The genetic diversity and population structure of Kudouzi (Sophora alopecuroides) population were revealed by using SNP markers combined with seed phenotypic traits

The genetic diversity and population structure of Kudouzi (Sophora alopecuroides) population were revealed by using SNP markers combined with seed phenotypic traits | 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 The genetic diversity and population structure of Kudouzi (Sophora alopecuroides) population were revealed by using SNP markers combined with seed phenotypic traits Cunkai luo, Fanyan Ma, Panxin Niu, Zhao Zhang, Weiting Chen, Ping Jiang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6061561/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Sophora alopecuroides L. is a perennial herb of Leguminosae. It is mainly distributed in northwest China and has important medicinal, feeding and ecological restoration values. However, in recent years, due to the intensification of human activities, its wild population resources have plummeted and genetic diversity has continued to decline. In order to fully reveal the genetic diversity and population structure characteristics of S. alopecuroides in the natural distribution area. In this study, Single nucleotide polymorphism (SNP) molecular markers combined with seed phenotypic traits were used to systematically study 65 wild samples of S. alopecuroides in northwest China. Results: The results showed that the coefficient of variation (CV) of 8 phenotypic traits of S. alopecuroides seeds ranged from 2.87 % to 7.94 %, and the diversity index (H) ranged from 1.639 to 1.767. There was a significant correlation between phenotypic traits (P < 0.01), indicating that the phenotypic diversity of S. alopecuroides seeds was rich. Cluster analysis based on phenotypic traits divided the S. alopecuroides population into two groups. At the same time, based on SNP molecular markers, the genetic diversity of S. alopecuroides was relatively low. The average values of expected heterozygosity (He), observed heterozygosity (Ho) and single nucleotide diversity index (Pi) were 0.22, 0.17 and 0.19, respectively. The results of molecular variance analysis (AMOVA) showed that the level of variation between individuals (132.83%) was higher than that between populations and within populations. The Pairwise population differentiation (Fst) was between 0.00 and 0.04, which confirmed that there was no obvious differentiation among the populations. Population structure analysis, principal component analysis (PCA) and phylogenetic tree analysis roughly divided all populations into two clusters, which was consistent with the phenotypic clustering results. It is worth noting that the distribution patterns of the samples in the Southern Tianshan Mountains (TSNL) and the Altai Mountains (ARTS) are more complex. In addition, redundancy analysis (RDA) showed that the cumulative interpretation rates of environmental factors on phenotypic traits and genetic diversity were 99.75 % and 67.89 %, respectively. Among them, the mean temperature of the driest quarter (MTD) and annual mean wind speed (YWS) were identified as the primary factors influencing phenotypic traits, while the precipitation of the coldest quarter (PC), isothermality (ISO), and precipitation of the wettest quarter (PWE) have an important impact on genetic diversity. Conclusions: This study provides a foundation for the genetic evaluation and conservation of S. alopecuroides genetic resources in China and offers important insights for its breeding programs. Sophora alopecuroides Single nucleotide polymorphism Genetic diversity Seed phenotypic traits Population structure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Sophora alopecuroides L. is a perennial herb of the Leguminosae family that is widely distributed across arid and semi-arid regions of West and Central Asia. In China, it is predominantly found in Xinjiang, Gansu, Ningxia, and Inner Mongolia [ 1 , 2 ]. Studies have shown that S. alopecuroides exhibits strong root development, drought resistance, salinity-alkalinity tolerance, and nitrogen fixation ability [ 1 , 3 ]. As such, it has become a crucial pioneer plant for environmental protection in northwest China, owing to its robust drought resistance, alkalinity tolerance, and wind-sand resistance. In addition to its ecological significance, S. alopecuroides possesses substantial medicinal and agricultural value. Its seeds, aerial parts, and roots contain bioactive compounds such as alkaloids, flavonoids, volatile oils, steroids, polysaccharides, and free fatty acids [ 4 – 7 ]. These compounds underpin its use in traditional medicine and potential application in biological pesticides [ 8 ], green manure [ 9 ], and high-quality forage [ 10 ]. Genetic diversity and population structure play a key role in species conservation, utilization and breeding [ 11 ]. Gao et al. [ 12 ] reported that the concentrations of key medicinal compounds, such as oxymatrine and total flavonoids, vary significantly in S. alopecuroides seeds from different geographical regions. Similarly, An et al. [ 13 ] found that the contents of genistein and sophoridine in seeds from northern Xinjiang were generally higher than those from southern Xinjiang. In addition, gene flow between different S. alopecuroides populations is limited, resulting in high genetic differentiation between populations [ 14 ]. Despite its ecological and medicinal importance, few fundamental studies on the large-scale population resources and genetic diversity of S. alopecuroides have been conducted. Seed traits are crucial for population reproduction and preservation, serving as important indicators of species inheritance [ 15 , 16 ]. Phenotypic plasticity, the ability of a single genotype to produce different phenotypes in varying environments, is largely a result of genetic selection and is closely related to phenotypic diversity [ 17 – 19 ]. Significant differences in seed traits, such as size and thousand-grain weight, among different wild populations of S. alopecuroides suggest rich ecological clusters and complex genetic backgrounds [ 20 ]. However, the correlation between seed phenotypic traits and genetic diversity in S. alopecuroides has not been thoroughly investigated. Genotyping-by-Sequencing (GBS) is a high-throughput, next-generation sequencing method that reduces genomic complexity using restriction enzymes. This technique generates high-density single nucleotide polymorphism (SNP) markers by tagging random genomic deoxyribonucleic acid (DNA) fragments common across samples with unique short DNA sequences (barcodes), pooling samples into a single sequencing run to detect and label mutations at a relatively low cost [ 21 ]. Currently, GBS serves as a powerful genotyping tool, widely applied in genetic mapping, marker-assisted selection [ 22 , 23 ], diversity analysis, germplasm and species identification [ 24 – 25 ]. Among molecular markers, SNP are key tools in population and quantitative genetics. SNP represent single-base differences among individuals of the same species and are the most abundant form of genetic variation [ 26 ]. As third-generation markers, SNP offer advantages such as biallelic nature, high density, genetic stability, easy detection, and genome-wide distribution [ 27 , 28 ]. With advances in sequencing and bioinformatics, SNP have been extensively used to study genetic diversity, population structure, and differentiation [ 29 – 33 ]. However, their application in the genetic diversity analysis of S. alopecuroides remains unreported. In this study, seeds were collected from 65 wild populations of S. alopecuroides across diverse geographic regions. Phenotypic traits were measured, and SNP markers were employed to assess the genetic structure and diversity within these populations. The main objectives of this study are as follows: (1) what is the level of genetic diversity and population structure of S. alopecuroides in these different populations? (2) What are the effects of geographical and environmental factors on the genetic diversity of S. alopecuroides? Based on the findings, this research aims to provide scientific basis for the effective conservation, management, and utilization of S. alopecuroides germplasm, as well as for the development of high-quality cultivars to meet market demands. Methods Plant Materials The field collection of S. alopecuroides samples was approved by the Chinese government and carried out in compliance with all relevant national regulations. Species identification was confirmed by Professor Zhuowen Zhang (College of Horticulture and Forestry, Huazhong Agricultural University), and voucher specimens were deposited in the Department of Forestry, College of Agriculture, Shihezi University (Accession Nos: SHAF20230801–SHAF20230865). Based on the main distribution range of S. alopecuroides in China [ 34 ] and the maturity stage of the plants, a total of 65 samples were collected between August and October 2023 from Xinjiang, Gansu, Ningxia, Inner Mongolia, and Shaanxi provinces (Fig. 1, Table 1 and Table S1 ). Sampling sites were spaced at least 20 km apart. At each site, 10–15 healthy and mature individuals were randomly selected, maintaining a minimum distance of 10 meters between sampled plants. From each individual, 80–120 mature seeds were collected from multiple pods. Seeds from all individuals within each site were pooled to form a composite sample representing the genetic diversity of that population. After field collection and natural air-drying, seeds were stored in a cool and dry environment [ 35 ]. Table 1 Geographic information of Sophora alopecuroides population Population Location Sample Number YLHG Yili, Xinjiang, China G1, G2, G3, G4, G5 5 TSBL Bortala, Xinjiang, China G6, G7, G15, G16, G17, G18, G19, G20, G21, G22 10 TCDQ Tacheng, Xinjiang, China G8, G9, G10, G11 4 ARTS Altay, Xinjiang, China G12, G13, G14 3 KLSM Bayingolin; Hotan; Kashgar, Xinjiang, China G23, G24, G28, G29, G30, G37, G38, G39, G40, G41, G42, G43, G44, G46 14 TSNL Aksu, Xinjiang, China G25, G26, G27, G31, G32, G33, G34, G35, G36, G45 10 THPD Turpan; Hami; Xinjiang, China G47, G48, G49, G50, G51 5 QLSM Gansu, China G52, G53, G54, G55 4 HTGY Ningxia; Shaanxi, China G56, G57, G58, G59, G60, G65 6 NMGY Inner Mongolia, China G61, G62, G63, G64 4 Based on their geographic origin, the 65 samples were grouped into 10 populations. Global Satellite Positioning System (GPS) coordinates for each sampling location were recorded using handheld GPS devices, and bioclimatic variables were obtained from the WorldCLIM global high resolution climate database ( http://www.worldclim.org/ ) and local meteorological stations (Table S2 and S3). A subset of seeds from each composite sample was randomly selected and sent to Beijing Nuohe Zhiyuan Bioinformatics Technology Co., Ltd. for GBS analysis. Measurement of seed phenotypic traits We used a Wanshen SC-G automatic seed testing instrument (Hangzhou Wanshen Detection Technology Co., Ltd., China) to measure the length, width, diameter, roundness, area, perimeter and shape index data of 65 samples of S. alopecuroides seeds. Each index was measured three times, 200 seeds were randomly selected for each repetition, and the mean value was taken. The thousand-grain weight was measured by a one-thousandth electronic balance, and 1,000 seeds were randomly selected each time. Three measurement replicates were set up, and the average value was taken [ 20 , 36 ]. Statistics and analysis of phenotypic traits Microsoft Excel 2016 was used to analyze the experimental data, Origin Pro 2021 software was used for plotting, SPSS 27.0 software was used to test the homogeneity of variance, and variance analysis was performed on the seed trait data. The least significant test (LSD) method was based on one-way analysis of variance (one-way ANOVA), which was performed ± the mean value of the trait. The coefficient of variation (CV) and phenotypic genetic Shannon diversity index (H) were calculated in Excel as follows: CV = (Standard deviation × Mean) × 100% (1) The observed values of each trait were divided into 10 levels according to the mean value (Xi) and standard deviation (SD). Each 0.5 SD was 1 level, from the first level (Xi < X − 2 SD) to the 10th level (Xi ≥ X + 2 SD). The relative frequency of each group was used to calculate H [ 42 ]. \(\:H=-\sum\:Pi{ln}Pi\) (2) In Eq. (2), \(\:i\) is the classification of a trait, and P \(\:i\) is the percentage of the number of materials in the total number of materials in the \(\:i\) th grade of the trait. On the basis of the mean values of the seed phenotypic traits, cluster analysis was carried out