Genetic Diversity and Chemotypic Variation of Gentiana rhodantha: Insights into the Genetic Basis of Active Compound Accumulation

Genetic Diversity and Chemotypic Variation of Gentiana rhodantha: Insights into the Genetic Basis of Active Compound Accumulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic Diversity and Chemotypic Variation of Gentiana rhodantha: Insights into the Genetic Basis of Active Compound Accumulation Qingqing Ye, Lingling Wei, Weixiang Xiao, Xing Tan, Li Yang, Deqiang Ren, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9367916/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Gentiana rhodantha is a commonly used ethnic medicine in Southwest China, possesses high medicinal value. Although the quality of medicinal materials from different producing areas varies considerably, no studies to date have investigated the chemotypes or genetic diversity of G. rhodantha across its distribution range. In this study, we combined genotyping-by-sequencing (GBS) and high-performance liquid chromatography (HPLC) to analyze the genetic diversity and active component content of G. rhodantha from ten different producing areas. The relationships of these parameters with geographical and environmental factors were also examined and discussed. A total of 4,524,308 high-quality single nucleotide polymorphism (SNP) loci were obtained. Principal component analysis and phylogenetic analysis based on these SNPs divided the ten populations into three major genetic lineages. The cross-validation error rate reached its minimum at K = 10, suggesting the presence of ten distinct subgroups within the sampled populations. Genetic diversity within populations was low, whereas genetic differentiation among populations was significant. The contents of six active components varied widely among populations. The content of mangiferin varied by up to 3.25-fold across populations, while that of swertiamarin showed a 61-fold difference. Genetic clustering corresponded well with chemotypes. Moreover, both genetic diversity and the degree of genetic differentiation were significantly correlated with component content and the extent of variation. Active components exhibited specific associations with geographical factors (latitude, longitude, and altitude). Positive correlations predominated among active components, although negative correlations were also observed for certain component pairs.This study reveals the relationship between genetic variation and active component accumulation in G. rhodantha , and clarifies the role of geographical factors in shaping chemotypic differentiation. These findings provide an important scientific basis for the conservation, evaluation, elite germplasm screening, and standardized cultivation of this medicinal plant. Gentiana rhodantha Population genomics Genotyping-by-sequencing (GBS) Genetic differentiation Germplasm resources Bioactive constituents Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Genetic diversity representing the total repertoire of heritable variations accumulated throughout a species' evolutionary history, constitutes the fundamental basis for its adaptation to environmental changes and its capacity for population survival and proliferation. Furthermore, it serves as a critical indicator for deciphering evolutionary trajectories and assessing future adaptive potential [1–2]. Genetic differentiation among geographical populations, arising from variations in genetic diversity and local adaptation processes, can further influence phenotypic traits, including the biosynthesis of secondary metabolites that are intrinsically linked to the quality of medicinal materials. For instance, in Scutellaria baicalensis , significant correlations have been established between population-level genetic diversity and the content of its major bioactive constituents, such as baicalin and baicalein. Population genetic structure analyses have revealed that specific populations harbor unique haplotypes of genes involved in flavonoid biosynthesis, thereby shaping the geographical chemotypic patterns of this medicinal plant [3]. Studies on Panax ginseng have demonstrated that disparities in genetic diversity between wild and cultivated populations directly impact the composition and accumulation of ginsenosides, with the higher genetic diversity observed in wild populations being associated with a more complex ginsenoside profile [4]. Similarly, in the analysis of cultivated Panax quinquefolius provenances, accessions exhibiting higher genetic diversity were frequently accompanied by an enriched content of flavonoid bioactive components, underscoring that genotypic diversity is a pivotal determinant of medicinal material quality [5]. Collectively, these investigations elucidate that genetic diversity not only underpins species evolution but also directly governs the formation and chemical stability of medicinal material quality by modulating genetic variation in key genes within secondary metabolic pathways. This provides a crucial genetic foundation for the conservation and sustainable utilization of medicinal plant resources. Genotyping-by-sequencing (GBS) technology, which integrates restriction enzyme digestion with high-throughput sequencing, enables the efficient acquisition of hundreds of thousands to millions of genome-wide single nucleotide polymorphisms (SNPs) [6–7]. Characterized by its high throughput, cost-effectiveness, and abundant information yield, GBS has become a cornerstone tool in population genetics and evolutionary research [8]. As a next-generation molecular marker, SNPs are distinguished by their high polymorphism, widespread genomic distribution, and co-dominant inheritance, offering unique value for dissecting genetic architecture and unraveling species evolutionary mechanisms at a genome-wide scale [9–10]. In recent years, GBS has been successfully applied in genetic studies across diverse species. For instance, in a population genomic analysis of Anemone shikokiana , 52,231 high-quality SNPs were generated via GBS, revealing two distinct genetic lineages with low intra-population diversity, where geographical isolation has driven significant genetic differentiation and limited gene flow [11]. Similarly, a GBS-based investigation of Tsoongiodendron odorum uncovered generally low genetic diversity within the species, with genetic variation primarily residing within populations, minimal differentiation among populations, and relatively high levels of gene flow [12]. Concurrently, GBS technology has demonstrated significant application potential in medicinal plant research. For example, a study employing GBS on 13 wild populations of Ardisia violacea , based on 246,000 SNP loci, systematically revealed a pattern of low overall genetic diversity coupled with significant inter-population differentiation [13]. In research on different chemotypes of Artemisia annua , GBS not only systematically elucidated the population genetic background but also, through genome-wide association analysis, identified genetic variants closely linked to the artemisinin biosynthetic pathway [14]. Collectively, these studies demonstrate that GBS, combined with SNP markers, provides an efficient and economical means of acquiring genome-wide genetic information. This approach offers robust support for elucidating species genetic structure, evolutionary history, and adaptive mechanisms, thereby laying a scientific foundation for the conservation of genetic resources in medicinal plants, the selection of elite germplasm, and the formulation of breeding strategies [15]. Gentiana rhodantha Franch. (Family Gentianaceae) is a perennial herbaceous species widely utilized as a traditional ethnic medicine among the Miao and Tujia communities in Southwest China. This species is officially documented in both the Chinese Pharmacopoeia (2025 edition) and the Quality Standards for Chinese Medicinal Materials and Ethnic Medicines of Guizhou Province (2003 edition), and is commonly known as “Xuelimei” or “Xuedanlong” [16]. The dried whole plant or root is used for medicinal purposes. In traditional Chinese medicine, it is characterized as having a cold property and a bitter taste, and is recognized for its effects in clearing heat, eliminating dampness, detoxifying, purging fire, alleviating coughing, and cooling the blood. Clinically, it is frequently employed in the treatment of conditions such as cough and dyspnea caused by lung heat, pneumonia, jaundice, dysentery, and sores or boils. Furthermore, it serves as a critical raw material in several Chinese patent medicines, including “Feilike Mixture” and “Xiao'er Xiaoji Zhike” Oral Liquid [17–18]. Phytochemical investigations have revealed that G. rhodantha primarily contains iridoids, flavonoids, and phenolic acids, which contribute to its diverse pharmacological properties, including anti-inflammatory, antioxidant, and antibacterial activities [19]. The species is predominantly distributed across the provinces of Guizhou, Yunnan, Sichuan, and Hunan in China, typically inhabiting slopes, shrublands, sparse forests, and rocky crevices at altitudes ranging from 300 to 2500 m [20]. However, due to multiple stressors, including habitat fragmentation, over-exploitation, and climate change, the distribution range of its wild populations is progressively contracting, and population sizes are continuously declining, leading to an increasingly precarious survival status. Consequently, a systematic assessment of its genetic diversity and population structure is of substantial theoretical and practical significance for formulating science-based conservation strategies and ensuring the sustainable utilization of this medicinal resource. Recent research on G. rhodantha has primarily focused on quality evaluation, analytical profiling, and the assessment of medicinal material characteristics across different production areas [21–23]. These investigations have yielded substantial knowledge regarding its growth characteristics and pharmacologically active constituents, thereby laying a foundation for its scientific cultivation, efficient extraction, and rational application. Relevant studies have demonstrated that the contents of bioactive compounds in G. rhodantha vary significantly among different habitats and geographical origins [24], suggesting differentiation in medicinal components across populations. However, the mechanisms underlying this differentiation remain unclear. To address this knowledge gap, the present study employed GBS and high-performance liquid chromatography (HPLC) to investigate 149 G. rhodantha samples collected from 10 populations across its main distribution areas in the four Chinese provinces of Hunan, Sichuan, Yunnan, and Guizhou. Through integrated analysis of population genetic diversity and bioactive compound content, we elucidated the association patterns between genetic variation and the accumulation of active constituents in G. rhodantha . These findings provide a critical scientific basis for germplasm resource evaluation, superior provenance selection, and the standardized cultivation and utilization of this medicinal species. Materials and methods Experimental samples A total of 149 wild G. rhodantha individuals were sampled from its primary distribution areas, encompassing one population in Hunan Province and three populations each in Yunnan Province, Guizhou Province, and southwestern Sichuan Province. Fifteen individuals per population (14 individuals for the MB population). All samples were identified by Professor Zhikun Wu from Guizhou University of Traditional Chinese Medicine as Gentiana rhodantha (family Gentianaceae), and all voucher specimens are deposited in the Herbarium of Guizhou University of Traditional Chinese Medicine (GZTM, Cheng-gang Hu, [email protected] ). This sampling scheme was designed to systematically investigate the genetic diversity of the species across its representative geographical range. For the analysis of bioactive compound contents, eight populations were selected; the MB and QL populations were excluded from this component due to limited sample sizes available from field collections. All samples were preserved using silica gel desiccation at low temperatures to ensure the integrity of subsequent experimental analyses. Detailed information regarding the sampling locations and population characteristics is provided in Table 1 and illustrated in Fig. 1. Table 1 Geographical distribution of the 10 populations of Gentiana rhodantha Sample Species Location Longitude (E) Latitude (N) Altitude (m) Habitat No. of voucher specimens HY G. rhodantha Huayuan County, Hunan Province 109.26 28.47 391 Moist cliff WLL-2024-005 XD G. rhodantha Xundian County, Yunnan Province 103.39 25.52 2085 Hillside shrubland WLL-2024-008 PG G. rhodantha Puge County, Sichuan Province 102.6 27.37 2000 Sparse pine forest, grassy slope WLL-2024-011 RH G. rhodantha Renhuai City, Guizhou Province 106.25 28.09 584 Roadside grassland WLL-2024-012 XY G. rhodantha Xiangyun County, Yunnan Province 100.82 25.4 2187 Grassy slope, cliff WLL-2024-013 HS G. rhodantha Huishui County, Guizhou Province 106.89 26 1246 Sparse pine forest WLL-2024-015 HZ G. rhodantha Hezhang County, Guizhou Province 104.59 27.25 1868 Hillside shrubland WLL-2024-016 ZY G. rhodantha Zhenyuan County, Yunnan Province 100.79 24.19 1338 Grassland on soil embankment WLL-2024-017 MB G. rhodantha Mabian County, Sichuan Province 103.49 28.99 703 Edge of sparse bamboo forest, moist clif WLL-2024-018 QL G. rhodantha Qionglai City, Sichuan Province 103.33 30.48 558 Edge of sparse forest WLL-2024-019 GBS library construction and sequencing Library construction, sequencing, and data analysis for all samples were performed on the BGI platform at Shanghai Majorbio Bio-pharm Technology Co., Ltd., utilizing the DNBSEQ-T7 sequencing system with the PE150 sequencing mode. The GBS library construction protocol commenced with an in silico evaluation and validation of the G. rhodantha reference genome to identify an optimal restriction enzyme combination, primarily employing MseI and TaqaI. This selection aimed to achieve appropriate fragment size ranges, an optimal number of tags, and sufficient reference genome coverage. Subsequently, genomic DNA (0.1–1 µg) was digested with the restriction enzyme MseI. Following digestion, Solexa P1 and P2 adapters, each incorporating a 6-bp barcode sequence, were ligated to the fragment ends. A secondary digestion using TaqaI was then performed to modulate the number of tags. The final GBS library was generated through PCR amplification, fragment size selection, and purification using AMPure XP beads, yielding high-quality libraries suitable for subsequent sequencing analysis. Bioinformatic analysis methods Quality control of raw sequencing data To ensure high data quality for downstream analyses, rigorous quality control of the raw sequencing reads was performed using Fastp [25]. The quality control pipeline primarily involved the following steps: adapter sequences were first removed from the reads, and sequences containing non-AGCT bases at the 5' end were discarded. Subsequently, read ends with sequencing quality values below Q20 were trimmed, and reads containing up to 10 ambiguous 'N' bases were filtered out. Finally, after the removal of adapters and low-quality bases, short reads with a length of less than 25 bp were discarded, yielding high-quality sequencing data for subsequent analyses. Variant discovery and molecular marker development Following quality control of the raw sequencing data, the cleaned reads were assigned to individual samples using the axe R package. Sequencing data from each individual were subsequently pooled at the population level, and consensus sequences were generated using Stacks [26]. Variants were concurrently detected across all samples utilizing the populations module of Stacks. Single nucleotide polymorphisms (SNPs) with a call rate exceeding 70% and a minimum depth of coverage greater than> 2X were retained for subsequent population genetic analyses. Phylogenetic analysis Following the identification of high-quality SNP markers, a maximum likelihood (ML) phylogenetic tree was constructed using IQ-TREE2 under the GTR + I+G4 substitution model with 1,000 bootstrap replicates [27]. For comparative purposes, a neighbor-joining (NJ) tree was also constructed using FastTree with the -gtr -gamma parameters and 1,000 bootstrap replicates [28]. Population structure analysis Population genetic structure was assessed using ADMIXTURE [29]. Clustering analysis was performed on all samples assuming a range of genetic clusters from 1 to 20. The optimal number of clusters was determined by identifying the K value corresponding to the lowest cross-validation (CV) error. Principal component analysis (PCA) Principal component analysis (PCA) was conducted using Plink to perform mathematical statistical analysis on the sequencing data [30]. This linear transformation reduced the dimensionality of the multivariate dataset, extracting the primary characteristic components to elucidate genetic relationships and distances among samples, thereby providing a foundation for subsequent evolutionary analyses. Genetic diversity analysis Genetic diversity within each population was quantified using the populations module of Stacks [26]. A comprehensive suite of key population genetic parameters was calculated to thoroughly characterize the level of genetic diversity within each group, offering robust data support for subsequent investigations into evolutionary relationships and population differentiation. Determination of bioactive compound contents To investigate the variation in accumulation patterns of pharmacologically active constituents among different geographical populations of G. rhodantha , the contents of six major bioactive compounds-loganic