Analysis of Genetic Diversity Among Wild Soybean (Glycine soja) in Hebei Province, China

Analysis of Genetic Diversity Among Wild Soybean (Glycine soja) in Hebei Province, China | 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 Data Note Analysis of Genetic Diversity Among Wild Soybean (Glycine soja) in Hebei Province, China Xintong Wu, Qike Feng, Zhanying Zhang, Xinyue Zhang, Zhi Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5909037/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Nov, 2025 Read the published version in BMC Genomic Data → Version 1 posted 10 You are reading this latest preprint version Abstract Objectives: Soybean serves as a crucial source of protein and oil. Wild soybean (Glycine soja) shares genetic similarities with cultivated soybean (Glycine max) but exhibits richer diversity due to lower genetic bottlenecks. The high allelic diversity in wild soybeans provides traits for environmental adaptation, which is useful for cultivated soybeans through breeding. Considering that soybeans originated in northern China and that Hengshui Lake, as a wetland environment, plays a crucial role in preserving species diversity, 17 wild soybean resources at this site were collected and then re-sequenced on the Illumina NovaSeq6000 platform with a depth of 10×. Data description: In this study, we collected 17 wild soybean accessions from Hengshui Lake in Hebei Province, China, and performed re-sequencing on the Illumina NovaSeq6000 platform, followed by SNPs identification. Subsequently, we incorporated an additional 62 wild soybean genomic datasets and utilized ADMIXTURE, neighbor-joining tree, and principal component analysis to elucidate population characteristics. The results revealed that these wild soybean accessions could be divided into seven distinct subpopulations. Wild soybean re-sequenced data soybean population genomics Objective Soybean plays a significant role as a primary source of protein feed and vegetable oil. Wild soybean ( Glycine soja Sieb. & Zucc.), as the close ancestral species of cultivated soybean, exhibits numerous genetic similarities to its domesticated counterpart. Due to genetic bottlenecks and human selection, cultivated soybeans exhibit much lower genetic diversity compared to their wild relatives [1]. In contrast, wild soybeans possess a richer genetic diversity, such as high protein, biotic and abiotic stress tolerances [2,3,4], making them a potential source of beneficial traits or genes for cultivated soybeans. Since there is no reproductive barrier between wild and cultivated soybeans, wild soybeans with high allelic diversity can be a resource for genes that adapt to specific environmental conditions, which can be reintroduced into domesticated soybeans through breeding [5,6]. Since soybeans originated in northern China, and Hengshui Lake, as wetland environment, has played a crucial role in preserving species diversity, we selected this site to conduct sampling of wild soybeans. Ultimately, we collected 17 samples of wild soybean resources, which were then re-sequenced on the Illumina NovaSeq6000 platform a minimum depth of 10×, and identified a total of 9,686,569 SNPs. Subsequently, we incorporated an additional 62 wild soybean genomic datasets and utilized ADMIXTURE, neighbor-joining tree, and principal component analysis to elucidate population characteristics. The results revealed that these wild soybean accessions could be divided into seven distinct subpopulations. Data description Samples of Glycine soja were collected from Hengshui Lake (37°31′39″ N, 115°27′45″ E) in Hebei province. High-quality genomic DNA was extracted from leaf tissues using DNAsecure Plant Kit (TIANGEN). Quantification and quality control of extracted DNA were conducted using a NanoDrop 2000 spectrophotometer (Thermo Fischer Scientific), garose gel electrophoresis, and a Qubit fluorometer (Invitrogen). Subsequently, qualified DNA samples (>1.5 ug, OD260/280=1.8-2.0) were