Genetic diversity and population structure revealed by Whole Genome Resequencing of Lushan Cloud Mist Tea (Camellia sinensis var. sinensis)

Genetic diversity and population structure revealed by Whole Genome Resequencing of Lushan Cloud Mist Tea (Camellia sinensis var. sinensis) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic diversity and population structure revealed by Whole Genome Resequencing of Lushan Cloud Mist Tea (Camellia sinensis var. sinensis) Yanli Niu, Dongzhou Xia, Yansong Peng, Zhongxin Duan, Deshui Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8748651/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Lushan Cloud Mist Tea is celebrated as one of China’s top ten famous teas, distinguished by its unique climate and geographical environment. Our analysis revealed varying levels of genetic differentiation among populations, with the most significant divergence observed between the mid-altitude JDX and low-altitude TYC groups. The low-altitude FSZ group exhibited high nucleotide diversity, suggesting a rich reservoir of genetic resources. In contrast, the low-altitude TYC group displayed comparatively low diversity, making it more susceptible to genetic drift. Additionally, the effective population size of the nine studied populations showed a degree of consistency yet indicated a increasing trend in modern times. For SIFT analysis, sift score showed the most amino acid was under TOLERATED group, indicated that the genome for this tea species was relatively stable. Overall, our findings provide essential insights into the genetic diversity and structure of Lushan Cloud Mist Tea, informing evidence-based strategies for germplasm conservation and utilization. Lushan Tea Genetic Diversity Whole Genome Resequencing Genetic Structure Demographic History SIFT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The tea plant ( Camellia sinensis L.), a pivotal perennial species within the Theaceae family, holds considerable importance as a beverage crop on a global scale.The germplasm resources of the tea plant are crucial for the sustainable development of the tea industry. These resources serve as the cornerstone for comprehensive research, the advancement of resource development, and the strategic utilization of tea plant varieties. Furthermore, they are integral to maintaining the adaptability and long-term survival of tea plant populations. In light of these factors, the examination of genetic diversity and population structure has become increasingly critical, serving as the basis for the development of informed scientific conservation and effective resource utilization approaches. It is remain uncertainty about the origin center of C. sinensis . Previous research indicated this species was originated in the Jiangnan Tea Region of China, or Central China. Cloud tea intend to grow in the perennial cloud shrouded mountains. For morphology, its leaves are in an elliptical or obovate-lanceolate shape. The edges of the leaves are serrated, which is a typical characteristic. The leaf color is a vibrant green, with a shiny and lustrous surface, indicating the high quality of the tea leaves. The tender leaves may have fine hairs on the back, which gradually shed as the leaves mature. Molecular marker technologies have been fundamental in understanding tea plants' genetic diversity. For example, Luo et al. ( 2002 ) used RAPD markers to elucidate the phylogenetic relationships among 31 distinct tea plant ecotypes in China (Luo et al. 2002 ). Similarly, Mishra et al. ( 2009 ) utilized AFLP markers to investigate the genetic diversity of Darjeeling tea cultivars in India(Mishra et al. 2009 ). Additionally, Tan et al. ( 2009 ) utilized SSR markers to evaluate the genetic variations within 43 tea plant germplasms, uncovering substantial diversity (Tan et al. 2009 ). Despite the invaluable insights gained from these traditional molecular approaches, their scope in resolving phylogenetic and evolutionary nuances among tea plants remains limited. In contrast, more recent studies have adopted Genotyping-by-Sequencing (GBS) technology, offering a more comprehensive perspective on the genetic diversity of local tea plant germplasm resources and marking a significant progression in the field (Niu et al. 2019 ; Huang et al. 2022 ). However, traditional molecular markers lack the accuracy and resolution to accurately find genetic variation at the whole genome level, and it is difficult to find new variants and genes. Whole-genome resequencing has revolutionized the field of genomics, capturing the complete evolutionary history of species and providing a framework for constructing robust phylogenetic relationships. Its applications extend across plant systematics, population genetics, and evolutionary studies. For example, Qi et al. ( 2013 ) leveraged this technology to untangle the evolutionary lineage of cucumber varieties (Qi et al. 2013 ), and Duan et al. ( 2017 ) applied it to shed light on the genetic history of apple cultivars(Duan et al. 2017 ). The technique has been indispensable in elucidating the genetic makeup of species like watermelon (Guo et al. 2019 ), grape (Liang et al. 2019 ), and cannabis (Ren et al. 2021 ), respectively.Recently, whole-genome resequencing has emerged as a key tool in conserving endangered plant species by offering unprecedented insights into their genetic structures. It is particularly impactful in tea plant genetics research, enhancing our comprehension of the genetic variation and evolutionary dynamics inherent within Camellia sinensis species. The tea plant, a perennial crop of economic significance, displays a wide range of genetic diversity shaped by human cultivation and natural cross-pollination. Despite this, the complexity of high heterozygosity and abundant repetitive sequences has historically impeded research. The seminal publication of the Yun Kang No. 10 tea plant genome heralded a transformative phase in tea genomics (Xia et al. 2017 ). This was further bolstered by the subsequent genome release of the cultivar Shuchazao (Xia et al. 2020 ) and the chromosomal-level assembly of reference genomes for cultivars such as Bi Yun (Zhang et al. 2020a ), wild DASZ (Zhang et al. 2020b ), and Longjing 43(Wang et al. 2020 ). Recent advancements in whole-genome resequencing have markedly clarified the genetic diversity and evolutionary trajectory of tea plant varieties. Wang et al. ( 2020 ) delineated the evolution of domesticated tea plants (Wang et al. 2020 ), while An et al. ( 2020 ) delved into the genetic diversity and adaptive evolution across different varieties (An et al. 2020 ). These endeavors have not only enriched our understanding of tea plant evolution but also opened new avenues for enhancing tea breeding programs. Lushan Cloud Mist Tea is acclaimed as one of China’s top ten teas, owing its reputation to the unique climate and geography of its origin. For a long time, Lushan tea has been predominantly propagated via asexual reproduction, yet the genetic consequences of this practice on these distinct geographic tea populations remain unexplored. As genetic diversity forms the cornerstone for species evolution and adaptation, preserving this variability is pivotal for the long-term survival of the species. Hence, this study employs whole-genome resequencing to identify SNP sites, laying the groundwork for analyzing the genetic diversity of Lushan tea resources. Our analysis provides a preliminary glimpse into the extent of genetic variation in Lushan tea, illuminates genetic relationships across varying altitudes and geographical locales in Lushan, and reveals traits of their geographic genetic makeup. These findings deliver theoretical support for the conservation and harnessing of Lushan tea germplasm, facilitating the mining of superior alleles and the selection of prime tea cultivars. Materials and Methods Sampling, DNA extraction and sequencing From March to May 2020, We collected 90 samples from eight sampling points across three different altitudinal gradients in the Lushan Scenic Area, namely DTC, DYS, FSZ, NWS, TH, XRD, JDX and TYC Fig. 1, Table 1 ) . Upon collection, we promptly froze the leaves in liquid nitrogen. Subsequently, we extracted total genomic DNA using a DNA kit (Tiangen, Beijing, China). Next, we prepared