A comparison of Chinese wild and cultivar soybean with European soybean collections on genetic diversity by Genome-Wide Scan

A comparison of Chinese wild and cultivar soybean with European soybean collections on genetic diversity by Genome-Wide Scan | 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 A comparison of Chinese wild and cultivar soybean with European soybean collections on genetic diversity by Genome-Wide Scan Jiangyuan Xu, Xindong Yao, Yuqing Lu, Rittler Leopold, Yongzhe Gu, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4647180/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2025 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 11 You are reading this latest preprint version Abstract Although soybeans ( Glycine max [L.] Merr.) originated in China and have spread worldwide, artificial selection for different breeding targets and methods in various regions can alter the genetic makeup of soybeans, enabling them to adapt to different environments. China has established a soybean germplasm gene bank that stores over 30,000 soybean germplasms from all over the world, but it contains few modern European varieties. The selective sweep analysis is an effective method for evaluating genetic diversity among populations and subpopulations. To compare the genetic diversity between Chinese and European germplasms, we genotyped 797 European varieties, 804 Chinese elite cultivars and landraces, and 54 Chinese wild varieties using the ZDX1 array, respectively. An analysis of 158,315 SNPs demonstrated a higher genetic diversity in Chinese wild soybeans and cultivars. Moreover, population structure findings indicated that European varieties possess partial Chinese ancestry. The joint analysis of pi, F ST and XP-CLR identified 140 selected regions between Chinese and European germplasms in total. Specifically, the Chinese collection had 124 regions distributed across 15 chromosomes, while the European collection had 16 regions spread over 10 chromosomes. The QTLs identified within these selected regions highlight the significant differences in breeding targets across regions, providing a scientific basis for both Chinese and European breeders to utilize these germplasm resources. Genetic diversity Selective sweep analysis European soybean Chinese soybean Figures Figure 1 Figure 2 Figure 3 Introduction Soybean ( Glycine max [L.] Merr.), as an essential source of vegetable protein and oil, traces its origin to China, boasting a cultivation history exceeding 3000 years (Li et al. 2008 ).. The crop made its way to Korea around the 2nd century BCE from China, subsequently reaching Japan from Korea. By the 6th century CE, late-ripening soybeans from southern China were introduced to the Kyushu region of Japan via maritime routes. Soybeans were brought to the Netherlands before 1737 and to the United States from China via the United Kingdom in 1765. The earliest records of soybeans in Argentina date back to 1882, with Brazil's introduction of the crop occurring relatively later. Artificial selection across different regions for various breeding objectives has significantly influenced the genetic diversity of soybeans, allowing them to adapt to diverse environments. According to the Food and Agriculture Organization (FAO), the global inventory of soybean germplasm resources surpasses 200,000 entries, including duplicates. The Chinese National Genebank holds over 43,000 cultivated soybean germplasms and more than 10,000 annual wild soybean germplasms (Insititute of Crop Sciences 2013). China leads globally with a collection exceeding 30,000 soybean germplasms, including more than 23,000 cultivated and over 7,000 wild types (Qiu et al. 2013 ); meanwhile, all soybeans in the United States are of foreign origin, with the country now preserving more than 20,000 soybean germplasms. Prior to World War II, soybean cultivation and production received limited attention. Post-1945, global soybean production saw a significant expansion, spearheaded by the United States, where soybean output surpassed China's by 1954. Since the 1970s, South American countries like Brazil, Argentina, and Paraguay have vigorously developed soybean production, with Brazil's yield exceeding China's by 1974. China's soybean germplasm resources have not only played a crucial role in domestic breeding and production but also have made indelible contributions to global soybean breeding and production. In China, soybeans rank as the fourth largest crop after corn, rice, and wheat. Between 2021 and 2022, China's total soybean production ranged from 16.4 to 20.28 million tons, with imports reaching 91.08 to 96.52 million tons, resulting in a self-sufficiency rate of merely 15–18%. The country's high dependency on soybean imports and the significant risk to its industry are notable. Against the backdrop of limited arable land and in the effort not to compete with staple crops, enhancing per unit area yield and increasing total soybean production are key strategies to alleviate import pressure. Currently, China has developed approximately 3000 varieties (Qiu & Wang 2007 , 2018 ). The modern breeding process grapples with the challenge of a narrow genetic base, with most varieties developed in China between 1923 and 1995 in the main soybean-producing regions of Northeast and Huang-Huai-Hai using local varieties as parents, leading to a narrowing genetic basis due to the over-concentration of parental sources (Xiong et al. 2010 ). Although Chinese soybeans possess a vast gene pool, traditional breeding has utilized only a fraction (Wang et al. 2006 ). Recently, the cultivation and utilization of key parental lines in breeding have resulted in closely related breeds with uniform traits, highlighting the genetic vulnerability of these varieties without breakthroughs in yield. Employing foreign germplasm as parents presents a viable and economical solution to this issue. Germplasm collections from various regions serve as important sources of beneficial genetic variation, and the introduction of foreign varieties as parents can effectively address the problem of a narrow genetic base (Wang et al. 2017 ; Hegstad et al. 2019 ). The soybean, native to China, has seen extensive research into the genetic relationships between Chinese and foreign germplasm. Studies spanning various countries, including China-USA (Lijuan et al. 1997 ; Li et al. 2001 ; Liu et al. 2017 ) and China-Japan (Guan et al. 2010 ), have laid a theoretical foundation for the introduction and utilization of foreign germplasm in Chinese breeding programs. The genetic relationships uncovered in these studies are pivotal. For instance, research by Guan Rongxia et al. ( 2007 ) using SSR markers to analyze the genetic diversity among 32 Chinese soybean varieties and 40 foreign soybean ancestors introduced to China revealed significant findings. Across 22 SSR loci, 170 allelic variations were detected, with Chinese and introduced foreign soybeans averaging 60 and 69 allelic variations, respectively, both showing a genetic diversity index of 0.71. The study identified 48 unique allelic variations in foreign varieties, compared to 22 in Chinese soybeans, with a significant frequency difference in shared allelic variations between Chinese and foreign soybeans. Cluster analysis indicated substantial differences between Chinese breeds and their foreign ancestors. Genomic composition analysis showed that the introduction of Amsoy (USA) and Tokachi Nagaha (Japan) added 23 unique foreign allelic variations to five Chinese soybean breeds, with a retention rate of 29.13%. However, the retention of these allelic variations varies across different genetic backgrounds, suggesting that many unique allelic variations remain unutilized and could further contribute to soybean improvement in China. Further studies by Qin Jun et al. (2006) on the pedigrees of widely cultivated varieties Suinong 14 and Hefeng 25, which contain foreign genetic material, used SSR markers for genomic analysis. Allelic variations from Tokachi Nagaha (Japanese germplasm) and Amsoy (American germplasm) passed on to Hefeng 25 and Suinong 14 were found to be associated with 100-seed weight and oil content, indicating potential functional gene clusters in certain chromosomal regions. Additionally, American and Japanese germplasms, such as Amsoy, Clark63, Mamoton, and Tokachi Nagaha, have significantly contributed genetic diversity to popular Chinese varieties like Hefeng 25, Suinong 14 (Qin et al. 2006 ), and Zhonghuang 13 (Zhang et al. 2020 ). These findings underscore the importance of integrating global genetic resources into Chinese soybean breeding, demonstrating how foreign