randomly broken into 350 bp fragments by a Covaris crusher. For library preparation, TruSeq Library Construction Kit (Illumina, San Diego, CA, USA) was employed following manufacturer instructions. Preliminary quantification was performed using a Qubit2.0, and the library was diluted to 1 ng/µl. The insert size was verified using an Agilent 2100, and the effective concentration (>2 nM) was accurately quantified using Q-PCR to ensure quality. Qualified libraries were pooled and sequenced using the Illumina NovaSeq6000 platform in PE150 mode. Quality control of the raw data was conducted using fastp (v.0.23.2) [7], both ends of low-quality sequences were trimmed. The cleaned data were then aligned to the soybean reference genome [8] using Sentieon [9] with parameters BWA (v.0.7.12) [10]. Here, a total of 79 datasets (including 62 previously reported wild soybean genomic datasets, https://www.ncbi.nlm.nih.gov/sra/?term=(SRP045129)+AND+%22Glycine+soja%22%5Borgn%3A__txid3848%5D+++%EF%BC%89) were utilized for further analysis. Sentieon was used to detect SNPs and InDels, which were subsequently annotated using ANNOVAR software [11]. SNPs with a RMS Mapping Quality (MQ) below 40, genotype quality (GQ) below 5 and a genotype depth (DP) below 4 were redefined as missing. After SNP filtering with the following conditions: QC < 20, MAF 0.2, 9,686,569 SNPs were obtained. Indels were similarly analyzed under the same criteria, resulted in a total of 1,650,281 indels. These high-quality SNPs were used to analyze the population structure by ADMIXTURE v.1.3.0 [12]. Initially, we determined the optimal number of ancestral populations (K) by conducting cross-validation (CV) across values ranging from 2 to 6. The minimum CV error indicated K=7 as the optimal number, leading to the division of all 79 cultivars into seven subgroups, designated as P1 to P7. These seven subpopulations comprised 4, 8, 4, 29, 10, 4, and 20 individuals, respectively, with P4 being the largest. Additionally, we constructed a neighbor-joining tree for these 79 wild soybeans using the ggtree package [13] in R (v.4.4.1). The visualization revealed seven clusters that corresponded to the results of our population analysis, mutually validating the accuracy of the population structure. Subsequently, we performed Principal Component Analysis (PCA), which similarly grouped these wild soybeans into the same seven subpopulations (Table 1). Table 1 : Overview of data files/data sets. Label Name of data file/data set File types (file extension) Data repository and identifier (DOI or accession number) Data file 1 Population structure of 79 wild soybean accessions Portable document format (.pdf) Figshare (https://doi.org/10.6084/m9.figshare.28282280.v1) Data set 1 Resequencing of 17 Wild Soybeans Fastq file (fastq.gz) CNCB (https://ngdc.cncb.ac.cn/gsa/s/518kUh0o) Limitations Wild soybean ( Glycine soja ), a valuable germplasm resource, is characterized by its high genetic diversity and adaptability to diverse environmental conditions. This genetic richness makes it a prime target for studying crop improvement and genetic resistance. In this study, we have only collected and re-sequenced wild soybeans from the Hengshui Lake, which may not fully capture the genetic diversity present within the wild soybean population. Besides that, the environmental and ecological factors that may influence the phenotypic expression of these wild soybean resources were not considered in this study, thus limiting the understanding of genotype-phenotype correlations. Declarations Ethics approval and consent to participate The current study complies with relevant institutional, national, and international guidelines and legislation for experimental research and field studies on plants (either cultivated or wild), including the collection of plant material. Permissions were obtained to collect Glycine max samples. Sampling was conducted in Institute of Cereal and Oil Crops (ICOC), Hebei Academy