a DNA sequencing library using the TruSeq DNA PCR-free prep kit from Illumina. Finally, we sequenced the library with an insert size of 400 bp on the BGI T7 platform by paired-end (PE) with a read length of 150 base pair (bp) long (Jierui Biotech, Guangzhou). Reads mapping and SNP calling Using the software Fastp (version: 0.24.0) (Chen et al. 2018), the raw reads were subject to adapter removal and quality filtering with default parameters. We used the chromosome-level assembled genome of cultivar Longjing 43 ( Camellia sinensis var. sinensis cv. Longjing 43) as the reference (http://tpia.teaplant.org/download.html). The genome contains 15 chromosomes and other 1318 contigs. The clean reads were mapped to this reference genome using the BWA-MEM (version: 0.7.19-r1273) program with default parameters (Li & Durbin 2009). The alignment outputs were sorted and indexed with SAMtools (version: 1.20) (Li et al. 2009), and duplicated reads were marked by Picard (version: 3.0) (http://broadinstitute.github.io/picard/). Base quality recalibration and local realignment were performed using the Genome Analysis Toolkit (GATK: v4.6.2.0) (McKenna et al. 2010). SNPs were called simultaneously on all samples by GATK Unified Genotyper. After joint calling, we romve InDels (Insertion and Deletion) firstly using Vcftools (version: 0.1.16) (Danecek et al. 2011), and then it was used for SNP filtration with criteria: min depth ≥ 4, max depth ≤ 100, min Q value ≥ 30, and min GQ ≥ 2. At last we diluted SNPs using --thin 20 parameter. Genetic diversity and population structure analysis Using the filtered SNP datasets, the genetic diversity metrics, including heterozygosity (H e , H o ), nucleotide diversity estimator of \(\:\pi\:\:\)(Nei & Li 1979), and private alleles were implemented using the populations module from Stacks (version: 2.68) (Catchen et al. 2013). Principal component analysis was performed using GCTA software (version: 1.94.3) (http://www.complextraitgenomics.com/software/gcta/) with all SNPs. Phylogenetic trees were constructed based on the maximum likelihood model using iqtree2 software (version: 2.4.0) (Minh et al. 2020), with the reliability of tree branches verified through bootstrapping (1000 repetitions). We also conduct further SNPs filtering to do genetic structure analysi. Here we romoved sites harbored allele frequency less than 0.05 and p value of Hardy-Weinberg that was samller than 0.0001 using plink software (version: 1.9.0 beta 8) (Purcell et al. 2007). The genetic structure of Lushan tea populations was analyzed using Admixture software (version: 1.3.0) (Alexander et al. 2009), we set K values from 1 to 15. The optimal number of clusters was determined based on the Cross-validation error. Demographic history inference based on genome SNP data Using a sequentially Markovian coalescent method implemented in SMC++ (version: v1.15.4) with default parameters (Terhorst et al., 2017), the historical effective population size for all samples was inferred based on whole-genome sequence data. Initially, we obtained VCF files for the population analysis and then converted these files into SMC format. This conversion facilitated the generation for figure plots. Finally, plots were conducted with a generation time of five years and a mutation rate per generation per site of 6.5×10^-9 (Zhang et al., 2021). Predicting effects of amino acid substitutions on proteins To assess whether amino acid substitutions have an impact on protein function and evaluate the harmfulness of genetic variations. We conducted SIFT (Sortig Intolerant From Tolerant) analyses using default parameters based on all SNPs (Sim et al., 2012). Results Population genomic data and Genetic Diversity A total of 287.8367 Gb of high-quality reads were obtained after filtering from the raw bases, amounting to 293.5857 Gb for the 90 collected samples. An average mapping rate of 99.72% (97.67% to 99.88%) was identified. These samples have an average repeat sequence ratio of 5.4599% and an average GC content of 39.46%. The average sequencing depth for these 90 samples were 5.7674×. (Supplementary Table 1). A total of 2,338,431 SNPs were identified across the eight populations. Approximately above 90% of these SNP sites are located in intergenic regions, with a nearly equal ratio of nonsynonymous to synonymous (Non-syn/Syn) substitutions. An analysis of the SNP site distribution across chromosomes revealed the highest number of SNPs on Chromosome 1 and the highest density on Chromosome 9. Conversely, the lowest number of SNPs was found on Chromosome 15, with the lowest density on Chromosome 6 ( see Fig. 2 ). The results of the population genetic diversity analysis are presented in Table 2 . The observed heterozygosity of the Lushan tea resources ranges from 0.12142 to 0.13159, averaging 0.12533, while nucleotide diversity (Pi value) ranges from 0.11046 to 0.11995, with an average of 0.11503. Notably, the FSZ population exhibits the highest nucleotide diversity and haplotype diversity, indicating the greatest genetic diversity in this group. In contrast, the TYC population show lower values in both nucleotide and haplotype diversity, suggesting reduced genetic diversity within this group, likely influenced by habitat disturbance as they are situated in core tourist areas. Furthermore, the genetic differentiation coefficient (Fst value) between the TYC and JDX populations is greater than 0.04 (Table 3 ), indicating a substantial degree of differentiation between them. Conversely, the Fst values between other populations range from 0.022119 to 0.039913, suggesting moderate genetic differentiation among them. Table 2 Genetic diversity parameters of eight C. sinensis populations Population Obs Het Obs Hom Exp Het Exp Hom Pi Private DTC 0.12834 0.87166 0.11220 0.88780 0.11726 18224 DYS 0.12315 0.87685 0.10966 0.89034 0.11355 32303 FSZ 0.13159 0.86841 0.11375 0.88625 0.11995 13054 NWS 0.12552 0.87448 0.10988 0.89012 0.11382 41243 TH 0.12172 0.87828 0.10747 0.89253 0.11334 16342 XRD 0.12711 0.87289 0.11290 0.88710 0.11759 28917 JDX 0.12376 0.87624 0.10834 0.89166 0.11425 18773 TYC 0.12142 0.87859 0.09941 0.90059 0.11046 5838 Table 3 Fixation indices(Fst) among eight C. sinensis populations DTC DYS FSZ NWS TH XRD JDX TYC DTC 0.026689 0.024839 0.026167 0.031640 0.022119 0.029098 0.037983 DYS 0.025316 0.029749 0.024540 0.029162 0.027122 0.028610 FSZ 0.028406 0.031390 0.024921 0.030048 0.038850 NWS 0.034128 0.026103 0.031481 0.039913 TH 0.033780 0.034208 0.038635 XRD 0.030080 0.039651 JDX 0.042547 Phylogenetic tree construction of Lushan tea populations based on SNPs Based on the all the SNPs, a maximum likelihood tree consisting of all samples from eight populations was obtained with high bootstrap values (Fig. 3 ). For the phylogenetic tree, the NWS, and DYC populations were two monogroups, and the remaining six groups were intermingled. The samples collected from DTC population formed 3 separate groups. This dispersion pattern may be related to geographical locations and topography (as Jiandao Gorge forms a curved valley), coupled with the dual natural and cultural attributes of Lushan Mountain. Tourism activities and other human interventions could also influence gene flow among the samples. Population Principal Component Analysis Principal component analysis (PCA) was used to further categorize the 90 samples. PC1 accounts for 2.76% of the variance, while PC2 accounts for 1.82%. As depicted in Fig. 4 , the NWS and XRD populations were significantly distinguished from TH, XRD, and DYS populations along the PC1 axis, indicating substantial genetic differences between them. PC2, on the other hand, separates the NWS population from the other seven populations. The samples from the FSZ, DYS and XRD populations are closely clustered together on both PC1 and PC2, suggesting a close genetic relationship between these two populations. There is an overlapping area between the samples of the JDX and THG populations, indicating gene flow between these groups. The PCA results also show that the JDX population was highly dispersed, which is consistent with the phylogenetic tree analysis results. This suggests extensive gene flow between the JDX population and other groups. Population Structure Analysis The genetic structure of a population is influenced