germplasm can enrich genetic diversity and contribute to crop improvement. The research not only facilitates the strategic use of global soybean genetic resources but also highlights the potential for discovering and utilizing unique genetic variations for future soybean enhancement. Soybean was first introduced from China to Europe in the 18th century, and soybean harvested area and yield in Europe began to increase rapidly in the 21st century, and the gap with China is getting smaller (Fogelberg & Recknagel 2017 ; Karges et al. 2022 ). Northeast China is the main soybean producing area in China, which is also at the same latitude with the central and north Europe, thus introduce early maturity varieties from Europe can expand the genetic background, thereby improve the main cultivars in Northeast China. But several analysis were carried on between European cultivars and Chinese germplasm bank. In this study, a high-throughput genotyping approach (Sun et al. 2022a ) was used to sequence the germplasm from Chinese gene bank and the introduced varieties from Europe. This study aimed to compare the genetic diversity and analyze population structure differences between Chinese and European soybean varieties, enhancing our understanding of their adaptation and evolution. Additionally, it aims to identify genomic regions that may have undergone selective sweeps in soybean breeding and explore their significance for future soybean improvement efforts in Northeast China. Materials and methods Plant materials To examine the genetic diversity between Chinese and European soybean germplasms, this study compiled a dataset of 1,655 accessions. Of these, 858 accessions originated from China, comprising 804 elite cultivars and landraces, referred to as Chinese cultivars, along with 54 Glycine soja specimens, denoting the wild soybean varieties native to China. Additionally, the study included 797 accessions from Europe, spanning 16 countries or regions. The entire collection of accessions was sourced from the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences. This comprehensive assembly facilitates a robust comparative analysis, aiming to uncover the genetic diversity and potential unique traits present within and between these distinct soybean populations, providing valuable insights for breeding and conservation efforts (Table S1 ). DNA extraction and SNP genotyping The methods for DNA extraction and SNP genotyping followed those described by Yao et al. ( 2023 ). The ZDX1 soybean array, designed by the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences and Beijing Compass Biotechnology Co., Ltd. (Beijing, China), was utilized for genotyping (Sun et al. 2022a ). Genetic diversity analysis To comprehensively assess the genetic diversity within our soybean germplasm collection, we calculated two key genetic diversity parameters: nucleotide diversity (Pi) and the fixation index (F ST ), utilizing VCFtools (Danecek et al. 2011 ). These calculations were performed over genomic windows of 100 kilobases (kb) with a stepwise progression of 10kb, allowing for a detailed examination of genetic variation across the genome. The nucleotide diversity (Pi) serves as a measure of the average number of nucleotide differences per site between any two DNA sequences randomly chosen from the sample population, offering insights into the genetic variation present. The fixation index (F ST ), on the other hand, is a statistic that quantifies the degree of genetic differentiation between populations, providing a measure of population divergence due to genetic structure. In addition to these parameters, the linkage disequilibrium (LD) within the populations was analyzed using PopLDdecay software (Zhang et al. 2019 ). This analysis involved calculating LD values (r 2 ) for all pairwise combinations of Single Nucleotide Polymorphisms (SNPs) to understand the non-random association of alleles at different loci. LD decay was assessed at the threshold of r 2 = 0.2 to gauge the extent of linkage disequilibrium, which can offer insights into the genetic architecture and breeding history of the populations under study. Population structure analysis To further elucidate the genetic structure of our soybean germplasm collection, Principal Component Analysis (PCA) was performed using PLINK 1.9 software (Chang et al. 2015 ). PCA is a statistical technique that transforms the original, possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This method is particularly useful in revealing the underlying genetic structure of populations by identifying patterns of variation that explain the greatest difference between individuals. Phylogenetic analyses were conducted using the FastTree software (Price et al. 2010 ) with a maximum likelihood estimation method. This approach generated a phylogenetic tree that represents the genetic relationships among the soybean accessions, effectively highlighting the evolutionary pathways and genetic differentiation within the collection. To complement the PCA and phylogenetic analyses, the ADMIXTURE software (Alexander et al. 2009 ) was employed to infer the optimal population structure within the study. ADMIXTURE utilizes a model-based clustering algorithm to assign individuals to populations based on their genotype data, estimating the proportions of individual genomes derived from each of the inferred ancestral populations. This method is invaluable for detecting admixture events and understanding the genetic lineage and composition of the soybean germplasm. Together, these analytical approaches offer a comprehensive understanding of the population structure, facilitating the identification of genetic relationships and the distribution of genetic diversity across the soybean collection, thereby informing conservation strategies and breeding programs. Selective Sweep Analysis In our study, we employed a multifaceted approach to identify genomic regions under selection between Chinese and European soybean germplasms. Initially, we determined the selection areas by selecting the top 10% of the F ST values, which indicate population differentiation, and focused on the overlapping areas of the top and bottom 5% of the pi-ratio, a measure of nucleotide diversity, between the two germplasms. In these overlapping areas, the top 5% of the pi-ratio was considered the selection area in the Chinese accessions, while the bottom 5% delineated the selection area in the European accessions. This methodology allows for the nuanced differentiation of selective pressures that have acted distinctly on the genetic makeup of the Chinese and European soybean populations. Building upon this foundation, we further utilized the Cross Population Composite Likelihood Ratio (XP-CLR) (Chen et al. 2010 ) method to pinpoint areas within the genome exhibiting signs of selective sweeps by contrasting genetic variances across the populations. The analysis was conducted using windows of 100,000 base pairs (100K) with a step size of 10,000 base pairs (10K). This specific setup was chosen to achieve an optimal balance between detecting selective sweeps with precision and maintaining computational efficiency. Through the application of the XP-CLR algorithm, our study systematically scanned the genome, calculating XP-CLR scores to identify regions where significantly elevated scores indicate the occurrence of recent positive selection. This thorough approach enabled a comprehensive examination of genomic regions subjected to evolutionary selection pressures, offering insights into the adaptive histories of the populations under investigation. Together, these strategies form a comprehensive framework for detecting and analyzing selection in soybean germplasms, reflecting the complex dynamics of evolution and adaptation. SoyBase ( https://www.soybase.org ) was utilized to conduct searches for Quantitative Trait Loci (QTLs) in selected regions. Results Genetic diversity and linkage disequilibrium analysis Based on an analysis of 158,315 SNPs, it was discovered that 16.36%, 14.73%, and 19.82% of the loci exhibited a minor allele frequency (MAF) of 0 in Chinese wild soybeans, Chinese cultivars, and European soybeans, respectively (Table 1 ). Conversely, a MAF of greater than or equal to 0.05 was observed in 74.81%, 63.93%, and 57.03% of the loci in Chinese wild soybeans, Chinese cultivars, and European soybeans, respectively. The polymorphic information content (PIC) values for Chinese wild soybeans, Chinese cultivars, and European soybeans were determined to be 0.2782, 0.2221, and 0.2078, respectively, indicating a higher genetic diversity in wild soybeans from China compared to both Chinese and