of Agricultural and Forestry Sciences field plots and permission was granted by the ICOC to perform data collection. Consent for publication Not applicable. Availability of data and materials The Data file 1 in this Data Note can be freely and openly accessed on FigShare (https://doi.org/10.6084/m9.figshare.28282280.v1) [14]. Sequence data that support the findings of this study can be freely and openly accessed on the Genome Sequence Archive in National Genomics Data Center China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences under GSA: CRA022612 (https://ngdc.cncb.ac.cn/gsa/s/518kUh0o) [15,16,17]. Competing interests The authors declare no conflicts of interest. Funding This study is supported by China Agriculture Research System of MOF and MARA (CARS-04-PS06), Hebei Agriculture Research System (HBCT2023040101), National Natural Science Foundation of China (32172100) Authors’ contributions XW data curation and writing-original draft; QF resources and data analysis; ZZ resources; XZ data analysis; ZL data curation and edited the manuscript; QY visualization of the work; XS supervision and fundings; MZ project administration; LY conceptualization and fundings. Acknowledgements Not applicable. References Kofsky J, Zhang H, Song BH. Genetic architecture of early vigor traits in wild soybean. Int J Mol Sci. 2020; 21(9): 3105. Qi X, Li MW, Xie M, Liu X, Ni M, Shao G, Song C, Kay-Yuen Yim A, Tao Y, Wong FL, Isobe S, Wong CF, Wong KS, Xu C, Li C, Wang Y, Guan R, Sun F, Fan G, Xiao Z, Zhou F, Phang TH, Liu X, Tong SW, Chan TF, Yiu SM, Tabata S, Wang J, Xu X, Lam HM. Identification of a novel salt tolerance gene in wild soybean by whole-genome sequencing. Nat Commun. 2014; 5: 4340. Bian XH, Li W, Niu CF, Wei W, Hu Y, Han JQ, Lu X, Tao JJ, Jin M, Qin H, Zhou B, Zhang WK, Ma B, Wang GD, Yu DY, Lai YC, Chen SY, Zhang JS. A class B heat shock factor selected for during soybean domestication contributes to salt tolerance by promoting flavonoid biosynthesis. New Phytol. 2020; 225(1): 268-283. Jin T, Sun Y, Shan Z, He J, Wang N, Gai J, Li Y. Natural variation in the promoter of GsERD15B affects salt tolerance in soybean. Plant Biotechnol J. 2021; 19(6): 1155-1169. Liu Y, Du H, Li P, Shen Y, Peng H, Liu S, Zhou GA, Zhang H, Liu Z, Shi M, Huang X, Li Y, Zhang M, Wang Z, Zhu B, Han B, Liang C, Tian Z. Pan-genome of wild and cultivated soybeans. Cell. 2020; 182(1): 162-176. Zheng Y, Cao X, Zhou Y, Ma S, Wang Y, Li Z, Zhao D, Yang Y, Zhang H, Meng C, Xie Z, Sui X, Xu K, Li Y, Zhang CS. Purines enrich root-associated Pseudomonas and improve wild soybean growth under salt stress. Nat Commun. 2024; 15(1): 3520. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018; 34(17): i884-i890. Jia KH, Zhang X, Li LL, Shi TL, Liu D, Yang Y, Cong Y, Li R, Pu Y, Gong Y, Chen X, Si YJ, Tian R, Qian Z, Ding H, Li N. Telomere-to-telomere genome assemblies of cultivated and wild soybean provide insights into evolution and domestication under structural variation. Plant Commun. 2024; 5(8): 100919. Pei S, Liu T, Ren X, Li W, Chen C, Xie Z. Benchmarking variant callers in next-generation and third-generation sequencing analysis. Brief Bioinform. 2021; 22(3):bbaa148. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25(16): 2078-2079. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38(16): e164. Liu CC, Shringarpure S, Lange K, Novembre J. Exploring population structure with admixture models and principal component analysis. Methods Mol Biol. 2020; 2090: 67-86. Yu G. Using ggtree to visualize data on tree-like structures. Curr Protocols Bioinf. 2020; 69: e96. Wu XT. Population structure of 79 wild soybean accessions.pdf. Figure. https://doi.org/10.6084/m9.figshare.28282280.v1. Wu XT. 17 Wild Soybean (Glycine soja) in Hebei Province (CRA022612). CNCB. 2025. https://ngdc.cncb.ac.cn/gsa/s/518kUh0o. Chen T, Chen X, Zhang S, Zhu J, Tang B, Wang A, Dong L, Zhang Z, Yu C, Sun Y, Chi L, Chen H, Zhai S, Sun Y, Lan L, Zhang X, Xiao J, Bao Y, Wang Y, Zhang Z, Zhao W. The genome sequence archive family: toward explosive data growth and diverse data types. Genom Proteom Bioinf. 