via OriginPro 2021 software. By controlling the data quality, the differences in the quantitative phenotypic traits of the 65 samples in this study were visually displayed. The cluster analysis of the phenotypic traits of 65 samples was carried out via the longest distance method of square Euclidean distance. To eliminate the influence of dimension on the clustering relationship, the seed trait data were standardized by the z value and then clustered [ 46 ]. The z value standardized calculation formula is as follows: \(\:{\text{X}}^{\text{*}}=\frac{x-\stackrel{-}{x}}{\sigma\:}\) (3) In Eq. (3), X* is the normalized value of z value, \(\:x\) is the original value, \(\:\stackrel{-}{x}\) is the sample mean, and \(\:\sigma\:\) is the sample standard deviation. DNA extraction, GBS library construction and sequencing (dup: abstract ?) The DNA of 65 S. alopecuroides seeds from different provenances was extracted via the CTAB method [ 37 ]. The integrity of the extracted DNA was detected via 1% agarose gel electrophoresis, and the concentration and quality of the extracted DNA (OD260/280 ratio) were measured via a Nanodrop UV‒visible spectrophotometer. After the quality test was performed, the extracted DNA stock solution was stored at − 20°C until use. The construction and sequencing of the GBS library begins with the digestion of genomic DNA. First, 0.1–1 µg of genomic DNA was extracted and digested with restriction endonucleases to obtain a suitable marker density. The two ends of the digested DNA fragment are connected with P1 and P2 adaptor sequences (which can be complementary to the digested DNA gap). The tag sequences containing P1 and P2 adapters at both ends were amplified by polymerase chain reaction (PCR). The amplified DNA fragments were subsequently separated and recovered via the DNA fragment mixing pool, and the DNA fragments in the expected size range were selected via agarose gel electrophoresis. After library construction was completed, a Qubit 2.0 instrument was used for preliminary quantification, and the library was diluted to 1 ng/µl. Then, an Agilent 2100 instrument was used to determine the size of the insert in the library. After the insert size was in line with expectations, the fluorescence polymerase chain reaction (q-PCR) method was used to accurately quantify the effective concentration of the library (the effective concentration of the library > 2 nM) to ensure the quality of the library. After the library inspection was performed, Illumina HiSeq PE150 sequencing was performed after pooling different libraries according to the effective concentration and the target offline data volume. DNA extraction, GBS library construction and sequencing The original image data files obtained via Illumina HiSeq sequencing were converted into sequenced reads via base-calling analysis, and the results were stored in FASTQ file format. The raw data obtained by sequencing were filtered to filter out the reads containing the adaptor sequence; when the N content in the single-ended sequencing read exceeded 10% of the length ratio of the read, the paired reads needed to be removed. When the number of low-quality ( < = 5) bases contained in the single-end sequencing read exceeds 50% of the read length ratio, this pair of paired reads needs to be removed. Finally, high-quality, clean data were obtained for subsequent analysis. Among the 65 samples, the sample labeled G60—which had the highest number of sequence tags—was selected to perform de novo stack clustering and construct a quasi-reference sequence for alignment. The clean reads of all samples were aligned to this quasi-reference using the BWA software [ 38 ] (parameters: mem -t 4 -k 32 -M). The alignment files were then sorted using SAMTOOLS [ 39 ] (sort function). SNPs across the population were detected using SAMTOOLS and related tools, yielding a total of 58,448 raw SNPs. After filtering with a call rate threshold of 0.8 and a minimum allele frequency (MAF) of 0.03, a final set of 10,584 high-quality SNP loci was retained for subsequent analyses. Population structure and diversity analysis The samples were selected on the basis of the filtered SNP loci, and the distance matrix was calculated via TreeBest software. A phylogenetic tree was subsequently constructed via the neighbor-joining method [ 40 ]. The feature vectors and eigenvalues were calculated via GCTA software, and the principal component analysis (PCA) distribution map was drawn R v. 4.4.3. Admixture was used to analyze the population structure [ 41 ]. First, the input file-Ped file of PLINK was created, and then the population genetic structure and population pedigree information were constructed via admixture software. The software Arlequin [ 42 ] was used to calculate the genetic diversity indices of each population: observed heterozygosity (Ho), expected heterozygosity (He), molecular variance analysis (AMOVA) and Fst between groups to measure possible differences between different groups. The method of Nei & Li [ 43 ] was used to analyze the population nucleotide diversity (π) and calculate the single nucleotide diversity index (Pi). Climatic association analysis To estimate the degree to which genomic variation is influenced by environmental variables, Redundancy analysis (RDA) was performed to examine the correlations between phenotypic characteristics, genetic diversity parameters and environmental factors with the package vegan in the R v. 4.4.3 statistical environment [ 44 ]. RDA involves a multiple linear regression followed by a PCA on the matrix of regression-fitted values. A dependent matrix of minor allele frequencies for each population and an independent matrice of environmental variables were included. To avoid high collinearity, we excluded those with a VIF over 20 [ 45 ]. Results Diversity analysis of seed phenotypic traits We measured 8 phenotypic traits of 65 S. alopecuroides samples and calculated their CV and H values (Table 2). The results revealed that the CV of the 8 phenotypic traits ranged from 2.87% to 7.94%, and the average CV was 4.48%. Among them, the CV of thousand-grain weight was the largest, whereas the CV of width was the smallest. The diversity indices of the 8 phenotypic traits ranged from 1.639 to 1.767, with an average of 1.713. All indicators were normally distributed (Fig. 2). This result indicated that the diversity of the 8 phenotypic traits in the 65 S. alopecuroides samples in this study was higher. Table 2 Diversity of seed phenotypic traits of S. alopecuroides samples parameter Length (mm) Width (mm) Diameter (mm) Roundness Area (mm 2 ) Perimeter (mm) Shape Weight (g) Maximum 4.271 3.023 3.626 0.840 1.434 10.356 12.136 29.060 Minimum 3.461 2.641 3.074 0.700 1.200 7.464 10.129 20.056 Mean 3.728 2.838 3.302 0.764 1.317 8.607 10.878 23.776 SD 0.161 0.081 0.105 0.029 0.049 0.553 0.388 1.888 CV 4.32% 2.87% 3.19% 3.80% 6.43% 3.56% 3.69% 7.94% H 1.654 1.767 1.725 1.736 1.721 1.639 1.737 1.724 SD: standard deviation; CV: coefficient of variation; H: Shannon 's diversity index; Shape: shape index; Weight: thousand-grain weight Correlation analysis of seed phenotypic traits Correlation analysis was performed on the phenotypic traits of all the tested S. alopecuroides seeds (Fig. 3). The results revealed that length was significantly positively correlated with width, diameter, shape index, area, perimeter and thousand-grain weight (P < 0.01) but negatively correlated with roundness (P < 0.01). Width was significantly positively correlated with diameter, area, perimeter and thousand-grain weight (P < 0.01) but weakly correlated with roundness and the shape index. There was a significant positive correlation between diameter and shape index, area, perimeter and thousand-grain weight (P < 0.01) and a significant negative correlation with roundness (P < 0.01). The roundness was significantly negatively correlated with the shape index, area and perimeter (P < 0.01), and the correlation with thousand-grain weight was weak. The shape index was significantly positively correlated with area and perimeter (P < 0.01) and weakly negatively correlated with thousand-grain weight. Area was significantly positively correlated with perimeter and thousand-grain weight (P < 0.01), and thousand-grain weight was also significantly positively correlated with perimeter (P < 0.01). Cluster analysis of seed phenotypic traits Systematic cluster analysis of the seed phenotypic traits of 65 S. alopecuroides samples revealed that the samples were divided into two groups (Fig. 4). Among them, group I is shown in blue in the figure and includes 19 samples from the YLHG, TSBL, KLSM, TSNL, HTGY, and NMGY populations. The samples of this group are characterized by high roundness but small other traits, indicating that the seeds of this group are small, the seed weight is light, and the comprehensive traits are poor. Group II, which included 46 samples, is shown in green in the figure, and all regions were included. The seeds in this group are generally larger in size, heavier in weight, and exhibit better performance in terms of seed length, width, thickness, and thousand-grain weight. These characteristics suggest superior seed quality and potential for improved yield. Sequencing Data Analysis A total of 65 S. alopecuroides samples were used for sequencing analysis. The total sequencing data volume was 61.20257 Gb, with an average of 941.578 Mb per sample. The results revealed that the sequencing quality was high (Q20 ≥ 93.97%, Q30 ≥ 84.85%), with a normal GC distribution (35.61%–39.78%) (Table 3). Table 3 Statistics of sequencing data Parameter Raw Base(bp) Clean Base(bp) Effective Rate (%) Error Rate (%) Q20(%) Q30(%) GC Content (%) Maximum 1,591,091,424 1,556,618,112 99.07 0.04 97.73 93.25 39.78 Minimum 533,957,184 480,835,584 84.08 0.03 93.97 84.85 35.61 Mean 827,187,193 775,839,775 93.31 0.03 96.51 90.24 38.06 Total 53,767,167,552 50,429,585,376 No samples were contaminated by adapter sequences, indicating successful library construction. The sequencing data of 65 S. alopecuroides were subsequently mapped to the reference genome. The average mapping rate of the population samples was 81.15% to 94.47%, and the average sequencing depth of the genome was 12.87-34.92×, with a 1 × coverage rate (at least one base coverage) for more than 0.10% of the samples (Table 4). Table 4 Statistics of sequencing depth and coverage Parameter Clean Reads Mapped Reads Mapping Rate (%) Average Depth Coverage at Least 1X (%) Coverage at Least 4X (%) Maximum 10,809,848 9,812,988 94.47% 34.92 0.10% 12.38% Minimum 3,339,136 2,709,853 81.15% 12.87 35.27% 57.47% Mean 5,387,776 4,732,293 87.45% 19.56 25.82% 48.67% Next, we detected a total of 58,448 SNP sites via SAMTOOLS, which were filtered to obtain high-quality SNPs. A total of 10,584 SNPs were obtained for subsequent analysis. Population genetic diversity analysis The S. alopecuroides samples were divided into 10 clusters according to geographical source: YLHG, TSBL, TCDQ, ARTS, KLSM, TSNL, THPD, QLSM, and NMGY, for a total of 65 samples. Moreover, the heterozygosity of each population was quite different (Table 5). The Ho values of the S. alopecuroides populations ranged from 0.20037 to 0.23511, with an average of 0.22213. The He ranged from 0.14387 to 0.18852, with an average of 0.16861, and the Pi ranged from 0.16442 to 0.19803, with an average of 0.18647. Among them, the Ho in the TCDQ was the highest, the He in the KLSM was the highest, and the Ho, He and Pi in the NMGY were the lowest. The genetic diversity was the highest in the TCDQ, and the NMGY had a relatively low level of genetic diversity. Table 5 Genetic diversity level of ten subpopulations Population Sample Size Observed Heterozygosity (Ho) Expected Heterozygosity (He) Nucleotide Diversity Index (Pi) YLHG 5 0.204 22 0.152 39 0.169 32 TSBL 10 0.223 56 0.181 86 0.191 43 TCDQ 4 0.235 11 0.173 28 0.198 03 ARTS 3 0.230 11 0.159 74 0.191 69 KLSM 14 0.230 89 0.188 52 0.195 50 TSNL 10 0.214 18 0.174 53 0.183 72 THPD 5 0.216 48 0.162 17 0.180 19 QLSM 4 0.234 44 0.170 96 0.195 38 HTGY 6 0.231 97 0.178 76 0.195 01 NMGY 4 0.200 37 0.143 87 0.164 42 Population structure analysis and principal component analysis In order to further understand the genetic background relationships of S. alopecuroides in different regions, Admixture software was used to analyze the population structure of 65 samples. The results revealed that when K = 2, the CV error was the smallest, indicating that the optimal grouping of the genetic structure of the 65 S. alopecuroides samples was two clusters (Fig. 5a and Fig. 5b). Moreover, to supplement the results of the population structure analysis, we used GCTA for