European cultivated soybeans. Table 1 Distribution of Minor Allele Frequencies (MAF) in SNPs from Different Soybean Sources Source MAF 0 > 0.05 CN_cultivate 23313 101205 EU_cultivate 31383 90284 CN_wild 25895 118438 In addition, the analysis of linkage disequilibrium (LD) revealed notable differences among the populations. The average r 2 value for 797 European varieties was 0.3859, with the r 2 value declining to half its maximum value at a distance of 99 kb. For the 54 Chinese wild soybeans, the average r 2 value was notably lower at 0.1428, with the r 2 value halving at just 14 kb. Similarly, for the 804 Chinese cultivars, the average r 2 value was 0.2372, with a halving distance of 84 kb (Fig. 1 ). These findings suggest a greater degree of homozygosity in European varieties compared to both wild and cultivated soybeans in China, providing insight into the genetic structure and evolutionary dynamics of these soybean populations. Population structure analysis The population structure of the soybean germplasm was meticulously analyzed through Admixture and Principal Component Analysis (PCA). At a specified value of K = 10, the distinctive population structures of the Chinese and European germplasms emerged with clarity (Fig. 2 C). Among the Chinese accessions, it was observed that landraces harbored a richer genetic diversity in comparison to the cultivated varieties, which exhibited a relatively uniform population structure. Further insights were gleaned from Principal Component Analysis, which highlighted that the first two principal components effectively differentiated wild soybeans in China from others, placing Chinese cultivars in closer genetic proximity to these wild counterparts (Fig. 2 A). Nevertheless, a noteworthy overlap between Chinese and European cultivated soybeans was observed, hinting at the possibility that the genetic discrepancies among the overlapping entities might be relatively minor. Moreover, the construction of an evolutionary tree using the neighbor-joining method underscored a distinct separation between Chinese wild soybeans and Chinese cultivar soybeans (Fig. 2 B). This clear delineation not only highlights the genetic diversity within the Chinese soybean germplasm but also emphasizes the complexities involved in soybean evolution and classification. However, the nuanced genetic variances among certain Chinese and European varieties posed challenges in their distinct classification through the evolutionary tree, indicating the complex nature of soybean genetic diversity and evolution. This complexity suggests a rich tapestry of genetic variation that spans across geographical boundaries, reflecting the adaptive responses of soybeans to diverse environments and selective pressures. Collectively, these three analytical approaches converge on a common inference: the genetic disparities between select segments of Chinese and European soybean germplasms are potentially minimal, underscoring a subtle complexity within the genetic landscape of soybean populations. Selective Sweep Analysis We utilized a 100 kb window and 10 kb step to identify selected blocks in Chinese and European accessions using F ST and pi values, resulting in 1579 blocks for Chinese germplasm and 311 blocks for European germplasm. We integrated the overlapping blocks to obtain a total of 109 and 32 selected regions for Chinese and European accessions, respectively. Additionally, XP-CLR identified 1591 selected regions, and by integrating the results of both methods, we screened 124 regions in China and 16 regions in Europe (Fig. 3 ). The selected regions in Chinese germplasm were distributed across all chromosomes except for 1, 5, 7, 10, and 13, spanning the remaining 15 chromosomes, with an average length of 1.31 Mb.These regions contained 41 QTLs related to various traits such as flowering and maturity, S, Zn or Cu content of plants, amino acid content of seeds, oil or oleic acid content of seeds, protein content of seeds, grain number per plant, Sclerotinia sclerotiorum resistance, seed coat color, and water use efficiency. Similarly, the selected regions in European germplasm were distributed on chromosomes 6, 7, 10, 12, 14, 18, and 19, with an average length of 1.76 Mb. These regions contained 7 QTLs related to flowering and palmitic acid content in grains (Table 2 , S2). Table 2 QTLs name and location in the select regions in Chinese varieties and European varieties Chinese varieties European varieties QTL name Chromosome Position QTL name Chromosome Position Seeds per plant 2-g4 2 15923907 First flower 6-g2 7 2434258 Plant height 2-g3 2 15923907 Seed weight 4-g7 7 2434258 Seed oleic 4-g3 6 8208575 First flower 3-g5 7 2434258 Seed linoleic 4-g2 6 8208575 Seed palmitic 2-g3.1 7 2438596 Reproductive period 4-g7 6 10283306 Seed long-chain fatty acid 1-g23.1 7 2438596 Seed protein 9-g3 6 10211244 Seed palmitic 2-g3.2 7 2439023 Seed Met 1-g1 6 10277748 Seed long-chain fatty acid 1-g23.2 7 2439023 Reproductive period 2-g7 6 10283306 Seed coat color 4-g3 8 20702756 Seed Gly 2-g4 9 42738613 Seed Thr 2-g4 9 42738613 Seed Val 2-g4 9 42738613 Seed Leu 2-g4 9 42738613 Shoot Mg 1-g10 12 32521921 Shoot S 1-g21 12 32521921 Shoot Cu 1-g10 12 32374613 Shoot Zn 1-g21 12 32347348 Shoot Cu 1-g9 12 32347348 Shoot Zn 1-g22.1 12 32374613 Shoot Zn 1-g22.2 12 32403359 Sclero 3-g5 12 32434240 Seed protein 5-g1 14 24367698 Reproductive stage length 4-g4 14 31326567 First flower 4-g57 15 1265085 Seed oil 10-g5 15 3913001 Seed oil 4-g11 15 3936757 Seed protein 3-g13 15 3936757 Seed protein 3-g14 15 3937899 Seed oil 4-g12 15 3937899 Seed oil 4-g13 15 3985288 Seed protein 3-g15 15 3985288 Seed Trp 1-g19 15 4006986 First flower 4-g62 15 45731853 Shoot S 1-g24 15 45851553 First flower 4-g74 19 5182596 Seed Trp 1-g23 19 5251007 WUE 2-g52 20 3196500 First flower 4-g84 20 3867301 R8 full maturity 3-g8 20 3903416 First flower 3-g11 20 3903416 Seed protein 7-g30 20 4036946 Discussion Comparative Analysis of Soybean Genetic Diversity Across Geographies In comparison to cultivated varieties, wild soybeans exhibit a higher degree of genetic diversity (Zhao et al. 2018 ; Li et al. 2020 ). Our study also revealed that the genetic diversity of wild soybeans in China surpassed that of both cultivated soybeans in China and Europe. As the original center of soybeans, China boasts abundant soybean resources, and previous research has shown that Chinese local varieties typically possess greater genetic diversity and more rapid LD decay rates (Dong et al. 2004 ; Li et al. 2020 ). In contrast, the breeding foundation for soybeans in Europe is relatively weaker due to the shorter history of soybean cultivation and smaller cultivation area (Haupt & Schmid 2020 ). Tavaud-Pirra et al. ( 2009 ) were the first to examine 32 European soybean populations from 1950–2000 and found that the genetic diversity of European soybean germplasm was significantly lower than that of North American and Asian soybean germplasm. Previous analysis of smaller-scale Chinese and European soybean populations often revealed clearer population structures (Saleem et al. 2021 ; Yao et al. 2023 ). However, our study found significant gene flow between Chinese and European soybean germplasms (Fig. 2 B, C), making it challenging to separate them using conventional population structure analysis. Liu et al. ( 2020 ) provided a thorough overview of the global spread of soybeans originating from China. However, their study was constrained by the limited variety of European soybean germplasm available, leading them to use soybean varieties from southern Sweden as a proxy for European germplasm. Despite these limitations, they asserted that the European soybean lineage can be traced back to Northeast China and North America, with the North American soybean germplasm itself having roots in Northeast China (Gizlice et al. 1994 ). In contrast, our research utilized a broader spectrum of European germplasm, comprising 797 soybean varieties preserved by the Chinese National Soybean GeneBank (CNSGB). This extensive collection facilitated a more comprehensive comparison between the soybean germplasms of Northeast China and Europe. Our findings indicate that certain European soybean germplasms exhibit minimal genetic differences when compared to the Northeast Chinese soybean germplasm, thereby providing molecular evidence supporting the Northeast Chinese ancestry of some European soybeans. This insight enhances our understanding of the genetic continuity and diversity resulting from decades of breeding work, underscoring the close genetic ties between European and Northeast Chinese soybeans. Selective Sweeps in CN and EU collections During the