2021; 19(4): 578-583. CNCB-NGDC Members and Partners. Database resources of the national genomics data center, china national center for bioinformation in 2022. Nucleic Acids Res. 2022; 50(D1): D27-D38. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Nov, 2025 Read the published version in BMC Genomic Data → Version 1 posted Editorial decision: Revision requested 10 Mar, 2025 Reviews received at journal 14 Feb, 2025 Reviewers agreed at journal 13 Feb, 2025 Reviews received at journal 12 Feb, 2025 Reviewers agreed at journal 12 Feb, 2025 Reviewers agreed at journal 04 Feb, 2025 Reviewers invited by journal 03 Feb, 2025 Editor assigned by journal 02 Feb, 2025 Submission checks completed at journal 30 Jan, 2025 First submitted to journal 26 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5909037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":410127936,"identity":"88b95306-5606-425a-96c9-7a14b2649990","order_by":0,"name":"Xintong Wu","email":"","orcid":"","institution":"Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xintong","middleName":"","lastName":"Wu","suffix":""},{"id":410127938,"identity":"39bed872-3654-4a01-8bad-922713047640","order_by":1,"name":"Qike Feng","email":"","orcid":"","institution":"Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qike","middleName":"","lastName":"Feng","suffix":""},{"id":410127941,"identity":"4524b3ce-a59d-427e-995e-7eb14dcb67cf","order_by":2,"name":"Zhanying Zhang","email":"","orcid":"","institution":"Hengshui People's Procuratorate","correspondingAuthor":false,"prefix":"","firstName":"Zhanying","middleName":"","lastName":"Zhang","suffix":""},{"id":410127944,"identity":"b2a4881f-0148-4076-af45-a8f5a5e3a3cb","order_by":3,"name":"Xinyue Zhang","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Zhang","suffix":""},{"id":410127947,"identity":"1c970384-44b7-423f-8e9c-83df9b0ab4c0","order_by":4,"name":"Zhi Liu","email":"","orcid":"","institution":"Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Liu","suffix":""},{"id":410127949,"identity":"d3e38887-21c8-4376-87d5-3330000798e8","order_by":5,"name":"Qing Yang","email":"","orcid":"","institution":"Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Yang","suffix":""},{"id":410127950,"identity":"e448a6e5-e417-46ae-b325-eceae1b67fc8","order_by":6,"name":"Xiaolei Shi","email":"","orcid":"","institution":"Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"Shi","suffix":""},{"id":410127951,"identity":"6413f02f-5b4a-4402-b0ac-8e1a7b1d9261","order_by":7,"name":"Mengchen Zhang","email":"","orcid":"","institution":"Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mengchen","middleName":"","lastName":"Zhang","suffix":""},{"id":410127952,"identity":"29b31552-1a4b-4ac9-ad69-a0b29246887a","order_by":8,"name":"Long Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDCCAzAGM/PxHx8qJOTkidfCzpYgOeOMhbFhA9Fa+HkMpHnbKhIRIjgA3+3Dj1983HFY3pyZx8CYd55EAmMD88NHN/BokTyXZmY588xhw53NbAWJc7dJ5LEzsBkb5+DRYnCGwcyYt+0w44bDzBsOvN0mUczYwMMmjV8L+zeQFvsNhxkMG3jnSCQ2HCCohcf4MVBL4obDLMaMvA1EaJE8w1PGOLMtPXnDYbY0xhnHJIwNmwn4he8M++YPH9usbTecP3yM4UNNnZw8e/PDx/i0AAGbBANDMxKfGb9ysJIPDAx1hJWNglEwCkbByAUAKBRQKHgsOHsAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2025-01-27 03:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5909037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5909037/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12863-025-01332-3","type":"published","date":"2025-11-04T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95564613,"identity":"83456e13-9417-4961-b69e-8eb975dbcc28","added_by":"auto","created_at":"2025-11-10 16:10:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":369116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5909037/v1/03d13739-b5b5-4a73-9bf6-af68945bc286.