acid, neomangiferin, swertiamarin, sweroside, mangiferin, and isoorientin-were quantified using HPLC in eight populations (HY, XD, PG, RH, XY, HS, HZ, and ZY). The MB and QL populations were excluded from this analysis due to limited sample availability. Sample preparation involved accurately weighing 0.5 g of powdered material, followed by ultrasonic extraction with 15 mL of 70% methanol for 30 min. The resulting extract was filtered, and the subsequent filtrate was collected for analysis. A mixed standard solution containing the six reference compounds was prepared for calibration. Chromatographic separation was achieved using an Agilent TC-C18 column (250 mm × 4.6 mm, 5 um) maintained at 28℃, with detection at 242 nm. The mobile phase consisted of acetonitrile and 0.03% phosphoric acid applied in a gradient elution at 0.8 mL/min. The injection volume was 8 uL, with a total run time of 52 min. Under these conditions, all target compounds exhibited well-resolved peak shapes with retention times consistent with those of the corresponding reference standards, confirming the suitability of this method for quantitative analysis. Integrative analysis of genetic and chemical data To elucidate the intrinsic relationship between the population genetic structure and the accumulation of bioactive constituents in G. rhodantha , an integrative analysis was performed combining genetic data with quantitative chemical profiles. Initially, genetic clustering characteristics of the different populations were delineated based on principal component analysis (PCA), phylogenetic reconstruction, and population structure analysis. This facilitated an exploration of the correspondence between genetic relatedness and chemotypic similarity among populations. Subsequently, correlation analyses between genetic diversity parameters and bioactive compound contents were conducted to assess the potential influence of genetic variation on the accumulation of secondary metabolites. Finally, utilizing the pairwise genetic differentiation index (FST) matrix, the relationship between the degree of genetic differentiation and disparities in bioactive compound contents was quantitatively evaluated. This analysis, integrated with the observed patterns of chemotypic similarity among population pairs exhibiting varying levels of genetic differentiation, revealed the regulatory role of genetic isolation in shaping chemotypic divergence. Correlation analysis between geographical factors and active ingredient contents To investigate the influence of geographical factors on the accumulation of bioactive components in G. rhodantha , correlation analysis was performed between geographical variables (longitude, latitude, and altitude) and the contents of six major active ingredients (loganic acid, neomangiferin, swertiamarin, sweroside, mangiferin, and isoorientin). Pearson's correlation coefficient was employed to assess linear relationships among variables, with coefficient values ranging from − 1 to 1, where positive and negative values indicate positive and negative correlations, respectively, and values closer to absolute 1 represent stronger correlations. Statistical analyses were conducted using SPSS software (version 26.0). Based on the analysis results, correlation heatmaps were generated using Origin software (version 2021) to visually illustrate the correlation patterns between geographical factors and active components, providing a reference for further elucidating the mechanisms by which environmental factors affect the quality formation of G. rhodantha . Results Sequencing data summary Utilizing GBS technology, sequencing libraries were successfully constructed for 149 G. rhodantha individuals collected from southwestern China. Sequencing generated a total of 2,866,380,310 raw reads, with an average of 19,109,202 reads per sample. The total number of bases obtained was 401,293,243,400 bp, corresponding to an average of 267,528,828,9 bp per sample. The GC content across samples ranged from 40.33% to 42.28%, with a mean value of 40.97%. All samples exhibited Q20 scores above 98.96% and Q30 scores above 96.74%, indicating high sequencing accuracy, low base error rates, and high-quality effective data suitable for subsequent analyses (Supplementary Table 1). SNP detection and marker development In this study, a total of 10,627,975 raw SNP loci were identified across the 149 G. rhodantha samples. Following stringent quality control, 4,524,308 high-quality SNPs were retained for subsequent analyses. The counts of transitions and transversions ranged from 12,665 to 21,934 and 7,868 to 14,059, respectively, with transition/transversion ratios varying between 1.52 and 1.67. All ratios exceeded the empirical threshold of 1.5, confirming high sequencing data quality and accurate SNP detection, thereby providing a reliable foundation for downstream population genetic analyses. Analysis of SNP-based genetic diversity revealed that the number of heterozygous and homozygous mutations per sample ranged from 3,720 to 17,902 and 7,352 to 30,216, respectively (Supplementary Table 2). The extensive variation observed in both mutation types indicates a relatively high level of genetic diversity within G. rhodantha populations. This characteristic likely reflects the combined influence of various evolutionary factors, including mutation accumulation, recombination events, and natural selection, and holds significant implications for the environmental adaptability and evolutionary potential of this species. Phylogenetic and population structure analysis Principal component analysis To investigate the genetic structure and clustering patterns among different geographical populations of G. rhodantha , PCA was performed on the 10 populations. The results revealed that the first principal component (PC1) and the second principal component (PC2) explained 20.81% and 14.92% of the total genetic variation, respectively, with a cumulative contribution of 35.73%. This indicates substantial genetic differentiation among populations from different geographical origins, exhibiting clear population stratification at the genomic level. The two-dimensional scatter plot based on principal component scores further elucidated the clustering relationships among populations. Joint analysis of PC1 and PC2 demonstrated that the 10 populations could be partitioned into three primary genetic clusters: PG, HZ, HS, XD, and HY clustered together; QL, MB, and RH formed a second cluster; and ZY and XY constituted a third cluster. Analysis of the PC1 versus PC3 scatter plot revealed that the HY population clustered independently along the PC1 axis, ZY and XY formed a distinct branch, while the remaining populations grouped into another major cluster. The PC2 versus PC3 scatter plot similarly supported the distinctiveness of the HY population, with PG, HZ, HS, and XD clustering together and the remaining populations forming another cluster (Fig. 2). Synthesizing the multi-dimensional PCA results, the HY population exhibited pronounced genetic distinctiveness and a higher degree of genetic differentiation. Conversely, close clustering relationships were observed between the ZY and XY populations, between MB and RH, and among HS and XD, suggesting closer genetic affinities among these population pairs or potential influences from similar habitat selection pressures, resulting in greater consistency in their genetic architecture (Fig. 3, Supplementary Table 3). Population structure analysis To elucidate the genetic structure and determine the optimal number of genetic groups within the G. rhodantha germplasm, population genetic structure was analyzed for the 149 individuals using ADMIXTURE. The optimal number of genetic clusters was identified by examining the trend of CV error rates across varying numbers of putative subpopulations (K values). The results demonstrated a continuous decrease in CV error with increasing K, reaching its minimum value of 0.28341 at K = 10. This indicates that partitioning the 149 G. rhodantha accessions into 10 genetic subpopulations represents the optimal grouping, suggesting that all individuals can be traced to 10 distinct ancestral gene pools (Fig. 4). The population structure plot based on K = 10 (Fig. 5) further elucidated the ancestry components characterizing each subpopulation. Across the 10 genetic subpopulations, the vast majority of individuals exhibited a highly homogeneous ancestral genetic background, with the proportion of their corresponding ancestral component exceeding 0.999. This suggests that these accessions possess relatively independent genetic origins without significant introgression or admixture. A minority of individuals, however, displayed pronounced mixed genetic backgrounds, harboring genetic information from two to five different ancestral populations, with their maximum ancestral component proportions all falling below 0.999. Individuals with mixed ancestral backgrounds were primarily distributed across three populations: RH, HZ, and MB, specifically including RH3, RH6, RH15, HZ1, HZ3, HZ7, HZ8, HZ14, MB5, MB6, and MB11. Notably, the HZ population contained the highest number and proportion of individuals with mixed genetic backgrounds, suggesting that this population may have experienced more frequent or complex historical inter-population gene flow events or admixture processes. Further analysis of the patterns of genetic introgression among different populations revealed significant shared genetic components between the RH and QL populations. The HZ population exhibited weak gene flow signals with the RH, HS, XD, and PG populations. Additionally, a subtle signal of genetic admixture was detected between the MB and QL populations. These patterns of introgression indicate close genetic affinities or historical gene flow events among the relevant populations, a finding that is highly consistent with the population clustering relationships revealed by the principal component analysis. Phylogenetic analysis To further elucidate the genetic evolutionary relationships among G. rhodantha germplasm resources, a phylogenetic tree was constructed for the 149 accessions using the NJ method based on genome-wide SNP data. The phylogenetic analysis revealed that the 149 G. rhodantha samples could be clustered into three primary genetic lineages (Fig. 6): Lineage I comprised populations HS, XD, HZ, and HY; Lineage II consisted of populations XY, ZY, and PG; and Lineage III encompassed populations QL, MB, and RH. This clustering pattern is highly congruent with the genetic differentiation patterns revealed by principal component analysis and population structure analysis. The consistency among these three analytical approaches collectively elucidates a clear geographical genetic structure within G. rhodantha populations. These findings further substantiate the existence of significant genetic differentiation among different geographical populations, providing a robust phylogenetic foundation for subsequent germplasm resource evaluation and the formulation of conservation strategies. Linkage disequilibrium analysis To evaluate the linkage disequilibrium (LD) characteristics and infer the genetic diversity levels among different geographical populations of G. rhodantha , the relationship between the average linkage disequilibrium coefficient (r²) and physical distance (kb) among markers was calculated using 168,057 high-quality SNPs. This analysis generated LD decay curves for each population, effectively reflecting the trend of LD attenuation with increasing genetic distance. The rate of this decay serves as an indirect indicator of a population's effective size, historical recombination events, and potential selective pressures. The results revealed substantial variation in LD levels among the 10 G. rhodantha populations. Ranked by decreasing LD magnitude (i.e., from highest to lowest r² values), the populations were ordered as follows: QL > XY > ZY > HZ > HY > MB > RH > HS > PG > XD. The difference in LD values between the populations exhibiting the highest and lowest levels exceeded 18-fold. A negative correlation was observed between LD coefficients and population genetic diversity; that is, higher degrees of linkage corresponded to lower genetic diversity. Based on this relationship, the genetic diversity levels of the 10 populations were inferred to be inversely ordered relative to LD magnitude: QL < XY < ZY < HZ < HY < MB < RH < HS < PG < XD. Further analysis of the LD decay rate, quantified as the physical distance at which r² decays to half its maximum value, revealed that populations PG, HS, and XD exhibited similar decay rates, with half-decay distances ranging from 12.640 to 13.183 kb. This rapid decay suggests relatively high genetic diversity within these populations. Populations MB and RH also displayed comparable decay rates, with half-decay distances between 20.768 and 22.921 kb, indicating intermediate levels of genetic diversity. In contrast, populations QL and XY exhibited the slowest LD decay, with half-decay distances substantially exceeding those of the other populations. This pattern suggests relatively low genetic diversity, restricted intra-population recombination, smaller effective population sizes, or potential exposure to strong natural selection pressures in these two populations (Fig. 7, Table 2). Table 2 LD-decay values for each population popID half.decay(kb) decay01(kb) Hs 13.183 47.649 Hy 31.342 113.292 Hz 42.319 152.975 Mb 22.921 82.853 Pg 12.829 46.368 Ql 228.726 826.826 Rh 20.768 75.067 Xd 12.640 45.685 Xy 106.079 383.462 ZY 57.675 208.486 Genetic diversity and population differentiation analysis Population genetic diversity analysis To comprehensively characterize the level of genetic diversity within G. rhodantha germplasm resources, population genetic parameters were systematically evaluated for the 10 geographical populations based on genome-wide SNP markers. The results revealed generally low genetic diversity at the population level for G. rhodantha (Table 3). Specifically, observed heterozygosity (Ho) ranged from 0.036 to 0.109, with a mean value of 0.075, while expected heterozygosity (He) ranged from 0.025 to 0.153, with a mean of 0.097. With the exception of the QL and ZY populations, where Ho slightly exceeded He suggesting a minor excess of heterozygotes, the remaining eight populations exhibited Ho values lower than He, indicating varying degrees of heterozygote deficiency. This trend was further corroborated by the inbreeding coefficient (FIS), which was positive across all 10 populations, ranging from 0.020 to 0.162 with a mean of 0.075. This positive FIS values suggest a trend toward relative excess of homozygotes in most populations, implying potential inbreeding or a Wahlund effect (i.e., the presence of substructure within populations). Nucleotide diversity (π), a core indicator reflecting the richness of genetic variation within a population, is closely associated with effective population size and selective pressures. In this study, π values varied significantly among populations, ranging from 0.026 to 0.160, with an overall mean of 0.102 (Table 3). Notably, populations HZ, PG, XD, and MB, located in the southwestern region, exhibited relatively higher π values (0.129–0.160), suggesting that these populations may have retained richer genetic variation and could represent a genetic diversity center for G. rhodantha . In stark contrast, populations QL, ZY, HY, and XY displayed markedly lower π values (0.026–0.070). Combined with field habitat surveys, these low-diversity populations are generally distributed across small, fragmented shrub-grassland areas. Limited habitat area may lead to smaller effective population sizes, thereby exacerbating genetic drift and inbreeding risks, ultimately constraining the long-term preservation and accumulation of genetic variation. Additional auxiliary genetic parameters further support the conclusion of limited intra-population genetic variation in G. rhodantha . Polymorphism information content (PIC) analysis revealed that PIC values across all populations ranged from 0.020 to 0.123, with a mean of 0.078. All values fell below the 0.25 threshold, classifying them as low polymorphism levels according to common evaluation standards [31]. Furthermore, minor allele frequency (MAF) and Shannon's information index (I) were also at low levels, with mean values of 0.072 and 0.147, respectively. These multiple genetic parameters mutually corroborate each other, collectively revealing the current state of impoverished genetic variation within G. rhodantha populations and providing an important theoretical basis for formulating germplasm conservation strategies for this species. Population differentiation analysis The FST serves as a critical metric for quantifying genetic divergence among populations, with its value directly reflecting the intensity of geographical isolation and the extent of restricted gene flow. According to established criteria, FST < 0.05 indicates negligible differentiation, 0.05 ≤ FST ≤ 0.15 represents moderate differentiation, 0.15 0.25 signifies extremely high genetic differentiation, implying pronounced reproductive isolation or geographical barriers [32]. Analysis of genetic differentiation among the 10 geographical populations of G. rhodantha in this study (Table 4, Fig. 8) revealed pairwise FST values ranging from 0.1392 to 0.7864, with a mean of 0.3789. The vast majority of pairwise FST values substantially exceeded 0.25, indicating that high levels of genetic differentiation have developed among populations, characterized by significant genetic structure divergence and geographical isolation patterns, with severely restricted inter-population gene flow. Further analysis revealed considerable heterogeneity in the degree of genetic differentiation among different population pairs. The lowest FST value