Potent SSR markers for the assessment of population structure, genetic diversity, and bioactive compounds in Atractylodis Rhizoma

Potent SSR markers for the assessment of population structure, genetic diversity, and bioactive compounds in Atractylodis Rhizoma | 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 Potent SSR markers for the assessment of population structure, genetic diversity, and bioactive compounds in Atractylodis Rhizoma Haotian Zhong, Lina Chen, Lei Chen, Xiao Huang, Ling Gong, Juan Hu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4801864/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Atractylodes lancea and A. chinensis , commonly referred to as Atractylodes Rhizome (AR), are significant traditional medicinal plants in China. AR exhibits a broad geographical distribution within the country. However, the escalating market demand and depletion of wild resources have led to a pressing need for increased cultivation of AR. Despite this urgency, research on the conservation of AR resources remains limited. Hence, it is imperative to conduct an analysis of the genetic background of the original plant and ascertain the specific variety of medicine AR. Results: This research utilized transcriptome data from A. lancea to develop SSR molecular markers, assess the population structure and genetic diversity of AR, and employed the mantel test to validate the relationship between volatile oil components and genetic distance among the samples. A set of 29 pairs of highly polymorphic SSR primers yielded a total of 264 different alleles. Clustering analysis identified three distinct populations: Mao Mountain, Dabie Mountain, and samples from other locations. A clear differentiation between A. lancea and A. chinensis was observed, facilitating effective discrimination of AR varieties. Screening based on GC-MS results revealed 24 potential differential metabolites between the two species, with correlation analysis indicating significant associations with 18 previously identified molecular markers. Conclusions: This study successfully developed SSR molecular markers for the purpose of analyzing genetic diversity in A. lancea and A. chinensis . Furthermore, a method was established for identifying the variety of medicine AR, with the confirmation that T72 exhibits the highest predictive ability for β-eudesmol, hinesol, and atractylon. These findings lay a solid groundwork for future quality control of medicine AR and the selection of superior germplasm. Atractylodes lancea Atractylodes chinensis SSR Genetic diversity Phylogenetic. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Atractylodis Rhizoma (AR) is a commonly utilized herbal remedy in East Asian countries, including China, Japan, Korea, and Thailand, for addressing various health conditions such as digestive disorders, rheumatic diseases, and night blindness. The primary bioactive constituents found in AR are terpenoids, alkynes, and their glycosides, including atractylodin, β-eudesmol, atractylon, and hinesol [ 1 ]. These compounds exhibit a wide range of pharmacological effects, such as antihypertensive, antihyperglycemic, anti-tumor, anti-inflammatory, disease-resistant against pathogenic microorganisms, hepatoprotective, and effects on the digestive and nervous systems [ 2 , 3 ]. AR is sourced from the rhizome of Atractylodes lancea (Thunb.) DC. and A. chinensis (DC.) Koidz as specified in the Pharmacopoeias of China and Japan. Historical Bencao literature, including the Compendium of Materia Medica (1578 AD), suggests that the rhizome of A. lancea contains a higher concentration of volatile oil and a more pronounced aromatic scent compared to A. chinensis , which has traditionally been believed to possess superior therapeutic properties. Additional characteristics, such as the morphology of the rhizome and its transverse section, as well as the presence of white cotton-like crystals, are frequently employed in the authentication of A. lancea as distinct from A. chinensis [ 4 ]. A. chinensis is considered a synonym of A. lancea in both the "Flora of China" and "Plants of the World Online," suggesting that the taxonomic classification of species within the genus Atractylodes , particularly Atractylodes lancea , has been a topic of debate [ 5 , 6 ]. Given the inconsistent quality of artemisinin, it is imperative to distinguish between various botanical sources of AR and devise precise and dependable identification techniques. In recent years, alongside traditional morphological identification techniques, molecular and metabolic methods have been increasingly utilized. Various chloroplast genomic regions, including trnL-F , trnK , atpB-rbcL , as well as the nuclear internal transcribed spacer (ITS), have been employed in numerous studies to ascertain the phylogenetic relationships within the Atractylodes genus [ 7 , 8 ], moreover, A. macrocephala, A. lancea , and their hybrids were differentiated using MLPA-qPCR targeting the nuclear genome [ 9 ], and SCAR (Sequence-Characterized Amplified Region) markers were employed in conjunction with sequencing techniques to characterize and identify Maoshan A. lancea [ 10 ]. The molecular markers derived from plastid have been demonstrated to possess the capability to differentiate among A. lancea , A. chinensis , and A. macrocephala [ 11 ]. The authentication of the geographic origins of A. lancea rhizome chemotypes was achieved through the use of metabolite markers, and further metabolomic research has contributed valuable information towards the development of a quality evaluation system for A. lancea [ 12 , 13 ]. Nevertheless, there exist numerous limitations linked to molecular markers (such as low resolution and biased results) and chemometrics (including complex composition and inconsistent standards) that must be addressed. Simple sequence repeats (SSR) molecular markers have been extensively utilized in genetic analysis of diverse crops and traditional Chinese medicine, primarily due to their attributes of codominance, high polymorphism, universality, and repeatability. The utilization of SSR markers, ranging from cluster analysis to germplasm selection and trait association, plays a significant role in the advancement of the crop and traditional Chinese medicine industries [ 14 – 17 ]. For AR, SSRs have been applied just on a small scale in population structure analysis of A. lancea in Hubei province [ 18 ] and A. chinensis in northern China [ 19 ]. The SSR loci in the chloroplast genome of Atractylodes have also been screened [ 7 ]. To date, there remains a deficiency in the availability of highly reliable SSR markers for distinguishing between A. lancea and A. chinensis , with no SSR markers having been identified for the characterization of the bioactive compounds present in these species. Genetic diversity, which serves as the foundation of biodiversity, enables species to effectively respond to a range of intricate environmental fluctuations, thus playing a crucial role in facilitating genetic breeding and enhancing germplasm quality [ 20 ]. A decrease in genetic diversity within a species results in diminished genetic variation and adaptability, thereby posing a threat to its long-term survival [ 21 ]. The establishment of core collections has grown in importance for preserving the genetic diversity of species. The abundance of germplasm resources is essential for crop breeding and improvement, and core collections provide a practical approach for conserving and effectively utilizing these resources. The core sample set represents a subset of germplasm resources that efficiently encompasses the genetic variability of the entire population, thus reducing the necessary number of samples [ 22 , 23 ]. Recent studies indicate that molecular markers may effectively capture the genetic diversity of germplasm resources by analyzing DNA sequences [ 24 ]. Markers such as SSR and SNP have been successfully employed in the development of core collections in various agricultural and economic crops, such as rice, wheat, strawberries, and cucumber [ 25 – 28 ]. In this study, SSR primers were developed using transcriptome data and metabolites from GC-MS data to assess the genetic diversity of AR and establish SSR markers for distinguishing between A. lancea and A. chinensis . The aims of the research were to elucidate the population structure and genetic variability of AR, establish a method for discriminating between A. lancea and A. chinensis using SSR markers, and validate the predictive capacity of SSR loci associated with active compounds. Results Characterization of the transcriptome derived SSRs The Molecular Information and Sequence Analysis (MISA) revealed the presence of SSR loci in 125711 sequences, with 1261 sequences exhibiting multiple SSRs. A total of 12578 SSRs were identified in the transcriptome data of A. lancea , with 450 of them forming compound structures. The SSRs displayed a variety of repeat types, with mononucleotide (5840, 46.43%) and dinucleotide (4448, 35.36%) repeats being the most prevalent, followed by trinucleotides (2129, 16.93%). Tetranucleotide, pentanucleotide, and hexanucleotide repeats each accounted for less than 3% of the total (Table S1 ). SSR primer validation and genetic diversity analysis Three hundred SSR loci were chosen in a uniform manner from the transcripts, followed by the design and screening of primers using 1% agarose gel electrophoresis and fluorescent capillary electrophoresis. Ultimately, 29 pairs of SSR primers were identified that exhibited high amplification efficiency, good reproducibility, and high polymorphism (Table S2). A total of 432 samples from 7 populations were analyzed using 29 pairs of primers, resulting in the amplification of 264 alleles. The number of alleles per locus ranged from 3 (T217) to 17 (T256), with an average of 9.276 alleles per locus. Among the primers used, T11, T31, and T284 exhibited the highest levels of polymorphism and identification efficiency (refer to Table 1 ). Additionally, a comparison of genetic diversity among samples collected from the 7 sampling sites indicated that MS displayed the highest genetic diversity, with 215 different alleles identified, followed by DB (refer to Table 2 ). Table 1 Genetic diversity statistics of the 29 microsatellite markers across 432 samples Primer N A N E I H O H E PIC T3 6.857 3.085 1.319 0.592 0.640 0.726 T10 4.000 1.653 0.632 0.239 0.341 0.357 T11 8.429 4.101 1.616 0.683 0.739 0.752 T12 4.143 1.673 0.658 0.310 0.325 0.403 T16 3.857 1.380 0.476 0.240 0.240 0.259 T26 4.714 2.561 0.994 0.561 0.561 0.576 T31 7.000 4.171 1.574 0.527 0.738 0.800 T36 5.143 1.894 0.843 0.289 0.442 0.463 T44 3.429 1.529 0.564 0.240 0.302 0.331 T45 4.143 2.038 0.865 0.482 0.484 0.493 T58 5.714 2.745 1.163 0.557 0.594 0.680 T72 3.571 1.844 0.708 0.313 0.415 0.410 T82 4.000 1.485 0.506 0.311 0.253 0.270 T86 3.286 2.168 0.817 0.534 0.499 0.490 T134 3.000 1.221 0.313 0.142 0.162 0.173 T146 3.000 1.705 0.376 0.140 0.169 0.259 T177 5.143 2.640 1.131 0.583 0.607 0.702 T217 2.286 1.439 0.414 0.217 0.258 0.276 T235 7.286 2.915 1.298 0.574 0.639 0.677 T238 3.286 1.255 0.331 0.155 0.165 0.183 T252 3.143 2.030 0.782 0.479 0.499 0.564 T253 4.143 1.461 0.553 0.306 0.280 0.463 T256 7.429 2.968 1.358 0.522 0.648 0.736 T262 4.143 1.356 0.447 0.198 0.208 0.223 T266 5.571 3.086 1.264 0.670 0.665 0.664 T277 5.714 2.904 1.236 0.523 0.653 0.697 T281 5.571 2.480 0.954 0.322 0.463 0.538 T284 6.429 3.505 1.410 0.685 0.703 0.752 T286 8.571 2.712 1.316 0.329 0.583 0.658 N A , Number of Alleles; N E , Number of Effective alleles; I , Shannon’s Information Index; H O , Observed Heterozygosity; H E , Expected Heterozygosity; PIC - Polymorphic Information Content. Table 2 Genetic parameters of 7 different sampling points and different sets Population No. Alleles N A N E I H O H E MS 153 215 7.414 2.784 1.175 0.516 0.573 DB 40 152 5.241 2.465 0.989 0.457 0.501 YX 55 138 4.759 2.238 0.896 0.371 0.460 TB 40 102 3.517 1.937 0.672 0.342 0.362 WC 52 139 4.793 2.283 0.876 0.375 0.446 CC 60 135 4.655 2.190 0.869 0.408 0.455 PG 32 120 4.138 2.036 0.779 0.360 0.408 Core set 106 229 7.897 2.786 1.209 0.442 0.575 Rest set 326 253 8.724 2.767 1.196 0.430 0.560 No., population sample size; alleles, number of different alleles within population; N A , Number of Alleles; N E , Number of Effective alleles; I , Shannon’s Information Index; H O , Observed Heterozygosity; H E , Expected Heterozygosity; PIC - Polymorphic Information Content. MS - Mao Mountain, Jiangsu; DB – Dabie Mountain, Hubei; YX – Shiyan Hubei; TB – Taibai, Shaanxi; WC – Weichang, Hebei; CC – Chicheng, Hebei; PG – Pianguan, Shanxi. Population structure analysis In our study, the population structure of the samples was analyzed