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Genetic Diversity Among Wild Soybean (Glycine soja) in Hebei Province, China","fulltext":[{"header":"Objective","content":"\u003cp\u003eSoybean plays a significant role as a primary source of protein feed and vegetable oil. Wild soybean (\u003cem\u003eGlycine soja\u003c/em\u003e Sieb. \u0026amp; Zucc.), as the close ancestral species of cultivated soybean, exhibits numerous genetic similarities to its domesticated counterpart. Due to genetic bottlenecks and human selection, cultivated soybeans exhibit much lower genetic diversity compared to their wild relatives [1]. In contrast, wild soybeans possess a richer genetic diversity, such as high protein, biotic and abiotic stress tolerances [2,3,4], making them a potential source of beneficial traits or genes for cultivated soybeans. Since there is no reproductive barrier between wild and cultivated soybeans, wild soybeans with high allelic diversity can be a resource for genes that adapt to specific environmental conditions, which can be reintroduced into domesticated soybeans through breeding [5,6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince soybeans originated in northern China, and Hengshui Lake, as wetland environment, has played a crucial role in preserving species diversity, we selected this site to conduct sampling of wild soybeans. Ultimately, we collected 17 samples of wild soybean resources, which were then re-sequenced on the Illumina NovaSeq6000 platform a minimum depth of 10\u0026times;, and identified a total of 9,686,569 SNPs. Subsequently, we incorporated an additional 62 wild soybean genomic datasets and utilized ADMIXTURE, neighbor-joining tree, and principal component analysis to elucidate population characteristics. The results revealed that these wild soybean accessions could be divided into seven distinct subpopulations.\u003c/p\u003e"},{"header":"Data description","content":"\u003cp\u003eSamples of \u003cem\u003eGlycine soja\u003c/em\u003e were collected from Hengshui Lake (37\u0026deg;31\u0026prime;39\u0026Prime; N, 115\u0026deg;27\u0026prime;45\u0026Prime; E) in Hebei province. High-quality genomic DNA was extracted from leaf tissues using DNAsecure Plant Kit (TIANGEN). Quantification and quality control of extracted DNA were conducted using a NanoDrop 2000 spectrophotometer (Thermo Fischer Scientific), garose gel electrophoresis, and a Qubit fluorometer (Invitrogen). Subsequently, qualified DNA samples (\u0026gt;1.5 ug, OD260/280=1.8-2.0) were randomly broken into 350 bp fragments by a Covaris crusher.\u003c/p\u003e\n\u003cp\u003eFor library preparation, TruSeq Library Construction Kit (Illumina, San Diego, CA, USA) was employed following manufacturer instructions. \u0026nbsp;Preliminary quantification was performed using a Qubit2.0, and the library was diluted to 1 ng/\u0026micro;l. \u0026nbsp;The insert size was verified using an Agilent 2100, and the effective concentration (\u0026gt;2 nM) was accurately quantified using Q-PCR to ensure quality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQualified libraries were pooled and sequenced using the Illumina NovaSeq6000 platform in PE150 mode. Quality control of the raw data was conducted using fastp (v.0.23.2) [7], both ends of low-quality sequences were trimmed. The cleaned data were then aligned to the soybean reference genome [8] using Sentieon [9] with parameters BWA (v.0.7.12) [10].\u003c/p\u003e\n\u003cp\u003eHere, a total of 79 datasets (including 62 previously reported wild soybean genomic datasets, https://www.ncbi.nlm.nih.gov/sra/?term=(SRP045129)+AND+%22Glycine+soja%22%5Borgn%3A__txid3848%5D+++%EF%BC%89) were utilized for further analysis. Sentieon was used to detect SNPs and InDels, which were subsequently annotated using ANNOVAR software [11]. SNPs with a RMS Mapping Quality (MQ) below 40, genotype quality (GQ) below 5 and a genotype depth (DP) below 4 were redefined as missing. After SNP filtering with the following conditions: QC\u0026thinsp;\u0026lt;\u0026thinsp;20, MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05, missing\u0026thinsp;\u0026gt;\u0026thinsp;0.2, 9,686,569 SNPs were obtained. Indels were similarly analyzed under the same criteria, resulted in a total of 1,650,281 indels.