was observed between the XD and HS populations (0.1392), followed by the PG and HZ pair (0.1436). Although these values fall within the moderate differentiation range, they nonetheless suggest that these populations may share relatively recent common ancestors or have experienced more frequent historical gene flow compared to other population pairs, thereby slowing the progression of genetic differentiation. In stark contrast, the FST value between the ZY and QL populations reached 0.7863, the highest among all population pairs. These two populations are geographically distant, and this pronounced spatial isolation severely restricts pollen and seed dispersal, leading to near-complete cessation of gene flow. Under such circumstances, genetic drift becomes the predominant evolutionary force, accumulating extremely high levels of genetic differentiation [33]. These results further corroborate that geographical isolation is a key driver of population genetic differentiation in G. rhodantha. Table 3 Genetic diversity statistics of Gentiana rhodantha across 10 populations Pop ID Number Ho He π Fis PIC MAF Shannon HS 15.000 0.081 0.121 0.127 0.120 0.097 0.089 0.183 HY 15.000 0.053 0.067 0.070 0.047 0.054 0.051 0.102 HZ 15.000 0.109 0.153 0.160 0.137 0.123 0.112 0.231 MB 14.000 0.090 0.123 0.129 0.104 0.098 0.090 0.184 PG 15.000 0.095 0.147 0.155 0.162 0.119 0.106 0.225 QL 15.000 0.036 0.025 0.026 0.020 0.020 0.020 0.038 RH 15.000 0.094 0.111 0.116 0.064 0.090 0.079 0.170 XD 15.000 0.087 0.124 0.130 0.113 0.100 0.091 0.189 XY 15.000 0.063 0.064 0.067 0.016 0.051 0.048 0.096 ZY 15.000 0.039 0.035 0.036 0.002 0.027 0.027 0.052 Table 4 Pairwise FST values between different populations of Gentiana rhodantha POP2 HS HY HZ MB PG QL RH XD XY ZY HS 0 0.3485 0.1822 0.3204 0.1983 0.5092 0.3430 0.1392 0.4404 0.5009 HY 0.3485 0 0.3140 0.4300 0.2936 0.6730 0.4503 0.3549 0.5764 0.6542 HZ 0.1822 0.3140 0 0.2533 0.1436 0.4160 0.2734 0.1808 0.3729 0.4239 MB 0.3204 0.4300 0.2533 0 0.2291 0.1950 0.1672 0.3231 0.4737 0.5327 PG 0.1983 0.2936 0.1436 0.2291 0 0.3930 0.2541 0.1968 0.3174 0.3672 QL 0.5092 0.6730 0.4160 0.1950 0.3930 0 0.2330 0.5036 0.7009 0.7863 RH 0.3430 0.4503 0.2734 0.1672 0.2541 0.2330 0 0.3425 0.4841 0.5381 XD 0.1392 0.3549 0.1808 0.3231 0.1968 0.5036 0.3425 0 0.4377 0.5008 XY 0.4404 0.5764 0.3729 0.4737 0.3174 0.7009 0.4841 0.4377 0 0.2807 ZY 0.5009 0.6542 0.4239 0.5327 0.3672 0.7863 0.5381 0.5008 0.2807 0 Analysis of bioactive compound contents in different G. rhodantha populations To further investigate the variation in the accumulation of medicinal bioactive constituents among different geographical populations of G. rhodantha , the contents of six major active compounds—loganic acid, neomangiferin, swertiamarin, sweroside, mangiferin, and isoorientin—were quantified in eight populations using HPLC. The results are presented in Table 5 and Fig. 9. Mangiferin, the predominant bioactive constituent of G. rhodantha and the indicator component specified by the 2025 edition of the Chinese Pharmacopoeia, exhibited the most pronounced variation among populations. The XD population displayed the highest mangiferin content at 122.8439 mg/g, significantly exceeding all other populations. This was followed by the XY population (78.3677 mg/g), PG (69.0507 mg/g), HS (59.4093 mg/g), HY (55.4126 mg/g), ZY (47.2276 mg/g), and RH (46.8388 mg/g), with the HZ population exhibiting the lowest content at 37.8322 mg/g. Across the eight populations, mangiferin content ranged from 37.8322 to 122.8439 mg/g, with a mean value of 64.6229 mg/g and a 3.25-fold variation, indicating substantial chemotypic differentiation among different geographical populations of G. rhodantha . The remaining five compounds also demonstrated considerable inter-population variation. Swertiamarin content was highest in the ZY population (1.7057 mg/g) and lowest in the PG population (0.0278 mg/g), representing a 61-fold difference, with a mean value of 0.5262 mg/g. Neomangiferin content peaked in the XD population (1.1604 mg/g) and reached its minimum in the HY population (0.2522 mg/g), averaging 0.7717 mg/g. Isoorientin content was highest in the XY population (1.4998 mg/g) and lowest in the RH population (0.4008 mg/g), with a mean of 0.8704 mg/g. Loganic acid content ranged from a high of 1.0464 mg/g in the HY population to a low of 0.1251 mg/g in the HS population, averaging 0.3583 mg/g. Sweroside content varied from 0.4645 mg/g in the ZY population to 0.1068 mg/g in the HZ population, with a mean of 0.2259 mg/g. This pronounced chemotypic differentiation indicates that the accumulation patterns of secondary metabolites in G. rhodantha are shaped by the combined influence of genetic background and habitat conditions. The substantial differences in secondary metabolite accumulation patterns among different geographical populations establish a critical foundation for subsequent investigations into the mechanisms associating genetic background with bioactive constituent accumulation. Table 5 Determination results of six compound Sample loganic acid neomangiferin swertiamarin sweroside mangiferin isoorientin HY 1.0464 0.2522 0.1534 0.4251 55.4126 0.6740 XD 0.1997 1.1604 0.4173 0.2458 122.8439 0.9994 PG 0.6275 0.9254 0.0278 0.1673 69.0507 0.9399 RH 0.2195 0.6038 0.5254 0.1192 46.8388 0.4008 XY 0.2920 0.9320 1.2016 0.1588 78.3677 1.4998 HS 0.1251 1.0912 0.1374 0.1199 59.4093 1.0735 HZ 0.1784 0.3942 0.0408 0.1068 37.8322 0.6766 ZY 0.1780 0.8146 1.7057 0.4645 47.2276 0.6995 Integrative analysis of genetic differentiation and bioactive compound accumulation Correspondence between genetic clustering and chemotypes Integrative comparison of bioactive compound contents with population genetic analysis results revealed a clear correspondence between genetic clustering and chemotypes. Based on principal component analysis and phylogenetic reconstruction, the 10 G. rhodantha populations were partitioned into three major genetic lineages. The first genetic lineage comprised populations HS, XD, HZ, and HY. Within this lineage, populations XD and HS exhibited close genetic clustering, and both demonstrated relatively high mangiferin contents of 122.8439 mg/g and 59.4093 mg/g, respectively, significantly exceeding those of most other populations. Furthermore, both populations displayed neomangiferin contents exceeding 1.0 mg/g (XD: 1.1604 mg/g; HS: 1.0912 mg/g), reflecting similar chemotypic profiles. The second genetic lineage consisted of populations XY, ZY, and PG. Within this lineage, populations XY and ZY clustered closely in principal component analysis, and both exhibited relatively high swertiamarin contents (XY: 1.2016 mg/g; ZY: 1.7057 mg/g), significantly surpassing those of other populations. The third genetic lineage encompassed populations QL, MB, and RH. Within this lineage, population RH showed evidence of weak gene flow with MB and QL, and its mangiferin content (46.8388 mg/g) fell within the moderate-to-low range. These findings demonstrate a strong correspondence between genetic relatedness and chemotypic similarity, indicating that the accumulation of secondary metabolites is significantly influenced by genetic background. This further corroborates the regulatory role of genetic background in shaping secondary metabolite accumulation patterns. Influence of genetic diversity levels on bioactive compound accumulation Genetic diversity analysis revealed that populations HZ, PG, and XD exhibited the highest π values among all populations, with values of 0.160, 0.155, and 0.130, respectively. In contrast, population ZY displayed a substantially lower π value of 0.036. Correspondingly, bioactive compound analysis demonstrated that population XD possessed a remarkably high mangiferin content of 122.8439 mg/g, and population PG exhibited a relatively high mangiferin content of 69.0507 mg/g, both ranking among the higher levels. Conversely, population ZY showed a mangiferin content of only 47.2276 mg/g. These findings are mutually corroborated by the linkage disequilibrium (LD) analysis results: populations XD and PG exhibited rapid LD decay, with LD declining to half its maximum value within 12.640 kb and 12.829 kb, respectively, indicating larger effective population sizes and active recombination that facilitate the retention of genetic variation and adaptive evolution of genes involved in secondary metabolism. In contrast, population ZY displayed slow LD decay, suggesting a smaller effective population size and stronger genetic drift effects that constrain both genetic diversity and the accumulation of bioactive constituents. These results collectively indicate a positive correlation between genetic diversity levels and bioactive compound contents. Regulatory role of genetic differentiation in chemotypic divergence To investigate the quantitative relationship between the degree of genetic differentiation among G. rhodantha populations and the accumulation patterns of their medicinal bioactive constituents, the contents of six major active compounds across eight geographical populations were analyzed in conjunction with pairwise FST values. The results revealed a distinct and consistent association between the extent of inter-population genetic differentiation and disparities in bioactive compound profiles (Table 5). Highly differentiated population pairs generally exhibited pronounced chemotypic divergence. For instance, the ZY and HY populations, characterized by a high FST value of 0.6542, displayed significant differences in the contents of loganic acid, neomangiferin, and swertiamarin. Similarly, the XY and HY populations, with an FST of 0.5764, showed substantial variation in the levels of loganic acid, swertiamarin, sweroside, and isoorientin. In contrast, population pairs characterized by low genetic differentiation tended to exhibit high similarity across multiple bioactive constituents. The XD and HS populations, which showed the lowest FST value (0.1392), demonstrated highly consistent contents of neomangiferin, isoorientin, swertiamarin, and sweroside. Likewise, the PG and HZ populations (FST = 0.1436) exhibited comparable levels of swertiamarin, sweroside, and isoorientin. In summary, the degree of genetic differentiation among G. rhodantha populations corresponds closely with the divergence in their medicinal bioactive constituent profiles: genetically proximate populations tend to share similar chemical fingerprints, whereas significantly differentiated populations tend to develop distinct chemotypes. This finding reveals the underlying regulatory influence of genetic variation on the accumulation of bioactive constituents and provides an important foundation for predicting chemotypic characteristics and facilitating targeted selection of elite germplasm sources. Correlation analysis between geographical factors and active ingredient contents To investigate the influence of geographical factors on the accumulation of bioactive components in G. rhodantha , Pearson's correlation coefficient matrix was employed to analyze the relationships between sampling site geographical factors (longitude, latitude, and altitude) and the contents of six major active ingredients (Fig. 10). The results revealed significant spatial associations among geographical factors, with a strong positive correlation observed between longitude and latitude, indicating a certain spatial regularity in the distribution of sampling sites. Altitude exhibited moderate negative correlations with both longitude and latitude, suggesting that high-altitude sampling sites within the study area tended to be located in regions with lower longitude and latitude. Correlation analysis between geographical factors and active ingredients showed that most compound contents exhibited negative correlations with longitude, latitude, and altitude; however, the influence of geographical factors on the accumulation of active ingredients displayed clear component specificity. Specifically, neomangiferin and isoorientin showed moderate negative correlations with longitude and latitude but strong positive correlations with altitude. In contrast, loganic acid presented moderate positive correlations with longitude and latitude and a weak negative correlation with altitude. Swertiamarin exhibited a strong negative correlation with longitude, a significant negative correlation with latitude, and a weak correlation with altitude. Mangiferin showed weak negative correlations with longitude and latitude but a strong positive correlation with altitude. By comparison, sweroside displayed weak negative correlations with latitude and altitude, and no correlation with longitude. Among the active ingredients, positive correlations predominated. Neomangiferin exhibited strong positive correlations with mangiferin and isoorientin, and a weak positive correlation with swertiamarin. Swertiamarin showed a moderate positive correlation with sweroside and a weak positive correlation with isoorientin. Mangiferin was moderately positively correlated with isoorientin, while sweroside demonstrated a moderate positive correlation with loganic acid. These patterns suggest that these compounds may share co-accumulation characteristics or be regulated by similar metabolic pathways within the plant. Meanwhile, certain negative correlations were also observed among components, with the strongest negative correlation occurring between loganic acid and neomangiferin, followed by loganic acid with swertiamarin, whereas loganic acid showed only a weak negative correlation with mangiferin and isoorientin. Sweroside also displayed weak negative correlations with neomangiferin and isoorientin. These positive and negative correlation patterns reflect potential synergistic and competitive relationships among different active ingredients in biosynthetic pathways, providing important clues for elucidating the accumulation mechanisms of secondary metabolites in G. rhodantha . In summary, geographical factors exert a significant negative influence on the accumulation of multiple active ingredients in G. rhodantha , while the positive and negative correlations among active ingredients reflect their co-accumulation characteristics and underlying metabolic regulatory relationships. These findings provide a reference for further elucidating the mechanisms by which environmental factors influence the quality formation of G. rhodantha and are consistent with the conclusions regarding chemotype differentiation among different populations drawn from the population genetic analysis in this study. Discussion Population genetic structure of G. rhodantha reveals pronounced differentiation driven by geographical isolation This study reveals a distinctive low intra-population and high inter-population genetic structure pattern in the Chinese endemic species G. rhodantha , characterized by extremely low intra-population genetic diversity coupled with remarkably high inter-population genetic differentiation. Consistent analyses based on SNP markers demonstrated that the mean He, π, and PIC across all populations were only 0.097, 0.102, and 0.078, respectively. These values are substantially below the established threshold for low polymorphism (PIC < 0.25), confirming the paucity of genetic variation within populations [31]. Concurrently, the mean FST among populations reached 0.3789, with the vast majority of population pairs classified as exhibiting extremely high genetic differentiation according to established criteria [32]. Phylogenetic reconstruction and principal component analysis further partitioned the 10 populations into three primary genetic lineages, with these complementary approaches collectively revealing a pronounced and well-defined geographical genetic structure for this species. The formation of this pattern arises from the combined effects of the species' biological characteristics and historical evolutionary processes. First, as a typical selfing or inbreeding species, the reproductive system of G. rhodantha inherently tends to maintain low intra-population genetic diversity, with the predominantly positive inbreeding coefficients (FIS ranging from 0.020 to 0.162) providing direct evidence for this. Second, and more critically, climatic fluctuations since the Quaternary period, particularly in the topographically complex southwestern region, have driven population contractions and habitat fragmentation for this species [34]. Field surveys revealed that populations exhibiting low genetic diversity (e.g., QL, ZY) are predominantly distributed in small, isolated shrub-grassland areas, where limited habitat extent severely constrains effective population sizes (Ne). Under these conditions, genetic drift rapidly becomes the predominant evolutionary force in small, isolated populations, not only accelerating the stochastic loss of genetic variation within populations but also driving profound differentiation among populations through the random fixation and loss of alleles [33]. The exceptionally high FST value of 0.7863 between the ZY and QL populations exemplifies the extreme outcome of prolonged spatial isolation coupled with genetic drift. This finding carries paramount implications for conservation strategies: conservation efforts should not focus solely on enhancing genetic diversity within individual populations but should prioritize the preservation of highly differentiated populations representing distinct evolutionary units and harboring unique allelic combinations, thereby maximizing the conservation of the species' evolutionary potential and overall genetic variation. Population demographic history inferred from linkage disequilibrium decay LD decay analysis provides critical temporal evidence for elucidating the evolutionary demographic history of G. rhodantha populations. In this study, substantial variation in LD decay rates was observed among the 10 populations: populations