using neighbor-joining (NJ) tree, principal component analysis (PCA), and STRUCTURE. The results revealed that all samples of A. chinensis were grouped together in a single cluster, whereas the MS and DB populations of A. lancea were clustered separately. (Fig. 1 A). The findings of the PCA were consistent with those of the NJ tree, indicating that all populations, with the exception of MS and DB, were grouped together in a single cluster, while MS and DB were classified in a separate group (Fig. 1 B). The STRUCTURE analysis identified two primary genetic clusters, one originating from individuals of MS and DB and the other from individuals of other populations, when K = 2. These findings can serve as a foundation for distinguishing between varieties of A. lancea and A. chinensis (Fig. 1 C). When K = 3, the findings were consistent with those obtained from PCA. The Analysis of molecular variance (AMOVA) analysis indicated that the majority of genetic diversity was found within populations, with only 15% of genetic variation observed among populations (Table 3 ). Based on the genetic differentiation coefficient observed between populations, it can be deduced that there was a significant level of gene flow among various populations of A. chinensis , whereas gene flow among populations of A. lancea was comparatively limited (Table 4 ). We subsequently utilized a limited set of primers to enumerate alleles exhibiting markedly elevated frequencies within each population, thereby facilitating the efficient identification of specific populations. Loci including T3, T11, and T235 were frequently observed across multiple populations (Fig. 2 ). Table 3 Analysis of molecular variance (AMOVA) for 7 different populations Source df SS MS % Among Pops 6 942.048 157.008 15% Within Pops 857 6195.243 7.229 85% Total 863 7137.292 100% df – degrees of freedom; SS – sum of square; MS – mean of square. Table 4 The matrix of pairwise genetic differentiation ( F ST ) among 7 populations F ST MS DB YX TB WC CC PG MS 0.000 DB 0.089 0.000 YX 0.064 0.121 0.000 TB 0.123 0.155 0.060 0.000 WC 0.105 0.145 0.042 0.047 0.000 CC 0.094 0.142 0.039 0.057 0.027 0.000 PG 0.109 0.158 0.043 0.058 0.033 0.025 0.000 Construction of the core collection The GenoCore analysis yielded a core subset of 106 samples, encompassing 99% of the alleles present in the original germplasm. The genetic diversity parameters N A , N E , H O , H E , and I were employed to assess the composition of the core subset (Table 2 ). The results indicated that the core sample set had higher N E , H O and H E compared to those of the original germplasm, and had the ability to measure more genetic information with fewer sample sizes. Moreover, it could be seen from the PCA diagram and phylogenetic (Fig. 3 ) that the materials in the core collection were not only similar to the distribution of the original germplasm, but also had a relatively comprehensive and uniform coverage, which represented the geometric distribution of the original germplasm, indicating that the genetic structure of the original germplasm was well maintained by the core collection and further confirming the representativeness of the selected core collection. Volatile components A total of 63 different volatile substances (Table S3) and hierarchical clustering heatmap (Fig. 4 A) were obtained in all samples, and had been identified as 9 different types. The clustering results using all compounds were not sufficient to distinguish between the two varieties, subsequently, Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was used to make samples clearly divided into two groups (Fig. 4 B). The OPLS-DA model revealed 24 differential volatile components with the cutoff of variable importance in projection (VIP) value > 1 and P value < 0.05, and three compounds with the highest VIP value, namely cryptomeridiol, γ-elemene, and 2-Methylene-5α-cholestan-3β-ol, were discovered (Fig. 4 C). Correlation between metabolites and SSR loci To go a step further, we estimated correlation between differential metabolites and SSR loci. The results of mantel test suggested that 24 differential metabolites significantly related to A. lancea and the A. chinensis (Fig. 5 ). It is noteworthy that atractylon was significantly correlated with A. lancea , and the other two were related to A. chinensis . SSR loci for the distinguishing of the activate components Based on the results of comparing allele frequencies between high and low content groups in 102 samples, seven pairs of primers were identified that exhibit preferential alleles between the two groups. Specifically, allele 301 in T72 was found to have a higher frequency in the high content group of β-eudesmol, hinesol, atractylon, and the total of four components, while allele 295 in T72 was superior in the low content group (Table 5 ). Table 5 Four compounds of high and low content group high frequency loci and alleles Group High Low β-Eudesmol T72(301)0.583 T72(295)0.633 T281(209)0.317 Atractylon + Hinesol T45(388)0.717 T72(301)0.5 T58(258)0.4 T72(295)0.7 Atractylodin T235(378)0.667 Total T72(301)0.5 T256(408)0.617 T3(377)0.383 T72(295)0.717 The numbers in parentheses represent different alleles, which behind parentheses represent the frequency of the alleles. Discussion Population structure and genetic diversity of A. lancea Transcriptome derived SSR markers have been widely applied in population genetic analysis of medicinal plants[ 29 , 30 ]. In this study, 29 pairs of high polymorphism SSR primers were designed, and used to analysis genetic diversity. Mao Mountain, recognized as the best producing area of A. lancea , has the highest genetic diversity among all the 7 populations, followed by Dabie Mountain. At present, the high genetic diversity in the samples of Yingshan, part of the Dabie Mountain, indicated that it had the potential to become the new most suitable producing area of A. lancea . Moreover, studies have found that A. lancea from Hubei province had small genetic variation [ 18 ]. In our analysis of population structure, it is evident that all sampling points of A. chinensis are part of the same population, suggesting limited genetic variation and aligning with previous assessments of genetic diversity in A. chinensis , which may be attributed to the evolutionary history of the specie s[ 19 ]. Moreover, our findings indicated that the genetic diversity was high in both sampling sites of A. lancea , leading to the formation of two distinct populations. Notably, samples collected from Yunxi in Hubei province exhibited a closer genetic relationship to the Shaanxi population rather than to the commonly believed A. lancea population. Identification of A. lancea and A. chinensis Based on the amplification results, the screening of preferred alleles was conducted to facilitate identification. The identification of preferred alleles in high-content samples can serve as a valuable reference for molecular marker-assisted breeding and quality prediction in the seedling stage. By integrating the preferred alleles from each sampling point, the quality of allelic richness at each point can be efficiently assessed from a molecular standpoint. The SSR molecular markers established in the present study have demonstrated the ability to effectively differentiate between A. lancea and A. chinensis , thereby mitigating the inaccuracies stemming from maternal inheritance of the chloroplast genome during identification. Additionally, the supervised OPLS-DA analysis of the GC-MS data successfully segregated the samples into distinct groups, further validating the robustness of our findings. The markers created in this study have the potential for broad application in verifying the authenticity of AR, including applications in plant seedlings and herbal slicing, thereby aiding in the quality control of medicinal products. Core collection construction Given the endangered status of wild A. lancea , it is crucial to gather and safeguard its germplasm resources. In this study, we established a core collection of AR to safeguard the genetic diversity of the species. The core sample set created in this research comprehensively represented the alleles of the original germplasm and offered several advantages over random sampling methods like Least Distance Stepwise Sampling (LDSS) [ 31 ]. The augmentation of genetic parameters within the core sample set compared to the original sample set suggests that the core collection maintains ample genetic diversity despite a substantial reduction in sample size, thereby eliminating duplicate alleles within the original germplasm and successfully achieving the objective of preserving greater genetic variation with fewer samples [ 32 , 33 ]. Furthermore, the 326 samples that were not used as core samples can also function as reserve samples for the preserved germplasm [ 29 ]. Nevertheless, the preserved germplasm exhibits a reduced size compared to the initial germplasm across all genetic diversity parameters discussed, indicating a notable level of redundancy in the residual components following the exclusion of the core germplasm [ 34 ]. Correlation between compounds and SSR The evaluation of germplasm containing high levels of effective substances and the investigation of the relationship between molecular markers and chemical component content are crucial for quality identification. Recent studies have utilized SSR and RAPD (Random Amplified Polymorphic DNA) markers to analyze the correlation between molecular markers and secondary metabolic components, revealing a strong association in species such as Juniperus rigida , Ferula communis , and Lagerstroemia speciosa [ 35 – 37 ]. Based on these studies, we confirm the predictive efficacy of molecular markers on compound traits and propose a methodology for the cultivation of superior A. lancea germplasm in forthcoming breeding efforts. Conclusions In summary, a total of 29 pairs of SSR primers were created and utilized for the authentication of samples from A. lancea and A. chinensis . Samples from the primary cultivation region were categorized into three distinct populations: Mao Mountain, Dabie Mountain, and others. The Mao Mountain population exhibited the greatest genetic diversity among the three populations. Furthermore, the core germplasm of A. lancea was established in order to enhance the preservation of genetic diversity and streamline the process of molecular breeding for this medicinal plant. Additionally, a correlation between volatile components and SSRs was identified, suggesting the potential predictive utility of primers for the four active substances present in A. lancea . Methods Botanical specimens A total of 432 rhizome samples of wild Atractylodes lancea (Thunb.) DC. and Atractylodes chinensis (DC.) Koidz. from seven primary producing regions were utilized in this study (see Table 6 ). Detailed sampling area names and geographical information are also provided in Table 6 . The plant material, which grew in mountain forests with minimal human disturbance, was collected as wild samples and subsequently identified by Professor Yu Kun. Hubei and Jiangsu provinces are widely acknowledged as the principal producing regions of Atractylodes lancea , whereas other regions predominantly produce Atractylodes chinensis . For the purposes of our research, we utilized Atractylodis Rhizoma (AR), derived from the traditionally medicinal part of A. lancea. Table 6 Data collected on the presence of rhizome samples from 7 regions in China Provience Position Code Longitude Latitude Numbers Species Names Shanxi Pianguan PG 111.52°E 39.44°N 32 Atractylodes chinensis Shaanxi Taibai Mountain TB 107.85°E 34.04°N 40 Atractylodes chinensis Jiangsu Mao Mountain MS 119.28°E 31.69°N 153 Atractylodes lancea Hubei Dabie Mountain DB 115.57°E 30.60°N 40 Atractylodes lancea Shiyan YX 110.43°E 33.00°N 55 Atractylodes chinensis Hebei Weichang WC 117.75°E 41.95°N 52 Atractylodes chinensis Chicheng CC 115.83°E 40.90°N 60 Atractylodes chinensis The provinces represent the sampling regions of China, specifically Shanxi, Shaanxi, Jiangsu, Hubei, and Hebei. Within these five provinces, seven distinct sampling areas are distributed. The sequencing and assembly of the transcriptome RNA was extracted from the roots of A. lancea collected from Mao Mountain and subjected to sequencing using the Illumina method. Following enrichment of mRNA, reverse transcription was conducted to generate the cDNA library, which was subsequently assessed for quality before undergoing sequencing on the Illumina Hiseq 4000 platform (Illumina, Inc., San Diego, CA, United States), and the raw data are available in NCBI (Accession number: SRR30010489; https://www.ncbi.nlm.nih.gov/sra/?term=SRR30010489 ). The reads