PCA. According to the degree of SNP difference between individuals, PCA (Fig.5c) showed that 65 S. alopecuroides samples could not be effectively divided into two groups, and the distribution of some populations gradually overlapped, which was similar to the results of population structure analysis. Phylogenetic tree analysis The 10584 filtered SNPs were used to analyze the phylogenetic tree of 65 samples from 10 populations of S. alopecuroides via the neighbor-joining method (Fig. 6). The results revealed that the 65 samples could be divided into two large clusters. In general, the samples belonging to the same geographical area have a relative aggregation phenomenon in the two large clusters and are not completely merged into one place. Some samples were distributed in both clusters. Among them, cluster I mainly included S. alopecuroides samples from the TSBL, QLSM, HTGY, NMGY and THPD, whereas the samples from the YLHG, TCDQ and KLSM were clustered into cluster II. The distribution of S. alopecuroides samples in ARTS and TSNL was more disordered in the two clusters. These findings indicate that there is geographical isolation between the germplasm resources of S. alopecuroides from different geographical sources. Analysis of molecular variance and Pairwise population differentiation The Fst based on SNP data ranged from 0.00 to 0.04 among the 10 populations (Fig. 7). The highest Fst value was observed between TSNL and ARTS, with relatively high values also found between YLHG, TSNL, and other populations. AMOVA results indicated that 1.94% of genetic variation was attributed to differences among populations, while 34.77% and 132.83% originated from among individuals and within individuals, respectively, and all differences were statistically significant (P < 0.05). These results suggest that most genetic variation in S. alopecuroides is derived from within-population differences. Notably, the occurrence of negative variance components may reflect limitations of the AMOVA model under conditions of small sample size and weak genetic structure. Table 6 Analysis of molecular variance analysis (AMOVA) of population Source of variation df Sum of squares Variance components Percentage of variation (%) Among populations 9 879.598 2.16256Va 1.94 Among individuals 55 3876.463 -38.71712Vb -34.77 Within individuals 65 9614.500 147.91538Vc 132.83 Total 129 14370.562 111.36083 100 Redundancy analysis RDA revealed the relationship between phenotypic traits, genetic diversity, and environmental factors in S. alopecuroides. As shown in Fig. 8a and 8b, the cumulative explanatory power of environmental factors for phenotypic traits and genetic diversity was 99.75% and 67.89%, respectively, suggesting that the first two RDA axes captured most of the variation. The length of the environmental vector indicates the strength of its influence, while the proximity between a sample point and an environmental vector reflects the degree of impact. In Fig. 8a, the key environmental factors influencing seed phenotypic traits included MTD (Mean Temperature of Driest Quarter), YWS (Annual Mean Wind Speed), Alt (Altitude), MAE (Mean Annual Evaporation), MTWE (Mean Temperature of Wettest Quarter), and MAS (Mean Annual Sunshine Time), with MTD, Alt, MAS, and YWS exerting the strongest effects. In Fig. 8b, genetic diversity was mainly affected by PWE (Precipitation of Wettest Quarter), ISO (Isothermality), and PC (Precipitation of Coldest Quarter). Specifically, PC was most associated with YLHG, TCDQ, ARTS, and TSBL populations; ISO influenced KLSM, QLSM, and TSNL; and PWE had the greatest impact on HTGY, THPD, and NMGY populations. Discussion Genetic diversity Genetic diversity analysis is of great significance in the evaluation, utilization, origin and evolution of plant germplasm resources [47, 48]. Phenotypic diversity is a comprehensive reflection of genetic diversity and environmental diversity [18]. Over long-term natural selection, different populations may undergo significant genetic variations, leading to relatively stable phenotypic traits [49]. In this study, the phenotypic traits of 65 S. alopecuroides seeds from various provenances were analyzed (Table 2), revealing a relatively rich phenotypic diversity. Among these, seed length, area, thousand-grain weight, and other traits exhibited greater variation, consistent with the findings of Yang et al. [20] and Wang et al. [36]. In addition, the H ranged from 1.639 to 1.767, and all traits were normally distributed (Fig. 2), which further verified that the phenotypic trait diversity was rich and statistically uniform. At the same time, seed phenotypic traits are mostly linked, so the correlation analysis between traits is very important in the study of seed phenotypic diversity [50, 51]. The correlation analysis of phenotypic traits in S. alopecuroides seeds revealed significant correlations between most traits (P < 0.01) (Fig. 3), suggesting that these traits exhibit a synergistic pattern in phenotypic expression, providing a basis for further exploration of the genetic mechanisms underlying phenotypic traits. Meanwhile, the application of SNP molecular markers has become an important tool for the identification and conservation of plant germplasm resources [52, 53]. In this study, the genetic diversity analysis based on SNP molecular markers showed that the genetic diversity of S. alopecuroides was low, Ho, He and Pi = 0.19 were 0.22, 0.17 and 0.19 (Table 5), respectively, which was similar to the results of previous genetic diversity analysis of S. alopecuroides [54-56]. Furthermore, it showed relatively low genetic diversity compared with other Sophora species [57, 58]. Additionally, most species of the genus Sophora are monoecious, insect pollinated and self-compatible [36]; S. alopecuroides is no exception. These factors will lead to a low level of genetic diversity. The AMOVA based on SNP markers showed that the genetic variation among individuals was -34.77%, while the genetic variation within individuals was as high as 132.83% (Table 6). This shows that there is a high genetic similarity within the population, resulting in a negative difference between individuals. At the same time, the Fst only between 0.00 and 0.04 (Fig. 7), which was also lower than that of other species of Sophora [59, 60]. In addition, small differences between populations and low genetic differentiation also indicate that these samples may be the same ancestral group and subsequently experienced geographical isolation. The occurrence of negative genetic variation may reflect the limitations of AMOVA when applied to poorly differentiated populations. Additionally, population size plays a critical role, as smaller populations reduce the evolutionary potential of wild species, thereby diminishing genetic diversity [61, 62]. Therefore, it is not excluded that the small number of samples collected in this study may lead to aberrant. Moreover, due to the small populations size of S. alopecuroides studied previously and the limited sample sizes, understanding of the genetic structure and variation of S. alopecuroides remains constrained [54, 56]. In contrast, we collected a larger number of germplasm resources from 65 regions across five provinces in northwest China. Although the limitations of previous studies have been overcome to some extent, the results still show that it is difficult to fully reveal the genetic diversity and population structure of S. alopecuroides populations. Population structure Population structure analysis in this study indicated that K = 2 is the optimal number of groups (Fig. 5). PCA and phylogenetic tree analysis further confirmed the division of the populations into two main clusters (Fig. 6). However, some populations were still divided into different clusters, which may be due to the different calculation focuses of the three analysis methods and the insufficient sample size, resulting in some populations with complex genetic relationships being divided into different clusters. Because geographical isolation also has a significant impact on the genetic structure of populations, it is a major pioneer in the formation of exotic species [63, 64]. The distribution of many S. alopecuroides populations across mountains and plateaus limits pollen and seed dispersal, creating barriers between populations [65]. Through the above analysis, we found that according to the characteristics of geographical, ecological and climatic conditions, S. alopecuroides can be divided into two clusters: north and south, with the Tianshan Mountains and the Qilian Mountains as the boundaries. The north-south geographical division is consistent with the population structure classification of the sample. However, the TSNL and ARTS populations showed more complex patterns, with higher genetic differentiation coefficients than other populations. At the same time, due to human activities, the suitable habitat area of S. alopecuroides has been greatly reduced, showing a fragmented pattern [34]. We speculate that the two populations were originally an ancestral population, and then experienced geographical isolation. Human factors also exacerbated the isolation effect, and TSBL may be a transitional group between the two populations. The results of phylogenetic tree analysis also verify the possibility of this situation. Bioclimatic redundancy analysis Environmental factors are the main limiting factors affecting the distribution and growth of plants on a large geographical scale. The differences in temperature, precipitation and other factors in different regions lead to significant differentiation in the growth adaptability of different populations to their habitats [66, 67]. To further assess the impact of environmental factors on genetic variation, we performed RDA. The phenotypic traits are the most direct indicators of the impact of environmental factors on plants. We found that the effects of environmental factors on the phenotype of S. alopecuroides seeds are manifested in external conditions such as average temperature in dry and wet seasons, average annual wind speed, evaporation, altitude and light. This is also an important factor affecting the distribution of S. alopecuroides [68, 69]. Rong et al. [34] also expressed that the future expansion trend of S. alopecuroides population was affected, and the main environmental factors were temperature and rainfall. The RDA based on the molecular level shows that the PC, ISO and PWE. PC had a strong influence on the genetic diversity of S. alopecuroides populations. This reflects that the seasonal variation of precipitation plays a key role in the gene flow and adaptability of S. alopecuroides population. ISO also has a significant effect on the genetic diversity of some populations, particularly in KLSM, QLSM and TSNL populations. Due to the relatively gentle climate change in areas with high ISO, it is conducive to the stable growth of plants for a long time, promoting gene flow and the accumulation of adaptive genetics [70]. This is also the main factor affecting the low genetic differentiation of populations in these areas. Conclusion This study utilized 10,584 SNP markers obtained via GBS technology, combined with seed phenotypic traits, to analyze the genetic diversity of 65 S. alopecuroides samples from 10 populations in northwest China. The results showed that the genetic diversity of S. alopecuroides populations was low and there was no obvious differentiation. The genetic variation mainly came from individuals. The population structure is mainly divided into two main clusters, which are related to environmental factors such as geographical distribution and climatic conditions. These findings underscore the need for further genetic protection and breeding efforts based on the geographical and genetic distance between populations. Therefore, on the basis of studying the genetic characteristics of S. alopecuroides populations, it is necessary to develop in situ conservation and regeneration strategies suitable for S. alopecuroides populations, such as giving priority to populations with relatively high genetic diversity and strengthening introduction measures to maximize the genetic diversity of S. alopecuroides populations. In future research, it is necessary to increase the size of the research population, which can be combined with population dynamics analysis, screening of adaptive genes, and interaction between environment and genetics. This study provides a foundation for the genetic evaluation and conservation of S. alopecuroides genetic resources in China and offers important insights for its breeding programs. Abbreviations GBS: genotyping-by-sequencing; SNP: single nucleotide polymorphism; PCA: principal component analysis; GPS: global satellite positioning system; DNA: deoxyribonucleic acid; PCR: polymerase chain reaction; Q-PCR: fluorescence polymerase chain reaction; He: expected heterozygosity; Ho: observed heterozygosity; AMOVA: molecular variance analysis; Fst: pairwise population differentiation; RDA: redundancy analysis; MTD: mean temperature of the driest quarter; YWS: annual mean wind; PC: precipitation of the coldest quarter; ISO: isothermality; PWE: precipitation of the wettest quarter; Pi: single nucleotide diversity index; LSD: least significant test; One-Way ANOVA: one-way analysis of variance; CV: coefficient of variation; H: Shannon’s diversity index; SD: standard deviation; Alt: Altitude; MAE: Mean Annual Evaporation; MTWE :Mean Temperature of Wettest Quarter; MAS: Mean Annual Sunshine Time Declarations Acknowledgements We acknowledge to Prof. Z.W Z. for specimens’ identification, and appreciate the assistance of Mr. X.X.Z., Mr. Z.Y.T., Mr. W.W.R., Mr. J.W.S., and Mr. R.Y. in sample collections. Authors’ contributions C.L., M.W., and X.H. conceptualized the topic. C.L. and F.N. analyzed the data. C.L., F.N., and P.N. set up the methodology. Z.Z., W.C., and G.M. provided resources. P.N. and Z.Z. conducted the investigation. M.W., G.C., and X.H. supervised the study. C.L. and F.M. wrote the main manuscript text. M.W., and X.H. reviewed and edited the manuscript. P.N. and P.J. contributed to visualization. M.W., P.J., and W.C. acquired funding. All authors have read and approved the final manuscript. Funding This research was funded by Corps Guiding Science and Technology Plan Project (Grant No. 2023ZD088), New Variety Cultivation Project of Shihezi University (Grant No. YZZX202303), College Students’ Innovation and Entrepreneurship Training Program (Grant No. SRP2024232). Data availability The raw sequencing data have been deposited at the National Center for Biotechnology Information database under BioProject PRJNA1225430. 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Prediction of the distribution area of major noxious weeds in Xinjiang based on maximum entropy model. J. Ecol. 2023;43:5096–5109. doi:10.5846/stxb202205061252. Bansal S, Harrington CA, Gould PJ, St Clair JB. Climate-related genetic variation in drought-resistance of Douglas-fir (Pseudotsuga menziesii). Glob Chang Biol. 2015;21(2):947-58. doi:10.1111/gcb.12719. Additional Declarations No competing interests reported. Supplementary Files TableS1S3.docx Supplementary Information Supplementary Material: Table S1 Detailed geographical location of Sophora alopecuroides samples. Table S2 Bioclimatic variables used in this study, and the mean (± SD) values of 65 S. alopecuroides samples. Table S3 22 bioclimatic variables in the 10 populations of S. alopecuroides. 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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-6061561","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449377825,"identity":"453ee917-63ca-45ee-8e71-0feebac828ef","order_by":0,"name":"Cunkai luo","email":"","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Cunkai","middleName":"","lastName":"luo","suffix":""},{"id":449377826,"identity":"5f879e88-715c-45a1-b65c-45f6240be263","order_by":1,"name":"Fanyan Ma","email":"","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Fanyan","middleName":"","lastName":"Ma","suffix":""},{"id":449377827,"identity":"a97fd8fa-075d-470c-bb96-0b2421a0c013","order_by":2,"name":"Panxin Niu","email":"","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Panxin","middleName":"","lastName":"Niu","suffix":""},{"id":449377828,"identity":"3ebb457d-0543-4a0a-89ba-09cca271b319","order_by":3,"name":"Zhao Zhang","email":"","orcid":"","institution":"Bozhou Water Conservancy Irrigation Test Station","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Zhang","suffix":""},{"id":449377829,"identity":"b94f7f18-716d-4d8c-aba5-bbc9de988a9d","order_by":4,"name":"Weiting Chen","email":"","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Weiting","middleName":"","lastName":"Chen","suffix":""},{"id":449377830,"identity":"9bb4a333-5109-4c21-8d0a-841e5f743d10","order_by":5,"name":"Ping Jiang","email":"","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Jiang","suffix":""},{"id":449377831,"identity":"bc806b63-3ea3-47d0-9cc8-e964e60336a4","order_by":6,"name":"Mei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3LsUpDMRTG8VMCcQntmlC4vsIpgThY6KskCJ0O0snVgBAXoaviS7iJ2y0d7lJ1jTgV4QrSoXAXBUEtLk65dRPMfzofnB9ALvdnw6EYnXr+PcqtyGRcoCh/RdZzjdJuSbC6nb9EZO5aPdfLtwBFN9pOM0mRxeF4n5C7m0vaQxVAq2hZ/zxBTElGEwrnH4nLQQB3FS1nIkXuVxsinX+oaukCHLeTSPqJEDVGMHIWwGIbGcWVYYS2UGdklL+Tg4vF8qSfImpKuqH3D9HbqWr1ejTc7VYHsyZFvuLyx9jcHZ8GAGzd9pHL5XL/vE8+L003WDsi4gAAAABJRU5ErkJggg==","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":true,"prefix":"","firstName":"Mei","middleName":"","lastName":"Wang","suffix":""},{"id":449377834,"identity":"d611ee49-39f5-47f4-bfda-62055d2bc8ca","order_by":7,"name":"Guangming Chu","email":"","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Chu","suffix":""},{"id":449377835,"identity":"686b1762-dcc0-4708-9c59-0043e4c47720","order_by":8,"name":"Xiang Huang","email":"","orcid":"","institution":"Agricultural College, Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-02-19 07:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6061561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6061561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81634772,"identity":"f671afc8-81f4-4a43-8d7c-b6ac312420d8","added_by":"auto","created_at":"2025-04-29 12:08:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384139,"visible":true,"origin":"","legend":"\u003cp\u003eSampling point distribution map of 10 \u003cem\u003eSophora alopecuroides\u003c/em\u003e populations\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/c277ab4411560c2e7c9a0f60.png"},{"id":81632389,"identity":"f5e13d56-f885-45e1-8b27-49b98f56ff35","added_by":"auto","created_at":"2025-04-29 11:44:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153594,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of frequency distribution of seeds phenotypic traits of \u003cem\u003eS. alopecuroides. \u003c/em\u003ea: length; b: width; c: diameter; d: roundness; e: shape index; f: perimeter; g: Area; h: thousand-grain weight\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/b4919e78ed5331c110f884c7.png"},{"id":81633972,"identity":"67f009a3-47ba-4425-b981-0c44f00a5151","added_by":"auto","created_at":"2025-04-29 12:00:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98154,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of seeds phenotypic traits of \u003cem\u003eS. alopecuroides. \u003c/em\u003e“**” indicates extremely significant correlation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01); A: length, B: width, C: diameter, D: roundness, E: shape index, F: perimeter, G: area, H: thousand-grain weight.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/a950366d0b411f88ee40f0c4.png"},{"id":81632395,"identity":"3240e353-5554-46f8-b269-a7095ad89419","added_by":"auto","created_at":"2025-04-29 11:44:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122605,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis of phenotypic traits of \u003cem\u003eS. alopecuroides\u003c/em\u003e seeds\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/d4b1dcbfbdccacab29376ff1.png"},{"id":81633969,"identity":"00ced5df-bafe-460a-bf51-79f2a16ae518","added_by":"auto","created_at":"2025-04-29 12:00:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90459,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure analysis and PCA of 65 samples of \u003cem\u003eS. alopecuroides\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/05fcb4587614cc849dc9c381.png"},{"id":81633973,"identity":"e31735ae-b4fb-452e-ba68-7b2e4dc857a4","added_by":"auto","created_at":"2025-04-29 12:00:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":239387,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree of ten subpopulations of \u003cem\u003eS. alopecuroides\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/cc73766001e97f358a1317f9.png"},{"id":81633974,"identity":"af9331c8-40c0-4419-9ee0-c1098c8f4b1f","added_by":"auto","created_at":"2025-04-29 12:00:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":122656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFst\u003c/em\u003e among 10 \u003cem\u003eS. alopecuroides\u003c/em\u003e populations\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/81cb428e2cb3900006fd2fb9.png"},{"id":81632865,"identity":"381ce60b-d47c-4460-a616-7bde6be9c2de","added_by":"auto","created_at":"2025-04-29 11:52:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":154924,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) of the relationship between seed phenotypic traits (a), genetic diversity (b) and environmental factors. The red arrows represent environmental factors, and different color circles represent sample points and populations\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/649b44a834defaada1c1bc35.png"},{"id":82839599,"identity":"b3d063d1-04ac-48b1-bf58-8839ed6a5a7d","added_by":"auto","created_at":"2025-05-15 20:46:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2279694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/764b6d98-cead-417d-bc7d-71479c9f0091.pdf"},{"id":81634798,"identity":"186179ac-d6d6-468f-baa6-149336f2149e","added_by":"auto","created_at":"2025-04-29 12:08:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Material: \u003cstrong\u003eTable S1 \u003c/strong\u003eDetailed geographical location of Sophora alopecuroides samples. \u003cstrong\u003eTable S2 \u003c/strong\u003eBioclimatic variables used in this study, and the mean (± SD) values of 65 S. alopecuroides samples. \u003cstrong\u003eTable S3\u003c/strong\u003e 22 bioclimatic variables in the 10 populations of S. alopecuroides.\u003c/p\u003e","description":"","filename":"TableS1S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6061561/v1/4928f87f3ed67b96fcd70457.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The genetic diversity and population structure of Kudouzi (Sophora alopecuroides) population were revealed by using SNP markers combined with seed phenotypic traits","fulltext":[{"header":"Background","content":"\u003cp\u003eSophora alopecuroides L. is a perennial herb of the Leguminosae family that is widely distributed across arid and semi-arid regions of West and Central Asia. In China, it is predominantly found in Xinjiang, Gansu, Ningxia, and Inner Mongolia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Studies have shown that S. alopecuroides exhibits strong root development, drought resistance, salinity-alkalinity tolerance, and nitrogen fixation ability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As such, it has become a crucial pioneer plant for environmental protection in northwest China, owing to its robust drought resistance, alkalinity tolerance, and wind-sand resistance. In addition to its ecological significance, S. alopecuroides possesses substantial medicinal and agricultural value. Its seeds, aerial parts, and roots contain bioactive compounds such as alkaloids, flavonoids, volatile oils, steroids, polysaccharides, and free fatty acids [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These compounds underpin its use in traditional medicine and potential application in biological pesticides [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], green manure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and high-quality forage [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic diversity and population structure play a key role in species conservation, utilization and breeding [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Gao et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported that the concentrations of key medicinal compounds, such as oxymatrine and total flavonoids, vary significantly in S. alopecuroides seeds from different geographical regions. Similarly, An et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] found that the contents of genistein and sophoridine in seeds from northern Xinjiang were generally higher than those from southern Xinjiang. In addition, gene flow between different S. alopecuroides populations is limited, resulting in high genetic differentiation between populations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite its ecological and medicinal importance, few fundamental studies on the large-scale population resources and genetic diversity of S. alopecuroides have been conducted.