PG, HS, and XD exhibited the most rapid decay, with half-decay distances ranging from 12.640 to 13.183 kb, whereas populations QL and XY displayed markedly slower decay rates. LD decay rates are intimately associated with historical effective population size, recombination rates, and selective pressures: larger effective population sizes and more frequent historical recombination events promote rapid LD decay, while population bottlenecks, inbreeding, or strong selection result in delayed LD decay [35]. Based on these principles, populations such as PG, HS, and XD are inferred to have maintained relatively large historical effective population sizes and experienced frequent recombination events, thereby retaining richer genetic diversity. This inference aligns closely with the π analysis results, where populations HZ, PG, and XD exhibited the highest π values (0.130–0.160). Collectively, these findings suggest that the southwestern region, centered around populations HZ, PG, and XD, may represent a genetic diversity center or glacial refugium for G. rhodantha . During the Quaternary glaciations, the complex topography and diverse microhabitats of this region likely provided relatively stable environments, enabling the preservation of a comparatively intact reservoir of genetic variation [36]. Conversely, the slow LD decay observed in populations QL and XY, combined with their extremely low π values, strongly suggests that these populations have experienced severe population bottlenecks or have been maintained at small population sizes for extended periods, resulting in significant genetic diversity depletion. This finding carries direct implications for germplasm collection strategies: populations exhibiting high genetic diversity, such as PG and XD, should be prioritized for germplasm conservation and collection to maximize the capture of the species' genetic variation. Positive association between genetic diversity and bioactive compound accumulation and their co-differentiation pattern Integrating population genetic data with secondary metabolite content analysis represents a central breakthrough of this study. The results reveal, for the first time in G. rhodantha , a positive association trend between genetic diversity levels and the contents of major medicinal bioactive constituents. Populations exhibiting high genetic diversity, such as XD, demonstrated significantly higher mangiferin contents compared to low-diversity populations like ZY. Furthermore, the rapid LD decay observed in populations XD and PG facilitates the retention of genetic variation, potentially providing richer raw material for the adaptive evolution of genes involved in secondary metabolism [37]. High genetic diversity implies a greater abundance of allelic variation in genes comprising secondary metabolic pathways, increasing the likelihood of efficient bioactive compound accumulation through gene dosage effects or optimized regulatory networks. Conversely, genetically depauperate populations, such as ZY, characterized by small effective population sizes and pronounced genetic drift, may have lost advantageous alleles favorable for secondary metabolite biosynthesis. This observed positive trend suggests that future efforts to screen for high-content medicinal germplasm should prioritize populations exhibiting high genetic diversity and rapid LD decay. Particularly noteworthy is the pronounced co-differentiation pattern observed between the degree of genetic differentiation among G. rhodantha populations and their chemotypic divergence. The three major genetic lineages delineated by SNP analysis were significantly associated with distinct chemotypic profiles. Within the first lineage (comprising HS, XD, HZ, and HY), populations XD and HS not only exhibited close genetic clustering but also demonstrated highly consistent contents of mangiferin and neomangiferin. Within the second lineage (comprising XY, ZY, and PG), populations XY and ZY jointly exhibited relatively high levels of swertiamarin accumulation. This coupling of genetic relatedness with chemotypic similarity strongly suggests that the key genes governing the biosynthesis of these bioactive constituents, or their regulatory regions, harbor genetic variation paralleling that of neutral markers, a phenomenon indicative of co-evolution or genetic linkage [38]. Quantitative analysis further substantiated this observation: weakly differentiated population pairs, such as XD and HS (FST = 0.1392), exhibited comparable contents across multiple bioactive constituents, whereas highly differentiated pairs, such as ZY and HY (FST = 0.6542), displayed significant disparities in several compounds. This compellingly demonstrates that as gene flow among populations becomes interrupted and genetic differentiation accumulates, the genetic machinery governing secondary metabolism likewise diverges, ultimately giving rise to heritable, stable chemotypes. Mangiferin, serving as the indicator component, exhibited a 3.25-fold variation in content across the eight populations (ranging from 37.8322 to 122.8439 mg/g), underscoring substantial chemotypic differentiation within this species. This variation may originate from genetic polymorphisms in key genes of the xanthone biosynthetic pathway or from differential regulation by upstream transcription factors. Studies on the congeneric species Gentiana macrophylla provide corroborating evidence for this inference [39]. Conclusions In summary, this study draws the following principal conclusions: (1) G. rhodantha populations in southwestern China exhibit a pronounced geographical genetic structure, characterized by extremely high levels of inter-population genetic differentiation (mean FST = 0.3789), with geographical isolation serving as the dominant factor driving this differentiation; (2) Intra-population genetic diversity is generally low (mean π = 0.102), with habitat fragmentation exacerbating genetic drift effects and inbreeding risks; (3) LD decay analysis reveals differential demographic histories among populations, suggesting that populations such as PG and XD may represent glacial refugia that have retained relatively high genetic diversity; (4) A positive trend exists between genetic diversity and the accumulation of bioactive constituents, and a pronounced co-differentiation pattern is observed between genetic differentiation and chemotypic divergence, providing direct evidence for the genetic basis underlying chemotypic differentiation. (5) Constituent-specific associations were observed between geographical factors and the accumulation of active ingredients. Neomangiferin, isoorientin, and mangiferin exhibited strong positive correlations with altitude, whereas loganic acid showed moderate positive correlations with longitude and latitude. Positive correlations predominated among the active ingredients, reflecting their co-accumulation characteristics and potential metabolic regulatory relationships. Synthesizing these findings, highly differentiated populations (e.g., ZY and QL populations with FST = 0.7863) represent distinct evolutionary units and should be prioritized as separate conservation targets to prevent the loss of adaptive gene combinations through admixture between different genetic lineages. Populations exhibiting both high genetic diversity and exceptional bioactive compound contents (e.g., XD and PG populations) are suitable candidates for targeted selection as elite germplasm for propagation and utilization. For low-diversity populations experiencing severe habitat fragmentation (e.g., QL, XY, and ZY), priority should be given to habitat restoration and population reinforcement efforts, implementing assisted moderate gene flow to mitigate inbreeding depression risks and enhance the long-term survival potential of these populations. Declarations Acknowledgements We are grateful to editors and anonymous reviewers for their helpful comments. Authors' contributions Qingqing Ye: Data curation, Software, Methodology, Formal analysis, Writing-review and editing. Lingling Wei: Investigation, Resources, Software, Data curation, Formal analysis, Writing-original draft. Weixiang Xiao: Investigation, Resources, Data curation. Xin Tan: Investigation, Resources, Data curation. Li Yang: Investigation, Resources, Supervision. Deqiang Ren: Software, Data curation, Supervision. Ning Ding: Software, Data curation,Supervision. Zhikun Wu: Conceptualization, Formal analysis, Investigation, Supervision, Writing-original draft, Writing-review and editing. Funding This research was supported by the Research on Green Prevention and Control Technology for Leaf Blight of Gentiana rhodantha under Forest Based on Pathogen-Biological Control-Plant-Environment Coordinated Regulation (Qianlin Kehe [2026] Zhicheng No. 004), the Chuan-Qian Collaboration Premium Seeds and Medicinals: Joint Research and Demonstration of Systematic Introduction, Domestication, and Key Industrialization Technologies of Gentiana rhodantha Germplasm Resources (2026YFHZ0116), the National Wild Plant Germplasm Resource Center for Guizhou University of Traditional Chinese Medicine (ZWGX‒2405), and the Science and Technology Plan Project of Guizhou Province ([2022]‒4016). 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Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 29 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Editor invited by journal 20 Apr, 2026 Submission checks completed at journal 19 Apr, 2026 First submitted to journal 19 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9367916","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631788560,"identity":"71e72555-69b7-4e7e-acbb-9178a198c783","order_by":0,"name":"Qingqing Ye","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qingqing","middleName":"","lastName":"Ye","suffix":""},{"id":631788561,"identity":"47821b71-60cd-4ea5-b7e8-a2a0601d9795","order_by":1,"name":"Lingling Wei","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese 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10:06:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9759593,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of the 10 samples of \u003cem\u003eGentiana rhodantha\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIGURE1Geographicdistributionofthe10samplesofGentianarhodantha.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/2697d56e7cdaa858f8ffdd0a.jpg"},{"id":108494369,"identity":"bed14b16-97ab-45e2-8a4d-332a406f0000","added_by":"auto","created_at":"2026-05-05 10:04:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2051214,"visible":true,"origin":"","legend":"\u003cp\u003ePCA scatter plot of \u003cem\u003eGentiana rhodantha\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIGURE2PCAscatterplotofGentianarhodantha.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/276ddc53f79ba96dbad292bc.jpg"},{"id":108494372,"identity":"72604e68-2288-4f9c-8244-bc2f7b62972a","added_by":"auto","created_at":"2026-05-05 10:04:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26134,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of explained variance by principal components in PCA\u003c/p\u003e","description":"","filename":"FIGURE3DistributionofexplainedvariancebyprincipalcomponentsinPCA.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/bdc39da22f856f370cc31052.jpg"},{"id":108494766,"identity":"32ac6228-122d-4710-b10e-6be23fe50149","added_by":"auto","created_at":"2026-05-05 10:07:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40020,"visible":true,"origin":"","legend":"\u003cp\u003eCross-validation error across different K values in population structure analysis\u003c/p\u003e","description":"","filename":"FIGURE4CrossvalidationerroracrossdifferentKvaluesinpopulationstructureanalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/8002e58363fa0b379e431dac.jpg"},{"id":108491415,"identity":"8d9547c2-1a29-43db-b5cf-368e0493bad0","added_by":"auto","created_at":"2026-05-05 09:53:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1371272,"visible":true,"origin":"","legend":"\u003cp\u003eADMIXTURE analysis of population genetic structure under assumed ancestral numbers K = 9 - 11\u003c/p\u003e","description":"","filename":"FIGURE5ADMIXTUREanalysisofpopulationgeneticstructureunderassumedancestralnumbersK911.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/81872df6a5d66fd57c41d67f.jpg"},{"id":109081135,"identity":"78cfa321-4a5e-4e52-ae9e-89430322b891","added_by":"auto","created_at":"2026-05-12 12:00:38","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6616482,"visible":true,"origin":"","legend":"\u003cp\u003eNJ phylogenetic tree of the 149 \u003cem\u003eGentiana rhodantha\u003c/em\u003e accessions\u003c/p\u003e","description":"","filename":"FIGURE6NJphylogenetictreeofthe149Gentianarhodanthaaccessions.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/842cc1fe88688b45a6d7cb56.jpg"},{"id":108491423,"identity":"5e83089d-5321-44d3-ac3d-ee1ab223b9b1","added_by":"auto","created_at":"2026-05-05 09:53:52","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1623874,"visible":true,"origin":"","legend":"\u003cp\u003eLD analysis of \u003cem\u003eGentiana rhodantha\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIGURE7LDanalysisofGentianarhodantha.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/7104a81e643f3a1a4d6c786f.jpg"},{"id":108494928,"identity":"c6e64321-9b14-474d-b959-456d2234d16f","added_by":"auto","created_at":"2026-05-05 10:08:07","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2186818,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic illustration of nucleotide diversity and population differentiation\u003c/p\u003e","description":"","filename":"FIGURE8Schematicillustrationofnucleotidediversityandpopulationdifferentiation.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/b061088c2ca02f93310036b6.jpg"},{"id":108494892,"identity":"b0e3c933-f641-483c-8d37-5fba9750a1b1","added_by":"auto","created_at":"2026-05-05 10:07:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":142687,"visible":true,"origin":"","legend":"\u003cp\u003eContents of six compounds in eight medicinal materials\u003c/p\u003e","description":"","filename":"FIGURE9Contentsofsixcompoundsineightmedicinalmaterials.png","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/07bfbccb2977c35cebddb82c.png"},{"id":108494385,"identity":"6bcfd68b-b1b3-482e-9755-8d78a37c8d46","added_by":"auto","created_at":"2026-05-05 10:04:37","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":27368329,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap of geographical factors and seven chemical constituents.\u003c/p\u003e","description":"","filename":"FIGURE1010Correlationheatmapofgeographicalfactorsandsevenchemicalconstituents..png","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/93fe3deaee1c31c5a3a6c868.png"},{"id":109082507,"identity":"4aff0769-0ffb-4071-bd5f-03c113aec869","added_by":"auto","created_at":"2026-05-12 12:40:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25091489,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/a6ea7aa4-b6ad-43ce-aecb-e8fdd1ef8fb4.pdf"},{"id":108494368,"identity":"2f99738d-2b9e-4c8c-92f8-f7e7b2d3922e","added_by":"auto","created_at":"2026-05-05 10:04:14","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":50040,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/a03024218ffa48f5a5894230.docx"},{"id":109067675,"identity":"50311364-fc33-4327-9e69-956ada1768bb","added_by":"auto","created_at":"2026-05-12 09:59:36","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":55220,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/23462053daabd10d98307c43.docx"},{"id":108495223,"identity":"ef17cfd3-3ca0-44e6-8589-9c51f7971ed0","added_by":"auto","created_at":"2026-05-05 10:09:23","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":35893,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9367916/v1/d2247214e00a15b9995719fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Diversity and Chemotypic Variation of Gentiana rhodantha: Insights into the Genetic Basis of Active Compound Accumulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenetic diversity representing the total repertoire of heritable variations accumulated throughout a species' evolutionary history, constitutes the fundamental basis for its adaptation to environmental changes and its capacity for population survival and proliferation. Furthermore, it serves as a critical indicator for deciphering evolutionary trajectories and assessing future adaptive potential [1\u0026ndash;2]. Genetic differentiation among geographical populations, arising from variations in genetic diversity and local adaptation processes, can further influence phenotypic traits, including the biosynthesis of secondary metabolites that are intrinsically linked to the quality of medicinal materials. For instance, in \u003cem\u003eScutellaria baicalensis\u003c/em\u003e, significant correlations have been established between population-level genetic diversity and the content of its major bioactive constituents, such as baicalin and baicalein. Population genetic structure analyses have revealed that specific populations harbor unique haplotypes of genes involved in flavonoid biosynthesis, thereby shaping the geographical chemotypic patterns of this medicinal plant [3]. Studies on \u003cem\u003ePanax ginseng\u003c/em\u003e have demonstrated that disparities in genetic diversity between wild and cultivated populations directly impact the composition and accumulation of ginsenosides, with the higher genetic diversity observed in wild populations being associated with a more complex ginsenoside profile [4]. Similarly, in the analysis of cultivated \u003cem\u003ePanax quinquefolius\u003c/em\u003e provenances, accessions exhibiting higher genetic diversity were frequently accompanied by an enriched content of flavonoid bioactive components, underscoring that genotypic diversity is a pivotal determinant of medicinal material quality [5]. Collectively, these investigations elucidate that genetic diversity not only underpins species evolution but also directly governs the formation and chemical stability of medicinal material quality by modulating genetic variation in key genes within secondary metabolic pathways. This provides a crucial genetic foundation for the conservation and sustainable utilization of medicinal plant resources.