of inferior quality were removed prior to conducting de novo assembly using Trinity and cd-hit [ 38 ] software was utilized to analyze unigenes (Table S4). SSR loci and primer screening MISA software [ 39 ] was used to search the SSR loci on unigenes from transcriptome data (Table S1 , S5). The SSR primers were designed with the following parameters: primer length 18–22 bp; PCR product size 100–500 bp; GC content 40%-60%; annealing temperature 55°C -65°C (Table S2). In addition, the forward primers were added with an M13 sequence (GTAAAACGACGGCCAGT). The PCR reaction system including (1) genomic DNA 1 µl; (2) the forward and reverse primer 1 µl; (3) 2 × Taq PCR MasterMix 12.5 µl; (4) dd H2O 9.5 µl; (5) M13-FAM/M13-HEX/M13-ROX 0.35 µl. The thermal cycling program followed for PCR amplifications of SSR was 95°C for 3 min, 35 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 30 s and a 72°C extension for 10 min. All designed primers were screened using 1% agarose gel electrophoresis and capillary fluorescence electrophoresis to obtain high polymorphism SSR loci. SSR markers data analysis Allele statistics was performed through GeneMarker[ 40 ] software (V2.2.0), subsequently GenAlEx[ 41 ] software (V6.5) was used to calculate genetic diversity parameters, including number of alleles ( N A ), effective number of alleles ( N E ), observed heterozygosity ( H O ), expected heterozygosity ( H E ), Shannon’s information index ( I ), and Fixation index ( F ST ). The primers polymorphism information content ( PIC ) were estimated using PIC_Calc [ 42 ] software (V0.6). The analysis of NJ tree and PCA were used R packages ape [ 43 ] (V5.7-1) and ropls [ 44 ] (V1.32.0), respectively. Furthermore, population structure was determined using STRUCTURE [ 45 ] (V2.3.4) and AMOVA was carried out using GenAlEx. The population characteristic alleles were investigated by calculating the allele frequencies in different populations. Construction of core collections The R script GenoCore[ 31 ] was used to obtain the core sample set, and the genetic parameters were compared with the original dataset to check retention degree of genetic diversity. To investigate and evaluate the core collections and the original samples, GenAlEx was used to estimate main genetic parameters followed by validating the distribution of core germplasm in the original samples by PCA analysis. GC-MS analysis of the volatile components Twelve samples were selected from the verified species of A. lancea and A. chinensis , and the volatile components of rhizome were extracted with n-hexane and analyzed by gas chromatography-mass spectrometry (GC–MS). The GC–MS analysis was carried out with a Thermo ISQ QD-TRACE 1300 GC-MS which used TG-1701MS (30 m, 0.25 mm, 0.25 µm) capillary column. The column temperature was programmed as follows: initial temperature 100°C for 2 min, 2 ℃/min to 180 ℃, 180 ℃ for 6 min, 30 ℃/min to 270 ℃, 270 ℃ for 5 min. An electron impact ionization system with ionization energy of 70 eV and electron ionization spectra with a mass scan range of 35–500 m/z were used [ 46 ]. The R packages ropls and pheatmap (v1.0.12) were utilized for conducting OPLS-DA and Hierarchical Clustering Analysis (HCA). Differential metabolites were identified based on the criteria of VIP > 1 from OPLS-DA and a P -value less than 0.05. Subsequently, a Mantel test was conducted using the R package LinkET [ 47 ] (V0.0.7.4) to investigate the correlation between SSR loci and differential metabolites. The validation of the predictive efficacy of SSR molecular markers In order to assess the predictive capacity of the SSR molecular markers identified in this investigation for the four bioactive constituents of A. lancea , a total of 102 samples from MS were analyzed to compare allele frequencies between groups with high and low content levels. Abbreviations AR: Atractylodes Rhizome; SSR: Simple Sequence Repeat; GC-MS: Gas Chromatography Mass Spectrometry; ITS: internal transcribed spacer; MLPA: Multiplex Ligation Dependent Probe Amplification; SCAR: Sequence-Characterized Amplified Region; SNP: Single Nucleotide Polymorphism; MISA: Molecular Information and Sequence Analysis; N A , Number of Alleles; N E , Number of Effective alleles; I , Shannon’s Information Index; H O , Observed Heterozygosity; H E , Expected Heterozygosity; PIC - Polymorphic Information Content; MS: Mao Mountain, Jiangsu; DB: Dabie Mountain, Hubei; YX: Shiyan Hubei; TB: Taibai, Shaanxi; WC: Weichang, Hebei; CC: Chicheng, Hebei; PG: Pianguan, Shanxi; NJ: Neighbour-Joining; PCA: Principal Components Analysis; AMOVA: Analysis of Molecular Variance; df: Degrees of Freedom; SS: Sum of Square; MS: Mean of Square; F ST : Fixation Index; OPLS-DA: Orthogonal Partial Least Squares-Discriminant Analysis; VIP: Variable Importance in Projection; LDSS: Least Distance Stepwise Sampling; RAPD: Random Amplified Polymorphic DNA; HCA: Hierarchical Clustering Analysis. Declarations Ethics approval and consent to participate Atractylodes lancea , Atractylodes chinensis is not endangered in China, and no specific permission was required for the collection. All A. lancea and A. chinensis materials in this study were collected in China with the permission. The study complied with relevant institutional, national, and international guidelines and legislation. The sample has been identified by Professor Yu Kun as A. lancea and A. chinensis , the sample has been preserved in the herbarium of traditional Chinese medicine of Hubei University of Chinese Medicine (CZ-20221220-zht). Availability of data and materials A total of 432 rhizome samples of wild Atractylodes lancea (Thunb.) DC. and Atractylodes chinensis (DC.) Koidz. from seven primary producing regions were collected as wild samples and subsequently identified by Professor Yu Kun. The sample for RNA-seq has been preserved in the herbarium of traditional Chinese medicine of Hubei University of Chinese Medicine (CZ-20221220-zht). The raw sequence data generated during the current study have been submitted to National Center for Biotechnology Information (NCBI) with accession numbers SRR30010489 (https://www.ncbi.nlm.nih.gov/sra/?term=SRR30010489), and under the bioproject which accession is PRJNA1140735. Consent for publication The authors all agreed for the publication. Competing interests The authors declare that they have no conflicts of interest. Authors’ contributions Lin Sen designed the study; Haotian Zhong, Lina Chen, Lei Chen, Xiao Huang, Ling Gong and Kun Yu collected samples; Haotian Zhong, Lina Chen, Yuling Zeng and Juan Hu completed experiments and analysis; Haotian Zhong wrote the manuscript; Kun Yu and Lin Sen revised the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgements This work was financially supported by the Natural Science Foundation of Hubei Province, China (Grant Nos. 2023BBB151, 2022CFB576), and the Projects of Department of Education of Hubei Province, China (T2022020). The authors would like to thank Xufang Tian for helping. References Ma Z, Liu G, Yang Z, Zhang G, Sun L, Wang M, et al. Species differentiation and quality evaluation for Atractylodes medicinal plants by GC/MS coupled with chemometric analysis. Chemistry & biodiversity 2023, 20(8):e202300793. Ishii T, Okuyama T, Noguchi N, Nishidono Y, Okumura T, Kaibori M, et al. Antiinflammatory constituents of Atractylodes chinensis rhizome improve glomerular lesions in immunoglobulin A nephropathy model mice. Journal of natural medicines 2020, 74(1):51-64. Jun X, Fu P, Lei Y, Cheng P. Pharmacological effects of medicinal components of Atractylodes lancea (Thunb.) DC. Chinese medicine 2018, 13:59. Chinese Pharmacopoeia Commission: Chinese pharmacopoeia, vol. 1. Beijing: China Medical Science Press; 2020. Flora of China Editorial Committee of Chinese Academy of Sciences: Flora of China, vol. 20-21. Beijing: Science Press; 2011. Atractylodes lancea . Plants of the World Online 2024, Available at: https://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:182976-1 (Accessed February 5, 2024). Wang Y, Wang S, Liu Y, Yuan Q, Sun J, Guo L. Chloroplast genome variation and phylogenetic relationships of Atractylodes species. BMC genomics 2021, 22(1):103. Liu J, Shi M, Zhang Z, Xie H, Kong W, Wang Q, et al. Phylogenomic analyses based on the plastid genome and concatenated nrDNA sequence data reveal cytonuclear discordance in genus Atractylodes (Asteraceae: Carduoideae). Frontiers in plant science 2022, 13:1045423. Mo J, Hu Y, Wang W, Cheng H, Liu H, Wang B. Rapid identification of Atractylodes macrocephala Koidz., Atractylodes lancea (Thunb.) DC. and hybrids with MLPA-qPCR based on nuclear genome. Journal of Applied Research on Medicinal and Aromatic Plants 2022, 28:100374. Wang MQ TS, Fang Y, Xue JY. Study on specific molecular marker of Atractylodes lancea in Maoshan area. journal of plant resources and environment 2017, 26(2):17-26. Wang L, Zhang H, Wu X, Wang Z, Fang W, Jiang M, et al. Phylogenetic relationships of Atractylodes lancea , A. chinensis and A. macrocephala , revealed by complete plastome and nuclear gene sequences. PloS one 2020, 15(1):e0227610. Zhang C, Wang H, Lyu C, Wang Y, Sun J, Zhang Y, et al. Authenticating the geographic origins of Atractylodes lancea rhizome chemotypes in China through metabolite marker identification. Frontiers in plant science 2023, 14:1237800. Qiu H, Shan C, Fei C, Xue P, Zhou Y, Yuan J, et al. Geoherbalism metabolomic analysis of Atractylodes lancea (Thunb.) DC. by LC-Triple TOF-MS/MS and GC-MS. Molecules (Basel, Switzerland) 2023, 28(16). Li X, Qiao L, Chen B, Zheng Y, Zhi C, Zhang S, et al. SSR markers development and their application in genetic diversity evaluation of garlic ( Allium sativum ) germplasm. Plant diversity 2022, 44(5):481-491. Nasim N, Sandeep IS, Sahoo A, Das S, Panda MK, Acharya L, et al. Population genetic structure and diversity analysis in economically important Pandanus odorifer (Forssk.) Kuntze accessions employing ISSR and SSR markers. IND CROP PROD 2020, 143. Zhou Q, Mu KM, Ni ZX, Liu XH, Li YG, Xu LA. Analysis of genetic diversity of ancient Ginkgo populations using SSR markers. IND CROP PROD 2020, 145. Sharan H, Kumar A, Singh S. Newly developed SSRs based genetic diversity patterns in trait specific populations of German chamomile ( Matricaria chamomilla L.). IND CROP PROD 2024, 210:118152. Dan L, Zhifang L, Can Z, Gang X, Jinqi X, Xuekui W. Genetic diversity of Atractylodes lancea in different regions of Hubei Province. Jorunal of Huazhong Agricultural University 2021, 40(6):7-18. Ma S, Zhao J, Su W, Zheng J, Zhang S, Zhao W, et al. Transcriptome-derived SSR markers for DNA fingerprinting and inter-populations genetic diversity assessment of Atractylodes chinensis . The Nucleus 2022, 65(3):321-329. Ndjiondjop MN, Semagn K, Gouda AC, Kpeki SB, Dro Tia D, Sow M, et al. Genetic variation and population structure of Oryza glaberrima and development of a Mini-Core collection using DArTseq. Frontiers in plant science 2017, 8:1748. Rodríguez-Nevado C, Lam TT, Holmes EC, Pagán I. The impact of host genetic diversity on virus evolution and emergence. Ecology letters 2018, 21(2):253-263. Guzmán LF, Machida-Hirano R, Borrayo E, Cortés-Cruz M, Espíndola-Barquera MD, Heredia García E. Genetic structure and selection of a core collection for long term conservation of avocado in Mexico. Frontiers in plant science 2017, 8:243. Liu FM, Zhang NN, Liu XJ, Yang ZJ, Jia HY, Xu DP. Genetic diversity and population structure analysis of Dalbergia odorifera germplasm and development of a core collection using microsatellite markers. Genes 2019, 10(4). Zane L, Bargelloni L, Patarnello T. Strategies for microsatellite isolation: a review. Molecular ecology 2002, 11(1):1-16. Zhang H, Zhang D, Wang M, Sun J, Qi Y, Li J, et al. A core collection and mini core collection of Oryza sativa L. in China. TAG Theoretical and applied genetics Theoretische und angewandte Genetik 2011, 122(1):49-61. Kobayashi F, Tanaka T, Kanamori H, Wu J, Katayose Y, Handa H. Characterization of a mini core collection of Japanese wheat varieties using single-nucleotide polymorphisms generated by genotyping-by-sequencing. Breeding science 2016, 66(2):213-225. Zurn JD, Hummer KE, Bassil NV. Exploring the diversity and genetic structure of the U.S. National Cultivated Strawberry Collection. Horticulture research 2022, 9:125. Wang X, Bao K, Reddy UK, Bai Y, Hammar SA, Jiao C, et al. The USDA cucumber ( Cucumis sativus L.) collection: genetic diversity, population structure, genome-wide association studies, and core collection development. Horticulture research 2018, 5:64. Liu M, Hu X, Wang X, Zhang J, Peng X, Hu Z, et al. Constructing a core collection of the medicinal plant Angelica biserrata using genetic and metabolic data. Frontiers