by multiple factors, including mutations, selection, migration, population size, and environmental conditions. Analyzing the genetic structure of populations helps in understanding the evolutionary processes of species. When K = 2, the cross-validation error rate (CV error) is minimal (see Supplementary Fig. 1 ). However, integrating results from PCA and phylogenetic tree analysis, a comprehensive analysis of the genetic structure of the six Lushan tea populations was conducted. Thus, genetic structure plots for K > 1 were created. According to the Admixture results (Fig. 5 ), most individuals exhibit a singular genetic background. At K = 2, the genetic structure of the DYS, and TH populations show nearly complete homozygosity without gene flow with other groups, indicating its distinct structure, whereas other populations demonstrate genetic admixture, suggesting extensive gene flow between populations. At K = 3, except for the MWS population, other population samples exhibit a common ancestral signal. At K = 4, JDX and TYC populations share a common ancestor. At K = 5 and K = 6, many samples show a relatively pure genetic background, with very few being mixed; there is frequent gene flow between populations, with each population displaying both similarities and genetic variations in their genetic backgrounds. Population demographic history analysis Our research results indicate that the effective population size has been fluctuating since approximately 20,000 years ago. The greatest decline in population size occurred around 2,000 years ago (see Fig. 6 ). This pattern holds true for all eight of the populations studied. This trend aligns with the ice age glacial cycle fluctuations, and Lushan is a diverse refuge. SIFT analyses For a variant site, a specific score will be given in the end ranging from 0 to 1. When it fall on 0-0.05, the mutation indicates that it has caused a change in the protein structure and is therefore harmful. The closer the score is to 1, the smaller the impact of the mutation on the protein. The results indicate that the majority of the mutations at these sites are non-synonymous mutations. Each type of mutation was showed in Fig. 7 . And among these non-synonymous mutations, the majority of the sites are harmful (Fig. 8 ). Discussions As of January 2021, a total of 388 whole genome sequences of plants have been globally published, providing extensive genomic information for various species. Whole genome sequencing yields a wealth of SNP and variation data, enabling in-depth studies on species characteristics, population evolution, trait gene localization, and more. Tea plants, known for their intricate taxonomy and genetics, pose significant challenges due to their complex genetic backgrounds resulting from cross-breeding among samples, unlike annual or perennial self-incompatible crops. In biological populations, genetic variation correlates with the rate of evolution. Characterizing genetic diversity unveils a species’ population evolutionary history and enables the evaluation of its evolutionary potential. Parameters like Ho, Pi, and Haplotype Diversity offer multifaceted insights into a population’s genetic diversity and potential. Higher values within a certain range indicate greater gene richness, genetic diversity, and evolutionary potential. Therefore, understanding the genetic background of germplasm resources is crucial. In this study, the nearly equal ratio of nonsynonymous mutations to synonymous mutations aligns with An et al.'s ( 2020 ) findings (An et al. 2020 ), where whole genome resequencing revealed a similar pattern across all cultivars. Additionally, Lu et al.'s (2021) sequencing of 120 strains from 8 ancient tea tree populations showed that the variation in ancient tea trees remained unaffected by external natural environmental pressures or artificial breeding, evidenced by a nonsynonymous mutations/synonymous mutations ratio of 1.05. Whole genome resequencing enhances our understanding of genetic variation and adaptive characteristics (Jing et al., 2023 ). Lei et al. ( 2022 ) also used whole genome sequencing technology to study Yunnan Lincang tea, shedding light on the genetic composition and evolutionary characteristics of the local tea populations (Lei et al. 2022 ). High-throughput sequencing technology provides a large number of molecular markers for tea plant genetic analysis and serves as a potent tool for studying population genetic patterns. This study, which is conducting a phylogenetic analysis based on whole genome resequencing, elucidates genetic diversity and structure among Lushan tea resources. Notably, the JDX population, situated in a remote location, exhibits the highest nucleotide diversity and richest genetic diversity, while the TYC and TH populations, located in the core tourist areas, display the lowest nucleotide diversity due to potential long-term artificial selection and breeding. These findings align with the results of the phylogenetic tree, principal component analysis, and population structure analysis. The phylogenetic tree demonstrates the wide distribution of DTC population individuals across all three branches, corroborated by scattered DTC population samples on both PC1 and PC2 in PCA analysis. Similarly, the population genetic structure graph indicates the NWS population’s complex genetic origin and richer genetic resources, confirming extensive individual and gene flow among NWS population samples. The Fst genetic differentiation coefficient unveils notable genetic differentiation between the JDX and TYC populations, likely influenced by the core tourist area’s location. Conversely, the TH and DYS populations exhibit minimal genetic differentiation, attributed to their relatively less human interference and complex geographical locations. Evidently, changes in the genetic structure of Lushan tea resources are associated with genetic drift and selection. The Lushan National Scenic Area, a popular tourist destination, experiences a peak tourist season during the National Day holiday coinciding with the prime flowering period of tea plants. The majority of tourist activities are concentrated in the mid-altitude regions (DTC, XRD, and JDX), leading to increased exchanges between tea individuals in these areas. The genetic diversity results indicate that the inbreeding coefficient Fis values are lowest in the low-altitude NWS and FSZ populations, signifying minimal inbreeding and robust gene flow with samples from other populations. Observations of heterozygosity also support these findings, revealing that mid-altitude regions have higher values than high-altitude (DYS) and low-altitude (TYC) populations. Furthermore, cluster analysis, principal component analysis, and population structure analysis demonstrate more distant relationships between samples in the mid-altitude JDX population compared to other populations. Similarly, samples from the high-altitude DYS population and low-altitude THG population exhibit closer relationships within their respective populations than with others. These results align with findings, indicating that increasing hybridization with the popularization of tea cultivation enhances heterozygosity and gene flow between tea populations (Feng et al. 2020). Tea trees, known for their large genomes, high heterozygosity, and species diversity, hold significant economic importance. In the case of Lushan tea resources, asexual propagation is common, allowing for the preservation of superior genotypes and the swift breeding of new varieties. However, extensive asexual propagation can lead to a loss of genetic diversity and the accumulation of detrimental mutations, potentially impacting tea quality and the tree’s ability to adapt to the environment. This can be verified from the results of our analysis of harmful mutations. Among the 31,834 non-synonymous mutation sites, 10,444 of them are harmful mutations, accounting for nearly one-third. This proportion is extremely high, indicating that there are significant challenges regarding its living environment. Hou et al. (2023) conducted high-depth whole genome resequencing of Tieguanyin mother trees and their asexually propagated progeny, revealing accumulated mutations in adversity-related genes across multiple generations of asexual