\u003c/p\u003e \u003cp\u003eSeed traits are crucial for population reproduction and preservation, serving as important indicators of species inheritance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Phenotypic plasticity, the ability of a single genotype to produce different phenotypes in varying environments, is largely a result of genetic selection and is closely related to phenotypic diversity [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Significant differences in seed traits, such as size and thousand-grain weight, among different wild populations of S. alopecuroides suggest rich ecological clusters and complex genetic backgrounds [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the correlation between seed phenotypic traits and genetic diversity in S. alopecuroides has not been thoroughly investigated.\u003c/p\u003e \u003cp\u003eGenotyping-by-Sequencing (GBS) is a high-throughput, next-generation sequencing method that reduces genomic complexity using restriction enzymes. This technique generates high-density single nucleotide polymorphism (SNP) markers by tagging random genomic deoxyribonucleic acid (DNA) fragments common across samples with unique short DNA sequences (barcodes), pooling samples into a single sequencing run to detect and label mutations at a relatively low cost [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Currently, GBS serves as a powerful genotyping tool, widely applied in genetic mapping, marker-assisted selection [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], diversity analysis, germplasm and species identification [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among molecular markers, SNP are key tools in population and quantitative genetics. SNP represent single-base differences among individuals of the same species and are the most abundant form of genetic variation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As third-generation markers, SNP offer advantages such as biallelic nature, high density, genetic stability, easy detection, and genome-wide distribution [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. With advances in sequencing and bioinformatics, SNP have been extensively used to study genetic diversity, population structure, and differentiation [\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, their application in the genetic diversity analysis of S. alopecuroides remains unreported.\u003c/p\u003e \u003cp\u003eIn this study, seeds were collected from 65 wild populations of S. alopecuroides across diverse geographic regions. Phenotypic traits were measured, and SNP markers were employed to assess the genetic structure and diversity within these populations. The main objectives of this study are as follows: (1) what is the level of genetic diversity and population structure of S. alopecuroides in these different populations? (2) What are the effects of geographical and environmental factors on the genetic diversity of S. alopecuroides? Based on the findings, this research aims to provide scientific basis for the effective conservation, management, and utilization of S. alopecuroides germplasm, as well as for the development of high-quality cultivars to meet market demands.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePlant Materials\u003c/h2\u003e\n \u003cp\u003eThe field collection of S. alopecuroides samples was approved by the Chinese government and carried out in compliance with all relevant national regulations. Species identification was confirmed by Professor Zhuowen Zhang (College of Horticulture and Forestry, Huazhong Agricultural University), and voucher specimens were deposited in the Department of Forestry, College of Agriculture, Shihezi University (Accession Nos: SHAF20230801\u0026ndash;SHAF20230865).\u003c/p\u003e\n \u003cp\u003eBased on the main distribution range of S. alopecuroides in China [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] and the maturity stage of the plants, a total of 65 samples were collected between August and October 2023 from Xinjiang, Gansu, Ningxia, Inner Mongolia, and Shaanxi provinces (Fig. 1, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sampling sites were spaced at least 20 km apart. At each site, 10\u0026ndash;15 healthy and mature individuals were randomly selected, maintaining a minimum distance of 10 meters between sampled plants. From each individual, 80\u0026ndash;120 mature seeds were collected from multiple pods. Seeds from all individuals within each site were pooled to form a composite sample representing the genetic diversity of that population. After field collection and natural air-drying, seeds were stored in a cool and dry environment [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Geographic information of Sophora alopecuroides population\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation\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\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\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\u003eYLHG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYili, Xinjiang, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG1, G2, G3, G4, G5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBortala, Xinjiang, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG6, G7, G15, G16, G17, G18, G19, G20, G21, G22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTacheng, Xinjiang, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG8, G9, G10, G11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltay, Xinjiang, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG12, G13, G14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKLSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayingolin; Hotan; Kashgar, Xinjiang, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG23, G24, G28, G29, G30, G37, G38, G39, G40, G41, G42, G43, G44, G46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSNL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAksu, Xinjiang, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG25, G26, G27, G31, G32, G33, G34, G35, G36, G45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurpan; Hami; Xinjiang, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG47, G48, G49, G50, G51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQLSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGansu, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG52, G53, G54, G55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHTGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNingxia; Shaanxi, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG56, G57, G58, G59, G60, G65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInner Mongolia, China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG61, G62, G63, G64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eBased on their geographic origin, the 65 samples were grouped into 10 populations. Global Satellite Positioning System (GPS) coordinates for each sampling location were recorded using handheld GPS devices, and bioclimatic variables were obtained from the WorldCLIM global high resolution climate database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org/\u003c/span\u003e\u003c/span\u003e) and local meteorological stations (Table S2 and S3). A subset of seeds from each composite sample was randomly selected and sent to Beijing Nuohe Zhiyuan Bioinformatics Technology Co., Ltd. for GBS analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMeasurement of seed phenotypic traits\u003c/h3\u003e\n\u003cp\u003eWe used a Wanshen SC-G automatic seed testing instrument (Hangzhou Wanshen Detection Technology Co., Ltd., China) to measure the length, width, diameter, roundness, area, perimeter and shape index data of 65 samples of S. alopecuroides seeds. Each index was measured three times, 200 seeds were randomly selected for each repetition, and the mean value was taken. The thousand-grain weight was measured by a one-thousandth electronic balance, and 1,000 seeds were randomly selected each time. Three measurement replicates were set up, and the average value was taken [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStatistics and analysis of phenotypic traits\u003c/h3\u003e\n\u003cp\u003eMicrosoft Excel 2016 was used to analyze the experimental data, Origin Pro 2021 software was used for plotting, SPSS 27.0 software was used to test the homogeneity of variance, and variance analysis was performed on the seed trait data. The least significant test (LSD) method was based on one-way analysis of variance (one-way ANOVA), which was performed\u0026thinsp;\u0026plusmn;\u0026thinsp;the mean value of the trait. The coefficient of variation (CV) and phenotypic genetic Shannon diversity index (H) were calculated in Excel as follows:\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV = (Standard deviation \u0026times; Mean) \u0026times; 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe observed values of each trait were divided into 10 levels according to the mean value (Xi) and standard deviation (SD). Each 0.5 SD was 1 level, from the first level (Xi\u0026thinsp;\u0026lt;\u0026thinsp;X \u0026minus;\u0026thinsp;2 SD) to the 10th level (Xi\u0026thinsp;\u0026ge;\u0026thinsp;X\u0026thinsp;+\u0026thinsp;2 SD). The relative frequency of each group was used to calculate H [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:H=-\\sum\\:Pi{ln}Pi\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\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\u003eIn Eq. (2), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e is the classification of a trait, and P\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e is the percentage of the number of materials in the total number of materials in the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003eth grade of the trait.\u003c/p\u003e\n\u003cp\u003eOn the basis of the mean values of the seed phenotypic traits, cluster analysis was carried out via OriginPro 2021 software. By controlling the data quality, the differences in the quantitative phenotypic traits of the 65 samples in this study were visually displayed. The cluster analysis of the phenotypic traits of 65 samples was carried out via the longest distance method of square Euclidean distance. To eliminate the influence of dimension on the clustering relationship, the seed trait data were standardized by the z value and then clustered [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. The z value standardized calculation formula is as follows:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}^{\\text{*}}=\\frac{x-\\stackrel{-}{x}}{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\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\u003eIn Eq.\u0026nbsp;(3), X* is the normalized value of z value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e is the original value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e is the sample mean, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e is the sample standard deviation.\u003c/p\u003e\n\u003ch3\u003eDNA extraction, GBS library construction and sequencing (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003eThe DNA of 65 S. alopecuroides seeds from different provenances was extracted via the CTAB method [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. The integrity of the extracted DNA was detected via 1% agarose gel electrophoresis, and the concentration and quality of the extracted DNA (OD260/280 ratio) were measured via a Nanodrop UV‒visible spectrophotometer. After the quality test was performed, the extracted DNA stock solution was stored at \u0026minus;\u0026thinsp;20\u0026deg;C until use.\u003c/p\u003e\n\u003cp\u003eThe construction and sequencing of the GBS library begins with the digestion of genomic DNA. First, 0.1\u0026ndash;1 \u0026micro;g of genomic DNA was extracted and digested with restriction endonucleases to obtain a suitable marker density. The two ends of the digested DNA fragment are connected with P1 and P2 adaptor sequences (which can be complementary to the digested DNA gap). The tag sequences containing P1 and P2 adapters at both ends were amplified by polymerase chain reaction (PCR). The amplified DNA fragments were subsequently separated and recovered via the DNA fragment mixing pool, and the DNA fragments in the expected size range were selected via agarose gel electrophoresis. After library construction was completed, a Qubit 2.0 instrument was used for preliminary quantification, and the library was diluted to 1 ng/\u0026micro;l. Then, an Agilent 2100 instrument was used to determine the size of the insert in the library. After the insert size was in line with expectations, the fluorescence polymerase chain reaction (q-PCR) method was used to accurately quantify the effective concentration of the library (the effective concentration of the library\u0026thinsp;\u0026gt;\u0026thinsp;2 nM) to ensure the quality of the library. After the library inspection was performed, Illumina HiSeq PE150 sequencing was performed after pooling different libraries according to the effective concentration and the target offline data volume.