\u003c/p\u003e \u003cp\u003eGenotyping-by-sequencing (GBS) technology, which integrates restriction enzyme digestion with high-throughput sequencing, enables the efficient acquisition of hundreds of thousands to millions of genome-wide single nucleotide polymorphisms (SNPs) [6\u0026ndash;7]. Characterized by its high throughput, cost-effectiveness, and abundant information yield, GBS has become a cornerstone tool in population genetics and evolutionary research [8]. As a next-generation molecular marker, SNPs are distinguished by their high polymorphism, widespread genomic distribution, and co-dominant inheritance, offering unique value for dissecting genetic architecture and unraveling species evolutionary mechanisms at a genome-wide scale [9\u0026ndash;10]. In recent years, GBS has been successfully applied in genetic studies across diverse species. For instance, in a population genomic analysis of \u003cem\u003eAnemone shikokiana\u003c/em\u003e, 52,231 high-quality SNPs were generated via GBS, revealing two distinct genetic lineages with low intra-population diversity, where geographical isolation has driven significant genetic differentiation and limited gene flow [11]. Similarly, a GBS-based investigation of \u003cem\u003eTsoongiodendron odorum\u003c/em\u003e uncovered generally low genetic diversity within the species, with genetic variation primarily residing within populations, minimal differentiation among populations, and relatively high levels of gene flow [12]. Concurrently, GBS technology has demonstrated significant application potential in medicinal plant research. For example, a study employing GBS on 13 wild populations of \u003cem\u003eArdisia violacea\u003c/em\u003e, based on 246,000 SNP loci, systematically revealed a pattern of low overall genetic diversity coupled with significant inter-population differentiation [13]. In research on different chemotypes of \u003cem\u003eArtemisia annua\u003c/em\u003e, GBS not only systematically elucidated the population genetic background but also, through genome-wide association analysis, identified genetic variants closely linked to the artemisinin biosynthetic pathway [14]. Collectively, these studies demonstrate that GBS, combined with SNP markers, provides an efficient and economical means of acquiring genome-wide genetic information. This approach offers robust support for elucidating species genetic structure, evolutionary history, and adaptive mechanisms, thereby laying a scientific foundation for the conservation of genetic resources in medicinal plants, the selection of elite germplasm, and the formulation of breeding strategies [15].\u003c/p\u003e \u003cp\u003e \u003cem\u003eGentiana rhodantha\u003c/em\u003e Franch. (Family Gentianaceae) is a perennial herbaceous species widely utilized as a traditional ethnic medicine among the Miao and Tujia communities in Southwest China. This species is officially documented in both the Chinese Pharmacopoeia (2025 edition) and the Quality Standards for Chinese Medicinal Materials and Ethnic Medicines of Guizhou Province (2003 edition), and is commonly known as \u0026ldquo;Xuelimei\u0026rdquo; or \u0026ldquo;Xuedanlong\u0026rdquo; [16]. The dried whole plant or root is used for medicinal purposes. In traditional Chinese medicine, it is characterized as having a cold property and a bitter taste, and is recognized for its effects in clearing heat, eliminating dampness, detoxifying, purging fire, alleviating coughing, and cooling the blood. Clinically, it is frequently employed in the treatment of conditions such as cough and dyspnea caused by lung heat, pneumonia, jaundice, dysentery, and sores or boils. Furthermore, it serves as a critical raw material in several Chinese patent medicines, including \u0026ldquo;Feilike Mixture\u0026rdquo; and \u0026ldquo;Xiao'er Xiaoji Zhike\u0026rdquo; Oral Liquid [17\u0026ndash;18]. Phytochemical investigations have revealed that \u003cem\u003eG. rhodantha\u003c/em\u003e primarily contains iridoids, flavonoids, and phenolic acids, which contribute to its diverse pharmacological properties, including anti-inflammatory, antioxidant, and antibacterial activities [19]. The species is predominantly distributed across the provinces of Guizhou, Yunnan, Sichuan, and Hunan in China, typically inhabiting slopes, shrublands, sparse forests, and rocky crevices at altitudes ranging from 300 to 2500 m [20]. However, due to multiple stressors, including habitat fragmentation, over-exploitation, and climate change, the distribution range of its wild populations is progressively contracting, and population sizes are continuously declining, leading to an increasingly precarious survival status. Consequently, a systematic assessment of its genetic diversity and population structure is of substantial theoretical and practical significance for formulating science-based conservation strategies and ensuring the sustainable utilization of this medicinal resource.\u003c/p\u003e \u003cp\u003eRecent research on \u003cem\u003eG. rhodantha\u003c/em\u003e has primarily focused on quality evaluation, analytical profiling, and the assessment of medicinal material characteristics across different production areas [21\u0026ndash;23]. These investigations have yielded substantial knowledge regarding its growth characteristics and pharmacologically active constituents, thereby laying a foundation for its scientific cultivation, efficient extraction, and rational application. Relevant studies have demonstrated that the contents of bioactive compounds in \u003cem\u003eG. rhodantha\u003c/em\u003e vary significantly among different habitats and geographical origins [24], suggesting differentiation in medicinal components across populations. However, the mechanisms underlying this differentiation remain unclear. To address this knowledge gap, the present study employed GBS and high-performance liquid chromatography (HPLC) to investigate 149 \u003cem\u003eG. rhodantha\u003c/em\u003e samples collected from 10 populations across its main distribution areas in the four Chinese provinces of Hunan, Sichuan, Yunnan, and Guizhou. Through integrated analysis of population genetic diversity and bioactive compound content, we elucidated the association patterns between genetic variation and the accumulation of active constituents in \u003cem\u003eG. rhodantha\u003c/em\u003e. These findings provide a critical scientific basis for germplasm resource evaluation, superior provenance selection, and the standardized cultivation and utilization of this medicinal species.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eExperimental samples\u003c/h2\u003e\n \u003cp\u003eA total of 149 wild \u003cem\u003eG. rhodantha\u003c/em\u003e individuals were sampled from its primary distribution areas, encompassing one population in Hunan Province and three populations each in Yunnan Province, Guizhou Province, and southwestern Sichuan Province. Fifteen individuals per population (14 individuals for the MB population). All samples were identified by Professor Zhikun Wu from Guizhou University of Traditional Chinese Medicine as \u003cem\u003eGentiana rhodantha\u003c/em\u003e (family Gentianaceae), and all voucher specimens are deposited in the Herbarium of Guizhou University of Traditional Chinese Medicine (GZTM, Cheng-gang Hu, [email protected]). This sampling scheme was designed to systematically investigate the genetic diversity of the species across its representative geographical range. For the analysis of bioactive compound contents, eight populations were selected; the MB and QL populations were excluded from this component due to limited sample sizes available from field collections. All samples were preserved using silica gel desiccation at low temperatures to ensure the integrity of subsequent experimental analyses. Detailed information regarding the sampling locations and population characteristics is provided in Table\u0026nbsp;1 and illustrated in Fig.\u0026nbsp;1.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGeographical distribution of the 10 populations of \u003cem\u003eGentiana rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLongitude (E)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatitude (N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAltitude (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHabitat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of voucher specimens\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuayuan County, Hunan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e109.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoist cliff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXundian County, Yunnan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e103.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHillside shrubland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePuge County, Sichuan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e102.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSparse pine forest, grassy slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenhuai City, Guizhou Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e106.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoadside grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXiangyun County, Yunnan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e100.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrassy slope, cliff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuishui County, Guizhou Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e106.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSparse pine forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHezhang County, Guizhou Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e104.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHillside shrubland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhenyuan County, Yunnan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e100.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrassland on soil embankment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMabian County, Sichuan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e103.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge of sparse bamboo forest, moist clif\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eG. rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQionglai City, Sichuan Province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e103.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdge of sparse forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLL-2024-019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eGBS library construction and sequencing\u003c/h3\u003e\n\u003cp\u003eLibrary construction, sequencing, and data analysis for all samples were performed on the BGI platform at Shanghai Majorbio Bio-pharm Technology Co., Ltd., utilizing the DNBSEQ-T7 sequencing system with the PE150 sequencing mode. The GBS library construction protocol commenced with an in silico evaluation and validation of the \u003cem\u003eG. rhodantha\u003c/em\u003e reference genome to identify an optimal restriction enzyme combination, primarily employing MseI and TaqaI. This selection aimed to achieve appropriate fragment size ranges, an optimal number of tags, and sufficient reference genome coverage. Subsequently, genomic DNA (0.1–1 µg) was digested with the restriction enzyme MseI. Following digestion, Solexa P1 and P2 adapters, each incorporating a 6-bp barcode sequence, were ligated to the fragment ends. A secondary digestion using TaqaI was then performed to modulate the number of tags. The final GBS library was generated through PCR amplification, fragment size selection, and purification using AMPure XP beads, yielding high-quality libraries suitable for subsequent sequencing analysis.\u003c/p\u003e\n\u003ch3\u003eBioinformatic analysis methods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eQuality control of raw sequencing data\u003c/h2\u003e\n \u003cp\u003eTo ensure high data quality for downstream analyses, rigorous quality control of the raw sequencing reads was performed using Fastp [25]. The quality control pipeline primarily involved the following steps: adapter sequences were first removed from the reads, and sequences containing non-AGCT bases at the 5' end were discarded. Subsequently, read ends with sequencing quality values below Q20 were trimmed, and reads containing up to 10 ambiguous 'N' bases were filtered out. Finally, after the removal of adapters and low-quality bases, short reads with a length of less than 25 bp were discarded, yielding high-quality sequencing data for subsequent analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eVariant discovery and molecular marker development\u003c/h3\u003e\n\u003cp\u003eFollowing quality control of the raw sequencing data, the cleaned reads were assigned to individual samples using the axe R package. Sequencing data from each individual were subsequently pooled at the population level, and consensus sequences were generated using Stacks [26]. Variants were concurrently detected across all samples utilizing the populations module of Stacks. Single nucleotide polymorphisms (SNPs) with a call rate exceeding 70% and a minimum depth of coverage greater than\u0026gt; 2X were retained for subsequent population genetic analyses.\u003c/p\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003ePhylogenetic analysis\u003c/h2\u003e\n \u003cp\u003eFollowing the identification of high-quality SNP markers, a maximum likelihood (ML) phylogenetic tree was constructed using IQ-TREE2 under the GTR + I+G4 substitution model with 1,000 bootstrap replicates [27]. For comparative purposes, a neighbor-joining (NJ) tree was also constructed using FastTree with the -gtr -gamma parameters and 1,000 bootstrap replicates [28].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePopulation structure analysis\u003c/h3\u003e\n\u003cp\u003ePopulation genetic structure was assessed using ADMIXTURE [29]. Clustering analysis was performed on all samples assuming a range of genetic clusters from 1 to 20. The optimal number of clusters was determined by identifying the K value corresponding to the lowest cross-validation (CV) error.\u003c/p\u003e\n\u003ch3\u003ePrincipal component analysis (PCA)\u003c/h3\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was conducted using Plink to perform mathematical statistical analysis on the sequencing data [30]. This linear transformation reduced the dimensionality of the multivariate dataset, extracting the primary characteristic components to elucidate genetic relationships and distances among samples, thereby providing a foundation for subsequent evolutionary analyses.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eGenetic diversity analysis\u003c/h2\u003e\n \u003cp\u003eGenetic diversity within each population was quantified using the populations module of Stacks [26]. A comprehensive suite of key population genetic parameters was calculated to thoroughly characterize the level of genetic diversity within each group, offering robust data support for subsequent investigations into evolutionary relationships and population differentiation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eDetermination of bioactive compound contents\u003c/h2\u003e\n \u003cp\u003eTo investigate the variation in accumulation patterns of pharmacologically active constituents among different geographical populations of \u003cem\u003eG. rhodantha\u003c/em\u003e, the contents of six major bioactive compounds-loganic acid, neomangiferin, swertiamarin, sweroside, mangiferin, and isoorientin-were quantified using HPLC in eight populations (HY, XD, PG, RH, XY, HS, HZ, and ZY). The MB and QL populations were excluded from this analysis due to limited sample availability. Sample preparation involved accurately weighing 0.5 g of powdered material, followed by ultrasonic extraction with 15 mL of 70% methanol for 30 min. The resulting extract was filtered, and the subsequent filtrate was collected for analysis. A mixed standard solution containing the six reference compounds was prepared for calibration. Chromatographic separation was achieved using an Agilent TC-C18 column (250 mm × 4.6 mm, 5 um) maintained at 28℃, with detection at 242 nm. The mobile phase consisted of acetonitrile and 0.03% phosphoric acid applied in a gradient elution at 0.8 mL/min. The injection volume was 8 uL, with a total run time of 52 min. Under these conditions, all target compounds exhibited well-resolved peak shapes with retention times consistent with those of the corresponding reference standards, confirming the suitability of this method for quantitative analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eIntegrative analysis of genetic and chemical data\u003c/h2\u003e\n \u003cp\u003eTo elucidate the intrinsic relationship between the population genetic structure and the accumulation of bioactive constituents in \u003cem\u003eG. rhodantha\u003c/em\u003e, an integrative analysis was performed combining genetic data with quantitative chemical profiles. Initially, genetic clustering characteristics of the different populations were delineated based on principal component analysis (PCA), phylogenetic reconstruction, and population structure analysis. This facilitated an exploration of the correspondence between genetic relatedness and chemotypic similarity among populations. Subsequently, correlation analyses between genetic diversity parameters and bioactive compound contents were conducted to assess the potential influence of genetic variation on the accumulation of secondary metabolites. Finally, utilizing the pairwise genetic differentiation index (FST) matrix, the relationship between the degree of genetic differentiation and disparities in bioactive compound contents was quantitatively evaluated. This analysis, integrated with the observed patterns of chemotypic similarity among population pairs exhibiting varying levels of genetic differentiation, revealed the regulatory role of genetic isolation in shaping chemotypic divergence.