domestication of soybean, some genes associated with certain traits were selected and fixed, resulting in a significant reduction in genetic diversity in the affected regions (Goettel et al. 2022 ; Qin et al. 2023 ). Selective sweep analysis can be used to locate these selected regions, and in combination with previously identified loci, can help infer differences in selection pressures between populations or aid in the discovery of new genes (Wen et al. 2015 ; Kim et al. 2021 ). In this study, we used F ST , pi, and XP-CLR to perform selective sweep analysis on Chinese Northeast and European soybean populations, which minimized the occurrence of false positives and facilitated the mapping and genetic analysis of the identified regions. Building on the foundation laid by genome-wide association studies and QTL mapping, our investigation delves into the genetic underpinnings of soybean adaptation to diverse climatic conditions. Several QTLs related to flowering and maturity were identified through a selective sweep, echoing the discoveries in Saleem's study (Saleem et al. 2021 ). Specifically, our research unveiled a region with pronounced selection signals in the vicinity of loci E4 , in addition to a robust selection region in the E1 loci. This observation aligns with the introduction of early-maturing European soybeans into our study, highlighting the genetic influence of breeding efforts aimed at adapting to different climatic conditions. Furthermore, we identified a strong selection signal adjacent to PRR genes, notable for their functional significance in the PRR3/7 subclade, particularly in response to long-day conditions (Lu et al. 2020 ). These findings underscore the profound impact of domestication and breeding practices on the genetic variation observed within soybean populations across China and Europe, as they have been tailored to thrive under the distinct latitudinal challenges presented by their respective environments. Seed protein content is one of the most important breeding objectives for soybeans (Singer et al. 2023 ). In our study, we identified six protein-related QTLs that have undergone selection in Chinese germplasm, yet none were identified within European germplasm. Despite this, preliminary findings indicate that European varieties generally possess higher protein content. This discrepancy leads us to suspect that there are additional protein-related QTLs and genes in European soybeans that remain undiscovered. This possibility highlights the need for further genetic exploration to fully understand and utilize the genetic potential of European soybean varieties for protein content enhancement. In addition, seven QTLs related to seed amino acid content were identified in Chinese germplasm, indicating that Chinese Northeast soybeans may have undergone specific selection for some amino acid content. The Northeast region of China is renowned as a major hub for high-oil soybean production. Breeding efforts in this area have been focused on enhancing the oil composition of soybeans towards healthier profiles, a trend also reflected in recent European varieties. Our research has identified several oil-related Quantitative Trait Loci (QTLs) in this region, emphasizing its importance in soybean oil production (Song et al. 2023 ). Soybean oil typically comprises 21.5% oleic acid, a beneficial unsaturated fatty acid, and 12.2% palmitic acid, a saturated fatty acid which, while stabilizing the oil, can be detrimental to health if consumed excessively (Abdelghany et al. 2020 ; Julibert et al. 2019 ). Given the health implications, increasing the proportion of unsaturated fatty acids is a significant breeding target to enhance the nutritional value of soybean oil (Thelen & Ohlrogge 2002 ). In line with these objectives, our study identified two QTLs related to seed oleic acid content in Chinese germplasm. Furthermore, we discovered an additional QTL in newly introduced European germplasm, indicating potential genetic avenues for improving oil quality in both regions. This cross-regional genetic investigation could further enhance the quality of soybeans by informing targeted breeding strategies. We also identified some QTL related to nutrient use efficiency and accumulation. The regulation mechanism of nutrient accumulation in soybean tissues is very complex and may vary depending on the soil and environmental conditions in different regions, leading to different selection directions (Ray et al. 2015 ; Dhanapal et al. 2018 ). ZDX1 soybean SNP array The ZDX1 soybean SNP array was developed using the largest scale of soybean core germplasm to date, encompassing 2214 soybean accessions from China and worldwide. This revolutionary array is capable of accurately identifying soybean germplasm from both China and abroad. When compared to other soybean array like SoySNP50K, 180 K AXIOM®, and NJAU 355 K SoySNP, the ZDX1 array boasts 80% unique SNPs, providing greater coverage of the soybean genome (Sun et al. 2022b ). Moreover, its functional sites can efficiently and precisely identify crucial agronomic traits. Soybean cyst nematode is a widespread issue that continues to affect soybean production regions across the world, and it is rapidly expanding to other areas (Tylka & Marett 2017 ). In this study, using the ZDX1 array to examine functional loci, we discovered 24 European varieties with resistance to soybean cyst nematode (Table S3). However, we also observed that the genetic diversity and quantity of resistance to soybean cyst nematode may be inadequate in European germplasm. This inadequacy may hinder the future expansion of soybean cultivation in Europe. Conclusion This study aimed to explore the genetic differences between soybeans from Northeast China and Europe. Our findings revealed that European soybean varieties exhibit relatively lower genetic diversity compared to the Chinese collections, which include elite cultivars, landraces, and wild accessions. This observation suggests a narrower genetic base within European varieties. Additionally, population structure analysis suggested that these European varieties likely originated from China, underscoring a historical connection in soybean cultivation between the regions. Through a combined analysis using F ST , pi, and XP-CLR, we identified distinct breeding targets between the Northeast Chinese and European varieties. This comparison provides insights into the developmental trajectory and adaptation strategies of soybean breeding in different geographical contexts. Declarations Author Contribution Lijuan Qiu and Zhangxiong Liu conceived and designed the experiments, analyzed the data, prepared figures and/or tables, and wrote or reviewed the draft manuscript. Jiangyuan Xu, Xindong Yao, and Yuqing Lu conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, and wrote or reviewed the draft manuscript. Rittler Leopold analyzed the data and prepared figures and/or tables. Yongzhe Gu, Ming Yuan, Yong Zhang, Rujian Sun, Yongguo Xue, Yeli Liu, Dezhi Han, Jinxing Wang, and Huawei Gao performed the experiments, analyzed the data, and wrote or reviewed the draft manuscript. All authors read and approved the final manuscript. Acknowledgement †Jiangyuan Xu, Xindong Yao and Yuqing Lu contributed equally to this work. Data Availability The datasets analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. References Abdelghany AM, Zhang S, Azam M, Shaibu AS, Feng Y, Li Y, Tian Y, Hong H, Li B, Sun J (2020) Profiling of seed fatty acid composition in 1025 Chinese soybean accessions from diverse ecoregions. 