in plant science 2020, 11:600249. Wu FF, Zhang SX, Gao Q, Liu F, Wang JL, Wang XG. Genetic diversity and population structure analysis in a large collection of Vicia amoena in China with newly developed SSR markers. BMC PLANT BIOLOGY 2021, 21(1):544. Jeong S, Kim JY, Jeong SC, Kang ST, Moon JK, Kim N. GenoCore: A simple and fast algorithm for core subset selection from large genotype datasets. PloS one 2017, 12(7):e0181420. Kumar A, Kumar S, Singh KBM, Prasad M, Thakur JK. Designing a Mini-Core collection effectively representing 3004 diverse rice accessions. Plant Communications 2020, 1(5):100049. Koorevaar T, Willemsen JH, Visser RGF, Arens P, Maliepaard C. Construction of a strawberry breeding core collection to capture and exploit genetic variation. BMC genomics 2023, 24(1):740. Egan LM, Conaty WC, Stiller WN. Core collections: is there any value for cotton breeding? Frontiers in plant science 2022, 13:895155. Liu Z, Kuang S, Qing M, Wang D, Li D. Metabolite profiles of essential oils and SSR molecular markers in Juniperus rigida Sieb. et Zucc. from different regions: A potential source of raw materials for the perfume and healthy products. IND CROP PROD 2019, 133:424-434. Rahali FZ, Lamine M, Gargouri M, Rebey IB, Hammami M, Sellami IH. Metabolite profiles of essential oils and molecular markers analysis to explore the biodiversity of Ferula communis : Towards conservation of the endemic giant fennel. Phytochemistry 2016, 124:58-67. Jayakumar KS, Sajan JS, Aswati Nair R, Padmesh Pillai P, Deepu S, Padmaja R, et al. Corosolic acid content and SSR markers in Lagerstroemia speciosa (L.) Pers.: a comparative analysis among populations across the Southern Western Ghats of India. Phytochemistry 2014, 106:94-103. Li W, Jaroszewski L, Godzik A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics (Oxford, England) 2001, 17(3):282-283. Beier S, Thiel T, Münch T, Scholz U, Mascher M. MISA-web: a web server for microsatellite prediction. Bioinformatics (Oxford, England) 2017, 33(16):2583-2585. Holland MM, Pack ED, McElhoe JA. Evaluation of GeneMarker(®) HTS for improved alignment of mtDNA MPS data, haplotype determination, and heteroplasmy assessment. Forensic science international Genetics 2017, 28:90-98. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics (Oxford, England) 2012, 28(19):2537-2539. Nagy S, Poczai P, Cernák I, Gorji AM, Hegedűs G, Taller J. PICcalc: an online program to calculate polymorphic information content for molecular genetic studies. Biochemical genetics 2012, 50(9-10):670-672. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics (Oxford, England) 2018, 35(3):526-528. Thévenot EA, Roux A, Xu Y, Ezan E, Junot C. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research 2015, 14(8):3322-3335. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000, 155(2):945-959. Wan QY: Study on the mechanism of morphological variation based on RNA sequencing in Atractylodes lancea . Hubei University of Chinese medicine; 2019. Huang HY. linkET: Everything is Linkable. R package version 0.0.3. 2021. Additional Declarations No competing interests reported. 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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-4801864","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344711275,"identity":"021bdbe1-1781-420c-9c44-3476bfacb233","order_by":0,"name":"Haotian Zhong","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haotian","middleName":"","lastName":"Zhong","suffix":""},{"id":344711277,"identity":"21b00026-e9b0-4365-96ef-b68822dfd0f0","order_by":1,"name":"Lina Chen","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Chen","suffix":""},{"id":344711279,"identity":"614c4e40-2a9f-425c-b20f-8acb3d60852d","order_by":2,"name":"Lei Chen","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Chen","suffix":""},{"id":344711282,"identity":"0ac62f90-59a2-4f28-a10b-b6034eae0ad0","order_by":3,"name":"Xiao Huang","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Huang","suffix":""},{"id":344711283,"identity":"4dc52126-0ee8-4315-8f2f-c5ecc3cb2243","order_by":4,"name":"Ling Gong","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Gong","suffix":""},{"id":344711285,"identity":"968ed719-3e39-410c-8e8d-ce2fef460f79","order_by":5,"name":"Juan Hu","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Hu","suffix":""},{"id":344711286,"identity":"7733de4d-41b2-4759-88dd-4cec03398266","order_by":6,"name":"Yuling Zeng","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuling","middleName":"","lastName":"Zeng","suffix":""},{"id":344711287,"identity":"abe0e4b4-d6f2-4cb3-a14a-4e9485d34dfc","order_by":7,"name":"Kun Yu","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Yu","suffix":""},{"id":344711288,"identity":"d3d291b3-0aa2-4443-b78b-6307a0acb583","order_by":8,"name":"Lin Sen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACfv7mAwcSDGzq2dgbiNQiOeNY4oMHBWkJ/DwHiNRicCDH2PDBh8MJkjMSiHXZgTNmEgkGzHkGNx9vvMFQYxNNUAdjc1sZUAtbscHttGILhmNpuQ2EtDAzHN4G1MLDuOF2jpkEY8NhwlrYGBJADpNg3HDzDJFaeBhSjA0SDAwSZ87gIVKLhAQwkBMMEoz5eYB+SSDGL/bnmw8c/PHnvxwb++GNNz7U2BDWggwMJBJIUQ7RQqqOUTAKRsEoGBkAACdmQyhKris2AAAAAElFTkSuQmCC","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Sen","suffix":""}],"badges":[],"createdAt":"2024-07-25 12:30:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4801864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4801864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63385886,"identity":"3d70c8eb-fd1b-4ffb-a4b3-050aa93163d9","added_by":"auto","created_at":"2024-08-27 14:38:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":945483,"visible":true,"origin":"","legend":"\u003cp\u003eThe cluster analysis of 432 samples collected from 7 locations.\u003cstrong\u003e \u003c/strong\u003eIn the NJ tree, the two distinct colors of the outer ring correspond to \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e, respectively. In the PCA analysis, circles of varying colors indicate the 95% confidence interval for each population. The population structure is depicted with each column representing an individual, where the length of the different color segments signifies the proportion of an ancestor. The parameter K = 2–6 denotes the number of ancestral groups assumed in this study, ranging from 2 to 6.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-4801864/v1/638032f2c825e3ef40276730.png"},{"id":63385887,"identity":"d765c7b4-7c2d-4a7a-bf3e-4cfa558e37ef","added_by":"auto","created_at":"2024-08-27 14:38:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":525095,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the frequency of alleles at 29 SSR loci.\u003c/p\u003e\n\u003cp\u003eVertical axis represents different alleles.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-4801864/v1/e01a293dea6eccbf8b457f25.png"},{"id":63385885,"identity":"9749c9a6-00b3-482a-af7f-dc0b417a8d05","added_by":"auto","created_at":"2024-08-27 14:38:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":467790,"visible":true,"origin":"","legend":"\u003cp\u003eClustering and distribution of core collection consisted of 106 germplasms.\u003c/p\u003e\n\u003cp\u003eA. NJ tree constructed using 106 core samples data; B. PCA analysis for overall and core samples.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-4801864/v1/b899564cde5994181d1899c3.png"},{"id":63385889,"identity":"bf100353-3368-42cf-9111-f7cdfac3654d","added_by":"auto","created_at":"2024-08-27 14:38:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":623930,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering (A) OPLS-DA analysis (B) and potential metabolites (C) of volatile oils from \u003cem\u003eA. lancea\u003c/em\u003e and the \u003cem\u003eA. chinensis\u003c/em\u003e.\u003cstrong\u003e \u003c/strong\u003eCompounds with * represent 2-(4a,8-Dimethyl-2,3,4,4a,5,6-hexahydro-naphthalen-2-yl)-prop-2-en-1-ol and Cyclopropanebutanoic acid, 2-[[2-[[2-[(2-pentylcyclopropyl) methyl] cyclopropyl] methyl] cyclopropyl] methyl]-, methyl ester from top to bottom, respectively.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-4801864/v1/6f18fc86db03e4156b6c721a.png"},{"id":63386855,"identity":"6a4edd37-2174-4ebc-b48d-f476631dab74","added_by":"auto","created_at":"2024-08-27 14:46:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":634152,"visible":true,"origin":"","legend":"\u003cp\u003eMantel test for differential metabolites and genetic distance between samples.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-4801864/v1/166f847c3b19d58a24e49fc3.png"},{"id":71887338,"identity":"21c2e0a1-25e5-45d8-be81-0a795159f389","added_by":"auto","created_at":"2024-12-19 12:33:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4535243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4801864/v1/0d089712-7382-4d6f-a49f-3cbdec8d74aa.pdf"},{"id":63385890,"identity":"34ecdd1a-6f8d-48d9-af26-d44ccc37c250","added_by":"auto","created_at":"2024-08-27 14:38:55","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":45900,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4801864/v1/a350c37d9719697447602817.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potent SSR markers for the assessment of population structure, genetic diversity, and bioactive compounds in Atractylodis Rhizoma","fulltext":[{"header":"Background","content":"\u003cp\u003eAtractylodis Rhizoma (AR) is a commonly utilized herbal remedy in East Asian countries, including China, Japan, Korea, and Thailand, for addressing various health conditions such as digestive disorders, rheumatic diseases, and night blindness. The primary bioactive constituents found in AR are terpenoids, alkynes, and their glycosides, including atractylodin, β-eudesmol, atractylon, and hinesol [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These compounds exhibit a wide range of pharmacological effects, such as antihypertensive, antihyperglycemic, anti-tumor, anti-inflammatory, disease-resistant against pathogenic microorganisms, hepatoprotective, and effects on the digestive and nervous systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. AR is sourced from the rhizome of \u003cem\u003eAtractylodes lancea\u003c/em\u003e (Thunb.) DC. and \u003cem\u003eA. chinensis\u003c/em\u003e (DC.) Koidz as specified in the Pharmacopoeias of China and Japan. Historical Bencao literature, including the Compendium of Materia Medica (1578 AD), suggests that the rhizome of \u003cem\u003eA. lancea\u003c/em\u003e contains a higher concentration of volatile oil and a more pronounced aromatic scent compared to \u003cem\u003eA. chinensis\u003c/em\u003e, which has traditionally been believed to possess superior therapeutic properties. Additional characteristics, such as the morphology of the rhizome and its transverse section, as well as the presence of white cotton-like crystals, are frequently employed in the authentication of \u003cem\u003eA. lancea\u003c/em\u003e as distinct from \u003cem\u003eA. chinensis\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003eA. chinensis\u003c/em\u003e is considered a synonym of \u003cem\u003eA. lancea\u003c/em\u003e in both the \"Flora of China\" and \"Plants of the World Online,\" suggesting that the taxonomic classification of species within the genus \u003cem\u003eAtractylodes\u003c/em\u003e, particularly \u003cem\u003eAtractylodes lancea\u003c/em\u003e, has been a topic of debate [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given the inconsistent quality of artemisinin, it is imperative to distinguish between various botanical sources of AR and devise precise and dependable identification techniques.\u003c/p\u003e \u003cp\u003eIn recent years, alongside traditional morphological identification techniques, molecular and metabolic methods have been increasingly utilized. Various chloroplast genomic regions, including \u003cem\u003etrnL-F\u003c/em\u003e, \u003cem\u003etrnK\u003c/em\u003e, \u003cem\u003eatpB-rbcL\u003c/em\u003e, as well as the nuclear internal transcribed spacer (ITS), have been employed in numerous studies to ascertain the phylogenetic relationships within the \u003cem\u003eAtractylodes\u003c/em\u003e genus [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], moreover, \u003cem\u003eA. macrocephala, A. lancea\u003c/em\u003e, and their hybrids were differentiated using MLPA-qPCR targeting the nuclear genome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and SCAR (Sequence-Characterized Amplified Region) markers were employed in conjunction with sequencing techniques to characterize and identify Maoshan \u003cem\u003eA. lancea\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The molecular markers derived from plastid have been demonstrated to possess the capability to differentiate among \u003cem\u003eA. lancea\u003c/em\u003e, \u003cem\u003eA. chinensis\u003c/em\u003e, and \u003cem\u003eA. macrocephala\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The authentication of the geographic origins of \u003cem\u003eA. lancea\u003c/em\u003e rhizome chemotypes was achieved through the use of metabolite markers, and further metabolomic research has contributed valuable information towards the development of a quality evaluation system for \u003cem\u003eA. lancea\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, there exist numerous limitations linked to molecular markers (such as low resolution and biased results) and chemometrics (including complex composition and inconsistent standards) that must be addressed.