
Total	10.041	308	152	84.007	5.044	16.565	15.429	32.694
Average	0.418	12.833	6.333	3.5	0.21	0.69	0.643	1.362

Note: MAF: Major allele frequency; GT: Number of genotypes; Na: Number of Alleles; Ne: Number of effective alleles; Ho: Observed heterozygosity; He: Expected heterozygosity; PIC: Polymorphism information content; I: Shannon–Wiener index.

Table 4. Information on 20 reference varieties.

No.	Variety Name	Sample Preservation Site	No.	Variety Name	Sample Preservation Site
1	Panlong204	New Plant Variety Preservation Center, MARA	11	Yuanbaosanhao	New Plant Variety Preservation Center, MARA
2	Naiyounangua	Anhui Jianghuai Horticulture Co., LTD	12	Ziguan No.8	New Plant Variety Preservation Center, MARA
3	Jiangyimopan16005	New Plant Variety Preservation Center, MARA	13	Jindianjiuhao	New Plant Variety Preservation Center, MARA
4	Jibei	New Plant Variety Preservation Center, MARA	14	HS512	New Plant Variety Preservation Center, MARA
5	Jiuyuanjinfeng108	New Plant Variety Preservation Center, MARA	15	Nongren22	New Plant Variety Preservation Center, MARA
6	Sizhuang21	New Plant Variety Preservation Center, MARA	16	HCA5	New Plant Variety Preservation Center, MARA
7	Linongjinxiang	New Plant Variety Preservation Center, MARA	17	Xiyangyang No.3	Anhui Jianghuai Horticulture Co., Ltd. (Hefei, Anhui, China)
8	PK529	New Plant Variety Preservation Center, MARA	18	Linonglüguizunangua	Guangdong Helinong Biological Seed Industry Co., Ltd. (Shantou, Guangdong, China)
9	Donghu No.16	New Plant Variety Preservation Center, MARA	19	Huiyou3Haobeibeinangua	Guangdong Helinong Biological Seed Industry Co., Ltd. (Shantou, Guangdong, China)
10	Huangbeibei	New Plant Variety Preservation Center, MARA	20	Jinbei No.5	Anhui Jianghuai Horticulture Co., Ltd. (Hefei, Anhui, China)

Table 5. Diversity indices of Cucurbita varieties in different regions.

Region	Shannon’s Information Index	Expected Heterozygosity
East China	1.267	0.648
North China	1.203	0.613
South China	1.031	0.566
Northwest China	1.338	0.692

Table 6. The description of the distinctive traits in DUS testing among varieties with zero locus difference in the molecular markers within groups 2 to 3.

Trait	Group 2		Group 3
Trait	HC203	PK No.2	Zhongtai268	Jiarui570	Jiuyuanjinfeng118
Mature fruit shape	6 (Long oval)	6 (Long oval)	10 (pear shape)	12 (bar shape)	12 (bar shape)
Mature fruit surface secondary color(stripe)	1 (milky white)	5 (orange)	5 (orange)	1 (milky white)	5 (orange)
Mature fruit stripe type	1 (dot)	4 (mixed)	1 (dot)	1 (dot)	1 (dot)

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Zou, J.; Wu, X.; Liu, X.; Peng, C.; Ma, Y.; Zhang, X.; Yang, X.; Luo, S.; Xing, W.; Hong, H.; et al. Identification and Genetic Diversity Analysis of Cucurbita Varieties Based on SSR Markers. Agronomy 2025, 15, 1420. https://doi.org/10.3390/agronomy15061420

AMA Style

Zou J, Wu X, Liu X, Peng C, Ma Y, Zhang X, Yang X, Luo S, Xing W, Hong H, et al. Identification and Genetic Diversity Analysis of Cucurbita Varieties Based on SSR Markers. Agronomy. 2025; 15(6):1420. https://doi.org/10.3390/agronomy15061420

Chicago/Turabian Style

Zou, Jialong, Xingting Wu, Xuejing Liu, Changcheng Peng, Yingxue Ma, Xiujie Zhang, Xuhong Yang, Shuailong Luo, Weigeng Xing, Hao Hong, and et al. 2025. "Identification and Genetic Diversity Analysis of Cucurbita Varieties Based on SSR Markers" Agronomy 15, no. 6: 1420. https://doi.org/10.3390/agronomy15061420

APA Style

Zou, J., Wu, X., Liu, X., Peng, C., Ma, Y., Zhang, X., Yang, X., Luo, S., Xing, W., Hong, H., Li, L., Tan, B., Jing, R., & Xu, Z. (2025). Identification and Genetic Diversity Analysis of Cucurbita Varieties Based on SSR Markers. Agronomy, 15(6), 1420. https://doi.org/10.3390/agronomy15061420

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Identification and Genetic Diversity Analysis of Cucurbita Varieties Based on SSR Markers

Abstract

1. Introduction

2. Materials and Methods

2.1. Materials

2.2. Source of SSR Primer

2.3. Field Planting and Traits Investigation

2.4. DNA Extraction

2.5. PCR Amplification

2.6. Polyacrylamide Gel Electrophoresis (PAGE)

2.6.1. Gel Making

2.6.2. Electrophoresis

2.6.3. Silver Staining

2.7. Fluorescent Capillary Electrophoresis

2.8. Data Analysis

3. Results

3.1. Primer Screening

3.2. Analysis of Core Primers

3.3. Construction of DNA Fingerprint Database

3.4. Selection of Reference Varieties

3.5. Analysis of Genetic Diversity of Cucurbita Varieties

3.6. Principal Component Analysis

3.7. Analysis of Population Genetic Structure

3.8. Analysis of Diversity of Cucurbita Varieties in Different Regions

3.9. Phenotypic Identification of Molecular-Undifferentiated Varieties

4. Discussion

4.1. Evaluation of SSR Core Primer Polymorphism and Variety Identification Ability

4.2. Identification of Germplasm Resources and Varieties Combined with Molecular Markers and Phenotypes

5. Conclusions

Author Contributions

Funding

Data Availability Statement

Conflicts of Interest

Appendix A

References

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