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Food Research International 164:112364. doi:https://doi.org/10.1016/j.foodres.2022.112364 Sun R, Sun B, Tian Y, Su S, Zhang Y, Zhang W, Wang J, Yu P, Guo B, Li H, Li Y, Gao H, Gu Y, Yu L, Ma Y, Su E, Li Q, Hu X, Zhang Q, Guo R, Chai S, Feng L, Wang J, Hong H, Xu J, Yao X, Wen J, Liu J, Li Y, Qiu L (2022a) Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array. Theoretical and Applied Genetics. doi:10.1007/s00122-022-04043-w Sun R, Sun B, Tian Y, Su S, Zhang Y, Zhang W, Wang J, Yu P, Guo B, Li H, Li Y, Gao H, Gu Y, Yu L, Ma Y, Su E, Li Q, Hu X, Zhang Q, Guo R, Chai S, Feng L, Wang J, Hong H, Xu J, Yao X, Wen J, Liu J, Li Y, Qiu L (2022b) Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array. Theoretical and Applied Genetics 135 (4):1413-1427. doi:10.1007/s00122-022-04043-w Tavaud-Pirra M, Sartre P, Nelson R, Santoni S, Texier N, Roumet P (2009) Genetic Diversity in a Soybean Collection. Crop Science 49 (3):895-902. doi:https://doi.org/10.2135/cropsci2008.05.0266 Thelen JJ, Ohlrogge JB (2002) Metabolic Engineering of Fatty Acid Biosynthesis in Plants. Metabolic Engineering 4 (1):12-21. doi:https://doi.org/10.1006/mben.2001.0204 Tylka GL, Marett CC (2017) Known Distribution of the Soybean Cyst Nematode, Heterodera glycines, in the United States and Canada, 1954 to 2017. Plant Health Progress 18 (3):167-168. doi:10.1094/PHP-05-17-0031-BR Wang C, Hu S, Gardner C, Lübberstedt T (2017) Emerging Avenues for Utilization of Exotic Germplasm. Trends in Plant Science 22 (7):624-637. doi:https://doi.org/10.1016/j.tplants.2017.04.002 Wang L, Guan Y, Guan R, Li Y, Ma Y, Dong Z, Liu X, Zhang H, Zhang Y, Liu Z, Chang R, Xu H, Li L, Lin F, Luan W, Yan Z, Ning X, Zhu L, Cui Y, Piao R, Liu Y, Chen P, Qiu L (2006) Establishment of Chinese soybean Glycine max core collections with agronomic traits and SSR markers. Euphytica 151 (2):215-223. doi:10.1007/s10681-006-9142-3 Wen Z, Boyse JF, Song Q, Cregan PB, Wang D (2015) Genomic consequences of selection and genome-wide association mapping in soybean. BMC Genomics 16 (1):671. doi:10.1186/s12864-015-1872-y Xiong D, Zhao T, Gai J (2010) Genetic bases of improved soybean cultivars released from 1923 to 2005 in China—A historical review. Frontiers of Agriculture in China 4 (4):383-393 Yao X, Xu J-y, Liu Z-x, Pachner M, Molin EM, Rittler L, Hahn V, Leiser W, Gu Y-z, Lu Y-q, Qiu L-j, Vollmann J (2023) Genetic diversity in early maturity Chinese and European elite soybeans: A comparative analysis. Euphytica 219 (1):17. doi:10.1007/s10681-022-03147-0 Zhang C, Dong S-S, Xu J-Y, He W-M, Yang T-L (2019) PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics 35 (10):1786-1788. doi:10.1093/bioinformatics/bty875 Zhang S, Li B, Chen Y, Shaibu AS, Zheng H, Sun J (2020) Molecular-Assisted Distinctness and Uniformity Testing Using SLAF-Sequencing Approach in Soybean. Genes 11 (2). doi:10.3390/genes11020175 Zhao H, Wang Y, Xing F, Liu X, Yuan C, Qi G, Guo J, Dong Y (2018) The Genetic Diversity and Geographic Differentiation of the Wild Soybean in Northeast China Based on Nuclear Microsatellite Variation. International Journal of Genomics 2018:8561458. doi:10.1155/2018/8561458 Additional Declarations No competing interests reported. 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09:01:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4647180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4647180/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-024-02294-8","type":"published","date":"2025-01-08T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61358299,"identity":"2e456ae4-8f36-4eb4-84ee-e33c6094be00","added_by":"auto","created_at":"2024-07-29 21:18:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75126,"visible":true,"origin":"","legend":"\u003cp\u003eComparative estimation of linkage disequilibrium (LD) decay with increasing genetic distance for European (Europe) or Chinese (China) soybean cultivars and Chinese wild soybean (China_Wild).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4647180/v1/b217b0dc7640ba37fa3369e9.png"},{"id":61358302,"identity":"ea6006cf-c117-47c4-80ef-26a5d992561e","added_by":"auto","created_at":"2024-07-29 21:18:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":242883,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Principal component analysis (PCA) of Chinese cultivars (Cn-cultivate), Chinese wild (Cn-wild) and newly introduced European cultivars (Eu-new) together with the storge stored European varieties (Eu-original) based on SNP data. (B) NJ tree based on SNP and clustered into four groups: Chinese wild (China_wild), Chinese cultivars (China_cul), European stored (EU_stored) and newly introduced from Europe (EU_new). (C) Graphical representation of the Admixtureresults for Chinese, Europe and wildcombined sample sets at optimum K = 10\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4647180/v1/13f4f6f8b1de2fd790bc0222.png"},{"id":61358300,"identity":"49bb64de-4371-45ac-8336-865ff6935fd6","added_by":"auto","created_at":"2024-07-29 21:18:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":289891,"visible":true,"origin":"","legend":"\u003cp\u003eCircos plot for distribution of genes on each chromosome (histogram), pi value of Chinese varieties (red line plot), pi value of European varieties (blue line plot), Fst between Chinese and European varieties (yellow line plot) and XP-CLR between Chinese and European varieties. The red lines through the circle indicated the strong selection area by two methods (Fst and XP-CLR)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4647180/v1/002f0c19f475bba7475c0bfd.png"},{"id":73693870,"identity":"543611d2-6d0f-430f-9367-e1f548d2ca7c","added_by":"auto","created_at":"2025-01-13 16:08:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1155636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4647180/v1/acf44cd5-e276-4c60-8c0b-7290d3f36272.pdf"},{"id":61358301,"identity":"7dff344f-b193-4d9a-a6e7-d68bb555f4f2","added_by":"auto","created_at":"2024-07-29 21:18:29","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":60134,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS1S3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4647180/v1/4d6f2d0a23d875c1336351a2.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A comparison of Chinese wild and cultivar soybean with European soybean collections on genetic diversity by Genome-Wide Scan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoybean (\u003cem\u003eGlycine max\u003c/em\u003e [L.] Merr.), as an essential source of vegetable protein and oil, traces its origin to China, boasting a cultivation history exceeding 3000 years (Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).. The crop made its way to Korea around the 2nd century BCE from China, subsequently reaching Japan from Korea. By the 6th century CE, late-ripening soybeans from southern China were introduced to the Kyushu region of Japan via maritime routes. Soybeans were brought to the Netherlands before 1737 and to the United States from China via the United Kingdom in 1765. The earliest records of soybeans in Argentina date back to 1882, with Brazil's introduction of the crop occurring relatively later. Artificial selection across different regions for various breeding objectives has significantly influenced the genetic diversity of soybeans, allowing them to adapt to diverse environments. According to the Food and Agriculture Organization (FAO), the global inventory of soybean germplasm resources surpasses 200,000 entries, including duplicates. The Chinese National Genebank holds over 43,000 cultivated soybean germplasms and more than 10,000 annual wild soybean germplasms (Insititute of Crop Sciences 2013). China leads globally with a collection exceeding 30,000 soybean germplasms, including more than 23,000 cultivated and over 7,000 wild types (Qiu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); meanwhile, all soybeans in the United States are of foreign origin, with the country now preserving more than 20,000 soybean germplasms. Prior to World War II, soybean cultivation and production received limited attention. Post-1945, global soybean production saw a significant expansion, spearheaded by the United States, where soybean output surpassed China's by 1954. Since the 1970s, South American countries like Brazil, Argentina, and Paraguay have vigorously developed soybean production, with Brazil's yield exceeding China's by 1974. China's soybean germplasm resources have not only played a crucial role in domestic breeding and production but also have made indelible contributions to global soybean breeding and production.