propagation. Whole genome resequencing proves crucial in identifying genomic variations and predicting hybrid performance for effective breeding strategies (Liu et al., 2021). This study’s detection of variations in Lushan tea resources through whole-genome resequencing highlights the importance of expanding the genetic variation of new varieties by leveraging beneficial genes from wild tea, paving the way for innovative germplasm, trait-related gene mining, and improved variety breeding in the future of tea cultivation. Declarations Funding This research was supported by Jiangxi provincial Key Laboratory of Plantation and High Value Utilization of Specialty Fruit Tree and Tea (20241ZDD02045), the Talents Program of Jiangxi Province (PR China) (jxsq2020104003), the Modern Agricultural Industrial Technology System of Jiangxi Province(JXARS-06), National Natural Science Foundation of China (32460785), and the “Xuncheng Talents” Program of Jiujiang City (JJXC2023136). Author contributions statement XF. J., and BS. L. conceived and designed the study and served as corresponding authors.YL. N., DZ. X., and YS. P. conducted the field sampling and collected tea plant materials. ZX. D., and DS. Y. performed the sequencing experiments and data acquisition. YL. N., and DZ. X. carried out the bioinformatic analyses and interpreted the sequencing data. YS. P. contributed to data validation and result visualization. BS. L. provided overall project supervision and resources. YL. N., and BS. 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Mol Plant 10:866–877 Xia E, Tong W, Hou Y, An Y, Chen L, Wu Q, Liu Y, Yu J, Li F, Li RJM (2020) The reference genome of tea plant and resequencing of 81 diverse accessions provide insights into its genome evolution and adaptation. Mol Plant 13:1013–1026 Zhang Q-J, Li W, Li K, Nan H, Shi C, Zhang Y, Dai Z-Y, Lin Y-L, Yang X-L, Tong YJMP (2020a) The chromosome-level reference genome of tea tree unveils recent bursts of non-autonomous LTR retrotransposons in driving genome size evolution. Mol Plant 13(7):935–938 Zhang W, Zhang Y, Qiu H, Guo Y, Wan H, Zhang X, Scossa F, Alseekh S, Zhang Q, Wang P, Xu L, Schmidt MHW, Jia X, Li D, Zhu A, Guo F, Chen W, Ni D, Usadel B, Fernie AR, Wen W (2020b) Genome assembly of wild tea tree DASZ reveals pedigree and selection history of tea varieties. Nat Commun 11(1):3719 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx FigureS1.png TableS1.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 31 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8748651","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590360705,"identity":"5721010c-7528-4998-b6ae-da09665e2041","order_by":0,"name":"Yanli Niu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanli","middleName":"","lastName":"Niu","suffix":""},{"id":590360708,"identity":"0aaec757-5351-4cb0-ba37-efbea6755b4f","order_by":1,"name":"Dongzhou Xia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dongzhou","middleName":"","lastName":"Xia","suffix":""},{"id":590360712,"identity":"30553d64-e74d-4b08-949f-8af69f31381e","order_by":2,"name":"Yansong Peng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yansong","middleName":"","lastName":"Peng","suffix":""},{"id":590360714,"identity":"b328aa51-2530-4454-8007-b7b9364b1732","order_by":3,"name":"Zhongxin Duan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhongxin","middleName":"","lastName":"Duan","suffix":""},{"id":590360716,"identity":"562d0e24-a801-4c16-b4a6-e46c0a044f5f","order_by":4,"name":"Deshui Yu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Deshui","middleName":"","lastName":"Yu","suffix":""},{"id":590360718,"identity":"4b27213b-a677-4c53-946f-b5c78c91ff98","order_by":5,"name":"Xinfeng Jiang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinfeng","middleName":"","lastName":"Jiang","suffix":""},{"id":590360723,"identity":"5c99f6c6-9056-4620-8f95-36cf1a870f80","order_by":6,"name":"Binsheng Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYBACPgYGNoYEBgY5PgYeIrWwQbUYs5GmBQgS24jXIpF87MHDHXXpbfxnD79gqLlDjJa0dIPEM4dz2yTy0iwYjj0jRkuOmURi2wGgFh4zA8aGw0RrqUtn4z9DmhbmBDaGHOMHxGnheQb0S9thQ5BfGBKOEaGFnz352MOfbXXy/MAQ+/ChhggtqI5MIE0DAwPzB1J1jIJRMApGwcgAAKzpMs6CZ/SNAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Binsheng","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2026-01-31 09:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8748651/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8748651/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102815787,"identity":"daddb6e6-7f7d-400f-9d03-f02c3277f590","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":427749,"visible":true,"origin":"","legend":"\u003cp\u003eThe geographical distribution of eight Lushan tea populations\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/da89c984ab7960d946842b7e.png"},{"id":102815782,"identity":"fa92cea6-cf55-4efa-9940-9295dd337217","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/7f9f7f4a11767d05545e00ed.png"},{"id":102815785,"identity":"12f187e8-6f91-4045-acd8-a3a65aae98c5","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":483497,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenomics tree among eight Lushan tea populations based on genome-wide SNPs. (The circle on the branch represent support rate, the bigger the higer.)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/67d7af0845ea2b3c761f9a10.png"},{"id":103049224,"identity":"779929ea-6607-4a64-86ca-10b7884d5dfe","added_by":"auto","created_at":"2026-02-20 07:38:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":416992,"visible":true,"origin":"","legend":"\u003cp\u003ePCA analysis of eight \u003cem\u003eC. sinensis\u003c/em\u003e populations for whole genome resequencing\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/e6078f52cb8fa9cfccd6f95d.png"},{"id":102815792,"identity":"0d5ada20-81cb-44fb-a25a-fca338f81873","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2065749,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation Genetic Structure Diagram\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/a1fc3f5544e044e0503e08ba.png"},{"id":102815790,"identity":"1af00211-f27e-49df-ae5f-7860b6149ab8","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":850644,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic history of eight \u003cem\u003eC. sinensis \u003c/em\u003epopulations in Lushan\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/25e92252fe8297f7b180bebc.png"},{"id":102962813,"identity":"432f946d-f86d-4f45-b5e0-e7713015421f","added_by":"auto","created_at":"2026-02-19 04:11:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112214,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of all variant types Distribution in CDS\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/72bf15aceeff7b15a09c9342.png"},{"id":102815789,"identity":"06600fc0-e9ad-4671-9257-173bdf939445","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":114703,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of SIFT prediction by variant type\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/a53d0c22432371926b72fb1e.png"},{"id":103050935,"identity":"15ecf181-94de-45fe-9274-e071ef2cb453","added_by":"auto","created_at":"2026-02-20 07:57:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5285482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/90adc5e5-d58c-4a6a-b574-cf6834765586.pdf"},{"id":102815783,"identity":"9237aa06-150e-455a-8486-552bc73a034b","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25302,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/5505bb43c8c60c98339638a3.docx"},{"id":102963063,"identity":"9235fff6-2d71-474f-a5bc-f493824841f8","added_by":"auto","created_at":"2026-02-19 04:13:06","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":80285,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/04a7ed45b0512c0f680d4b9d.png"},{"id":102815788,"identity":"539223a3-b34b-4242-bc6a-563780f36949","added_by":"auto","created_at":"2026-02-17 05:52:09","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23013,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.csv","url":"https://assets-eu.researchsquare.com/files/rs-8748651/v1/061ff786f9d01090b5539282.