\u003c/p\u003e \u003cp\u003eSimple sequence repeats (SSR) molecular markers have been extensively utilized in genetic analysis of diverse crops and traditional Chinese medicine, primarily due to their attributes of codominance, high polymorphism, universality, and repeatability. The utilization of SSR markers, ranging from cluster analysis to germplasm selection and trait association, plays a significant role in the advancement of the crop and traditional Chinese medicine industries [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For AR, SSRs have been applied just on a small scale in population structure analysis of \u003cem\u003eA. lancea\u003c/em\u003e in Hubei province [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and \u003cem\u003eA. chinensis\u003c/em\u003e in northern China [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The SSR loci in the chloroplast genome of \u003cem\u003eAtractylodes\u003c/em\u003e have also been screened [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To date, there remains a deficiency in the availability of highly reliable SSR markers for distinguishing between \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e, with no SSR markers having been identified for the characterization of the bioactive compounds present in these species.\u003c/p\u003e \u003cp\u003eGenetic diversity, which serves as the foundation of biodiversity, enables species to effectively respond to a range of intricate environmental fluctuations, thus playing a crucial role in facilitating genetic breeding and enhancing germplasm quality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A decrease in genetic diversity within a species results in diminished genetic variation and adaptability, thereby posing a threat to its long-term survival [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The establishment of core collections has grown in importance for preserving the genetic diversity of species. The abundance of germplasm resources is essential for crop breeding and improvement, and core collections provide a practical approach for conserving and effectively utilizing these resources. The core sample set represents a subset of germplasm resources that efficiently encompasses the genetic variability of the entire population, thus reducing the necessary number of samples [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Recent studies indicate that molecular markers may effectively capture the genetic diversity of germplasm resources by analyzing DNA sequences [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Markers such as SSR and SNP have been successfully employed in the development of core collections in various agricultural and economic crops, such as rice, wheat, strawberries, and cucumber [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, SSR primers were developed using transcriptome data and metabolites from GC-MS data to assess the genetic diversity of AR and establish SSR markers for distinguishing between \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e. The aims of the research were to elucidate the population structure and genetic variability of AR, establish a method for discriminating between \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e using SSR markers, and validate the predictive capacity of SSR loci associated with active compounds.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of the transcriptome derived SSRs\u003c/h2\u003e \u003cp\u003eThe Molecular Information and Sequence Analysis (MISA) revealed the presence of SSR loci in 125711 sequences, with 1261 sequences exhibiting multiple SSRs. A total of 12578 SSRs were identified in the transcriptome data of \u003cem\u003eA. lancea\u003c/em\u003e, with 450 of them forming compound structures. The SSRs displayed a variety of repeat types, with mononucleotide (5840, 46.43%) and dinucleotide (4448, 35.36%) repeats being the most prevalent, followed by trinucleotides (2129, 16.93%). Tetranucleotide, pentanucleotide, and hexanucleotide repeats each accounted for less than 3% of the total (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSSR primer validation and genetic diversity analysis\u003c/h2\u003e \u003cp\u003eThree hundred SSR loci were chosen in a uniform manner from the transcripts, followed by the design and screening of primers using 1% agarose gel electrophoresis and fluorescent capillary electrophoresis. Ultimately, 29 pairs of SSR primers were identified that exhibited high amplification efficiency, good reproducibility, and high polymorphism (Table S2).\u003c/p\u003e \u003cp\u003eA total of 432 samples from 7 populations were analyzed using 29 pairs of primers, resulting in the amplification of 264 alleles. The number of alleles per locus ranged from 3 (T217) to 17 (T256), with an average of 9.276 alleles per locus. Among the primers used, T11, T31, and T284 exhibited the highest levels of polymorphism and identification efficiency (refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, a comparison of genetic diversity among samples collected from the 7 sampling sites indicated that MS displayed the highest genetic diversity, with 215 different alleles identified, followed by DB (refer to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity statistics of the 29 microsatellite markers across 432 samples\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\u003ePrimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePIC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e 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\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.658\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 \u003cem\u003eN\u003c/em\u003e \u003csub\u003eA\u003c/sub\u003e, Number of Alleles; \u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, Number of Effective alleles; \u003cem\u003eI\u003c/em\u003e, Shannon\u0026rsquo;s Information Index; \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e, Observed Heterozygosity; \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, Expected Heterozygosity; \u003cem\u003ePIC\u003c/em\u003e - Polymorphic Information Content.\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 parameters of 7 different sampling points and different sets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRest set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.560\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\u003eNo., population sample size; alleles, number of different alleles within population; \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e, Number of Alleles; \u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, Number of Effective alleles; \u003cem\u003eI\u003c/em\u003e, Shannon\u0026rsquo;s Information Index; \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e, Observed Heterozygosity; \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, Expected Heterozygosity; \u003cem\u003ePIC\u003c/em\u003e - Polymorphic Information Content. MS - Mao Mountain, Jiangsu; DB \u0026ndash; Dabie Mountain, Hubei; YX \u0026ndash; Shiyan Hubei; TB \u0026ndash; Taibai, Shaanxi; WC \u0026ndash; Weichang, Hebei; CC \u0026ndash; Chicheng, Hebei; PG \u0026ndash; Pianguan, Shanxi.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure analysis\u003c/h2\u003e \u003cp\u003eIn our study, the population structure of the samples was analyzed using neighbor-joining (NJ) tree, principal component analysis (PCA), and STRUCTURE. The results revealed that all samples of \u003cem\u003eA. chinensis\u003c/em\u003e were grouped together in a single cluster, whereas the MS and DB populations of \u003cem\u003eA. lancea\u003c/em\u003e were clustered separately. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The findings of the PCA were consistent with those of the NJ tree, indicating that all populations, with the exception of MS and DB, were grouped together in a single cluster, while MS and DB were classified in a separate group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The STRUCTURE analysis identified two primary genetic clusters, one originating from individuals of MS and DB and the other from individuals of other populations, when K\u0026thinsp;=\u0026thinsp;2. These findings can serve as a foundation for distinguishing between varieties of \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). When K\u0026thinsp;=\u0026thinsp;3, the findings were consistent with those obtained from PCA. The Analysis of molecular variance (AMOVA) analysis indicated that the majority of genetic diversity was found within populations, with only 15% of genetic variation observed among populations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the genetic differentiation coefficient observed between populations, it can be deduced that there was a significant level of gene flow among various populations of \u003cem\u003eA. chinensis\u003c/em\u003e, whereas gene flow among populations of \u003cem\u003eA. lancea\u003c/em\u003e was comparatively limited (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe subsequently utilized a limited set of primers to enumerate alleles exhibiting markedly elevated frequencies within each population, thereby facilitating the efficient identification of specific populations. Loci including T3, T11, and T235 were frequently observed across multiple populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular variance (AMOVA) for 7 different populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e942.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Pops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6195.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7137.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\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\u003edf \u0026ndash; degrees of freedom; SS \u0026ndash; sum of square; MS \u0026ndash; mean of square.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe matrix of pairwise genetic differentiation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) among 7 populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\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 \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the core collection\u003c/h2\u003e \u003cp\u003eThe GenoCore analysis yielded a core subset of 106 samples, encompassing 99% of the alleles present in the original germplasm. The genetic diversity parameters \u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, and I were employed to assess the composition of the core subset (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results indicated that the core sample set had higher \u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e and \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e compared to those of the original germplasm, and had the ability to measure more genetic information with fewer sample sizes. Moreover, it could be seen from the PCA diagram and phylogenetic (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) that the materials in the core collection were not only similar to the distribution of the original germplasm, but also had a relatively comprehensive and uniform coverage, which represented the geometric distribution of the original germplasm, indicating that the genetic structure of the original germplasm was well maintained by the core collection and further confirming the representativeness of the selected core collection.