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eCorrelation analysis between geographical factors and active ingredient contents\u003c/h2\u003e\n \u003cp\u003eTo investigate the influence of geographical factors on the accumulation of bioactive components in \u003cem\u003eG. rhodantha\u003c/em\u003e, correlation analysis was performed between geographical variables (longitude, latitude, and altitude) and the contents of six major active ingredients (loganic acid, neomangiferin, swertiamarin, sweroside, mangiferin, and isoorientin). Pearson's correlation coefficient was employed to assess linear relationships among variables, with coefficient values ranging from − 1 to 1, where positive and negative values indicate positive and negative correlations, respectively, and values closer to absolute 1 represent stronger correlations. Statistical analyses were conducted using SPSS software (version 26.0). Based on the analysis results, correlation heatmaps were generated using Origin software (version 2021) to visually illustrate the correlation patterns between geographical factors and active components, providing a reference for further elucidating the mechanisms by which environmental factors affect the quality formation of \u003cem\u003eG. rhodantha\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eSequencing data summary\u003c/h2\u003e\n \u003cp\u003eUtilizing GBS technology, sequencing libraries were successfully constructed for 149 \u003cem\u003eG. rhodantha\u003c/em\u003e individuals collected from southwestern China. Sequencing generated a total of 2,866,380,310 raw reads, with an average of 19,109,202 reads per sample. The total number of bases obtained was 401,293,243,400 bp, corresponding to an average of 267,528,828,9 bp per sample. The GC content across samples ranged from 40.33% to 42.28%, with a mean value of 40.97%. All samples exhibited Q20 scores above 98.96% and Q30 scores above 96.74%, indicating high sequencing accuracy, low base error rates, and high-quality effective data suitable for subsequent analyses (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eSNP detection and marker development\u003c/h2\u003e\n \u003cp\u003eIn this study, a total of 10,627,975 raw SNP loci were identified across the 149 \u003cem\u003eG. rhodantha\u003c/em\u003e samples. Following stringent quality control, 4,524,308 high-quality SNPs were retained for subsequent analyses. The counts of transitions and transversions ranged from 12,665 to 21,934 and 7,868 to 14,059, respectively, with transition/transversion ratios varying between 1.52 and 1.67. All ratios exceeded the empirical threshold of 1.5, confirming high sequencing data quality and accurate SNP detection, thereby providing a reliable foundation for downstream population genetic analyses. Analysis of SNP-based genetic diversity revealed that the number of heterozygous and homozygous mutations per sample ranged from 3,720 to 17,902 and 7,352 to 30,216, respectively (Supplementary Table\u0026nbsp;2). The extensive variation observed in both mutation types indicates a relatively high level of genetic diversity within \u003cem\u003eG. rhodantha\u003c/em\u003e populations. This characteristic likely reflects the combined influence of various evolutionary factors, including mutation accumulation, recombination events, and natural selection, and holds significant implications for the environmental adaptability and evolutionary potential of this species.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003ePhylogenetic and population structure analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003ePrincipal component analysis\u003c/h2\u003e\n \u003cp\u003eTo investigate the genetic structure and clustering patterns among different geographical populations of \u003cem\u003eG. rhodantha\u003c/em\u003e, PCA was performed on the 10 populations. The results revealed that the first principal component (PC1) and the second principal component (PC2) explained 20.81% and 14.92% of the total genetic variation, respectively, with a cumulative contribution of 35.73%. This indicates substantial genetic differentiation among populations from different geographical origins, exhibiting clear population stratification at the genomic level. The two-dimensional scatter plot based on principal component scores further elucidated the clustering relationships among populations. Joint analysis of PC1 and PC2 demonstrated that the 10 populations could be partitioned into three primary genetic clusters: PG, HZ, HS, XD, and HY clustered together; QL, MB, and RH formed a second cluster; and ZY and XY constituted a third cluster. Analysis of the PC1 versus PC3 scatter plot revealed that the HY population clustered independently along the PC1 axis, ZY and XY formed a distinct branch, while the remaining populations grouped into another major cluster. The PC2 versus PC3 scatter plot similarly supported the distinctiveness of the HY population, with PG, HZ, HS, and XD clustering together and the remaining populations forming another cluster (Fig.\u0026nbsp;2). Synthesizing the multi-dimensional PCA results, the HY population exhibited pronounced genetic distinctiveness and a higher degree of genetic differentiation. Conversely, close clustering relationships were observed between the ZY and XY populations, between MB and RH, and among HS and XD, suggesting closer genetic affinities among these population pairs or potential influences from similar habitat selection pressures, resulting in greater consistency in their genetic architecture (Fig.\u0026nbsp;3, Supplementary Table\u0026nbsp;3).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003ePopulation structure analysis\u003c/h2\u003e\n \u003cp\u003eTo elucidate the genetic structure and determine the optimal number of genetic groups within the \u003cem\u003eG. rhodantha\u003c/em\u003e germplasm, population genetic structure was analyzed for the 149 individuals using ADMIXTURE. The optimal number of genetic clusters was identified by examining the trend of CV error rates across varying numbers of putative subpopulations (K values). The results demonstrated a continuous decrease in CV error with increasing K, reaching its minimum value of 0.28341 at K = 10. This indicates that partitioning the 149 \u003cem\u003eG. rhodantha\u003c/em\u003e accessions into 10 genetic subpopulations represents the optimal grouping, suggesting that all individuals can be traced to 10 distinct ancestral gene pools (Fig.\u0026nbsp;4).\u003c/p\u003e\n \u003cp\u003eThe population structure plot based on K = 10 (Fig.\u0026nbsp;5) further elucidated the ancestry components characterizing each subpopulation. Across the 10 genetic subpopulations, the vast majority of individuals exhibited a highly homogeneous ancestral genetic background, with the proportion of their corresponding ancestral component exceeding 0.999. This suggests that these accessions possess relatively independent genetic origins without significant introgression or admixture. A minority of individuals, however, displayed pronounced mixed genetic backgrounds, harboring genetic information from two to five different ancestral populations, with their maximum ancestral component proportions all falling below 0.999. Individuals with mixed ancestral backgrounds were primarily distributed across three populations: RH, HZ, and MB, specifically including RH3, RH6, RH15, HZ1, HZ3, HZ7, HZ8, HZ14, MB5, MB6, and MB11. Notably, the HZ population contained the highest number and proportion of individuals with mixed genetic backgrounds, suggesting that this population may have experienced more frequent or complex historical inter-population gene flow events or admixture processes.\u003c/p\u003e\n \u003cp\u003eFurther analysis of the patterns of genetic introgression among different populations revealed significant shared genetic components between the RH and QL populations. The HZ population exhibited weak gene flow signals with the RH, HS, XD, and PG populations. Additionally, a subtle signal of genetic admixture was detected between the MB and QL populations. These patterns of introgression indicate close genetic affinities or historical gene flow events among the relevant populations, a finding that is highly consistent with the population clustering relationships revealed by the principal component analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003ePhylogenetic analysis\u003c/h2\u003e\n \u003cp\u003eTo further elucidate the genetic evolutionary relationships among \u003cem\u003eG. rhodantha\u003c/em\u003e germplasm resources, a phylogenetic tree was constructed for the 149 accessions using the NJ method based on genome-wide SNP data. The phylogenetic analysis revealed that the 149 \u003cem\u003eG. rhodantha\u003c/em\u003e samples could be clustered into three primary genetic lineages (Fig.\u0026nbsp;6): Lineage I comprised populations HS, XD, HZ, and HY; Lineage II consisted of populations XY, ZY, and PG; and Lineage III encompassed populations QL, MB, and RH. This clustering pattern is highly congruent with the genetic differentiation patterns revealed by principal component analysis and population structure analysis. The consistency among these three analytical approaches collectively elucidates a clear geographical genetic structure within \u003cem\u003eG. rhodantha\u003c/em\u003e populations. These findings further substantiate the existence of significant genetic differentiation among different geographical populations, providing a robust phylogenetic foundation for subsequent germplasm resource evaluation and the formulation of conservation strategies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003eLinkage disequilibrium analysis\u003c/h2\u003e\n \u003cp\u003eTo evaluate the linkage disequilibrium (LD) characteristics and infer the genetic diversity levels among different geographical populations of \u003cem\u003eG. rhodantha\u003c/em\u003e, the relationship between the average linkage disequilibrium coefficient (r²) and physical distance (kb) among markers was calculated using 168,057 high-quality SNPs. This analysis generated LD decay curves for each population, effectively reflecting the trend of LD attenuation with increasing genetic distance. The rate of this decay serves as an indirect indicator of a population's effective size, historical recombination events, and potential selective pressures. The results revealed substantial variation in LD levels among the 10 \u003cem\u003eG. rhodantha\u003c/em\u003e populations. Ranked by decreasing LD magnitude (i.e., from highest to lowest r² values), the populations were ordered as follows: QL \u0026gt; XY \u0026gt; ZY \u0026gt; HZ \u0026gt; HY \u0026gt; MB \u0026gt; RH \u0026gt; HS \u0026gt; PG \u0026gt; XD. The difference in LD values between the populations exhibiting the highest and lowest levels exceeded 18-fold. A negative correlation was observed between LD coefficients and population genetic diversity; that is, higher degrees of linkage corresponded to lower genetic diversity. Based on this relationship, the genetic diversity levels of the 10 populations were inferred to be inversely ordered relative to LD magnitude: QL \u0026lt; XY \u0026lt; ZY \u0026lt; HZ \u0026lt; HY \u0026lt; MB \u0026lt; RH \u0026lt; HS \u0026lt; PG \u0026lt; XD.\u003c/p\u003e\n \u003cp\u003eFurther analysis of the LD decay rate, quantified as the physical distance at which r² decays to half its maximum value, revealed that populations PG, HS, and XD exhibited similar decay rates, with half-decay distances ranging from 12.640 to 13.183 kb. This rapid decay suggests relatively high genetic diversity within these populations. Populations MB and RH also displayed comparable decay rates, with half-decay distances between 20.768 and 22.921 kb, indicating intermediate levels of genetic diversity. In contrast, populations QL and XY exhibited the slowest LD decay, with half-decay distances substantially exceeding those of the other populations. This pattern suggests relatively low genetic diversity, restricted intra-population recombination, smaller effective population sizes, or potential exposure to strong natural selection pressures in these two populations (Fig.\u0026nbsp;7, Table\u0026nbsp;2).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLD-decay values for each population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epopID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ehalf.decay(kb)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edecay01(kb)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e13.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e47.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e31.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e113.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e42.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e152.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e22.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e82.853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e46.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e228.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e826.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e75.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e12.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e45.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e106.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e383.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e57.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e208.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic diversity and population differentiation analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation genetic diversity analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo comprehensively characterize the level of genetic diversity within \u003cem\u003eG. rhodantha\u003c/em\u003e germplasm resources, population genetic parameters were systematically evaluated for the 10 geographical populations based on genome-wide SNP markers. The results revealed generally low genetic diversity at the population level for \u003cem\u003eG. rhodantha\u003c/em\u003e (Table\u0026nbsp;3). Specifically, observed heterozygosity (Ho) ranged from 0.036 to 0.109, with a mean value of 0.075, while expected heterozygosity (He) ranged from 0.025 to 0.153, with a mean of 0.097. With the exception of the QL and ZY populations, where Ho slightly exceeded He suggesting a minor excess of heterozygotes, the remaining eight populations exhibited Ho values lower than He, indicating varying degrees of heterozygote deficiency. This trend was further corroborated by the inbreeding coefficient (FIS), which was positive across all 10 populations, ranging from 0.020 to 0.162 with a mean of 0.075. This positive FIS values suggest a trend toward relative excess of homozygotes in most populations, implying potential inbreeding or a Wahlund effect (i.e., the presence of substructure within populations).\u003c/p\u003e\n \u003cp\u003eNucleotide diversity (π), a core indicator reflecting the richness of genetic variation within a population, is closely associated with effective population size and selective pressures. In this study, π values varied significantly among populations, ranging from 0.026 to 0.160, with an overall mean of 0.102 (Table\u0026nbsp;3). Notably, populations HZ, PG, XD, and MB, located in the southwestern region, exhibited relatively higher π values (0.129–0.160), suggesting that these populations may have retained richer genetic variation and could represent a genetic diversity center for \u003cem\u003eG. rhodantha\u003c/em\u003e. In stark contrast, populations QL, ZY, HY, and XY displayed markedly lower π values (0.026–0.070). Combined with field habitat surveys, these low-diversity populations are generally distributed across small, fragmented shrub-grassland areas. Limited habitat area may lead to smaller effective population sizes, thereby exacerbating genetic drift and inbreeding risks, ultimately constraining the long-term preservation and accumulation of genetic variation.\u003c/p\u003e\n \u003cp\u003eAdditional auxiliary genetic parameters further support the conclusion of limited intra-population genetic variation in \u003cem\u003eG. rhodantha\u003c/em\u003e. Polymorphism information content (PIC) analysis revealed that PIC values across all populations ranged from 0.020 to 0.123, with a mean of 0.078. All values fell below the 0.25 threshold, classifying them as low polymorphism levels according to common evaluation standards [31]. Furthermore, minor allele frequency (MAF) and Shannon's information index (I) were also at low levels, with mean values of 0.072 and 0.147, respectively. These multiple genetic parameters mutually corroborate each other, collectively revealing the current state of impoverished genetic variation within \u003cem\u003eG. rhodantha\u003c/em\u003e populations and providing an important theoretical basis for formulating germplasm conservation strategies for this species.