\u003c/p\u003e \u003cp\u003eIn China, soybeans rank as the fourth largest crop after corn, rice, and wheat. Between 2021 and 2022, China's total soybean production ranged from 16.4 to 20.28\u0026nbsp;million tons, with imports reaching 91.08 to 96.52\u0026nbsp;million tons, resulting in a self-sufficiency rate of merely 15\u0026ndash;18%. The country's high dependency on soybean imports and the significant risk to its industry are notable. Against the backdrop of limited arable land and in the effort not to compete with staple crops, enhancing per unit area yield and increasing total soybean production are key strategies to alleviate import pressure. Currently, China has developed approximately 3000 varieties (Qiu \u0026amp; Wang \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The modern breeding process grapples with the challenge of a narrow genetic base, with most varieties developed in China between 1923 and 1995 in the main soybean-producing regions of Northeast and Huang-Huai-Hai using local varieties as parents, leading to a narrowing genetic basis due to the over-concentration of parental sources (Xiong et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Although Chinese soybeans possess a vast gene pool, traditional breeding has utilized only a fraction (Wang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Recently, the cultivation and utilization of key parental lines in breeding have resulted in closely related breeds with uniform traits, highlighting the genetic vulnerability of these varieties without breakthroughs in yield. Employing foreign germplasm as parents presents a viable and economical solution to this issue. Germplasm collections from various regions serve as important sources of beneficial genetic variation, and the introduction of foreign varieties as parents can effectively address the problem of a narrow genetic base (Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hegstad et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe soybean, native to China, has seen extensive research into the genetic relationships between Chinese and foreign germplasm. Studies spanning various countries, including China-USA (Lijuan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and China-Japan (Guan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), have laid a theoretical foundation for the introduction and utilization of foreign germplasm in Chinese breeding programs.\u003c/p\u003e \u003cp\u003eThe genetic relationships uncovered in these studies are pivotal. For instance, research by Guan Rongxia et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) using SSR markers to analyze the genetic diversity among 32 Chinese soybean varieties and 40 foreign soybean ancestors introduced to China revealed significant findings. Across 22 SSR loci, 170 allelic variations were detected, with Chinese and introduced foreign soybeans averaging 60 and 69 allelic variations, respectively, both showing a genetic diversity index of 0.71. The study identified 48 unique allelic variations in foreign varieties, compared to 22 in Chinese soybeans, with a significant frequency difference in shared allelic variations between Chinese and foreign soybeans. Cluster analysis indicated substantial differences between Chinese breeds and their foreign ancestors. Genomic composition analysis showed that the introduction of Amsoy (USA) and Tokachi Nagaha (Japan) added 23 unique foreign allelic variations to five Chinese soybean breeds, with a retention rate of 29.13%. However, the retention of these allelic variations varies across different genetic backgrounds, suggesting that many unique allelic variations remain unutilized and could further contribute to soybean improvement in China.\u003c/p\u003e \u003cp\u003eFurther studies by Qin Jun et al. (2006) on the pedigrees of widely cultivated varieties Suinong 14 and Hefeng 25, which contain foreign genetic material, used SSR markers for genomic analysis. Allelic variations from Tokachi Nagaha (Japanese germplasm) and Amsoy (American germplasm) passed on to Hefeng 25 and Suinong 14 were found to be associated with 100-seed weight and oil content, indicating potential functional gene clusters in certain chromosomal regions. Additionally, American and Japanese germplasms, such as Amsoy, Clark63, Mamoton, and Tokachi Nagaha, have significantly contributed genetic diversity to popular Chinese varieties like Hefeng 25, Suinong 14 (Qin et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and Zhonghuang 13 (Zhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings underscore the importance of integrating global genetic resources into Chinese soybean breeding, demonstrating how foreign germplasm can enrich genetic diversity and contribute to crop improvement. The research not only facilitates the strategic use of global soybean genetic resources but also highlights the potential for discovering and utilizing unique genetic variations for future soybean enhancement.\u003c/p\u003e \u003cp\u003eSoybean was first introduced from China to Europe in the 18th century, and soybean harvested area and yield in Europe began to increase rapidly in the 21st century, and the gap with China is getting smaller (Fogelberg \u0026amp; Recknagel \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Karges et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Northeast China is the main soybean producing area in China, which is also at the same latitude with the central and north Europe, thus introduce early maturity varieties from Europe can expand the genetic background, thereby improve the main cultivars in Northeast China. But several analysis were carried on between European cultivars and Chinese germplasm bank. In this study, a high-throughput genotyping approach (Sun et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) was used to sequence the germplasm from Chinese gene bank and the introduced varieties from Europe. This study aimed to compare the genetic diversity and analyze population structure differences between Chinese and European soybean varieties, enhancing our understanding of their adaptation and evolution. Additionally, it aims to identify genomic regions that may have undergone selective sweeps in soybean breeding and explore their significance for future soybean improvement efforts in Northeast China.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePlant materials\u003c/p\u003e \u003cp\u003eTo examine the genetic diversity between Chinese and European soybean germplasms, this study compiled a dataset of 1,655 accessions. Of these, 858 accessions originated from China, comprising 804 elite cultivars and landraces, referred to as Chinese cultivars, along with 54 Glycine soja specimens, denoting the wild soybean varieties native to China. Additionally, the study included 797 accessions from Europe, spanning 16 countries or regions. The entire collection of accessions was sourced from the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences. This comprehensive assembly facilitates a robust comparative analysis, aiming to uncover the genetic diversity and potential unique traits present within and between these distinct soybean populations, providing valuable insights for breeding and conservation efforts (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDNA extraction and SNP genotyping\u003c/p\u003e \u003cp\u003eThe methods for DNA extraction and SNP genotyping followed those described by Yao et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The ZDX1 soybean array, designed by the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences and Beijing Compass Biotechnology Co., Ltd. (Beijing, China), was utilized for genotyping (Sun et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic diversity analysis\u003c/p\u003e \u003cp\u003eTo comprehensively assess the genetic diversity within our soybean germplasm collection, we calculated two key genetic diversity parameters: nucleotide diversity (Pi) and the fixation index (F\u003csub\u003eST\u003c/sub\u003e), utilizing VCFtools (Danecek et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These calculations were performed over genomic windows of 100 kilobases (kb) with a stepwise progression of 10kb, allowing for a detailed examination of genetic variation across the genome. The nucleotide diversity (Pi) serves as a measure of the average number of nucleotide differences per site between any two DNA sequences randomly chosen from the sample population, offering insights into the genetic variation present. The fixation index (F\u003csub\u003eST\u003c/sub\u003e), on the other hand, is a statistic that quantifies the degree of genetic differentiation between populations, providing a measure of population divergence due to genetic structure.\u003c/p\u003e \u003cp\u003eIn addition to these parameters, the linkage disequilibrium (LD) within the populations was analyzed using PopLDdecay software (Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This analysis involved calculating LD values (r\u003csup\u003e2\u003c/sup\u003e) for all pairwise combinations of Single Nucleotide Polymorphisms (SNPs) to understand the non-random association of alleles at different loci. LD decay was assessed at the threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2 to gauge the extent of linkage disequilibrium, which can offer insights into the genetic architecture and breeding history of the populations under study.