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eVolatile components\u003c/h2\u003e \u003cp\u003eA total of 63 different volatile substances (Table S3) and hierarchical clustering heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) were obtained in all samples, and had been identified as 9 different types. The clustering results using all compounds were not sufficient to distinguish between the two varieties, subsequently, Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was used to make samples clearly divided into two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The OPLS-DA model revealed 24 differential volatile components with the cutoff of variable importance in projection (VIP) value\u0026thinsp;\u0026gt;\u0026thinsp;1 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and three compounds with the highest VIP value, namely cryptomeridiol, γ-elemene, and 2-Methylene-5α-cholestan-3β-ol, were discovered (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between metabolites and SSR loci\u003c/h2\u003e \u003cp\u003eTo go a step further, we estimated correlation between differential metabolites and SSR loci. The results of mantel test suggested that 24 differential metabolites significantly related to \u003cem\u003eA. lancea\u003c/em\u003e and the \u003cem\u003eA. chinensis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It is noteworthy that atractylon was significantly correlated with \u003cem\u003eA. lancea\u003c/em\u003e, and the other two were related to \u003cem\u003eA. chinensis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSSR loci for the distinguishing of the activate components\u003c/h2\u003e \u003cp\u003eBased on the results of comparing allele frequencies between high and low content groups in 102 samples, seven pairs of primers were identified that exhibit preferential alleles between the two groups. Specifically, allele 301 in T72 was found to have a higher frequency in the high content group of β-eudesmol, hinesol, atractylon, and the total of four components, while allele 295 in T72 was superior in the low content group (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFour compounds of high and low content group high frequency loci and alleles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Eudesmol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT72(301)0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT72(295)0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT281(209)0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtractylon\u0026thinsp;+\u0026thinsp;Hinesol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT45(388)0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT72(301)0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT58(258)0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT72(295)0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtractylodin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT235(378)0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT72(301)0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT256(408)0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3(377)0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT72(295)0.717\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\u003eThe numbers in parentheses represent different alleles, which behind parentheses represent the frequency of the alleles.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003ePopulation structure and genetic diversity of\u003c/b\u003e \u003cb\u003eA. lancea\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTranscriptome derived SSR markers have been widely applied in population genetic analysis of medicinal plants[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, 29 pairs of high polymorphism SSR primers were designed, and used to analysis genetic diversity. Mao Mountain, recognized as the best producing area of \u003cem\u003eA. lancea\u003c/em\u003e, has the highest genetic diversity among all the 7 populations, followed by Dabie Mountain. At present, the high genetic diversity in the samples of Yingshan, part of the Dabie Mountain, indicated that it had the potential to become the new most suitable producing area of \u003cem\u003eA. lancea\u003c/em\u003e. Moreover, studies have found that \u003cem\u003eA. lancea\u003c/em\u003e from Hubei province had small genetic variation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our analysis of population structure, it is evident that all sampling points of \u003cem\u003eA. chinensis\u003c/em\u003e are part of the same population, suggesting limited genetic variation and aligning with previous assessments of genetic diversity in \u003cem\u003eA. chinensis\u003c/em\u003e, which may be attributed to the evolutionary history of the specie s[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, our findings indicated that the genetic diversity was high in both sampling sites of \u003cem\u003eA. lancea\u003c/em\u003e, leading to the formation of two distinct populations. Notably, samples collected from Yunxi in Hubei province exhibited a closer genetic relationship to the Shaanxi population rather than to the commonly believed \u003cem\u003eA. lancea\u003c/em\u003e population.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of\u003c/b\u003e \u003cb\u003eA. lancea\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eA. chinensis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on the amplification results, the screening of preferred alleles was conducted to facilitate identification. The identification of preferred alleles in high-content samples can serve as a valuable reference for molecular marker-assisted breeding and quality prediction in the seedling stage. By integrating the preferred alleles from each sampling point, the quality of allelic richness at each point can be efficiently assessed from a molecular standpoint.\u003c/p\u003e \u003cp\u003eThe SSR molecular markers established in the present study have demonstrated the ability to effectively differentiate between \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e, thereby mitigating the inaccuracies stemming from maternal inheritance of the chloroplast genome during identification. Additionally, the supervised OPLS-DA analysis of the GC-MS data successfully segregated the samples into distinct groups, further validating the robustness of our findings. The markers created in this study have the potential for broad application in verifying the authenticity of AR, including applications in plant seedlings and herbal slicing, thereby aiding in the quality control of medicinal products.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCore collection construction\u003c/h2\u003e \u003cp\u003eGiven the endangered status of wild \u003cem\u003eA. lancea\u003c/em\u003e, it is crucial to gather and safeguard its germplasm resources. In this study, we established a core collection of AR to safeguard the genetic diversity of the species. The core sample set created in this research comprehensively represented the alleles of the original germplasm and offered several advantages over random sampling methods like Least Distance Stepwise Sampling (LDSS) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The augmentation of genetic parameters within the core sample set compared to the original sample set suggests that the core collection maintains ample genetic diversity despite a substantial reduction in sample size, thereby eliminating duplicate alleles within the original germplasm and successfully achieving the objective of preserving greater genetic variation with fewer samples [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, the 326 samples that were not used as core samples can also function as reserve samples for the preserved germplasm [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Nevertheless, the preserved germplasm exhibits a reduced size compared to the initial germplasm across all genetic diversity parameters discussed, indicating a notable level of redundancy in the residual components following the exclusion of the core germplasm [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between compounds and SSR\u003c/h2\u003e \u003cp\u003eThe evaluation of germplasm containing high levels of effective substances and the investigation of the relationship between molecular markers and chemical component content are crucial for quality identification. Recent studies have utilized SSR and RAPD (Random Amplified Polymorphic DNA) markers to analyze the correlation between molecular markers and secondary metabolic components, revealing a strong association in species such as \u003cem\u003eJuniperus rigida\u003c/em\u003e, \u003cem\u003eFerula communis\u003c/em\u003e, and \u003cem\u003eLagerstroemia speciosa\u003c/em\u003e [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Based on these studies, we confirm the predictive efficacy of molecular markers on compound traits and propose a methodology for the cultivation of superior \u003cem\u003eA. lancea\u003c/em\u003e germplasm in forthcoming breeding efforts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, a total of 29 pairs of SSR primers were created and utilized for the authentication of samples from \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e. Samples from the primary cultivation region were categorized into three distinct populations: Mao Mountain, Dabie Mountain, and others. The Mao Mountain population exhibited the greatest genetic diversity among the three populations. Furthermore, the core germplasm of \u003cem\u003eA. lancea\u003c/em\u003e was established in order to enhance the preservation of genetic diversity and streamline the process of molecular breeding for this medicinal plant. Additionally, a correlation between volatile components and SSRs was identified, suggesting the potential predictive utility of primers for the four active substances present in \u003cem\u003eA. lancea\u003c/em\u003e.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eBotanical specimens\u003c/h2\u003e\u003cp\u003eA total of 432 rhizome samples of wild \u003cem\u003eAtractylodes lancea\u003c/em\u003e (Thunb.) DC. and \u003cem\u003eAtractylodes chinensis\u003c/em\u003e (DC.) Koidz. from seven primary producing regions were utilized in this study (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Detailed sampling area names and geographical information are also provided in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The plant material, which grew in mountain forests with minimal human disturbance, was collected as wild samples and subsequently identified by Professor Yu Kun. Hubei and Jiangsu provinces are widely acknowledged as the principal producing regions of \u003cem\u003eAtractylodes lancea\u003c/em\u003e, whereas other regions predominantly produce \u003cem\u003eAtractylodes chinensis\u003c/em\u003e. For the purposes of our research, we utilized Atractylodis Rhizoma (AR), derived from the traditionally medicinal part of \u003cem\u003eA. lancea.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData collected on the presence of rhizome samples from 7 regions in China\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvience\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumbers\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecies Names\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShanxi\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePianguan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.52°E\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.44°N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtractylodes chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShaanxi\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaibai Mountain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.85°E\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.04°N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtractylodes chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJiangsu\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMao Mountain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.28°E\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.69°N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtractylodes lancea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHubei\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDabie Mountain\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDB\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.57°E\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.60°N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtractylodes lancea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShiyan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.43°E\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.00°N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtractylodes chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHebei\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeichang\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.75°E\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.95°N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtractylodes chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChicheng\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.83°E\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.90°N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAtractylodes chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe provinces represent the sampling regions of China, specifically Shanxi, Shaanxi, Jiangsu, Hubei, and Hebei. Within these five provinces, seven distinct sampling areas are distributed.