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation differentiation analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe FST serves as a critical metric for quantifying genetic divergence among populations, with its value directly reflecting the intensity of geographical isolation and the extent of restricted gene flow. According to established criteria, FST \u0026lt; 0.05 indicates negligible differentiation, 0.05 ≤ FST ≤ 0.15 represents moderate differentiation, 0.15 \u0026lt; FST ≤ 0.25 denotes high differentiation, and FST \u0026gt; 0.25 signifies extremely high genetic differentiation, implying pronounced reproductive isolation or geographical barriers [32]. Analysis of genetic differentiation among the 10 geographical populations of \u003cem\u003eG. rhodantha\u003c/em\u003e in this study (Table\u0026nbsp;4, Fig.\u0026nbsp;8) revealed pairwise FST values ranging from 0.1392 to 0.7864, with a mean of 0.3789. The vast majority of pairwise FST values substantially exceeded 0.25, indicating that high levels of genetic differentiation have developed among populations, characterized by significant genetic structure divergence and geographical isolation patterns, with severely restricted inter-population gene flow.\u003c/p\u003e\n \u003cp\u003eFurther analysis revealed considerable heterogeneity in the degree of genetic differentiation among different population pairs. The lowest FST value was observed between the XD and HS populations (0.1392), followed by the PG and HZ pair (0.1436). Although these values fall within the moderate differentiation range, they nonetheless suggest that these populations may share relatively recent common ancestors or have experienced more frequent historical gene flow compared to other population pairs, thereby slowing the progression of genetic differentiation. In stark contrast, the FST value between the ZY and QL populations reached 0.7863, the highest among all population pairs. These two populations are geographically distant, and this pronounced spatial isolation severely restricts pollen and seed dispersal, leading to near-complete cessation of gene flow. Under such circumstances, genetic drift becomes the predominant evolutionary force, accumulating extremely high levels of genetic differentiation [33]. These results further corroborate that geographical isolation is a key driver of population genetic differentiation in \u003cem\u003eG. rhodantha.\u003c/em\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGenetic diversity statistics of \u003cem\u003eGentiana rhodantha\u003c/em\u003e across 10 populations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePop ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHe\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eπ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e14.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePairwise FST values between different populations of \u003cem\u003eGentiana rhodantha\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePOP2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eXD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eXY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZY\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis of bioactive compound contents in different\u003c/strong\u003e \u003cstrong\u003eG. rhodantha\u003c/strong\u003e \u003cstrong\u003epopulations\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo further investigate the variation in the accumulation of medicinal bioactive constituents among different geographical populations of \u003cem\u003eG. rhodantha\u003c/em\u003e, the contents of six major active compounds—loganic acid, neomangiferin, swertiamarin, sweroside, mangiferin, and isoorientin—were quantified in eight populations using HPLC. The results are presented in Table\u0026nbsp;5 and Fig.\u0026nbsp;9.\u003c/p\u003e\n \u003cp\u003eMangiferin, the predominant bioactive constituent of \u003cem\u003eG. rhodantha\u003c/em\u003e and the indicator component specified by the 2025 edition of the Chinese Pharmacopoeia, exhibited the most pronounced variation among populations. The XD population displayed the highest mangiferin content at 122.8439 mg/g, significantly exceeding all other populations. This was followed by the XY population (78.3677 mg/g), PG (69.0507 mg/g), HS (59.4093 mg/g), HY (55.4126 mg/g), ZY (47.2276 mg/g), and RH (46.8388 mg/g), with the HZ population exhibiting the lowest content at 37.8322 mg/g. Across the eight populations, mangiferin content ranged from 37.8322 to 122.8439 mg/g, with a mean value of 64.6229 mg/g and a 3.25-fold variation, indicating substantial chemotypic differentiation among different geographical populations of \u003cem\u003eG. rhodantha\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eThe remaining five compounds also demonstrated considerable inter-population variation. Swertiamarin content was highest in the ZY population (1.7057 mg/g) and lowest in the PG population (0.0278 mg/g), representing a 61-fold difference, with a mean value of 0.5262 mg/g. Neomangiferin content peaked in the XD population (1.1604 mg/g) and reached its minimum in the HY population (0.2522 mg/g), averaging 0.7717 mg/g. Isoorientin content was highest in the XY population (1.4998 mg/g) and lowest in the RH population (0.4008 mg/g), with a mean of 0.8704 mg/g. Loganic acid content ranged from a high of 1.0464 mg/g in the HY population to a low of 0.1251 mg/g in the HS population, averaging 0.3583 mg/g. Sweroside content varied from 0.4645 mg/g in the ZY population to 0.1068 mg/g in the HZ population, with a mean of 0.2259 mg/g. This pronounced chemotypic differentiation indicates that the accumulation patterns of secondary metabolites in \u003cem\u003eG. rhodantha\u003c/em\u003e are shaped by the combined influence of genetic background and habitat conditions. The substantial differences in secondary metabolite accumulation patterns among different geographical populations establish a critical foundation for subsequent investigations into the mechanisms associating genetic background with bioactive constituent accumulation.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDetermination results of six compound\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eloganic acid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eneomangiferin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eswertiamarin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esweroside\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emangiferin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eisoorientin\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.0464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.2522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.4251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e55.4126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.6740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.1604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.4173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.2458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e122.8439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.6275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e69.0507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.2195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.6038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.5254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e46.8388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.4008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.2920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.9320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e78.3677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.4998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.0912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e59.4093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.0735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.3942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.0408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37.8322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.6766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.1780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.8146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.7057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.4645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e47.2276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.6995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003eIntegrative analysis of genetic differentiation and bioactive compound accumulation\u003c/h2\u003e\n \u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003eCorrespondence between genetic clustering and chemotypes\u003c/h2\u003e\n \u003cp\u003eIntegrative comparison of bioactive compound contents with population genetic analysis results revealed a clear correspondence between genetic clustering and chemotypes. Based on principal component analysis and phylogenetic reconstruction, the 10 \u003cem\u003eG. rhodantha\u003c/em\u003e populations were partitioned into three major genetic lineages. The first genetic lineage comprised populations HS, XD, HZ, and HY. Within this lineage, populations XD and HS exhibited close genetic clustering, and both demonstrated relatively high mangiferin contents of 122.8439 mg/g and 59.4093 mg/g, respectively, significantly exceeding those of most other populations. Furthermore, both populations displayed neomangiferin contents exceeding 1.0 mg/g (XD: 1.1604 mg/g; HS: 1.0912 mg/g), reflecting similar chemotypic profiles. The second genetic lineage consisted of populations XY, ZY, and PG. Within this lineage, populations XY and ZY clustered closely in principal component analysis, and both exhibited relatively high swertiamarin contents (XY: 1.2016 mg/g; ZY: 1.7057 mg/g), significantly surpassing those of other populations. The third genetic lineage encompassed populations QL, MB, and RH. Within this lineage, population RH showed evidence of weak gene flow with MB and QL, and its mangiferin content (46.8388 mg/g) fell within the moderate-to-low range. These findings demonstrate a strong correspondence between genetic relatedness and chemotypic similarity, indicating that the accumulation of secondary metabolites is significantly influenced by genetic background. This further corroborates the regulatory role of genetic background in shaping secondary metabolite accumulation patterns.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003eInfluence of genetic diversity levels on bioactive compound accumulation\u003c/h2\u003e\n \u003cp\u003eGenetic diversity analysis revealed that populations HZ, PG, and XD exhibited the highest π values among all populations, with values of 0.160, 0.155, and 0.130, respectively. In contrast, population ZY displayed a substantially lower π value of 0.036. Correspondingly, bioactive compound analysis demonstrated that population XD possessed a remarkably high mangiferin content of 122.8439 mg/g, and population PG exhibited a relatively high mangiferin content of 69.0507 mg/g, both ranking among the higher levels. Conversely, population ZY showed a mangiferin content of only 47.2276 mg/g. These findings are mutually corroborated by the linkage disequilibrium (LD) analysis results: populations XD and PG exhibited rapid LD decay, with LD declining to half its maximum value within 12.640 kb and 12.829 kb, respectively, indicating larger effective population sizes and active recombination that facilitate the retention of genetic variation and adaptive evolution of genes involved in secondary metabolism. In contrast, population ZY displayed slow LD decay, suggesting a smaller effective population size and stronger genetic drift effects that constrain both genetic diversity and the accumulation of bioactive constituents. These results collectively indicate a positive correlation between genetic diversity levels and bioactive compound contents.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\"\u003e\n \u003ch2\u003eRegulatory role of genetic differentiation in chemotypic divergence\u003c/h2\u003e\n \u003cp\u003eTo investigate the quantitative relationship between the degree of genetic differentiation among \u003cem\u003eG. rhodantha\u003c/em\u003e populations and the accumulation patterns of their medicinal bioactive constituents, the contents of six major active compounds across eight geographical populations were analyzed in conjunction with pairwise FST values. The results revealed a distinct and consistent association between the extent of inter-population genetic differentiation and disparities in bioactive compound profiles (Table\u0026nbsp;5). Highly differentiated population pairs generally exhibited pronounced chemotypic divergence. For instance, the ZY and HY populations, characterized by a high FST value of 0.6542, displayed significant differences in the contents of loganic acid, neomangiferin, and swertiamarin. Similarly, the XY and HY populations, with an FST of 0.5764, showed substantial variation in the levels of loganic acid, swertiamarin, sweroside, and isoorientin. In contrast, population pairs characterized by low genetic differentiation tended to exhibit high similarity across multiple bioactive constituents. The XD and HS populations, which showed the lowest FST value (0.1392), demonstrated highly consistent contents of neomangiferin, isoorientin, swertiamarin, and sweroside. Likewise, the PG and HZ populations (FST = 0.1436) exhibited comparable levels of swertiamarin, sweroside, and isoorientin. In summary, the degree of genetic differentiation among \u003cem\u003eG. rhodantha\u003c/em\u003e populations corresponds closely with the divergence in their medicinal bioactive constituent profiles: genetically proximate populations tend to share similar chemical fingerprints, whereas significantly differentiated populations tend to develop distinct chemotypes. This finding reveals the underlying regulatory influence of genetic variation on the accumulation of bioactive constituents and provides an important foundation for predicting chemotypic characteristics and facilitating targeted selection of elite germplasm sources.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\"\u003e\n \u003ch2\u003eCorrelation analysis between geographical factors and active ingredient contents\u003c/h2\u003e\n \u003cp\u003eTo investigate the influence of geographical factors on the accumulation of bioactive components in \u003cem\u003eG. rhodantha\u003c/em\u003e, Pearson's correlation coefficient matrix was employed to analyze the relationships between sampling site geographical factors (longitude, latitude, and altitude) and the contents of six major active ingredients (Fig.\u0026nbsp;10). The results revealed significant spatial associations among geographical factors, with a strong positive correlation observed between longitude and latitude, indicating a certain spatial regularity in the distribution of sampling sites. Altitude exhibited moderate negative correlations with both longitude and latitude, suggesting that high-altitude sampling sites within the study area tended to be located in regions with lower longitude and latitude.\u003c/p\u003e\n \u003cp\u003eCorrelation analysis between geographical factors and active ingredients showed that most compound contents exhibited negative correlations with longitude, latitude, and altitude; however, the influence of geographical factors on the accumulation of active ingredients displayed clear component specificity. Specifically, neomangiferin and isoorientin showed moderate negative correlations with longitude and latitude but strong positive correlations with altitude. In contrast, loganic acid presented moderate positive correlations with longitude and latitude and a weak negative correlation with altitude. Swertiamarin exhibited a strong negative correlation with longitude, a significant negative correlation with latitude, and a weak correlation with altitude. Mangiferin showed weak negative correlations with longitude and latitude but a strong positive correlation with altitude. By comparison, sweroside displayed weak negative correlations with latitude and altitude, and no correlation with longitude.\u003c/p\u003e\n \u003cp\u003eAmong the active ingredients, positive correlations predominated. Neomangiferin exhibited strong positive correlations with mangiferin and isoorientin, and a weak positive correlation with swertiamarin. Swertiamarin showed a moderate positive correlation with sweroside and a weak positive correlation with isoorientin. Mangiferin was moderately positively correlated with isoorientin, while sweroside demonstrated a moderate positive correlation with loganic acid. These patterns suggest that these compounds may share co-accumulation characteristics or be regulated by similar metabolic pathways within the plant. Meanwhile, certain negative correlations were also observed among components, with the strongest negative correlation occurring between loganic acid and neomangiferin, followed by loganic acid with swertiamarin, whereas loganic acid showed only a weak negative correlation with mangiferin and isoorientin. Sweroside also displayed weak negative correlations with neomangiferin and isoorientin. These positive and negative correlation patterns reflect potential synergistic and competitive relationships among different active ingredients in biosynthetic pathways, providing important clues for elucidating the accumulation mechanisms of secondary metabolites in \u003cem\u003eG. rhodantha\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eIn summary, geographical factors exert a significant negative influence on the accumulation of multiple active ingredients in \u003cem\u003eG. rhodantha\u003c/em\u003e, while the positive and negative correlations among active ingredients reflect their co-accumulation characteristics and underlying metabolic regulatory relationships. These findings provide a reference for further elucidating the mechanisms by which environmental factors influence the quality formation of \u003cem\u003eG. rhodantha\u003c/em\u003e and are consistent with the conclusions regarding chemotype differentiation among different populations drawn from the population genetic analysis in this study.