\u003c/p\u003e\n\u003ch3\u003eDNA extraction, GBS library construction and sequencing\u003c/h3\u003e\n\u003cp\u003eThe original image data files obtained via Illumina HiSeq sequencing were converted into sequenced reads via base-calling analysis, and the results were stored in FASTQ file format. The raw data obtained by sequencing were filtered to filter out the reads containing the adaptor sequence; when the N content in the single-ended sequencing read exceeded 10% of the length ratio of the read, the paired reads needed to be removed. When the number of low-quality (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;5) bases contained in the single-end sequencing read exceeds 50% of the read length ratio, this pair of paired reads needs to be removed. Finally, high-quality, clean data were obtained for subsequent analysis.\u003c/p\u003e\n\u003cp\u003eAmong the 65 samples, the sample labeled G60\u0026mdash;which had the highest number of sequence tags\u0026mdash;was selected to perform de novo stack clustering and construct a quasi-reference sequence for alignment. The clean reads of all samples were aligned to this quasi-reference using the BWA software [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] (parameters: mem -t 4 -k 32 -M). The alignment files were then sorted using SAMTOOLS [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] (sort function). SNPs across the population were detected using SAMTOOLS and related tools, yielding a total of 58,448 raw SNPs. After filtering with a call rate threshold of 0.8 and a minimum allele frequency (MAF) of 0.03, a final set of 10,584 high-quality SNP loci was retained for subsequent analyses.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003ePopulation structure and diversity analysis\u003c/h2\u003e\n \u003cp\u003eThe samples were selected on the basis of the filtered SNP loci, and the distance matrix was calculated via TreeBest software. A phylogenetic tree was subsequently constructed via the neighbor-joining method [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The feature vectors and eigenvalues were calculated via GCTA software, and the principal component analysis (PCA) distribution map was drawn R v. 4.4.3. Admixture was used to analyze the population structure [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. First, the input file-Ped file of PLINK was created, and then the population genetic structure and population pedigree information were constructed via admixture software. The software Arlequin [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] was used to calculate the genetic diversity indices of each population: observed heterozygosity (Ho), expected heterozygosity (He), molecular variance analysis (AMOVA) and Fst between groups to measure possible differences between different groups. The method of Nei \u0026amp; Li [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] was used to analyze the population nucleotide diversity (\u0026pi;) and calculate the single nucleotide diversity index (Pi).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eClimatic association analysis\u003c/h3\u003e\n\u003cp\u003eTo estimate the degree to which genomic variation is influenced by environmental variables, Redundancy analysis (RDA) was performed to examine the correlations between phenotypic characteristics, genetic diversity parameters and environmental factors with the package vegan in the R v. 4.4.3 statistical environment [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. RDA involves a multiple linear regression followed by a PCA on the matrix of regression-fitted values. A dependent matrix of minor allele frequencies for each population and an independent matrice of environmental variables were included. To avoid high collinearity, we excluded those with a VIF over 20 [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDiversity analysis of seed phenotypic traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe measured 8 phenotypic traits of 65\u0026nbsp;S. alopecuroides samples and calculated their CV and H values (Table 2). The results revealed that the CV of the 8 phenotypic traits ranged from 2.87% to 7.94%, and the average CV was 4.48%. Among them, the CV of thousand-grain weight was the largest, whereas the CV of width was the smallest. The diversity indices of the 8 phenotypic traits ranged from 1.639 to 1.767, with an average of 1.713. All indicators were normally distributed (Fig. 2). This result indicated that the diversity of the 8 phenotypic traits in the 65 S. alopecuroides samples in this study was higher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eDiversity of seed phenotypic traits of S. alopecuroides samples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eparameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoundness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003cp\u003e(mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePerimeter\u003c/p\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eShape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: standard deviation; CV: coefficient of variation; H: Shannon \u0026apos;s diversity index; Shape: shape index; Weight: thousand-grain weight\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis of seed phenotypic traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis was performed on the phenotypic traits of all the tested S. alopecuroides seeds (Fig. 3). The results revealed that length was significantly positively correlated with width, diameter, shape index, area, perimeter and thousand-grain weight (P \u0026lt; 0.01) but negatively correlated with roundness (P \u0026lt; 0.01). Width was significantly positively correlated with diameter, area, perimeter and thousand-grain weight (P \u0026lt; 0.01) but weakly correlated with roundness and the shape index. There was a significant positive correlation between diameter and shape index, area, perimeter and thousand-grain weight (P \u0026lt; 0.01) and a significant negative correlation with roundness (P \u0026lt; 0.01). The roundness was significantly negatively correlated with the shape index, area and perimeter (P \u0026lt; 0.01), and the correlation with thousand-grain weight was weak. The shape index was significantly positively correlated with area and perimeter (P \u0026lt; 0.01) and weakly negatively correlated with thousand-grain weight. Area was significantly positively correlated with perimeter and thousand-grain weight (P \u0026lt; 0.01), and thousand-grain weight was also significantly positively correlated with perimeter (P \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster analysis of seed phenotypic traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSystematic cluster analysis of the seed phenotypic traits of 65 S. alopecuroides samples revealed that the samples were divided into two groups (Fig. 4). Among them, group I is shown in blue in the figure and includes 19 samples from the YLHG, TSBL, KLSM, TSNL, HTGY, and NMGY populations. The samples of this group are characterized by high roundness but small other traits, indicating that the seeds of this group are small, the seed weight is light, and the comprehensive traits are poor. Group II, which included 46 samples, is shown in green in the figure, and all regions were included. The seeds in this group are generally larger in size, heavier in weight, and exhibit better performance in terms of seed length, width, thickness, and thousand-grain weight. These characteristics suggest superior seed quality and potential for improved yield.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 65\u0026nbsp;S. alopecuroides samples were used for sequencing analysis. The total sequencing data volume was 61.20257 Gb, with an average of 941.578 Mb per sample. The results revealed that the sequencing quality was high (Q20 \u0026ge; 93.97%, Q30 \u0026ge; 84.85%), with a normal GC distribution (35.61%\u0026ndash;39.78%) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eStatistics of sequencing data\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eRaw Base(bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eClean Base(bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eEffective Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eError Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eQ20(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eQ30(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eGC Content (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1,591,091,424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1,556,618,112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e99.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e97.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e93.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e39.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e533,957,184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e480,835,584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e84.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e93.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e84.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e35.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e827,187,193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e775,839,775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e93.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e96.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e90.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e38.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e53,767,167,552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e50,429,585,376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\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\u003eNo samples were contaminated by adapter sequences, indicating successful library construction. The sequencing data of 65\u0026nbsp;S. alopecuroides were subsequently mapped to the reference genome. The average mapping rate of the population samples was 81.15% to 94.47%, and the average sequencing depth of the genome was 12.87-34.92\u0026times;, with a 1 \u0026times; coverage rate (at least one base coverage) for more than 0.10% of the samples (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eStatistics of sequencing depth and coverage\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eClean Reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eMapped Reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eMapping Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eAverage Depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eCoverage at Least 1X (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eCoverage at Least 4X (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e10,809,848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e9,812,988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e94.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e34.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e12.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3,339,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e2,709,853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e81.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e12.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e35.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e57.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e5,387,776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e4,732,293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e87.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e19.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e25.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e48.