\u003c/p\u003e\u003ch2\u003eThe sequencing and assembly of the transcriptome\u003c/h2\u003e\u003cp\u003eRNA was extracted from the roots of \u003cem\u003eA. lancea\u003c/em\u003e collected from Mao Mountain and subjected to sequencing using the Illumina method. Following enrichment of mRNA, reverse transcription was conducted to generate the cDNA library, which was subsequently assessed for quality before undergoing sequencing on the Illumina Hiseq 4000 platform (Illumina, Inc., San Diego, CA, United States), and the raw data are available in NCBI (Accession number: SRR30010489; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra/?term=SRR30010489\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra/?term=SRR30010489\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The reads of inferior quality were removed prior to conducting de novo assembly using Trinity and cd-hit [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] software was utilized to analyze unigenes (Table S4).\u003c/p\u003e\u003ch2\u003eSSR loci and primer screening\u003c/h2\u003e\u003cp\u003eMISA software [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] was used to search the SSR loci on unigenes from transcriptome data (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S5). The SSR primers were designed with the following parameters: primer length 18–22 bp; PCR product size 100–500 bp; GC content 40%-60%; annealing temperature 55°C -65°C (Table S2). In addition, the forward primers were added with an M13 sequence (GTAAAACGACGGCCAGT). The PCR reaction system including (1) genomic DNA 1 µl; (2) the forward and reverse primer 1 µl; (3) 2 × Taq PCR MasterMix 12.5 µl; (4) dd H2O 9.5 µl; (5) M13-FAM/M13-HEX/M13-ROX 0.35 µl. The thermal cycling program followed for PCR amplifications of SSR was 95°C for 3 min, 35 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 30 s and a 72°C extension for 10 min. All designed primers were screened using 1% agarose gel electrophoresis and capillary fluorescence electrophoresis to obtain high polymorphism SSR loci.\u003c/p\u003e\u003ch2\u003eSSR markers data analysis\u003c/h2\u003e\u003cp\u003eAllele statistics was performed through GeneMarker[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] software (V2.2.0), subsequently GenAlEx[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] software (V6.5) was used to calculate genetic diversity parameters, including number of alleles (\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e), effective number of alleles (\u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e), expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e), Shannon’s information index (\u003cem\u003eI\u003c/em\u003e), and Fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e). The primers polymorphism information content (\u003cem\u003ePIC\u003c/em\u003e) were estimated using PIC_Calc [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] software (V0.6). The analysis of NJ tree and PCA were used R packages ape [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] (V5.7-1) and ropls [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] (V1.32.0), respectively. Furthermore, population structure was determined using STRUCTURE [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] (V2.3.4) and AMOVA was carried out using GenAlEx. The population characteristic alleles were investigated by calculating the allele frequencies in different populations.\u003c/p\u003e\u003ch2\u003eConstruction of core collections\u003c/h2\u003e\u003cp\u003eThe R script GenoCore[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] was used to obtain the core sample set, and the genetic parameters were compared with the original dataset to check retention degree of genetic diversity. To investigate and evaluate the core collections and the original samples, GenAlEx was used to estimate main genetic parameters followed by validating the distribution of core germplasm in the original samples by PCA analysis.\u003c/p\u003e\u003ch2\u003eGC-MS analysis of the volatile components\u003c/h2\u003e\u003cp\u003eTwelve samples were selected from the verified species of \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e, and the volatile components of rhizome were extracted with n-hexane and analyzed by gas chromatography-mass spectrometry (GC–MS). The GC–MS analysis was carried out with a Thermo ISQ QD-TRACE 1300 GC-MS which used TG-1701MS (30 m, 0.25 mm, 0.25 µm) capillary column. The column temperature was programmed as follows: initial temperature 100°C for 2 min, 2 ℃/min to 180 ℃, 180 ℃ for 6 min, 30 ℃/min to 270 ℃, 270 ℃ for 5 min. An electron impact ionization system with ionization energy of 70 eV and electron ionization spectra with a mass scan range of 35–500 m/z were used [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe R packages ropls and pheatmap (v1.0.12) were utilized for conducting OPLS-DA and Hierarchical Clustering Analysis (HCA). Differential metabolites were identified based on the criteria of VIP \u0026gt; 1 from OPLS-DA and a \u003cem\u003eP\u003c/em\u003e-value less than 0.05. Subsequently, a Mantel test was conducted using the R package LinkET [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] (V0.0.7.4) to investigate the correlation between SSR loci and differential metabolites.\u003c/p\u003e\u003ch2\u003eThe validation of the predictive efficacy of SSR molecular markers\u003c/h2\u003e\u003cp\u003eIn order to assess the predictive capacity of the SSR molecular markers identified in this investigation for the four bioactive constituents of \u003cem\u003eA. lancea\u003c/em\u003e, a total of 102 samples from MS were analyzed to compare allele frequencies between groups with high and low content levels.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAR: Atractylodes Rhizome; SSR: Simple Sequence Repeat; GC-MS: Gas Chromatography Mass Spectrometry; ITS: internal transcribed spacer; MLPA: Multiplex Ligation Dependent Probe Amplification; SCAR: Sequence-Characterized Amplified Region; SNP: Single Nucleotide Polymorphism; MISA: Molecular Information and Sequence Analysis;\u0026nbsp;\u003cem\u003eN\u003c/em\u003e\u003csub\u003eA\u003c/sub\u003e, Number of Alleles; \u003cem\u003eN\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, Number of Effective alleles; \u003cem\u003eI\u003c/em\u003e, Shannon\u0026rsquo;s Information Index; \u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e, Observed Heterozygosity; \u003cem\u003eH\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e, Expected Heterozygosity;\u003cem\u003e\u0026nbsp;PIC\u0026nbsp;\u003c/em\u003e- Polymorphic Information Content; MS: Mao Mountain, Jiangsu; DB: Dabie Mountain, Hubei; YX: Shiyan Hubei; TB: Taibai, Shaanxi; WC: Weichang, Hebei; CC: Chicheng, Hebei; PG: Pianguan, Shanxi;\u0026nbsp;NJ:\u0026nbsp;Neighbour-Joining; PCA: Principal Components Analysis; AMOVA: Analysis of Molecular Variance; df: Degrees of Freedom; SS: Sum of Square; MS: Mean of Square;\u0026nbsp;\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e: Fixation Index; OPLS-DA: Orthogonal Partial Least Squares-Discriminant Analysis; VIP: Variable Importance in Projection; LDSS: Least Distance Stepwise Sampling; RAPD: Random Amplified Polymorphic DNA; HCA: Hierarchical Clustering Analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAtractylodes\u003c/em\u003e \u003cem\u003elancea\u003c/em\u003e, \u003cem\u003eAtractylodes\u003c/em\u003e \u003cem\u003echinensis\u003c/em\u003e is not endangered in China, and no specific permission was required for the collection. All \u003cem\u003eA.\u003c/em\u003e \u003cem\u003elancea\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;A.\u003c/em\u003e \u003cem\u003echinensis\u003c/em\u003e materials in this study were collected in China with the permission. The study complied with relevant institutional, national, and international guidelines and legislation. The sample has been identified by Professor Yu Kun as \u003cem\u003eA.\u003c/em\u003e \u003cem\u003elancea\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;A.\u003c/em\u003e \u003cem\u003echinensis\u003c/em\u003e, the sample has been preserved in the herbarium of traditional Chinese medicine of Hubei University of Chinese Medicine (CZ-20221220-zht).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 432 rhizome samples of wild \u003cem\u003eAtractylodes lancea\u003c/em\u003e (Thunb.) DC. and \u003cem\u003eAtractylodes chinensis\u003c/em\u003e (DC.) Koidz. from seven primary producing regions were collected as wild samples and subsequently identified by Professor Yu Kun.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sample for RNA-seq has been preserved in the herbarium of traditional Chinese medicine of Hubei University of Chinese Medicine (CZ-20221220-zht).\u003c/p\u003e\n\u003cp\u003eThe raw sequence data generated during the current study have been submitted to National Center for Biotechnology Information (NCBI)\u0026nbsp;with accession numbers SRR30010489 (https://www.ncbi.nlm.nih.gov/sra/?term=SRR30010489), and under the bioproject which accession is PRJNA1140735.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors all agreed for the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLin Sen designed the study; Haotian Zhong,\u0026nbsp;Lina Chen, Lei Chen, Xiao Huang, Ling Gong and\u0026nbsp;Kun Yu collected samples; Haotian Zhong,\u0026nbsp;Lina Chen,\u0026nbsp;Yuling Zeng and Juan Hu completed experiments and analysis; Haotian Zhong\u0026nbsp;wrote the manuscript; Kun Yu and Lin Sen\u0026nbsp;revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the Natural Science Foundation of Hubei Province, China (Grant Nos. 2023BBB151, 2022CFB576), and the Projects of Department of Education of Hubei Province, China (T2022020). The authors would like to thank Xufang Tian for helping.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMa Z, Liu G, Yang Z, Zhang G, Sun L, Wang M, et al. Species differentiation and quality evaluation for \u003cem\u003eAtractylodes\u003c/em\u003e medicinal plants by GC/MS coupled with chemometric analysis. Chemistry \u0026amp; biodiversity\u003cem\u003e \u003c/em\u003e2023, 20(8):e202300793.\u003c/li\u003e\n\u003cli\u003eIshii T, Okuyama T, Noguchi N, Nishidono Y, Okumura T, Kaibori M, et al. Antiinflammatory constituents of \u003cem\u003eAtractylodes chinensis\u003c/em\u003e rhizome improve glomerular lesions in immunoglobulin A nephropathy model mice. Journal of natural medicines\u003cem\u003e \u003c/em\u003e2020, 74(1):51-64.\u003c/li\u003e\n\u003cli\u003eJun X, Fu P, Lei Y, Cheng P. Pharmacological effects of medicinal components of \u003cem\u003eAtractylodes lancea\u003c/em\u003e (Thunb.) DC. Chinese medicine\u003cem\u003e \u003c/em\u003e2018, 13:59.\u003c/li\u003e\n\u003cli\u003eChinese Pharmacopoeia Commission: Chinese pharmacopoeia, vol. 1. Beijing: China Medical Science Press; 2020.\u003c/li\u003e\n\u003cli\u003eFlora of China Editorial Committee of Chinese Academy of Sciences: Flora of China, vol. 20-21. Beijing: Science Press; 2011.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eAtractylodes lancea\u003c/em\u003e. Plants of the World Online\u003cem\u003e \u003c/em\u003e2024, Available at: https://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:182976-1 (Accessed February 5, 2024).\u003c/li\u003e\n\u003cli\u003eWang Y, Wang S, Liu Y, Yuan Q, Sun J, Guo L. Chloroplast genome variation and phylogenetic relationships of \u003cem\u003eAtractylodes\u003c/em\u003e species. BMC genomics\u003cem\u003e \u003c/em\u003e2021, 22(1):103.\u003c/li\u003e\n\u003cli\u003eLiu J, Shi M, Zhang Z, Xie H, Kong W, Wang Q, et al. Phylogenomic analyses based on the plastid genome and concatenated nrDNA sequence data reveal cytonuclear discordance in genus \u003cem\u003eAtractylodes\u003c/em\u003e (Asteraceae: Carduoideae). Frontiers in plant science\u003cem\u003e \u003c/em\u003e2022, 13:1045423.\u003c/li\u003e\n\u003cli\u003eMo J, Hu Y, Wang W, Cheng H, Liu H, Wang B. Rapid identification of Atractylodes macrocephala Koidz., Atractylodes lancea (Thunb.) DC. and hybrids with MLPA-qPCR based on nuclear genome. Journal of Applied Research on Medicinal and Aromatic Plants\u003cem\u003e \u003c/em\u003e2022, 28:100374.\u003c/li\u003e\n\u003cli\u003eWang MQ TS, Fang Y, Xue JY. Study on specific molecular marker of \u003cem\u003eAtractylodes lancea\u003c/em\u003e in Maoshan area. journal of plant resources and environment\u003cem\u003e \u003c/em\u003e2017, 26(2):17-26.