Analysis of Genetic Diversity in Tea Plant Population and Construction of DNA Fingerprint Profile Using SNP Markers Identified by SLAF-Seq

Abstract

The analysis and identification of the genetic diversity of plant germplasm resources and varieties are crucial for plant breeding. DNA fingerprinting using genomic molecular markers is crucial for precisely identifying germplasm resources. In this study, the SLAF-seq was performed using 47 germplasm resources of the Wanzhou tea plant population and 5 common cultivated varieties from the Yunnan, Sichuan, and Fujian provinces. A total of 7,447,940 SNPs were identified from 1,641,569 SLAF tags with an averaged sequencing depth of 11.73 (Q30 94.93% and GC 41.37%), which were used to analyze the population composition and genetic diversity. Results showed a large degree of genetic diversity and genetic variation among the samples. The cluster analysis showed that the tea plant population was categorized into three groups, indicating that these germplasms could not be fully classified by their geographical origin, and the linkage disequilibrium analysis indicates that the population resources of the XJ production area are more modern in evolution. A total of 371 uniformly distributed SNP loci were selected and successfully employed to construct the first SNP fingerprint and quick response code (QR code) for tea resources in Wanzhou. These findings offer new insights for genotyping, classifying, and identifying germplasm and genetic resources in the breeding of the Wanzhou tea population.

1. Introduction

The tea plant (Camellia sinensis L.) is a perennial, self-incompatible species characterized by high genetic heterogeneity and relies on cross-pollination for reproduction. It is an important economic crop that is now widely grown in over 60 countries and regions worldwide, and the tea produced from it is recognized as one of the most popular non-alcoholic beverages [1]. China is recognized as the first country in the world to discover, use, and cultivate tea, with a history that dates back over 5000 years [2,3,4]. In addition, it boasts the highest genetic diversity of tea plant resources globally, with more than 20 provinces and regions engaged in the cultivation of tea. Furthermore, each tea-producing region in China is characterized by a rich diversity of local tea varieties [5,6,7,8]. The Sichuan and Chongqing regions, recognized as some of the historical birthplaces of tea in China, are renowned for their abundant tea resources. Previous studies indicated that small- and medium-sized leaf group species in Sichuan represent the primary tea varieties found in these areas, which are widely distributed throughout the region [9]. These rich germplasm resources serve as gene carriers that regulate phenotypic traits and provide the foundational materials for breeding new tea varieties. We can reveal the degree of similarity, variation law, and evolutionary potential of tea by studying the genetic diversity of tea germplasm resources. However, due to frequent introduction, domestication, hybridization, and polyploidization, its origin, evolution, and classification are complex and diverse. It is difficult to effectively protect its development and utilization. Classifying these tea germplasm resources using appropriate methods and studying the various resources of small- and medium-sized leaf populations in Sichuan are crucial for breeding new tea varieties and for the collection and preservation of tea germplasm resources.

In recent years, the advancement and maturation of molecular biotechnology have made molecular markers one of the primary methods used in the study of the population genetics of tea plants. Molecular markers can directly reflect variations at the genomic level by analyzing differential fragments or bases present in the genomes of biological individuals or populations [10]. With the rapid advancement of biological sequencing technology, a wide array of molecular markers has been developed for germplasm screening, variety identification, intellectual property rights protection, and other related applications. The molecular marker techniques used in the previous studies of tea mainly include RFLP, AFLP, SSR, and ISSR. Chen et al. used the RFLP technique to analyze the cp DNA of 30 tea cultivars in Sichuan Province, and a total of 98 polymorphic bands were generated, which produced a good distinction between the groups [11]. Using AFLP technology, AM Devi et al. screened 5 primers with a better effect from 36 primers, and a total of 100 markers were screened in a 1:1 ratio in the F1 population of tea [12]. Based on this, the genetic map was constructed. Fingerprinting was performed through 15 SSR markers in 11 tea germplasm resources from Duyun by the SSR technique [13].