\u003c/p\u003e\n\u003cp\u003eThese high-quality SNPs were used to analyze the population structure by ADMIXTURE v.1.3.0 [12]. Initially, we determined the optimal number of ancestral populations (K) by conducting cross-validation (CV) across values ranging from 2 to 6. The minimum CV error indicated K=7 as the optimal number, leading to the division of all 79 cultivars into seven subgroups, designated as P1 to P7. These seven subpopulations comprised 4, 8, 4, 29, 10, 4, and 20 individuals, respectively, with P4 being the largest. Additionally, we constructed a neighbor-joining tree for these 79 wild soybeans using the ggtree package [13] in R (v.4.4.1). The visualization revealed seven clusters that corresponded to the results of our population analysis, mutually validating the accuracy of the population structure. Subsequently, we performed Principal Component Analysis (PCA), which similarly grouped these wild soybeans into the same seven subpopulations (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Overview of data files/data sets.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5461%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of data file/data set\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFile types\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(file extension)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.1064%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData repository and identifier (DOI or accession number)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9433%;\"\u003e\n \u003cp\u003eData file 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5461%;\"\u003e\n \u003cp\u003ePopulation structure of 79 wild soybean accessions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003ePortable document format (.pdf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.1064%;\"\u003e\n \u003cp\u003eFigshare (https://doi.org/10.6084/m9.figshare.28282280.v1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.9433%;\"\u003e\n \u003cp\u003eData set 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5461%;\"\u003e\n \u003cp\u003eResequencing of 17 Wild Soybeans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4043%;\"\u003e\n \u003cp\u003eFastq file (fastq.gz)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.1064%;\"\u003e\n \u003cp\u003eCNCB (https://ngdc.cncb.ac.cn/gsa/s/518kUh0o)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eWild soybean (\u003cem\u003eGlycine soja\u003c/em\u003e), a valuable germplasm resource, is characterized by its high genetic diversity and adaptability to diverse environmental conditions. This genetic richness makes it a prime target for studying crop improvement and genetic resistance. In this study, we have only collected and re-sequenced wild soybeans from the Hengshui Lake, which may not fully capture the genetic diversity present within the wild soybean population. Besides that, the environmental and ecological factors that may influence the phenotypic expression of these wild soybean resources were not considered in this study, thus limiting the understanding of genotype-phenotype correlations.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eThe current study complies with relevant institutional, national, and international guidelines and legislation for experimental research and field studies on plants (either cultivated or wild), including the collection of plant material. Permissions were obtained to collect Glycine max samples. Sampling was conducted in Institute of Cereal and Oil Crops (ICOC), Hebei Academy of Agricultural and Forestry Sciences field plots and permission was granted by the ICOC to perform data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;The Data file 1 in this Data Note can be