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic diversity and population structure revealed by Whole Genome Resequencing of Lushan Cloud Mist Tea (Camellia sinensis var. sinensis)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe tea plant (\u003cem\u003eCamellia sinensis\u003c/em\u003e L.), a pivotal perennial species within the Theaceae family, holds considerable importance as a beverage crop on a global scale.The germplasm resources of the tea plant are crucial for the sustainable development of the tea industry. These resources serve as the cornerstone for comprehensive research, the advancement of resource development, and the strategic utilization of tea plant varieties. Furthermore, they are integral to maintaining the adaptability and long-term survival of tea plant populations. In light of these factors, the examination of genetic diversity and population structure has become increasingly critical, serving as the basis for the development of informed scientific conservation and effective resource utilization approaches.\u003c/p\u003e \u003cp\u003eIt is remain uncertainty about the origin center of \u003cem\u003eC. sinensis\u003c/em\u003e. Previous research indicated this species was originated in the Jiangnan Tea Region of China, or Central China. Cloud tea intend to grow in the perennial cloud shrouded mountains. For morphology, its leaves are in an elliptical or obovate-lanceolate shape. The edges of the leaves are serrated, which is a typical characteristic. The leaf color is a vibrant green, with a shiny and lustrous surface, indicating the high quality of the tea leaves. The tender leaves may have fine hairs on the back, which gradually shed as the leaves mature.\u003c/p\u003e \u003cp\u003eMolecular marker technologies have been fundamental in understanding tea plants' genetic diversity. For example, Luo et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) used RAPD markers to elucidate the phylogenetic relationships among 31 distinct tea plant ecotypes in China (Luo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Similarly, Mishra et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) utilized AFLP markers to investigate the genetic diversity of Darjeeling tea cultivars in India(Mishra et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, Tan et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) utilized SSR markers to evaluate the genetic variations within 43 tea plant germplasms, uncovering substantial diversity (Tan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite the invaluable insights gained from these traditional molecular approaches, their scope in resolving phylogenetic and evolutionary nuances among tea plants remains limited. In contrast, more recent studies have adopted Genotyping-by-Sequencing (GBS) technology, offering a more comprehensive perspective on the genetic diversity of local tea plant germplasm resources and marking a significant progression in the field (Niu et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, traditional molecular markers lack the accuracy and resolution to accurately find genetic variation at the whole genome level, and it is difficult to find new variants and genes.\u003c/p\u003e \u003cp\u003eWhole-genome resequencing has revolutionized the field of genomics, capturing the complete evolutionary history of species and providing a framework for constructing robust phylogenetic relationships. Its applications extend across plant systematics, population genetics, and evolutionary studies. For example, Qi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) leveraged this technology to untangle the evolutionary lineage of cucumber varieties (Qi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Duan et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) applied it to shed light on the genetic history of apple cultivars(Duan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The technique has been indispensable in elucidating the genetic makeup of species like watermelon (Guo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), grape (Liang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and cannabis (Ren et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), respectively.Recently, whole-genome resequencing has emerged as a key tool in conserving endangered plant species by offering unprecedented insights into their genetic structures. It is particularly impactful in tea plant genetics research, enhancing our comprehension of the genetic variation and evolutionary dynamics inherent within \u003cem\u003eCamellia sinensis\u003c/em\u003e species.\u003c/p\u003e \u003cp\u003eThe tea plant, a perennial crop of economic significance, displays a wide range of genetic diversity shaped by human cultivation and natural cross-pollination. Despite this, the complexity of high heterozygosity and abundant repetitive sequences has historically impeded research. The seminal publication of the Yun Kang No. 10 tea plant genome heralded a transformative phase in tea genomics (Xia et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This was further bolstered by the subsequent genome release of the cultivar Shuchazao (Xia et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the chromosomal-level assembly of reference genomes for cultivars such as Bi Yun (Zhang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), wild DASZ (Zhang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), and Longjing 43(Wang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent advancements in whole-genome resequencing have markedly clarified the genetic diversity and evolutionary trajectory of tea plant varieties. Wang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) delineated the evolution of domesticated tea plants (Wang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while An et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) delved into the genetic diversity and adaptive evolution across different varieties (An et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These endeavors have not only enriched our understanding of tea plant evolution but also opened new avenues for enhancing tea breeding programs.\u003c/p\u003e \u003cp\u003eLushan Cloud Mist Tea is acclaimed as one of China\u0026rsquo;s top ten teas, owing its reputation to the unique climate and geography of its origin. For a long time, Lushan tea has been predominantly propagated via asexual reproduction, yet the genetic consequences of this practice on these distinct geographic tea populations remain unexplored. As genetic diversity forms the cornerstone for species evolution and adaptation, preserving this variability is pivotal for the long-term survival of the species. Hence, this study employs whole-genome resequencing to identify SNP sites, laying the groundwork for analyzing the genetic diversity of Lushan tea resources. Our analysis provides a preliminary glimpse into the extent of genetic variation in Lushan tea, illuminates genetic relationships across varying altitudes and geographical locales in Lushan, and reveals traits of their geographic genetic makeup. These findings deliver theoretical support for the conservation and harnessing of Lushan tea germplasm, facilitating the mining of superior alleles and the selection of prime tea cultivars.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eSampling, DNA extraction and sequencing\u003c/h2\u003e\n \u003cp\u003eFrom March to May 2020, We collected 90 samples from eight sampling points across three different altitudinal gradients in the Lushan Scenic Area, namely DTC, DYS, FSZ, NWS, TH, XRD, JDX and TYC Fig.\u0026nbsp;1, Table\u0026nbsp;1\u003cstrong\u003e)\u003c/strong\u003e. Upon collection, we promptly froze the leaves in liquid nitrogen. Subsequently, we extracted total genomic DNA using a DNA kit (Tiangen, Beijing, China). Next, we prepared a DNA sequencing library using the TruSeq DNA PCR-free prep kit from Illumina. Finally, we sequenced the library with an insert size of 400 bp on the BGI T7 platform by paired-end (PE) with a read length of 150 base pair (bp) long (Jierui Biotech, Guangzhou).