Among the aforementioned methods, RFLP and AFLP represent first-generation technologies, while SSR and ISSR are considered second-generation technologies. However, these technologies exhibit drawbacks such as their reduced accuracy and time-intensive and high-cost nature. These limitations have prompted the adoption of third-generation technologies, among which SNP stands as a representative. SNP technology directly identifies variations in DNA sequences, replacing the conventional gel electrophoresis detection method with its analysis, and its efficiency is significantly superior when compared to other methods. As one of the methods for SNP sequencing, Specific-Locus Amplified Fragment Sequencing (SLAF-seq) is a simplified genome sequencing technology, whose efficiency can be significantly improved while reducing the experimental cost by sequencing part of the genome sequence of species [14]. This technique has been extensively used in the analysis of genetic diversity, molecular fingerprinting, and germplasm resource identification [15,16,17]. In tea plants, a total of 125 germplasm resources were divided into four groups by using 807,743 SNPs developed by SLAF-seq [18]. Geng employed SLAF technology in the study of ancient tea in Guizhou to develop a considerable number of polymorphic tags, offering a reference for the construction of high-density genetic maps of tea [19]. As genome sequencing progresses, the application of DNA fingerprinting technology in breeding has become increasingly widespread, playing a key role in molecular marker-assisted selection. This technology enhances the efficiency and accuracy of trait selection, allowing breeders to develop improved varieties with desirable characteristics [20]. DNA fingerprinting markers consist of a small number of highly representative ones. The combination of these markers can effectively distinguish between different materials within the same species with stable results and convenient identification. Therefore, it is largely applied in crop germplasm resource diversity studies and in the determination and protection of variety property rights [21,22]. SNP technology features high polymorphism and a large quantity, and the markers are manifested as either dominant or codominant. It has been designated as the prioritized recommended marker method by the International Union for the Protection of New Varieties of Plants [23]. When Liu et al. conducted a study on the genetic diversity of tea in Wuyi Mountain, they selected 47 markers exhibiting superior polymorphism from a total of 96 SNPs. Based on these markers, they constructed a DNA fingerprinting map for the tea germplasm resources [24]. Fang et al. successfully genotyped 40 varieties of tea plants using 60 SNP markers that were developed and selected from the expressed sequence tag (EST) database. This work provides a valuable tool for the identification of different tea varieties [25]. Tadeo et al. successfully categorized 376 tea resources into 8 distinct groups by utilizing 8480 high-quality SNP markers obtained through diversity array technology. This study elucidates the internal relationships among tea populations in Uganda [26].

In the present study, SLAF-seq technology was utilized for the first time to conduct a genetic diversity analysis of population-based tea varieties in Wanzhou District, Chongqing. We collected 47 samples distributed in Wanzhou District, Chongqing, which exhibited remarkable differences in tree shape, leaf shape, and leaf color. Meanwhile, the original tea plant varieties from Sichuan, Fujian, and Yunnan were utilized as controls. The phylogenetic relationships among these germplasms were assessed through high-quality SNP loci after screening and filtering, and the fingerprint maps of the tea plant varieties in Wanzhou District were constructed using core SNPs. The genetic diversity of the population-based tea germplasms in Wanzhou District has been preliminarily elucidated, and the genetic relationships between the tea varieties in Wanzhou and those from the Fujian, Sichuan, and Yunnan provinces have been investigated. This study offers a scientific foundation for identifying and utilizing superior local tea germplasm resources specific to Chongqing, as well as for the innovative application of these germplasm resources.

2. Materials and Methods

2.1. Plant Materials

A total of 52 experimental materials were utilized in this study (Table S1). Among them, 47 materials of the Wanzhou small and medium leaf group variety were collected from Xinxiang Town (30°46′40″ N, 108°30′15″ E), Yanshan Town (30°52′00″ N, 108°35′19″ E), Jiuling Mountain (31°80′11″ N, 108°78′41″ E), Longju Town (30°72′24″ N, 108°50′94″ E), Fenshui Town (30°80′97″ N, 108°50′93″ E), and Tiefeng Township (29°96′07″ N, 108°40′93″ E) in Wanzhou District, Chongqing City. Among the five materials from other provinces, they originated, respectively, from the tea germplasm resource garden of the Tea Research Institute of Fujian Academy of Agricultural Sciences (26°07′23″, 119°14′18″), the Excellent Tea Germplasm Resource Conservation Area of Fujian Province (27°03′32″, 118°38′21″), the tea germplasm resource garden of Sichuan Province (30°37′27″, 103°52′32″), and the tea germplasm resource garden of Yunnan Province (25°01′27″, 102°46′21″). All the plant materials were collected during 2023–2024. The fresh one-bud-two-leaf samples of each material were rapidly frozen in liquid nitrogen and then stored in a −80 °C refrigerator for standby use.