freely and openly accessed on FigShare (https://doi.org/10.6084/m9.figshare.28282280.v1) [14]. Sequence data that support the findings of this study can be freely and openly accessed on the Genome Sequence Archive in National Genomics Data Center China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences under GSA: CRA022612 (https://ngdc.cncb.ac.cn/gsa/s/518kUh0o) [15,16,17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eThis study is supported by China Agriculture Research System of MOF and MARA (CARS-04-PS06), Hebei Agriculture Research System (HBCT2023040101), National Natural Science Foundation of China (32172100)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003cbr\u003e\u003c/strong\u003eXW data curation and writing-original draft; QF\u0026nbsp;resources and\u0026nbsp;data analysis; ZZ resources; XZ\u0026nbsp;data analysis;\u0026nbsp;ZL\u0026nbsp;data curation\u0026nbsp;and edited the manuscript;\u0026nbsp;QY visualization of the work; XS supervision and fundings; MZ\u0026nbsp;project administration; LY conceptualization and fundings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKofsky J, Zhang H, Song BH. Genetic architecture of early vigor traits in wild soybean. Int J Mol Sci. 2020; 21(9): 3105.\u003c/li\u003e\n \u003cli\u003eQi X, Li MW, Xie M, Liu X, Ni M, Shao G, Song C, Kay-Yuen Yim A, Tao Y, Wong FL, Isobe S, Wong CF, Wong KS, Xu C, Li C, Wang Y, Guan R, Sun F, Fan G, Xiao Z, Zhou F, Phang TH, Liu X, Tong SW, Chan TF, Yiu SM, Tabata S, Wang J, Xu X, Lam HM. Identification of a novel salt tolerance gene in wild soybean by whole-genome sequencing. Nat Commun. 2014; 5: 4340.\u003c/li\u003e\n \u003cli\u003eBian XH, Li W, Niu CF, Wei W, Hu Y, Han JQ, Lu X, Tao JJ, Jin M, Qin H, Zhou B, Zhang WK, Ma B, Wang GD, Yu DY, Lai YC, Chen SY, Zhang JS. A class B heat shock factor selected for during soybean domestication contributes to salt tolerance by promoting flavonoid biosynthesis. New Phytol. 2020; 225(1): 268-283.\u003c/li\u003e\n \u003cli\u003eJin T, Sun Y, Shan Z, He J, Wang N, Gai J, Li Y. Natural variation in the promoter of GsERD15B affects salt tolerance in soybean. Plant Biotechnol J. 2021; 19(6): 1155-1169.\u003c/li\u003e\n \u003cli\u003eLiu Y, Du H, Li P, Shen Y, Peng H, Liu S, Zhou GA, Zhang H, Liu Z, Shi M, Huang X, Li Y, Zhang M, Wang Z, Zhu B, Han B, Liang C, Tian Z. Pan-genome of wild and cultivated soybeans. Cell. 2020; 182(1): 162-176.\u003c/li\u003e\n \u003cli\u003eZheng Y, Cao X, Zhou Y, Ma S, Wang Y, Li Z, Zhao D, Yang Y, Zhang H, Meng C, Xie Z, Sui X, Xu K, Li Y, Zhang CS. Purines enrich root-associated Pseudomonas and improve wild soybean growth under salt stress. Nat Commun. 2024; 15(1): 3520.\u003c/li\u003e\n \u003cli\u003eChen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018; 34(17): i884-i890.\u003c/li\u003e\n \u003cli\u003eJia KH, Zhang X, Li LL, Shi TL, Liu D, Yang Y, Cong Y, Li R, Pu Y, Gong Y, Chen X, Si YJ, Tian R, Qian Z, Ding H, Li N. Telomere-to-telomere genome assemblies of cultivated and wild soybean provide insights into evolution and domestication under structural variation. Plant Commun. 2024; 5(8): 100919.\u003c/li\u003e\n \u003cli\u003ePei S, Liu T, Ren X, Li W, Chen C, Xie Z. Benchmarking variant callers in next-generation and third-generation sequencing analysis. Brief Bioinform. 2021; 22(3):bbaa148.\u003c/li\u003e\n \u003cli\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25(16): 2078-2079.\u003c/li\u003e\n \u003cli\u003eWang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38(16): e164.\u003c/li\u003e\n \u003cli\u003eLiu CC, Shringarpure S, Lange K, Novembre J. Exploring population structure with admixture models and principal component analysis. Methods Mol Biol. 2020; 2090: 67-86.\u003c/li\u003e\n \u003cli\u003eYu G. Using ggtree to visualize data on tree-like structures. Curr Protocols Bioinf. 2020; 69: e96.\u003c/li\u003e\n \u003cli\u003eWu XT. Population structure of 79 wild soybean accessions.pdf. Figure. https://doi.org/10.6084/m9.figshare.28282280.v1.