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNext, we detected a total of 58,448 SNP sites via SAMTOOLS, which were filtered to obtain high-quality SNPs. A total of 10,584 SNPs were obtained for subsequent analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation genetic diversity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;S. alopecuroides samples were divided into 10 clusters according to geographical source: YLHG, TSBL, TCDQ, ARTS, KLSM, TSNL, THPD, QLSM, and NMGY, for a total of 65 samples. Moreover, the heterozygosity of each population was quite different (Table 5). The Ho values of the S. alopecuroides populations ranged from 0.20037 to 0.23511, with an average of 0.22213. The He ranged from 0.14387 to 0.18852, with an average of 0.16861, and the Pi ranged from 0.16442 to 0.19803, with an average of 0.18647. Among them, the Ho in the TCDQ was the highest, the He in the KLSM was the highest, and the Ho, He and Pi in the NMGY were the lowest. The genetic diversity was the highest in the TCDQ, and the NMGY had a relatively low level of genetic diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eGenetic diversity level of ten subpopulations\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSample Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003cp\u003eHeterozygosity (Ho)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExpected\u003c/p\u003e\n \u003cp\u003eHeterozygosity (He)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNucleotide\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDiversity Index (Pi)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYLHG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.204 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.152 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.169 32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTSBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.223 56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.181 86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.191 43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.235 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.173 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.198 03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eARTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.230 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.159 74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.191 69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKLSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.230 89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.188 52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195 50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTSNL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.214 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.174 53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.183 72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTHPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.216 48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.162 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.180 19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQLSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.234 44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.170 96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195 38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHTGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.231 97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.178 76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195 01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNMGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.200 37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.143 87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.164 42\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\u003ePopulation structure analysis and principal component analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to further understand the genetic background relationships of S. alopecuroides in different regions, Admixture software was used to analyze the population structure of 65 samples. The results revealed that when K = 2, the CV error was the smallest, indicating that the optimal grouping of the genetic structure of the 65 S. alopecuroides samples was two clusters (Fig. 5a and Fig. 5b). Moreover, to supplement the results of the population structure analysis, we used GCTA for PCA. According to the degree of SNP difference between individuals, PCA (Fig.5c) showed that 65 S. alopecuroides samples could not be effectively divided into two groups, and the distribution of some populations gradually overlapped, which was similar to the results of population structure analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic tree analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 10584 filtered SNPs were used to analyze the phylogenetic tree of 65 samples from 10 populations of S. alopecuroides via the neighbor-joining method (Fig. 6). The results revealed that the 65 samples could be divided into two large clusters. In general, the samples belonging to the same geographical area have a relative aggregation phenomenon in the two large clusters and are not completely merged into one place. Some samples were distributed in both clusters. Among them, cluster I mainly included S. alopecuroides samples from the TSBL, QLSM, HTGY, NMGY and THPD, whereas the samples from the YLHG, TCDQ and KLSM were clustered into cluster II. The distribution of S. alopecuroides samples in ARTS and TSNL was more disordered in the two clusters. These findings indicate that there is geographical isolation between the germplasm resources of S. alopecuroides from different geographical sources.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of molecular variance and Pairwise population differentiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;Fst\u0026nbsp;based on SNP data ranged from 0.00 to 0.04 among the 10 populations (Fig. 7). The highest\u0026nbsp;Fst\u0026nbsp;value was observed between TSNL and ARTS, with relatively high values also found between YLHG, TSNL, and other populations. AMOVA results indicated that 1.94% of genetic variation was attributed to differences among populations, while 34.77% and 132.83% originated from among individuals and within individuals, respectively, and all differences were statistically significant (P\u0026nbsp;\u0026lt; 0.05). These results suggest that most genetic variation in\u0026nbsp;S. alopecuroides\u0026nbsp;is derived from within-population differences. Notably, the occurrence of negative variance components may reflect limitations of the AMOVA model under conditions of small sample size and weak genetic structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003eAnalysis of molecular variance analysis (AMOVA) of population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource of variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVariance components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePercentage of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmong populations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e879.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.16256Va\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAmong individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3876.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-38.71712Vb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-34.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9614.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e147.91538Vc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14370.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111.36083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRedundancy analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRDA revealed the relationship between phenotypic traits, genetic diversity, and environmental factors in S. alopecuroides. As shown in Fig. 8a and 8b, the cumulative explanatory power of environmental factors for phenotypic traits and genetic diversity was 99.75% and 67.89%, respectively, suggesting that the first two RDA axes captured most of the variation. The length of the environmental vector indicates the strength of its influence, while the proximity between a sample point and an environmental vector reflects the degree of impact. In Fig. 8a, the key environmental factors influencing seed phenotypic traits included MTD (Mean Temperature of Driest Quarter), YWS (Annual Mean Wind Speed), Alt (Altitude), MAE (Mean Annual Evaporation), MTWE (Mean Temperature of Wettest Quarter), and MAS (Mean Annual Sunshine Time), with MTD, Alt, MAS, and YWS exerting the strongest effects. In Fig. 8b, genetic diversity was mainly affected by PWE (Precipitation of Wettest Quarter), ISO (Isothermality), and PC (Precipitation of Coldest Quarter). Specifically, PC was most associated with YLHG, TCDQ, ARTS, and TSBL populations; ISO influenced KLSM, QLSM, and TSNL; and PWE had the greatest impact on HTGY, THPD, and NMGY populations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eGenetic diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic diversity analysis is of great significance in the evaluation, utilization, origin and evolution of plant germplasm resources [47, 48]. Phenotypic diversity is a comprehensive reflection of genetic diversity and environmental diversity [18]. Over long-term natural selection, different populations may undergo significant genetic variations, leading to relatively stable phenotypic traits [49]. In this study, the phenotypic traits of 65\u0026nbsp;S. alopecuroides\u0026nbsp;seeds from various provenances were analyzed (Table 2), revealing a relatively rich phenotypic diversity. Among these, seed length, area, thousand-grain weight, and other traits exhibited greater variation, consistent with the findings of Yang et al. [20] and Wang et al. [36]. In addition, the\u0026nbsp;H\u0026nbsp;ranged from 1.639 to 1.767, and all traits were normally distributed (Fig. 2), which further verified that the phenotypic trait diversity was rich and statistically uniform. At the same time, seed phenotypic traits are mostly linked, so the correlation analysis between traits is very important in the study of seed phenotypic diversity [50, 51]. The correlation analysis of phenotypic traits in\u0026nbsp;S. alopecuroides\u0026nbsp;seeds revealed significant correlations between most traits (P\u0026nbsp;\u0026lt; 0.01) (Fig. 3), suggesting that these traits exhibit a synergistic pattern in phenotypic expression, providing a basis for further exploration of the genetic mechanisms underlying phenotypic traits.\u003c/p\u003e\n\u003cp\u003eMeanwhile, the application of SNP molecular markers has become an important tool for the identification and conservation of plant germplasm resources [52, 53]. In this study, the genetic diversity analysis based on SNP molecular markers showed that the genetic diversity of\u0026nbsp;S. alopecuroides\u0026nbsp;was low,\u0026nbsp;Ho,\u0026nbsp;He\u0026nbsp;and\u0026nbsp;Pi\u0026nbsp;= 0.19 were 0.22, 0.17 and 0.19 (Table 5), respectively, which was similar to the results of previous genetic diversity analysis of\u0026nbsp;S. alopecuroides\u0026nbsp;[54-56]. Furthermore, it showed relatively low genetic diversity compared with other\u0026nbsp;Sophora\u0026nbsp;species [57, 58]. Additionally, most species of the genus\u0026nbsp;Sophora\u0026nbsp;are monoecious, insect pollinated and self-compatible [36];\u0026nbsp;S. alopecuroides\u0026nbsp;is no exception. These factors will lead to a low level of genetic diversity.