2.2. SLAF Library Construction and Sequencing

Total genomic DNA was extracted from young leaves of tea using cetyltrimethyl annonium bromide (CTAB) protocol. The SLAF library was generated by using the method as described before with minor revision [27]. Genomic DNA of each sample was digested with RsaI and HinCII. Then, the ATP and dual-index sequencing adapter were added at the 3′ and 5′ end of the digested DNA products, respectively. PCR was performed and the products were purified using E.Z.N.A.H Cycle Pure Kit (Omega Bio-tek, Inc., Norcross, GA, USA). The purified products were mixed and incubated with these two restricted enzymes again. After ligation of ATP and Solexa adapter in the pair-end, the reaction products were purified using a Quick Spin column (Qiagen, Venlo, The Netherlands) and segregated on a 2% agarose gel. Fragments were isolated using a Gel Extraction Kit (TianGen Biotech (Beijing) Co., Ltd., Beijing, China). These SLAFs were subjected to PCR to add barcodes. The PCR products were re-purified and then prepared for paired-end sequencing on an Illumina HiSeq sequencing platform (Illumina, San Diego, CA, USA).

2.3. Data Analysis Procedure

The raw data obtained through sequencing were identified by dual-index to acquire the reads of each sample. After filtering the adapters of the sequencing reads, the sequencing quality and data volume were assessed. The cutting efficiency of the enzymatic digestion scheme was evaluated via the BMK_Control data to determine the accuracy and validity of the experimental process. Electronic digestion prediction was carried out on the reference genome of Camellia sinensis: CSS_ChrLev (http://tpia.teaplant.org/index.html, accessed on 15 June 2023). In accordance with the selection principle of the digestion scheme, the combination of restriction endonucleases was determined to be RsaI + HinCII. Sequences with the lengths of digestion fragments ranging from 364 to 414 bp were defined as SLAF tags. Read lengths of 126 bp × 2 were employed for subsequent data assessment and analysis. The comparison statistics method for alignment with the reference genome is presented as follows: Mapped (%): The proportion of clean reads that were located on the reference genome among all clean reads, which is accomplished by employing the samtools flagstat command. Properly mapped (%): Both ends of the sequencing reads are mapped onto the reference genome and the distance complies with the length distribution of the sequencing fragments. This is realized by using the samtools flagstat command. The analysis of the insert fragment size for the sequencing data of each sample was accomplished by utilizing the CollectInsertSizeMetric.jar software within the Picard (1.94) software toolkit. The SLAF tags were mapped onto the reference genome by means of the BWA (0.7.10-r789) software, and the SLAF tags and polymorphic SLAF tags on distinct chromosomes were enumerated. The development of SNP markers was performed using the genome of Camellia sinensis as the reference. Sequencing reads were aligned to the reference genome utilizing bwa [28]. SNP markers were generated through two distinct approaches: GATK (v3.8) [29] and samtools (v1.9) [30]. The intersection of SNP markers identified by both methods was considered the final reliable dataset for SNP markers. The SNPs obtained were annotated by means of the SnpEff (v3.6c) software. SNPs were categorized as intergenic regions, upstream or downstream regions, as well as exons or introns. SNPs located in the coding regions were further classified as synonymous SNPs or non-synonymous SNPs, and INDELs located in the coding regions were determined as to whether they caused frameshift mutations.

2.4. The Analysis of the Population Structure of Tea Plants

In this project, the MEGA (10.2) [31] software was employed to construct the phylogenetic trees of each sample based on the neighbor-joining method, adopting the Kimura 2-parameter model and with 1000 bootstrap replications. Based on SNPs, the admixture (v1.22) software was adopted to analyze the population structure of the research materials. For the research population, the number of subgroups (K value) was pre-set ranging from 1 to 10 for clustering, and the clustering results were cross-validated. The optimal number of subgroups was determined based on the valley of the cross-validation error rate. The R package Pophelper (v1.22) was utilized to generate a stacked Q matrix plot for each K value (http://royfrancis.github.io/pophelper, accessed on 15 June 2023). The smartPCA program within the EIGENSOFT (v6.0) software package was employed to perform principal component analysis based on SNP data, thereby obtaining the clustering of samples. The linkage disequilibrium between SNPs was calculated using the PopLDdecay (v3.41) software.