\u003c/p\u003e \u003cp\u003ePopulation structure analysis\u003c/p\u003e \u003cp\u003eTo further elucidate the genetic structure of our soybean germplasm collection, Principal Component Analysis (PCA) was performed using PLINK 1.9 software (Chang et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). PCA is a statistical technique that transforms the original, possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This method is particularly useful in revealing the underlying genetic structure of populations by identifying patterns of variation that explain the greatest difference between individuals. Phylogenetic analyses were conducted using the FastTree software (Price et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) with a maximum likelihood estimation method. This approach generated a phylogenetic tree that represents the genetic relationships among the soybean accessions, effectively highlighting the evolutionary pathways and genetic differentiation within the collection.\u003c/p\u003e \u003cp\u003eTo complement the PCA and phylogenetic analyses, the ADMIXTURE software (Alexander et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) was employed to infer the optimal population structure within the study. ADMIXTURE utilizes a model-based clustering algorithm to assign individuals to populations based on their genotype data, estimating the proportions of individual genomes derived from each of the inferred ancestral populations. This method is invaluable for detecting admixture events and understanding the genetic lineage and composition of the soybean germplasm. Together, these analytical approaches offer a comprehensive understanding of the population structure, facilitating the identification of genetic relationships and the distribution of genetic diversity across the soybean collection, thereby informing conservation strategies and breeding programs.\u003c/p\u003e \u003cp\u003eSelective Sweep Analysis\u003c/p\u003e \u003cp\u003eIn our study, we employed a multifaceted approach to identify genomic regions under selection between Chinese and European soybean germplasms. Initially, we determined the selection areas by selecting the top 10% of the F\u003csub\u003eST\u003c/sub\u003e values, which indicate population differentiation, and focused on the overlapping areas of the top and bottom 5% of the pi-ratio, a measure of nucleotide diversity, between the two germplasms. In these overlapping areas, the top 5% of the pi-ratio was considered the selection area in the Chinese accessions, while the bottom 5% delineated the selection area in the European accessions. This methodology allows for the nuanced differentiation of selective pressures that have acted distinctly on the genetic makeup of the Chinese and European soybean populations.\u003c/p\u003e \u003cp\u003eBuilding upon this foundation, we further utilized the Cross Population Composite Likelihood Ratio (XP-CLR) (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) method to pinpoint areas within the genome exhibiting signs of selective sweeps by contrasting genetic variances across the populations. The analysis was conducted using windows of 100,000 base pairs (100K) with a step size of 10,000 base pairs (10K). This specific setup was chosen to achieve an optimal balance between detecting selective sweeps with precision and maintaining computational efficiency. Through the application of the XP-CLR algorithm, our study systematically scanned the genome, calculating XP-CLR scores to identify regions where significantly elevated scores indicate the occurrence of recent positive selection. This thorough approach enabled a comprehensive examination of genomic regions subjected to evolutionary selection pressures, offering insights into the adaptive histories of the populations under investigation. Together, these strategies form a comprehensive framework for detecting and analyzing selection in soybean germplasms, reflecting the complex dynamics of evolution and adaptation. SoyBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.soybase.org\u003c/span\u003e\u003cspan address=\"https://www.soybase.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to conduct searches for Quantitative Trait Loci (QTLs) in selected regions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGenetic diversity and linkage disequilibrium analysis\u003c/p\u003e \u003cp\u003eBased on an analysis of 158,315 SNPs, it was discovered that 16.36%, 14.73%, and 19.82% of the loci exhibited a minor allele frequency (MAF) of 0 in Chinese wild soybeans, Chinese cultivars, and European soybeans, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Conversely, a MAF of greater than or equal to 0.05 was observed in 74.81%, 63.93%, and 57.03% of the loci in Chinese wild soybeans, Chinese cultivars, and European soybeans, respectively. The polymorphic information content (PIC) values for Chinese wild soybeans, Chinese cultivars, and European soybeans were determined to be 0.2782, 0.2221, and 0.2078, respectively, indicating a higher genetic diversity in wild soybeans from China compared to both Chinese and European cultivated soybeans.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Minor Allele Frequencies (MAF) in SNPs from Different Soybean Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCN_cultivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU_cultivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCN_wild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, the analysis of linkage disequilibrium (LD) revealed notable differences among the populations. The average r\u003csup\u003e2\u003c/sup\u003e value for 797 European varieties was 0.3859, with the r\u003csup\u003e2\u003c/sup\u003e value declining to half its maximum value at a distance of 99 kb. For the 54 Chinese wild soybeans, the average r\u003csup\u003e2\u003c/sup\u003e value was notably lower at 0.1428, with the r\u003csup\u003e2\u003c/sup\u003e value halving at just 14 kb. Similarly, for the 804 Chinese cultivars, the average r\u003csup\u003e2\u003c/sup\u003e value was 0.2372, with a halving distance of 84 kb (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings suggest a greater degree of homozygosity in European varieties compared to both wild and cultivated soybeans in China, providing insight into the genetic structure and evolutionary dynamics of these soybean populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePopulation structure analysis\u003c/p\u003e \u003cp\u003eThe population structure of the soybean germplasm was meticulously analyzed through Admixture and Principal Component Analysis (PCA). At a specified value of K\u0026thinsp;=\u0026thinsp;10, the distinctive population structures of the Chinese and European germplasms emerged with clarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Among the Chinese accessions, it was observed that landraces harbored a richer genetic diversity in comparison to the cultivated varieties, which exhibited a relatively uniform population structure.\u003c/p\u003e \u003cp\u003eFurther insights were gleaned from Principal Component Analysis, which highlighted that the first two principal components effectively differentiated wild soybeans in China from others, placing Chinese cultivars in closer genetic proximity to these wild counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Nevertheless, a noteworthy overlap between Chinese and European cultivated soybeans was observed, hinting at the possibility that the genetic discrepancies among the overlapping entities might be relatively minor.\u003c/p\u003e \u003cp\u003eMoreover, the construction of an evolutionary tree using the neighbor-joining method underscored a distinct separation between Chinese wild soybeans and Chinese cultivar soybeans (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This clear delineation not only highlights the genetic diversity within the Chinese soybean germplasm but also emphasizes the complexities involved in soybean evolution and classification. However, the nuanced genetic variances among certain Chinese and European varieties posed challenges in their distinct classification through the evolutionary tree, indicating the complex nature of soybean genetic diversity and evolution. This complexity suggests a rich tapestry of genetic variation that spans across geographical boundaries, reflecting the adaptive responses of soybeans to diverse environments and selective pressures.