\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eReads mapping and SNP calling\u003c/h3\u003e\n\u003cp\u003eUsing the software Fastp (version: 0.24.0) (Chen et al. 2018), the raw reads were subject to adapter removal and quality filtering with default parameters. We used the chromosome-level assembled genome of cultivar Longjing 43 (\u003cem\u003eCamellia sinensis\u003c/em\u003e var. \u003cem\u003esinensis\u003c/em\u003e cv. Longjing 43) as the reference (http://tpia.teaplant.org/download.html). The genome contains 15 chromosomes and other 1318 contigs. The clean reads were mapped to this reference genome using the BWA-MEM (version: 0.7.19-r1273) program with default parameters (Li \u0026amp; Durbin 2009). The alignment outputs were sorted and indexed with SAMtools (version: 1.20) (Li et al. 2009), and duplicated reads were marked by Picard (version: 3.0) (http://broadinstitute.github.io/picard/). Base quality recalibration and local realignment were performed using the Genome Analysis Toolkit (GATK: v4.6.2.0) (McKenna et al. 2010). SNPs were called simultaneously on all samples by GATK Unified Genotyper. After joint calling, we romve InDels (Insertion and Deletion) firstly using Vcftools (version: 0.1.16) (Danecek et al. 2011), and then it was used for SNP filtration with criteria: min depth\u0026thinsp;\u0026ge;\u0026thinsp;4, max depth\u0026thinsp;\u0026le;\u0026thinsp;100, min Q value\u0026thinsp;\u0026ge;\u0026thinsp;30, and min GQ\u0026thinsp;\u0026ge;\u0026thinsp;2. At last we diluted SNPs using --thin 20 parameter.\u003c/p\u003e\n\u003ch3\u003eGenetic diversity and population structure analysis\u003c/h3\u003e\n\u003cp\u003eUsing the filtered SNP datasets, the genetic diversity metrics, including heterozygosity (H\u003csub\u003ee\u003c/sub\u003e, H\u003csub\u003eo\u003c/sub\u003e), nucleotide diversity estimator of \\(\\:\\pi\\:\\:\\)(Nei \u0026amp; Li 1979), and \u003cem\u003eprivate alleles\u003c/em\u003e were implemented using the \u003cem\u003epopulations\u003c/em\u003e module from Stacks (version: 2.68) (Catchen et al. 2013). Principal component analysis was performed using GCTA software (version: 1.94.3) (http://www.complextraitgenomics.com/software/gcta/) with all SNPs. Phylogenetic trees were constructed based on the maximum likelihood model using iqtree2 software (version: 2.4.0) (Minh et al. 2020), with the reliability of tree branches verified through bootstrapping (1000 repetitions). We also conduct further SNPs filtering to do genetic structure analysi. Here we romoved sites harbored allele frequency less than 0.05 and p value of Hardy-Weinberg that was samller than 0.0001 using plink software (version: 1.9.0 beta 8) (Purcell et al. 2007). The genetic structure of Lushan tea populations was analyzed using Admixture software (version: 1.3.0) (Alexander et al. 2009), we set K values from 1 to 15. The optimal number of clusters was determined based on the Cross-validation error.\u003c/p\u003e\n\u003ch3\u003eDemographic history inference based on genome SNP data\u003c/h3\u003e\n\u003cp\u003eUsing a sequentially Markovian coalescent method implemented in SMC++ (version: v1.15.4) with default parameters (Terhorst et al., 2017), the historical effective population size for all samples was inferred based on whole-genome sequence data. Initially, we obtained VCF files for the population analysis and then converted these files into SMC format. This conversion facilitated the generation for figure plots. Finally, plots were conducted with a generation time of five years and a mutation rate per generation per site of 6.5\u0026times;10^-9 (Zhang et al., 2021).\u003c/p\u003e\n\u003ch3\u003ePredicting effects of amino acid substitutions on proteins\u003c/h3\u003e\n\u003cp\u003eTo assess whether amino acid substitutions have an impact on protein function and evaluate the harmfulness of genetic variations. We conducted SIFT (Sortig Intolerant From Tolerant) analyses using default parameters based on all SNPs (Sim et al., 2012).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulation genomic data and Genetic Diversity\u003c/h2\u003e \u003cp\u003eA total of 287.8367 Gb of high-quality reads were obtained after filtering from the raw bases, amounting to 293.5857 Gb for the 90 collected samples. An average mapping rate of 99.72% (97.67% to 99.88%) was identified. These samples have an average repeat sequence ratio of 5.4599% and an average GC content of 39.46%. The average sequencing depth for these 90 samples were 5.7674\u0026times;. (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eA total of 2,338,431 SNPs were identified across the eight populations. Approximately above 90% of these SNP sites are located in intergenic regions, with a nearly equal ratio of nonsynonymous to synonymous (Non-syn/Syn) substitutions. An analysis of the SNP site distribution across chromosomes revealed the highest number of SNPs on Chromosome 1 and the highest density on Chromosome 9. Conversely, the lowest number of SNPs was found on Chromosome 15, with the lowest density on Chromosome 6 (\u003cb\u003esee Fig.\u0026nbsp;2\u003c/b\u003e). The results of the population genetic diversity analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The observed heterozygosity of the Lushan tea resources ranges from 0.12142 to 0.13159, averaging 0.12533, while nucleotide diversity (Pi value) ranges from 0.11046 to 0.11995, with an average of 0.11503. Notably, the FSZ population exhibits the highest nucleotide diversity and haplotype diversity, indicating the greatest genetic diversity in this group. In contrast, the TYC population show lower values in both nucleotide and haplotype diversity, suggesting reduced genetic diversity within this group, likely influenced by habitat disturbance as they are situated in core tourist areas. Furthermore, the genetic differentiation coefficient (Fst value) between the TYC and JDX populations is greater than 0.04 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating a substantial degree of differentiation between them. Conversely, the Fst values between other populations range from 0.022119 to 0.039913, suggesting moderate genetic differentiation among them.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity parameters of eight \u003cem\u003eC. sinensis\u003c/em\u003e populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs Het\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs Hom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExp Het\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExp Hom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDYS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJDX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFixation indices(Fst) among eight \u003cem\u003eC. sinensis\u003c/em\u003e populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDYS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFSZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNWS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eXRD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJDX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTYC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.029098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.037983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDYS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.027122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.028610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.030048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.038850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.026103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.031481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.039913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.034208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.038635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.030080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.039651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJDX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhylogenetic tree construction of Lushan tea populations based on SNPs\u003c/h3\u003e\n\u003cp\u003eBased on the all the SNPs, a maximum likelihood tree consisting of all samples from eight populations was obtained with high bootstrap values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the phylogenetic tree, the NWS, and DYC populations were two monogroups, and the remaining six groups were intermingled. The samples collected from DTC population formed 3 separate groups. This dispersion pattern may be related to geographical locations and topography (as Jiandao Gorge forms a curved valley), coupled with the dual natural and cultural attributes of Lushan Mountain. Tourism activities and other human interventions could also influence gene flow among the samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Principal Component Analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was used to further categorize the 90 samples. PC1 accounts for 2.76% of the variance, while PC2 accounts for 1.82%. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the NWS and XRD populations were significantly distinguished from TH, XRD, and DYS populations along the PC1 axis, indicating substantial genetic differences between them. PC2, on the other hand, separates the NWS population from the other seven populations. The samples from the FSZ, DYS and XRD populations are closely clustered together on both PC1 and PC2, suggesting a close genetic relationship between these two populations. There is an overlapping area between the samples of the JDX and THG populations, indicating gene flow between these groups. The PCA results also show that the JDX population was highly dispersed, which is consistent with the phylogenetic tree analysis results. This suggests extensive gene flow between the JDX population and other groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Structure Analysis\u003c/h2\u003e \u003cp\u003eThe genetic structure of a population is influenced by multiple factors, including mutations, selection, migration, population size, and environmental conditions. Analyzing the genetic structure of populations helps in understanding the evolutionary processes of species. When K\u0026thinsp;=\u0026thinsp;2, the cross-validation error rate (CV error) is minimal (see \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). However, integrating results from PCA and phylogenetic tree analysis, a comprehensive analysis of the genetic structure of the six Lushan tea populations was conducted. Thus, genetic structure plots for K\u0026thinsp;\u0026gt;\u0026thinsp;1 were created. According to the Admixture results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), most individuals exhibit a singular genetic background. At K\u0026thinsp;=\u0026thinsp;2, the genetic structure of the DYS, and TH populations show nearly complete homozygosity without gene flow with other groups, indicating its distinct structure, whereas other populations demonstrate genetic admixture, suggesting extensive gene flow between populations. At K\u0026thinsp;=\u0026thinsp;3, except for the MWS population, other population samples exhibit a common ancestral signal. At K\u0026thinsp;=\u0026thinsp;4, JDX and TYC populations share a common ancestor. At K\u0026thinsp;=\u0026thinsp;5 and K\u0026thinsp;=\u0026thinsp;6, many samples show a relatively pure genetic background, with very few being mixed; there is frequent gene flow between populations, with each population displaying both similarities and genetic variations in their genetic backgrounds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePopulation demographic history analysis\u003c/h2\u003e \u003cp\u003eOur research results indicate that the effective population size has been fluctuating since approximately 20,000 years ago. The greatest decline in population size occurred around 2,000 years ago (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This pattern holds true for all eight of the populations studied. This trend aligns with the ice age glacial cycle fluctuations, and Lushan is a diverse refuge.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSIFT analyses\u003c/h2\u003e \u003cp\u003eFor a variant site, a specific score will be given in the end ranging from 0 to 1. When it fall on 0-0.05, the mutation indicates that it has caused a change in the protein structure and is therefore harmful. The closer the score is to 1, the smaller the impact of the mutation on the protein.\u003c/p\u003e \u003cp\u003eThe results indicate that the majority of the mutations at these sites are non-synonymous mutations. Each type of mutation was showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e. And among these non-synonymous mutations, the majority of the sites are harmful (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eAs of January 2021, a total of 388 whole genome sequences of plants have been globally published, providing extensive genomic information for various species. Whole genome sequencing yields a wealth of SNP and variation data, enabling in-depth studies on species characteristics, population evolution, trait gene localization, and more. Tea plants, known for their intricate taxonomy and genetics, pose significant challenges due to their complex genetic backgrounds resulting from cross-breeding among samples, unlike annual or perennial self-incompatible crops.\u003c/p\u003e \u003cp\u003eIn biological populations, genetic variation correlates with the rate of evolution. Characterizing genetic diversity unveils a species\u0026rsquo; population evolutionary history and enables the evaluation of its evolutionary potential. Parameters like Ho, Pi, and Haplotype Diversity offer multifaceted insights into a population\u0026rsquo;s genetic diversity and potential. Higher values within a certain range indicate greater gene richness, genetic diversity, and evolutionary potential. Therefore, understanding the genetic background of germplasm resources is crucial.\u003c/p\u003e \u003cp\u003eIn this study, the nearly equal ratio of nonsynonymous mutations to synonymous mutations aligns with An et al.'s (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) findings (An et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), where whole genome resequencing revealed a similar pattern across all cultivars. Additionally, Lu et al.'s (2021) sequencing of 120 strains from 8 ancient tea tree populations showed that the variation in ancient tea trees remained unaffected by external natural environmental pressures or artificial breeding, evidenced by a nonsynonymous mutations/synonymous mutations ratio of 1.05.\u003c/p\u003e \u003cp\u003eWhole genome resequencing enhances our understanding of genetic variation and adaptive characteristics (Jing et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lei et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also used whole genome sequencing technology to study Yunnan Lincang tea, shedding light on the genetic composition and evolutionary characteristics of the local tea populations (Lei et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). High-throughput sequencing technology provides a large number of molecular markers for tea plant genetic analysis and serves as a potent tool for studying population genetic patterns. This study, which is conducting a phylogenetic analysis based on whole genome resequencing, elucidates genetic diversity and structure among Lushan tea resources. Notably, the JDX population, situated in a remote location, exhibits the highest nucleotide diversity and richest genetic diversity, while the TYC and TH populations, located in the core tourist areas, display the lowest nucleotide diversity due to potential long-term artificial selection and breeding. These findings align with the results of the phylogenetic tree, principal component analysis, and population structure analysis. The phylogenetic tree demonstrates the wide distribution of DTC population individuals across all three branches, corroborated by scattered DTC population samples on both PC1 and PC2 in PCA analysis. Similarly, the population genetic structure graph indicates the NWS population\u0026rsquo;s complex genetic origin and richer genetic resources, confirming extensive individual and gene flow among NWS population samples.\u003c/p\u003e \u003cp\u003eThe Fst genetic differentiation coefficient unveils notable genetic differentiation between the JDX and TYC populations, likely influenced by the core tourist area\u0026rsquo;s location. Conversely, the TH and DYS populations exhibit minimal genetic differentiation, attributed to their relatively less human interference and complex geographical locations. Evidently, changes in the genetic structure of Lushan tea resources are associated with genetic drift and selection.