3. Results

3.1. SLAF-Seq Analysis and SNP Data Identification

In this study, we constructed DNA libraries of 52 tea plants by sequencing and obtained a total of 479.18 Mb of clean data based on SLAF sequencing. The average Q30 and GC content were 94.9% and 41.37%, respectively. The GC content obtained by this sequencing ranged from 39.95% to 42.89%, with an average of 41.37%. A quality score ≥ 30 (Q30) ranges from 93.01% to 95.39%, with an average value of 94.93%. A total of 1,641,569 SLAF tags were developed in this project, and the average sequencing depth of the tags was 11.73×. For each sample, the average sequencing depth ranged from 7.42×-fold to 20.27×-fold (Table S2). Out of the 52 tea samples, the mean proportion of clean reads located on the reference genome accounted for 96.85% of the total number of clean reads, and 81.28% were properly mapped, indicating high-quality comparison results and potential for a further sequencing data analysis (Table S3).

Using the clean sequence readings obtained, we quantified the number of polymorphic SLAF tags, which totaled 636,727. Subsequently, we mapped the SLAF tags to the reference genome of Camellia sinensis and generated a chromosome-specific distribution map based on their locations (Figure 1).

A total of 7,447,940 population SNPs were obtained by comparing the sequencing readings with the reference genome using the intersection of SNPs obtained by both methods as the final reliable SNPs dataset, and their distribution on the chromosomes were mapped (Figure 2). This analysis was based on SNP data detected by variation and was screened according to the minor allele frequency (MAF: 0.05) and site integrity (INT: 0.5), and 1,827,503 SNPs with a high consistency were obtained for the downstream analysis. According to the location of the mutation site on the reference genome and the gene location information on the reference genome, it is possible to determine the genomic region where the mutation occurs, and the impact of the mutation can be determined. The results of the SNP annotation show that the top five regions in terms of distribution numbers were the intergenic regions, 89.58%, the intron, 4.69%, the gene upstream region (less than 5 kb), 2.14%, the gene downstream region (less than 5 kb), 2.00%, and the coding sequence (CDS), 1.33%. The obtained clean reads were further aligned with the reference genome to detect the insertion and deletion of small fragments between the sample and the reference genome (small InDel). The length of the InDel in the CDS region and the whole genome was calculated, and the result showed that it was mainly distributed between −3 and 3. Furthermore, the annotation of these InDels showed that the top four regions were the intergenic region, 82.72%, the intron, 9.24%, the gene downstream region (less than 5 kb), 3.40%, and the gene upstream region (less than 5 kb), 3.10% (Figure S1).

3.2. Genetic Diversity and Population Structure of Tea Plant

The analysis of genetic diversity explores the mechanisms of evolution by examining gene and genotype frequencies within a population. This investigation also considers various factors that influence these frequencies. In this study, we analyzed the genetic diversity of 47 tea samples from Chongqing and 5 cultivated species from Sichuan, Yunnan, and Fujian based on developed SNPs. First, the genetic data of the population were utilized to deduce the genetic distances. This analysis resulted in the creation of a distance matrix, which subsequently enabled the construction of a phylogenetic tree. The phylogenetic tree based on the neighbor-joining method showed that the 52 samples could be clustered into three groups: the A group included Z4-Z8, XIN2-2, and YZ-3, the B group included TP1-1-TP1-3, ZI-1-Z1-3, YZ-1, YZ-2, X2-9, XZ-2, and XZ-6, while other samples, including cultivated species from Sichuan, Fujian, and Yunnan, were grouped into te C Group (Figure 3). Results suggested that the 47 tea plant samples were not completely clustered by geographical location.

A principal component analysis (PCA) was performed on 52 germplasm resources. PC1 and PC2 represent axes 1 and 2, respectively, which account for 8.01% and 6.78% of the total variance (Figure 4). The data showed that all samples were divided into three well-separated clusters. In this study, we conducted a population structure analysis based on SNPs, and the number of subpopulations (K) is defined in advance in the range, while the population structure corresponding to each K value (K from 1 to 10) is cross-validated to obtain the most suitable K value for clustering. Furthermore, the cross-validation error rate was lowest when K = 3, which further indicates that the 52 samples can be divided into three clusters (Figure 5 and Figure S2). Subsequently, a linkage disequilibrium analysis was performed on all the population-based tea varieties. The results showed that the tea in different regions presented varying degrees of genetic diversity. The germplasm in the FQ region was more ancient with a relatively simple diversity, while the germplasm in the XJ region was more modern and had the most abundant genetic diversity among all the materials, and the other materials lie between the two extremes (Figure S3). Moreover, according to the PCA and phylogenetic tree analysis, all the 52 tree samples were not clustered strictly by region.