\u003c/li\u003e\n \u003cli\u003eWu XT. 17 Wild Soybean (Glycine soja) in Hebei Province (CRA022612). CNCB. 2025. https://ngdc.cncb.ac.cn/gsa/s/518kUh0o.\u003c/li\u003e\n \u003cli\u003eChen T, Chen X, Zhang S, Zhu J, Tang B, Wang A, Dong L, Zhang Z, Yu C, Sun Y, Chi L, Chen H, Zhai S, Sun Y, Lan L, Zhang X, Xiao J, Bao Y, Wang Y, Zhang Z, Zhao W. The genome sequence archive family: toward explosive data growth and diverse data types. Genom Proteom Bioinf. 2021; 19(4): 578-583.\u003c/li\u003e\n \u003cli\u003eCNCB-NGDC Members and Partners. Database resources of the national genomics data center, china national center for bioinformation in 2022. Nucleic Acids Res. 2022; 50(D1): D27-D38.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wild soybean, re-sequenced data, soybean population genomics","lastPublishedDoi":"10.21203/rs.3.rs-5909037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5909037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Objectives:\nSoybean serves as a crucial source of protein and oil. Wild soybean (Glycine soja) shares genetic similarities with cultivated soybean (Glycine max) but exhibits richer diversity due to lower genetic bottlenecks. The high allelic diversity in wild soybeans provides traits for environmental adaptation, which is useful for cultivated soybeans through breeding. Considering that soybeans originated in northern China and that Hengshui Lake, as a wetland environment, plays a crucial role in preserving species diversity, 17 wild soybean resources at this site were collected and then re-sequenced on the Illumina NovaSeq6000 platform with a depth of 10×.\nData description:\nIn this study, we collected 17 wild soybean accessions from Hengshui Lake in Hebei Province, China, and performed re-sequencing on the Illumina NovaSeq6000 platform, followed by SNPs identification. Subsequently, we incorporated an additional 62 wild soybean genomic datasets and utilized ADMIXTURE, neighbor-joining tree, and principal component analysis to elucidate population characteristics. The results revealed that these wild soybean accessions could be divided into seven distinct subpopulations.","manuscriptTitle":"Analysis of Genetic Diversity Among Wild Soybean (Glycine soja) in Hebei Province, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 08:20:23","doi":"10.21203/rs.3.rs-5909037/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-10T16:29:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-14T14:18:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286583936495900290683665836479945412635","date":"2025-02-14T00:19:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-12T20:49:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36398391589869338990497233400431490784","date":"2025-02-12T09:13:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135174099080418126531536567463098250417","date":"2025-02-04T05:25:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-03T15:18:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-02T14:19:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-30T11:09:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomic Data","date":"2025-01-27T03:18:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"748219c3-1f13-4ae4-a9d1-89ed6e15b62e","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:08:34+00:00","versionOfRecord":{"articleIdentity":"rs-5909037","link":"https://doi.org/10.1186/s12863-025-01332-3","journal":{"identity":"bmc-genomic-data","isVorOnly":false,"title":"BMC Genomic Data"},"publishedOn":"2025-11-04 15:57:51","publishedOnDateReadable":"November 4th, 2025"},"versionCreatedAt":"2025-02-03 08:20:23","video":"","vorDoi":"10.1186/s12863-025-01332-3","vorDoiUrl":"https://doi.org/10.1186/s12863-025-01332-3","workflowStages":[]},"version":"v1","identity":"rs-5909037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5909037","identity":"rs-5909037","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}