\u003c/p\u003e\n\u003cp\u003eThe AMOVA based on SNP markers showed that the genetic variation among individuals was -34.77%, while the genetic variation within individuals was as high as 132.83% (Table 6). This shows that there is a high genetic similarity within the population, resulting in a negative difference between individuals. At the same time, the\u0026nbsp;Fst\u0026nbsp;only between 0.00 and 0.04 (Fig. 7), which was also lower than that of other species of\u0026nbsp;Sophora\u0026nbsp;[59, 60]. In addition, small differences between populations and low genetic differentiation also indicate that these samples may be the same ancestral group and subsequently experienced geographical isolation. The occurrence of negative genetic variation may reflect the limitations of AMOVA when applied to poorly differentiated populations. Additionally, population size plays a critical role, as smaller populations reduce the evolutionary potential of wild species, thereby diminishing genetic diversity [61, 62]. Therefore, it is not excluded that the small number of samples collected in this study may lead to aberrant. Moreover, due to the small populations size of\u0026nbsp;S. alopecuroides\u0026nbsp;studied previously and the limited sample sizes, understanding of the genetic structure and variation of\u0026nbsp;S. alopecuroides\u0026nbsp;remains constrained [54, 56]. In contrast, we collected a larger number of germplasm resources from 65 regions across five provinces in northwest China. Although the limitations of previous studies have been overcome to some extent, the results still show that it is difficult to fully reveal the genetic diversity and population structure of\u0026nbsp;S. alopecuroides\u0026nbsp;populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation structure analysis in this study indicated that K = 2 is the optimal number of groups (Fig. 5). PCA and phylogenetic tree analysis further confirmed the division of the populations into two main clusters (Fig. 6). However, some populations were still divided into different clusters, which may be due to the different calculation focuses of the three analysis methods and the insufficient sample size, resulting in some populations with complex genetic relationships being divided into different clusters. Because geographical isolation also has a significant impact on the genetic structure of populations, it is a major pioneer in the formation of exotic species [63, 64]. The distribution of many\u0026nbsp;S. alopecuroides\u0026nbsp;populations across mountains and plateaus limits pollen and seed dispersal, creating barriers between populations [65]. Through the above analysis, we found that according to the characteristics of geographical, ecological and climatic conditions,\u0026nbsp;S. alopecuroides\u0026nbsp;can be divided into two clusters: north and south, with the Tianshan Mountains and the Qilian Mountains as the boundaries. The north-south geographical division is consistent with the population structure classification of the sample. However, the TSNL and ARTS populations showed more complex patterns, with higher genetic differentiation coefficients than other populations. At the same time, due to human activities, the suitable habitat area of\u0026nbsp;S. alopecuroides\u0026nbsp;has been greatly reduced, showing a fragmented pattern [34]. We speculate that the two populations were originally an ancestral population, and then experienced geographical isolation. Human factors also exacerbated the isolation effect, and TSBL may be a transitional group between the two populations. The results of phylogenetic tree analysis also verify the possibility of this situation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioclimatic redundancy analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnvironmental factors are the main limiting factors affecting the distribution and growth of plants on a large geographical scale. The differences in temperature, precipitation and other factors in different regions lead to significant differentiation in the growth adaptability of different populations to their habitats [66, 67]. To further assess the impact of environmental factors on genetic variation, we performed RDA. The phenotypic traits are the most direct indicators of the impact of environmental factors on plants. We found that the effects of environmental factors on the phenotype of S. alopecuroides seeds are manifested in external conditions such as average temperature in dry and wet seasons, average annual wind speed, evaporation, altitude and light. This is also an important factor affecting the distribution of S. alopecuroides [68, 69]. Rong et al. [34] also expressed that the future expansion trend of S. alopecuroides population was affected, and the main environmental factors were temperature and rainfall. The RDA based on the molecular level shows that the PC, ISO and PWE. PC had a strong influence on the genetic diversity of S. alopecuroides populations. This reflects that the seasonal variation of precipitation plays a key role in the gene flow and adaptability of S. alopecuroides population. ISO also has a significant effect on the genetic diversity of some populations, particularly in KLSM, QLSM and TSNL populations. Due to the relatively gentle climate change in areas with high ISO, it is conducive to the stable growth of plants for a long time, promoting gene flow and the accumulation of adaptive genetics [70]. This is also the main factor affecting the low genetic differentiation of populations in these areas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study utilized 10,584 SNP markers obtained via GBS technology, combined with seed phenotypic traits, to analyze the genetic diversity of 65 S. alopecuroides samples from 10 populations in northwest China. The results showed that the genetic diversity of S. alopecuroides populations was low and there was no obvious differentiation. The genetic variation mainly came from individuals. The population structure is mainly divided into two main clusters, which are related to environmental factors such as geographical distribution and climatic conditions. These findings underscore the need for further genetic protection and breeding efforts based on the geographical and genetic distance between populations. Therefore, on the basis of studying the genetic characteristics of S. alopecuroides populations, it is necessary to develop in situ conservation and regeneration strategies suitable for S. alopecuroides populations, such as giving priority to populations with relatively high genetic diversity and strengthening introduction measures to maximize the genetic diversity of S. alopecuroides populations. In future research, it is necessary to increase the size of the research population, which can be combined with population dynamics analysis, screening of adaptive genes, and interaction between environment and genetics. This study provides a foundation for the genetic evaluation and conservation of S. alopecuroides genetic resources in China and offers important insights for its breeding programs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGBS: genotyping-by-sequencing; SNP: single nucleotide polymorphism; PCA: principal component analysis; GPS: global satellite positioning system; DNA: deoxyribonucleic acid; PCR: polymerase chain reaction; Q-PCR: fluorescence polymerase chain reaction; He: expected heterozygosity; Ho: observed heterozygosity; AMOVA: molecular variance analysis; Fst: pairwise population differentiation; RDA: redundancy analysis; MTD: mean temperature of the driest quarter; YWS: annual mean wind; PC: precipitation of the coldest quarter; ISO: isothermality; PWE: precipitation of the wettest quarter; Pi: single nucleotide diversity index; LSD: least significant test; One-Way ANOVA: one-way analysis of variance; CV: coefficient of variation; H: Shannon\u0026rsquo;s diversity index; SD: standard deviation; Alt: Altitude; MAE: Mean Annual Evaporation; MTWE :Mean Temperature of Wettest Quarter; MAS: Mean Annual Sunshine Time\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge to Prof. Z.W Z. for specimens\u0026rsquo; identification, and appreciate the assistance of Mr. X.X.Z., Mr. Z.Y.T., Mr. W.W.R., Mr. J.W.S., and Mr. R.Y. in sample collections.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.L., M.W., and X.H. conceptualized the topic. C.L. and F.N. analyzed the data. C.L., F.N., and P.N. set up the methodology. Z.Z., W.C., and G.M. provided resources. P.N. and Z.Z. conducted the investigation. M.W., G.C., and X.H. supervised the study. C.L. and F.M. wrote the main manuscript text. M.W., and X.H. reviewed and edited the manuscript. P.N. and P.J. contributed to visualization. M.W., P.J., and W.C. acquired funding. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Corps Guiding Science and Technology Plan Project (Grant No. 2023ZD088), New Variety Cultivation Project of Shihezi University (Grant No. YZZX202303), College Students\u0026rsquo; Innovation and Entrepreneurship Training Program (Grant No. SRP2024232).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data have been deposited at the National Center for Biotechnology Information database under BioProject PRJNA1225430. The dataset associated with this study will be made publicly available under the accession numbers SRP564835, SRR32391254~SRR32391318 after the release date.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study including the collection on plants material complies with relevant institutional, national, and international guidelines and legislation.\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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eAgricultural College, Shihezi University, Shihezi 832003, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eXinjiang Yuli National Positioning Observation and Research Station for Desert Ecosystems, Yuli 841500, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eBozhou Water Conservancy Irrigation Test Station, Bole 833400, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWang RZ, Deng XX, Gao QX, Wu XL, Han L, Gao XJ, et al. 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Glob Chang Biol. 2015;21(2):947-58. doi:10.1111/gcb.12719.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sophora alopecuroides, Single nucleotide polymorphism, Genetic diversity, Seed phenotypic traits, Population structure","lastPublishedDoi":"10.21203/rs.3.rs-6061561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6061561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSophora alopecuroides L. is a perennial herb of Leguminosae. It is mainly distributed in northwest China and has important medicinal, feeding and ecological restoration values. However, in recent years, due to the intensification of human activities, its wild population resources have plummeted and genetic diversity has continued to decline. In order to fully reveal the genetic diversity and population structure characteristics of S. alopecuroides in the natural distribution area. In this study, Single nucleotide polymorphism (SNP) molecular markers combined with seed phenotypic traits were used to systematically study 65 wild samples of S. alopecuroides in northwest China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe results showed that the coefficient of variation (CV) of 8 phenotypic traits of S. alopecuroides seeds ranged from 2.87 % to 7.94 %, and the diversity index (H) ranged from 1.639 to 1.767. There was a significant correlation between phenotypic traits (P \u0026lt; 0.01), indicating that the phenotypic diversity of S. alopecuroides seeds was rich. Cluster analysis based on phenotypic traits divided the S. alopecuroides population into two groups. At the same time, based on SNP molecular markers, the genetic diversity of S. alopecuroides was relatively low. The average values of expected heterozygosity (He), observed heterozygosity (Ho) and single nucleotide diversity index (Pi) were 0.22, 0.17 and 0.19, respectively. The results of molecular variance analysis (AMOVA) showed that the level of variation between individuals (132.83%) was higher than that between populations and within populations. The Pairwise population differentiation (Fst) was between 0.00 and 0.04, which confirmed that there was no obvious differentiation among the populations. Population structure analysis, principal component analysis (PCA) and phylogenetic tree analysis roughly divided all populations into two clusters, which was consistent with the phenotypic clustering results. It is worth noting that the distribution patterns of the samples in the Southern Tianshan Mountains (TSNL) and the Altai Mountains (ARTS) are more complex. In addition, redundancy analysis (RDA) showed that the cumulative interpretation rates of environmental factors on phenotypic traits and genetic diversity were 99.75 % and 67.89 %, respectively. Among them, the mean temperature of the driest quarter (MTD) and annual mean wind speed (YWS) were identified as the primary factors influencing phenotypic traits, while the precipitation of the coldest quarter (PC), isothermality (ISO), and precipitation of the wettest quarter (PWE) have an important impact on genetic diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study provides a foundation for the genetic evaluation and conservation of S. alopecuroides genetic resources in China and offers important insights for its breeding programs.\u003c/p\u003e","manuscriptTitle":"The genetic diversity and population structure of Kudouzi (Sophora alopecuroides) population were revealed by using SNP markers combined with seed phenotypic traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 11:44:41","doi":"10.21203/rs.3.rs-6061561/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf3e7b0c-dc19-466e-9087-e6cd64f679ad","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-15T20:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 11:44:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6061561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6061561","identity":"rs-6061561","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}