3.3. Development of DNA Fingerprinting Markers

Fingerprint markers should be designed to minimize redundancy to enhance the efficiency and clarity of genetic analyses. In this study, we screened SNP sites based on the following principles: 1. the markers were evenly distributed on the genome; 2. there was no deletion site in the marker, that is, the site integrity was 100%; 3. the minor Allele Frequency (MAF) was <20%; 4. loci with a polymorphism information content (PIC) less than 0.35 were discarded; 5. the Hardy–Weinberg test retained the sites with p-values greater than 0.01; and 6. there was no other site mutation at 100 bp before and after the screened marker. Based on the above conditions, the research materials were screened for markers, and 371 mutation sites were finally determined as candidate markers. First, we analyzed the genetic diversity of the selected candidate markers, and the indicators are shown (

Table 1. Genetic diversity assessment table of SNP markers based on different indicators.

Shannon–Wiener Index	Ave	Min–Max
Observed allele number	2.000	2.000–2.000
Expected allele number	1.934	1.837–2.000
Observed heterozygous number	0.469	0.298–0.681
Expected heterozygous number	0.483	0.456–0.500
Nei diversity index	0.488	0.461–0.505
Shannon Wiener index	0.676	0.648–0.693
Polymorphism information content	0.366	0.352–0.375
Average MAF	0.419	-
Number of poly markers	371.000	-

, Figure S3). Then, a graph was plotted based on four indicators of observed heterozygosity, MAF, Nei diversity, and PIC (Figure S4). Furthermore, the genetic diversity, phylogenetic tree, population structure, and principal component analysis were further evaluated. The screened DNA markers were used to distinguish the materials and construct fingerprints for each material marker, and all tea materials can be distinguished clearly and effectively by the fingerprint (Figure 6). Furthermore, we constructed a 2D barcode for each tea plant sample using the core tag of the qrencode pair screening. The QR code can be linked to relevant information about the sample, including the variable name, type, and botanical classification. This integration makes it easy to identify and understand the data using mobile devices (Figure S5).

4. Discussion

Tea is a self-incompatible species, exhibiting a relatively high genetic variability. This trait is advantageous for the development of new varieties. The traditional classification method based on biological traits is vulnerable to several factors, such as environmental conditions, difficulties in identifying traits, and the instability of genetic characteristics. With the advancement in molecular markers within the field of molecular biology, differences in genetic information can be accurately represented, thereby enhancing classical classification methods [32]. This provides important technical support for the assessment of tea germplasm resources and the discovery of breeding materials. SLAF-seq technology, a recently developed technique, is characterized by its high throughput, precision, cost-effectiveness, and rapid turnaround time. This novel approach efficiently overcomes the complexities linked with the genomic analysis. Wang et al. employed SLAF technology to genotype 234 taro varieties from 16 different regions, identifying a total of 132,869 polymorphic single nucleotide polymorphism (SNP) markers. Subsequently, they classified all plants into eight distinct groups [33]. In the Lou onion, Fang successfully identified 162,321 high-quality SNPs utilizing SLAF technology, and building upon this foundation, a population structure analysis was performed, resulting in the clustering of all samples into three distinct groups [34]. There are comparable studies documenting numerous other plant species, such as rice, mango, miscanthus, and sugarcane [35,36,37,38]. In previous studies examining the population diversity of tea plants, methodologies such as ISSR [39], SSR [40], and RAPD [41] have been extensively utilized. However, in comparison to SLAF technology, these molecular markers developed through traditional approaches often exhibit lower accuracy and a limited number of polymorphic loci. The accuracy of analyzing the natural population structure relies heavily on the number of polymorphic loci. Traditional methods have significantly constrained the evaluation of diversity within local tea populations and impeded the exploration of germplasm resources. In the present study, SLAF-seq was utilized, which is relatively more precise and efficient compared with other approaches. For the analysis of the population structure of germplasm resources, a total of 479.18 Mb of reads were obtained. Based on the bioinformatics analysis, we identified 1,641,569 SLAF tags, among which 636,727 were found to be polymorphic SLAF tags, which suggests that the genetic diversity of the tea in the population in Wanzhou is abundant. Meanwhile, SLAF and SNP markers are uniformly distributed across each chromosome, and these polymorphic molecular markers possess a high degree of discriminatory power.