\u003c/p\u003e \u003cp\u003eThe Lushan National Scenic Area, a popular tourist destination, experiences a peak tourist season during the National Day holiday coinciding with the prime flowering period of tea plants. The majority of tourist activities are concentrated in the mid-altitude regions (DTC, XRD, and JDX), leading to increased exchanges between tea individuals in these areas. The genetic diversity results indicate that the inbreeding coefficient Fis values are lowest in the low-altitude NWS and FSZ populations, signifying minimal inbreeding and robust gene flow with samples from other populations.\u003c/p\u003e \u003cp\u003eObservations of heterozygosity also support these findings, revealing that mid-altitude regions have higher values than high-altitude (DYS) and low-altitude (TYC) populations. Furthermore, cluster analysis, principal component analysis, and population structure analysis demonstrate more distant relationships between samples in the mid-altitude JDX population compared to other populations. Similarly, samples from the high-altitude DYS population and low-altitude THG population exhibit closer relationships within their respective populations than with others. These results align with findings, indicating that increasing hybridization with the popularization of tea cultivation enhances heterozygosity and gene flow between tea populations (Feng et al. 2020).\u003c/p\u003e \u003cp\u003eTea trees, known for their large genomes, high heterozygosity, and species diversity, hold significant economic importance. In the case of Lushan tea resources, asexual propagation is common, allowing for the preservation of superior genotypes and the swift breeding of new varieties. However, extensive asexual propagation can lead to a loss of genetic diversity and the accumulation of detrimental mutations, potentially impacting tea quality and the tree\u0026rsquo;s ability to adapt to the environment. This can be verified from the results of our analysis of harmful mutations. Among the 31,834 non-synonymous mutation sites, 10,444 of them are harmful mutations, accounting for nearly one-third. This proportion is extremely high, indicating that there are significant challenges regarding its living environment.\u003c/p\u003e \u003cp\u003eHou et al. (2023) conducted high-depth whole genome resequencing of Tieguanyin mother trees and their asexually propagated progeny, revealing accumulated mutations in adversity-related genes across multiple generations of asexual propagation. Whole genome resequencing proves crucial in identifying genomic variations and predicting hybrid performance for effective breeding strategies (Liu et al., 2021). This study\u0026rsquo;s detection of variations in Lushan tea resources through whole-genome resequencing highlights the importance of expanding the genetic variation of new varieties by leveraging beneficial genes from wild tea, paving the way for innovative germplasm, trait-related gene mining, and improved variety breeding in the future of tea cultivation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Jiangxi provincial Key Laboratory of Plantation and High Value Utilization of Specialty Fruit Tree and Tea (20241ZDD02045), the Talents Program of Jiangxi Province (PR China) (jxsq2020104003), the Modern Agricultural Industrial Technology System of Jiangxi Province(JXARS-06), National Natural Science Foundation of China (32460785), and the\u0026nbsp;\u0026ldquo;Xuncheng Talents\u0026rdquo;\u0026nbsp;Program of Jiujiang City (JJXC2023136).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXF. J., and BS. L. conceived and designed the study and served as corresponding authors.YL. N., DZ. X., and YS. P. conducted the field sampling and collected tea plant materials. ZX. D., and DS. Y. \u0026nbsp;performed the sequencing experiments and data acquisition. YL. N., and DZ. X. carried out the bioinformatic analyses and interpreted the sequencing data. YS. P. contributed to data validation and result visualization. BS. L. provided overall project supervision and resources. YL. N., and BS. \u0026nbsp;L. \u0026nbsp;drafted the manuscript. All authors reviewed, revised, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data sequenced in this research was doposit in NCBI database with a \u0026nbsp;Project NO. PRJNA1110598 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1110598) and the SRA NO. was between SRX24527752-SRX24527781, SRX30756424-SRX30756483,respectively.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlexander DH, Novembre J, Lange, KJGr (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655\u0026ndash;1664\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn Y, Mi X, Zhao S, Guo R, Xia X, Liu S, Wei C (2020) Revealing Distinctions in Genetic Diversity and Adaptive Evolution Between Two Varieties of Camellia sinensis by Whole-Genome Resequencing. Front Plant Sci 11:603819\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. 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Mol Plant 13(7):935\u0026ndash;938\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Zhang Y, Qiu H, Guo Y, Wan H, Zhang X, Scossa F, Alseekh S, Zhang Q, Wang P, Xu L, Schmidt MHW, Jia X, Li D, Zhu A, Guo F, Chen W, Ni D, Usadel B, Fernie AR, Wen W (2020b) Genome assembly of wild tea tree DASZ reveals pedigree and selection history of tea varieties. Nat Commun 11(1):3719\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lushan Tea, Genetic Diversity, Whole Genome Resequencing, Genetic Structure, Demographic History, SIFT","lastPublishedDoi":"10.21203/rs.3.rs-8748651/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8748651/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLushan Cloud Mist Tea is celebrated as one of China\u0026rsquo;s top ten famous teas, distinguished by its unique climate and geographical environment. Our analysis revealed varying levels of genetic differentiation among populations, with the most significant divergence observed between the mid-altitude JDX and low-altitude TYC groups. The low-altitude FSZ group exhibited high nucleotide diversity, suggesting a rich reservoir of genetic resources. In contrast, the low-altitude TYC group displayed comparatively low diversity, making it more susceptible to genetic drift. Additionally, the effective population size of the nine studied populations showed a degree of consistency yet indicated a increasing trend in modern times. For SIFT analysis, sift score showed the most amino acid was under TOLERATED group, indicated that the genome for this tea species was relatively stable. Overall, our findings provide essential insights into the genetic diversity and structure of Lushan Cloud Mist Tea, informing evidence-based strategies for germplasm conservation and utilization.\u003c/p\u003e","manuscriptTitle":"Genetic diversity and population structure revealed by Whole Genome Resequencing of Lushan Cloud Mist Tea (Camellia sinensis var. sinensis)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 05:51:59","doi":"10.21203/rs.3.rs-8748651/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-16T15:51:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T07:53:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T07:53:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320495604867189795674980912452381042663","date":"2026-03-29T23:39:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48079271236913507960237910426073287531","date":"2026-03-29T11:00:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73126623588483925636124790164413424275","date":"2026-03-28T18:32:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97521045167554980952880594330107487130","date":"2026-03-24T03:10:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T14:49:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T10:00:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T09:59:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2026-01-31T09:38:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b365d55a-2760-4085-8f70-2b50ddc1025c","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T05:51:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 05:51:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8748651","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8748651","identity":"rs-8748651","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}