\u003c/li\u003e\n\u003cli\u003eWang L, Zhang H, Wu X, Wang Z, Fang W, Jiang M, et al. Phylogenetic relationships of \u003cem\u003eAtractylodes lancea\u003c/em\u003e, \u003cem\u003eA. chinensis\u003c/em\u003e and \u003cem\u003eA. macrocephala\u003c/em\u003e, revealed by complete plastome and nuclear gene sequences. PloS one\u003cem\u003e \u003c/em\u003e2020, 15(1):e0227610.\u003c/li\u003e\n\u003cli\u003eZhang C, Wang H, Lyu C, Wang Y, Sun J, Zhang Y, et al. Authenticating the geographic origins of \u003cem\u003eAtractylodes lancea\u003c/em\u003e rhizome chemotypes in China through metabolite marker identification. Frontiers in plant science\u003cem\u003e \u003c/em\u003e2023, 14:1237800.\u003c/li\u003e\n\u003cli\u003eQiu H, Shan C, Fei C, Xue P, Zhou Y, Yuan J, et al. Geoherbalism metabolomic analysis of \u003cem\u003eAtractylodes lancea\u003c/em\u003e (Thunb.) DC. by LC-Triple TOF-MS/MS and GC-MS. Molecules (Basel, Switzerland)\u003cem\u003e \u003c/em\u003e2023, 28(16).\u003c/li\u003e\n\u003cli\u003eLi X, Qiao L, Chen B, Zheng Y, Zhi C, Zhang S, et al. SSR markers development and their application in genetic diversity evaluation of garlic (\u003cem\u003eAllium sativum\u003c/em\u003e) germplasm. Plant diversity\u003cem\u003e \u003c/em\u003e2022, 44(5):481-491.\u003c/li\u003e\n\u003cli\u003eNasim N, Sandeep IS, Sahoo A, Das S, Panda MK, Acharya L, et al. Population genetic structure and diversity analysis in economically important Pandanus odorifer (Forssk.) Kuntze accessions employing ISSR and SSR markers. IND CROP PROD\u003cem\u003e \u003c/em\u003e2020, 143.\u003c/li\u003e\n\u003cli\u003eZhou Q, Mu KM, Ni ZX, Liu XH, Li YG, Xu LA. Analysis of genetic diversity of ancient Ginkgo populations using SSR markers. IND CROP PROD\u003cem\u003e \u003c/em\u003e2020, 145.\u003c/li\u003e\n\u003cli\u003eSharan H, Kumar A, Singh S. Newly developed SSRs based genetic diversity patterns in trait specific populations of German chamomile (\u003cem\u003eMatricaria chamomilla\u003c/em\u003e L.). IND CROP PROD\u003cem\u003e \u003c/em\u003e2024, 210:118152.\u003c/li\u003e\n\u003cli\u003eDan L, Zhifang L, Can Z, Gang X, Jinqi X, Xuekui W. Genetic diversity of \u003cem\u003eAtractylodes lancea\u003c/em\u003e in different regions of Hubei Province. Jorunal of Huazhong Agricultural University\u003cem\u003e \u003c/em\u003e2021, 40(6):7-18.\u003c/li\u003e\n\u003cli\u003eMa S, Zhao J, Su W, Zheng J, Zhang S, Zhao W, et al. Transcriptome-derived SSR markers for DNA fingerprinting and inter-populations genetic diversity assessment of \u003cem\u003eAtractylodes chinensis\u003c/em\u003e. The Nucleus\u003cem\u003e \u003c/em\u003e2022, 65(3):321-329.\u003c/li\u003e\n\u003cli\u003eNdjiondjop MN, Semagn K, Gouda AC, Kpeki SB, Dro Tia D, Sow M, et al. Genetic variation and population structure of \u003cem\u003eOryza glaberrima\u003c/em\u003e and development of a Mini-Core collection using DArTseq. Frontiers in plant science\u003cem\u003e \u003c/em\u003e2017, 8:1748.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Nevado C, Lam TT, Holmes EC, Pag\u0026aacute;n I. The impact of host genetic diversity on virus evolution and emergence. Ecology letters\u003cem\u003e \u003c/em\u003e2018, 21(2):253-263.\u003c/li\u003e\n\u003cli\u003eGuzm\u0026aacute;n LF, Machida-Hirano R, Borrayo E, Cort\u0026eacute;s-Cruz M, Esp\u0026iacute;ndola-Barquera MD, Heredia Garc\u0026iacute;a E. Genetic structure and selection of a core collection for long term conservation of avocado in Mexico. Frontiers in plant science\u003cem\u003e \u003c/em\u003e2017, 8:243.\u003c/li\u003e\n\u003cli\u003eLiu FM, Zhang NN, Liu XJ, Yang ZJ, Jia HY, Xu DP. Genetic diversity and population structure analysis of \u003cem\u003eDalbergia odorifera\u003c/em\u003e germplasm and development of a core collection using microsatellite markers. Genes\u003cem\u003e \u003c/em\u003e2019, 10(4).\u003c/li\u003e\n\u003cli\u003eZane L, Bargelloni L, Patarnello T. Strategies for microsatellite isolation: a review. Molecular ecology\u003cem\u003e \u003c/em\u003e2002, 11(1):1-16.\u003c/li\u003e\n\u003cli\u003eZhang H, Zhang D, Wang M, Sun J, Qi Y, Li J, et al. A core collection and mini core collection of \u003cem\u003eOryza sativa\u003c/em\u003e L. in China. TAG Theoretical and applied genetics Theoretische und angewandte Genetik\u003cem\u003e \u003c/em\u003e2011, 122(1):49-61.\u003c/li\u003e\n\u003cli\u003eKobayashi F, Tanaka T, Kanamori H, Wu J, Katayose Y, Handa H. Characterization of a mini core collection of Japanese wheat varieties using single-nucleotide polymorphisms generated by genotyping-by-sequencing. Breeding science\u003cem\u003e \u003c/em\u003e2016, 66(2):213-225.\u003c/li\u003e\n\u003cli\u003eZurn JD, Hummer KE, Bassil NV. Exploring the diversity and genetic structure of the U.S. National Cultivated Strawberry Collection. Horticulture research\u003cem\u003e \u003c/em\u003e2022, 9:125.\u003c/li\u003e\n\u003cli\u003eWang X, Bao K, Reddy UK, Bai Y, Hammar SA, Jiao C, et al. The USDA cucumber (\u003cem\u003eCucumis sativus\u003c/em\u003e L.) collection: genetic diversity, population structure, genome-wide association studies, and core collection development. Horticulture research\u003cem\u003e \u003c/em\u003e2018, 5:64.\u003c/li\u003e\n\u003cli\u003eLiu M, Hu X, Wang X, Zhang J, Peng X, Hu Z, et al. Constructing a core collection of the medicinal plant \u003cem\u003eAngelica biserrata\u003c/em\u003e using genetic and metabolic data. Frontiers in plant science\u003cem\u003e \u003c/em\u003e2020, 11:600249.\u003c/li\u003e\n\u003cli\u003eWu FF, Zhang SX, Gao Q, Liu F, Wang JL, Wang XG. Genetic diversity and population structure analysis in a large collection of \u003cem\u003eVicia amoena\u003c/em\u003e in China with newly developed SSR markers. BMC PLANT BIOLOGY\u003cem\u003e \u003c/em\u003e2021, 21(1):544.\u003c/li\u003e\n\u003cli\u003eJeong S, Kim JY, Jeong SC, Kang ST, Moon JK, Kim N. GenoCore: A simple and fast algorithm for core subset selection from large genotype datasets. PloS one\u003cem\u003e \u003c/em\u003e2017, 12(7):e0181420.\u003c/li\u003e\n\u003cli\u003eKumar A, Kumar S, Singh KBM, Prasad M, Thakur JK. Designing a Mini-Core collection effectively representing 3004 diverse rice accessions. Plant Communications\u003cem\u003e \u003c/em\u003e2020, 1(5):100049.\u003c/li\u003e\n\u003cli\u003eKoorevaar T, Willemsen JH, Visser RGF, Arens P, Maliepaard C. Construction of a strawberry breeding core collection to capture and exploit genetic variation. BMC genomics\u003cem\u003e \u003c/em\u003e2023, 24(1):740.\u003c/li\u003e\n\u003cli\u003eEgan LM, Conaty WC, Stiller WN. Core collections: is there any value for cotton breeding? Frontiers in plant science\u003cem\u003e \u003c/em\u003e2022, 13:895155.\u003c/li\u003e\n\u003cli\u003eLiu Z, Kuang S, Qing M, Wang D, Li D. Metabolite profiles of essential oils and SSR molecular markers in \u003cem\u003eJuniperus rigida\u003c/em\u003e Sieb. et Zucc. from different regions: A potential source of raw materials for the perfume and healthy products. IND CROP PROD\u003cem\u003e \u003c/em\u003e2019, 133:424-434.\u003c/li\u003e\n\u003cli\u003eRahali FZ, Lamine M, Gargouri M, Rebey IB, Hammami M, Sellami IH. Metabolite profiles of essential oils and molecular markers analysis to explore the biodiversity of \u003cem\u003eFerula communis\u003c/em\u003e: Towards conservation of the endemic giant fennel. Phytochemistry\u003cem\u003e \u003c/em\u003e2016, 124:58-67.\u003c/li\u003e\n\u003cli\u003eJayakumar KS, Sajan JS, Aswati Nair R, Padmesh Pillai P, Deepu S, Padmaja R, et al. Corosolic acid content and SSR markers in \u003cem\u003eLagerstroemia speciosa\u003c/em\u003e (L.) Pers.: a comparative analysis among populations across the Southern Western Ghats of India. Phytochemistry\u003cem\u003e \u003c/em\u003e2014, 106:94-103.\u003c/li\u003e\n\u003cli\u003eLi W, Jaroszewski L, Godzik A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics (Oxford, England)\u003cem\u003e \u003c/em\u003e2001, 17(3):282-283.\u003c/li\u003e\n\u003cli\u003eBeier S, Thiel T, M\u0026uuml;nch T, Scholz U, Mascher M. MISA-web: a web server for microsatellite prediction. Bioinformatics (Oxford, England)\u003cem\u003e \u003c/em\u003e2017, 33(16):2583-2585.\u003c/li\u003e\n\u003cli\u003eHolland MM, Pack ED, McElhoe JA. Evaluation of GeneMarker(\u0026reg;) HTS for improved alignment of mtDNA MPS data, haplotype determination, and heteroplasmy assessment. Forensic science international Genetics\u003cem\u003e \u003c/em\u003e2017, 28:90-98.\u003c/li\u003e\n\u003cli\u003ePeakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics (Oxford, England)\u003cem\u003e \u003c/em\u003e2012, 28(19):2537-2539.\u003c/li\u003e\n\u003cli\u003eNagy S, Poczai P, Cern\u0026aacute;k I, Gorji AM, Hegedűs G, Taller J. PICcalc: an online program to calculate polymorphic information content for molecular genetic studies. Biochemical genetics\u003cem\u003e \u003c/em\u003e2012, 50(9-10):670-672.\u003c/li\u003e\n\u003cli\u003eParadis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics (Oxford, England)\u003cem\u003e \u003c/em\u003e2018, 35(3):526-528.\u003c/li\u003e\n\u003cli\u003eTh\u0026eacute;venot EA, Roux A, Xu Y, Ezan E, Junot C. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research\u003cem\u003e \u003c/em\u003e2015, 14(8):3322-3335.\u003c/li\u003e\n\u003cli\u003ePritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics\u003cem\u003e \u003c/em\u003e2000, 155(2):945-959.\u003c/li\u003e\n\u003cli\u003eWan QY: Study on the mechanism of morphological variation based on RNA sequencing in \u003cem\u003eAtractylodes lancea\u003c/em\u003e. Hubei University of Chinese medicine; 2019.\u003c/li\u003e\n\u003cli\u003eHuang HY. linkET: Everything is Linkable. R package version 0.0.3. 2021.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atractylodes lancea, Atractylodes chinensis, SSR, Genetic diversity, Phylogenetic.","lastPublishedDoi":"10.21203/rs.3.rs-4801864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4801864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003e\u003cem\u003eAtractylodes lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e, commonly referred to as Atractylodes Rhizome (AR), are significant traditional medicinal plants in China. AR exhibits a broad geographical distribution within the country. However, the escalating market demand and depletion of wild resources have led to a pressing need for increased cultivation of AR. Despite this urgency, research on the conservation of AR resources remains limited. Hence, it is imperative to conduct an analysis of the genetic background of the original plant and ascertain the specific variety of medicine AR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThis research utilized transcriptome data from \u003cem\u003eA. lancea\u003c/em\u003e to develop SSR molecular markers, assess the population structure and genetic diversity of AR, and employed the mantel test to validate the relationship between volatile oil components and genetic distance among the samples. A set of 29 pairs of highly polymorphic SSR primers yielded a total of 264 different alleles. Clustering analysis identified three distinct populations: Mao Mountain, Dabie Mountain, and samples from other locations. A clear differentiation between \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e was observed, facilitating effective discrimination of AR varieties. Screening based on GC-MS results revealed 24 potential differential metabolites between the two species, with correlation analysis indicating significant associations with 18 previously identified molecular markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study successfully developed SSR molecular markers for the purpose of analyzing genetic diversity in \u003cem\u003eA. lancea\u003c/em\u003e and \u003cem\u003eA. chinensis\u003c/em\u003e. Furthermore, a method was established for identifying the variety of medicine AR, with the confirmation that T72 exhibits the highest predictive ability for β-eudesmol, hinesol, and atractylon. These findings lay a solid groundwork for future quality control of medicine AR and the selection of superior germplasm.\u003c/p\u003e","manuscriptTitle":"Potent SSR markers for the assessment of population structure, genetic diversity, and bioactive compounds in Atractylodis Rhizoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 14:38:50","doi":"10.21203/rs.3.rs-4801864/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d74206b7-afc7-4520-8f73-907bdaf76f47","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-19T12:24:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 14:38:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4801864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4801864","identity":"rs-4801864","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}