From the perspective of tea cultivation science, tea is divided into two categories: one is sexual line varieties, and the other is asexual line varieties [42]. Sexual line varieties, also referred to as “population varieties”, are reproduced through seeds under specific natural environmental conditions. They have been gradually selected and cultivated by tea farmers throughout the long history of tea planting and production. Their main feature lies in “the symbiosis and mixed breeding of multiple varieties”, and they are highly adapted to the ecological conditions of the tea-growing area where they are situated [43,44]. In recent years, with the rapid advancement of the tea industry, the general public has raised higher requirements for the taste of tea. The tea from group-bred varieties possesses a more mellow and full-bodied flavor, a richer aroma, and a more pronounced tea flavor. Consequently, research on group-bred tea has emerged as one of the current research focuses [45,46,47]. In this study, through conducting a linkage disequilibrium analysis on 47 germplasm resources of the Wanzhou population type tea and 5 representative cultivated varieties from Sichuan, Yunnan, and Fujian, we revealed that the germplasm resources growing in different regions possess different genetic diversities (Figure S3). Meanwhile, regarding the population structure and cluster analysis, all the germplasm resources could be roughly classified into three categories. Among them, the germplasm resources (Z) originating from Zhushan exhibit a relatively large genetic distance from those of other regional populations. Meanwhile, they present more significant differences compared with the common cultivated varieties in the Sichuan, Yunnan, and Fujian regions. However, with the exception of the Zhushan and Tieping regions, the germplasm resources of other population types were not clustered strictly according to regions. The majority of them have a closer genetic affinity with the cultivated varieties in the tea regions of Sichuan and Fujian, while a small proportion have a shorter genetic distance with the cultivated varieties in Yunnan (Figure 3 and Figure 4). These germplasm resources display significant differences in their leaf shape from the traditional small- and medium-sized leaves, with the leaf area falling between large-leaf and small-leaf varieties. This could be attributed to the historical introduction of wild varieties from other areas into the Wanzhou production area, which were hybridized with local varieties and propagated through cultivation, thereby breaking the geographical isolation and resulting in a mixed genetic background. Simultaneously, during the 1970s and 1980s, the development of the tea industry prompted local farmers to spontaneously hybridize or interplant tea from different origins, which may have resulted in the homogenization of the genetic background. In the PCA, the first two dimensions account for only 8% and 6.8% of the variance, respectively. This observation further supports this conclusion. The frequent gene flow has led to the formation of highly heterogeneous natural populations, thereby increasing the complexity of these populations. In such cases, the PCA may struggle to capture distinct hierarchical structures. Similar findings were reported in Elhaik’s research on a human population structure analysis [48].

The analysis of genetic diversity can elucidate the genetic relationships among tea plant populations across various regions. Additionally, it can uncover the genetic patterns associated with quantitative traits and explore the relationship between genotypes and phenotypes. In recent years, research on genetic diversity and population structures has been conducted across various regional populations, including those in Guangxi, Hunan, Anhui, and Fujian [49,50,51,52]. These studies have provided significant insights for the traceability and classification of regional varieties. However, there is a notable lack of related research in Sichuan and Chongqing, which poses challenges to the conservation and utilization of local tea germplasm resources. Furthermore, with the increasing introduction of foreign varieties in the Wanzhou area, it is imperative to elucidate the population structure and core germplasm before the further intermixing of genetic backgrounds occurs. This study has carried out related research in these aspects and offered necessary references.

5. Conclusions

In this study, the SLAF-seq technique was employed to analyze the population structure and genetic diversity of 52 tea germplasm resources. The population could be distinctly divided into three separate clusters. However, the distribution of the germplasm resources exhibited no significant correlation with their geographical origin. A total of 371 SNP markers were developed, and based on these core SNPs, the fingerprint profiles of 52 tea germplasm resources were constructed. The findings of this study offer an effective approach for the genotyping, classification, and identification of the germplasm and resources in tea plant breeding. Our research outcomes will provide a novel perspective for the improvement and exploitation of tea varieties in the Wanzhou area in the future.