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003ePopulation genetic structure of\u003c/strong\u003e \u003cstrong\u003eG. rhodantha\u003c/strong\u003e \u003cstrong\u003ereveals pronounced differentiation driven by geographical isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study reveals a distinctive low intra-population and high inter-population genetic structure pattern in the Chinese endemic species \u003cem\u003eG. rhodantha\u003c/em\u003e, characterized by extremely low intra-population genetic diversity coupled with remarkably high inter-population genetic differentiation. Consistent analyses based on SNP markers demonstrated that the mean He, π, and PIC across all populations were only 0.097, 0.102, and 0.078, respectively. These values are substantially below the established threshold for low polymorphism (PIC \u0026lt; 0.25), confirming the paucity of genetic variation within populations [31]. Concurrently, the mean FST among populations reached 0.3789, with the vast majority of population pairs classified as exhibiting extremely high genetic differentiation according to established criteria [32]. Phylogenetic reconstruction and principal component analysis further partitioned the 10 populations into three primary genetic lineages, with these complementary approaches collectively revealing a pronounced and well-defined geographical genetic structure for this species.\u003c/p\u003e\n\u003cp\u003eThe formation of this pattern arises from the combined effects of the species' biological characteristics and historical evolutionary processes. First, as a typical selfing or inbreeding species, the reproductive system of \u003cem\u003eG. rhodantha\u003c/em\u003e inherently tends to maintain low intra-population genetic diversity, with the predominantly positive inbreeding coefficients (FIS ranging from 0.020 to 0.162) providing direct evidence for this. Second, and more critically, climatic fluctuations since the Quaternary period, particularly in the topographically complex southwestern region, have driven population contractions and habitat fragmentation for this species [34]. Field surveys revealed that populations exhibiting low genetic diversity (e.g., QL, ZY) are predominantly distributed in small, isolated shrub-grassland areas, where limited habitat extent severely constrains effective population sizes (Ne). Under these conditions, genetic drift rapidly becomes the predominant evolutionary force in small, isolated populations, not only accelerating the stochastic loss of genetic variation within populations but also driving profound differentiation among populations through the random fixation and loss of alleles [33]. The exceptionally high FST value of 0.7863 between the ZY and QL populations exemplifies the extreme outcome of prolonged spatial isolation coupled with genetic drift. This finding carries paramount implications for conservation strategies: conservation efforts should not focus solely on enhancing genetic diversity within individual populations but should prioritize the preservation of highly differentiated populations representing distinct evolutionary units and harboring unique allelic combinations, thereby maximizing the conservation of the species' evolutionary potential and overall genetic variation.\u003c/p\u003e\n\u003cdiv id=\"Sec29\"\u003e\n \u003ch2\u003ePopulation demographic history inferred from linkage disequilibrium decay\u003c/h2\u003e\n \u003cp\u003eLD decay analysis provides critical temporal evidence for elucidating the evolutionary demographic history of \u003cem\u003eG. rhodantha\u003c/em\u003e populations. In this study, substantial variation in LD decay rates was observed among the 10 populations: populations PG, HS, and XD exhibited the most rapid decay, with half-decay distances ranging from 12.640 to 13.183 kb, whereas populations QL and XY displayed markedly slower decay rates. LD decay rates are intimately associated with historical effective population size, recombination rates, and selective pressures: larger effective population sizes and more frequent historical recombination events promote rapid LD decay, while population bottlenecks, inbreeding, or strong selection result in delayed LD decay [35]. Based on these principles, populations such as PG, HS, and XD are inferred to have maintained relatively large historical effective population sizes and experienced frequent recombination events, thereby retaining richer genetic diversity. This inference aligns closely with the π analysis results, where populations HZ, PG, and XD exhibited the highest π values (0.130–0.160). Collectively, these findings suggest that the southwestern region, centered around populations HZ, PG, and XD, may represent a genetic diversity center or glacial refugium for \u003cem\u003eG. rhodantha\u003c/em\u003e. During the Quaternary glaciations, the complex topography and diverse microhabitats of this region likely provided relatively stable environments, enabling the preservation of a comparatively intact reservoir of genetic variation [36]. Conversely, the slow LD decay observed in populations QL and XY, combined with their extremely low π values, strongly suggests that these populations have experienced severe population bottlenecks or have been maintained at small population sizes for extended periods, resulting in significant genetic diversity depletion. This finding carries direct implications for germplasm collection strategies: populations exhibiting high genetic diversity, such as PG and XD, should be prioritized for germplasm conservation and collection to maximize the capture of the species' genetic variation.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePositive association between genetic diversity and bioactive compound accumulation and their co-differentiation pattern\u003c/h3\u003e\n\u003cp\u003eIntegrating population genetic data with secondary metabolite content analysis represents a central breakthrough of this study. The results reveal, for the first time in \u003cem\u003eG. rhodantha\u003c/em\u003e, a positive association trend between genetic diversity levels and the contents of major medicinal bioactive constituents. Populations exhibiting high genetic diversity, such as XD, demonstrated significantly higher mangiferin contents compared to low-diversity populations like ZY. Furthermore, the rapid LD decay observed in populations XD and PG facilitates the retention of genetic variation, potentially providing richer raw material for the adaptive evolution of genes involved in secondary metabolism [37]. High genetic diversity implies a greater abundance of allelic variation in genes comprising secondary metabolic pathways, increasing the likelihood of efficient bioactive compound accumulation through gene dosage effects or optimized regulatory networks. Conversely, genetically depauperate populations, such as ZY, characterized by small effective population sizes and pronounced genetic drift, may have lost advantageous alleles favorable for secondary metabolite biosynthesis. This observed positive trend suggests that future efforts to screen for high-content medicinal germplasm should prioritize populations exhibiting high genetic diversity and rapid LD decay.\u003c/p\u003e\n\u003cp\u003eParticularly noteworthy is the pronounced co-differentiation pattern observed between the degree of genetic differentiation among \u003cem\u003eG. rhodantha\u003c/em\u003e populations and their chemotypic divergence. The three major genetic lineages delineated by SNP analysis were significantly associated with distinct chemotypic profiles. Within the first lineage (comprising HS, XD, HZ, and HY), populations XD and HS not only exhibited close genetic clustering but also demonstrated highly consistent contents of mangiferin and neomangiferin. Within the second lineage (comprising XY, ZY, and PG), populations XY and ZY jointly exhibited relatively high levels of swertiamarin accumulation. This coupling of genetic relatedness with chemotypic similarity strongly suggests that the key genes governing the biosynthesis of these bioactive constituents, or their regulatory regions, harbor genetic variation paralleling that of neutral markers, a phenomenon indicative of co-evolution or genetic linkage [38]. Quantitative analysis further substantiated this observation: weakly differentiated population pairs, such as XD and HS (FST = 0.1392), exhibited comparable contents across multiple bioactive constituents, whereas highly differentiated pairs, such as ZY and HY (FST = 0.6542), displayed significant disparities in several compounds. This compellingly demonstrates that as gene flow among populations becomes interrupted and genetic differentiation accumulates, the genetic machinery governing secondary metabolism likewise diverges, ultimately giving rise to heritable, stable chemotypes. Mangiferin, serving as the indicator component, exhibited a 3.25-fold variation in content across the eight populations (ranging from 37.8322 to 122.8439 mg/g), underscoring substantial chemotypic differentiation within this species. This variation may originate from genetic polymorphisms in key genes of the xanthone biosynthetic pathway or from differential regulation by upstream transcription factors. Studies on the congeneric species \u003cem\u003eGentiana macrophylla\u003c/em\u003e provide corroborating evidence for this inference [39].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study draws the following principal conclusions: (1) \u003cem\u003eG. rhodantha\u003c/em\u003e populations in southwestern China exhibit a pronounced geographical genetic structure, characterized by extremely high levels of inter-population genetic differentiation (mean FST = 0.3789), with geographical isolation serving as the dominant factor driving this differentiation; (2) Intra-population genetic diversity is generally low (mean π = 0.102), with habitat fragmentation exacerbating genetic drift effects and inbreeding risks; (3) LD decay analysis reveals differential demographic histories among populations, suggesting that populations such as PG and XD may represent glacial refugia that have retained relatively high genetic diversity; (4) A positive trend exists between genetic diversity and the accumulation of bioactive constituents, and a pronounced co-differentiation pattern is observed between genetic differentiation and chemotypic divergence, providing direct evidence for the genetic basis underlying chemotypic differentiation. (5) Constituent-specific associations were observed between geographical factors and the accumulation of active ingredients. Neomangiferin, isoorientin, and mangiferin exhibited strong positive correlations with altitude, whereas loganic acid showed moderate positive correlations with longitude and latitude. Positive correlations predominated among the active ingredients, reflecting their co-accumulation characteristics and potential metabolic regulatory relationships.\u003c/p\u003e\n\u003cp\u003eSynthesizing these findings, highly differentiated populations (e.g., ZY and QL populations with FST = 0.7863) represent distinct evolutionary units and should be prioritized as separate conservation targets to prevent the loss of adaptive gene combinations through admixture between different genetic lineages. Populations exhibiting both high genetic diversity and exceptional bioactive compound contents (e.g., XD and PG populations) are suitable candidates for targeted selection as elite germplasm for propagation and utilization. For low-diversity populations experiencing severe habitat fragmentation (e.g., QL, XY, and ZY), priority should be given to habitat restoration and population reinforcement efforts, implementing assisted moderate gene flow to mitigate inbreeding depression risks and enhance the long-term survival potential of these populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe are grateful to editors and anonymous reviewers for their helpful comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQingqing Ye: Data curation, Software, Methodology, Formal analysis, Writing-review and editing. Lingling Wei: Investigation, Resources, Software, Data curation, Formal analysis, Writing-original draft. Weixiang Xiao: Investigation, Resources, Data curation. Xin Tan: Investigation, Resources, Data curation. Li Yang: Investigation, Resources, Supervision. Deqiang Ren: Software, Data curation, Supervision. Ning Ding: Software, Data curation,Supervision. Zhikun Wu: Conceptualization, Formal analysis, Investigation, Supervision, Writing-original draft, Writing-review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Research on Green Prevention and Control Technology for Leaf Blight of Gentiana rhodantha under Forest Based on Pathogen-Biological Control-Plant-Environment Coordinated Regulation (Qianlin Kehe [2026] Zhicheng No. 004), the Chuan-Qian Collaboration Premium Seeds and Medicinals: Joint Research and Demonstration of Systematic Introduction, Domestication, and Key Industrialization Technologies of Gentiana rhodantha Germplasm Resources (2026YFHZ0116), the National Wild Plant Germplasm Resource Center for Guizhou University of Traditional Chinese Medicine (ZWGX‒2405), and the Science and Technology Plan Project of Guizhou Province ([2022]‒4016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarchant 1JAS, Pe\u0026ntilde;uelas R. 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BMC Plant Biol. 2024;24:66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-024-04727-z\u003c/span\u003e\u003cspan address=\"10.1186/s12870-024-04727-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gentiana rhodantha, Population genomics, Genotyping-by-sequencing (GBS), Genetic differentiation, Germplasm resources, Bioactive constituents","lastPublishedDoi":"10.21203/rs.3.rs-9367916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9367916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eGentiana rhodantha\u003c/em\u003e is a commonly used ethnic medicine in Southwest China, possesses high medicinal value. Although the quality of medicinal materials from different producing areas varies considerably, no studies to date have investigated the chemotypes or genetic diversity of \u003cem\u003eG. rhodantha\u003c/em\u003e across its distribution range. In this study, we combined genotyping-by-sequencing (GBS) and high-performance liquid chromatography (HPLC) to analyze the genetic diversity and active component content of \u003cem\u003eG. rhodantha\u003c/em\u003e from ten different producing areas. The relationships of these parameters with geographical and environmental factors were also examined and discussed. A total of 4,524,308 high-quality single nucleotide polymorphism (SNP) loci were obtained. Principal component analysis and phylogenetic analysis based on these SNPs divided the ten populations into three major genetic lineages. The cross-validation error rate reached its minimum at K\u0026thinsp;=\u0026thinsp;10, suggesting the presence of ten distinct subgroups within the sampled populations. Genetic diversity within populations was low, whereas genetic differentiation among populations was significant. The contents of six active components varied widely among populations. The content of mangiferin varied by up to 3.25-fold across populations, while that of swertiamarin showed a 61-fold difference. Genetic clustering corresponded well with chemotypes. Moreover, both genetic diversity and the degree of genetic differentiation were significantly correlated with component content and the extent of variation. Active components exhibited specific associations with geographical factors (latitude, longitude, and altitude). Positive correlations predominated among active components, although negative correlations were also observed for certain component pairs.This study reveals the relationship between genetic variation and active component accumulation in \u003cem\u003eG. rhodantha\u003c/em\u003e, and clarifies the role of geographical factors in shaping chemotypic differentiation. These findings provide an important scientific basis for the conservation, evaluation, elite germplasm screening, and standardized cultivation of this medicinal plant.\u003c/p\u003e","manuscriptTitle":"Genetic Diversity and Chemotypic Variation of Gentiana rhodantha: Insights into the Genetic Basis of Active Compound Accumulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 14:46:59","doi":"10.21203/rs.3.rs-9367916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T07:13:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T12:57:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T08:11:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204773651287889603171229352973903677665","date":"2026-04-24T07:27:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253054317190825691753889872059119654251","date":"2026-04-22T08:50:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T08:29:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T08:20:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T04:51:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-19T07:03:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-04-19T06:56:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7bd1dbf6-40e5-40b5-9add-c703912a83e2","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T07:13:27+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T05:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 14:46:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9367916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9367916","identity":"rs-9367916","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}