\u003c/p\u003e \u003cp\u003eCollectively, these three analytical approaches converge on a common inference: the genetic disparities between select segments of Chinese and European soybean germplasms are potentially minimal, underscoring a subtle complexity within the genetic landscape of soybean populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSelective Sweep Analysis\u003c/p\u003e \u003cp\u003eWe utilized a 100 kb window and 10 kb step to identify selected blocks in Chinese and European accessions using F\u003csub\u003eST\u003c/sub\u003e and pi values, resulting in 1579 blocks for Chinese germplasm and 311 blocks for European germplasm. We integrated the overlapping blocks to obtain a total of 109 and 32 selected regions for Chinese and European accessions, respectively. Additionally, XP-CLR identified 1591 selected regions, and by integrating the results of both methods, we screened 124 regions in China and 16 regions in Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe selected regions in Chinese germplasm were distributed across all chromosomes except for 1, 5, 7, 10, and 13, spanning the remaining 15 chromosomes, with an average length of 1.31 Mb.These regions contained 41 QTLs related to various traits such as flowering and maturity, S, Zn or Cu content of plants, amino acid content of seeds, oil or oleic acid content of seeds, protein content of seeds, grain number per plant, Sclerotinia sclerotiorum resistance, seed coat color, and water use efficiency. Similarly, the selected regions in European germplasm were distributed on chromosomes 6, 7, 10, 12, 14, 18, and 19, with an average length of 1.76 Mb. These regions contained 7 QTLs related to flowering and palmitic acid content in grains (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQTLs name and location in the select regions in Chinese varieties and European varieties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eChinese varieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eEuropean varieties\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTL name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQTL name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeeds per plant 2-g4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15923907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst flower 6-g2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2434258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height 2-g3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15923907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeed weight 4-g7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2434258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed oleic 4-g3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8208575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst flower 3-g5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2434258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed linoleic 4-g2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8208575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeed palmitic 2-g3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2438596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive period 4-g7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10283306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeed long-chain fatty acid 1-g23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2438596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed protein 9-g3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10211244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeed palmitic 2-g3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2439023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Met 1-g1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10277748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeed long-chain fatty acid 1-g23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2439023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive period 2-g7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10283306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed coat color 4-g3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20702756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Gly 2-g4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42738613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Thr 2-g4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42738613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Val 2-g4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42738613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Leu 2-g4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42738613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Mg 1-g10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32521921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot S 1-g21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32521921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Cu 1-g10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32374613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Zn 1-g21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32347348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Cu 1-g9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32347348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Zn 1-g22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32374613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot Zn 1-g22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32403359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSclero 3-g5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32434240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed protein 5-g1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24367698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive stage length 4-g4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31326567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst flower 4-g57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1265085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed oil 10-g5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3913001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed oil 4-g11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3936757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed protein 3-g13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3936757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed protein 3-g14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3937899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed oil 4-g12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3937899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed oil 4-g13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3985288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed protein 3-g15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3985288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Trp 1-g19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4006986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst flower 4-g62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45731853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoot S 1-g24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45851553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst flower 4-g74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5182596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Trp 1-g23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5251007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWUE 2-g52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3196500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst flower 4-g84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3867301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR8 full maturity 3-g8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3903416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst flower 3-g11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3903416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed protein 7-g30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4036946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eComparative Analysis of Soybean Genetic Diversity Across Geographies\u003c/p\u003e \u003cp\u003eIn comparison to cultivated varieties, wild soybeans exhibit a higher degree of genetic diversity (Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our study also revealed that the genetic diversity of wild soybeans in China surpassed that of both cultivated soybeans in China and Europe. As the original center of soybeans, China boasts abundant soybean resources, and previous research has shown that Chinese local varieties typically possess greater genetic diversity and more rapid LD decay rates (Dong et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, the breeding foundation for soybeans in Europe is relatively weaker due to the shorter history of soybean cultivation and smaller cultivation area (Haupt \u0026amp; Schmid \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tavaud-Pirra et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) were the first to examine 32 European soybean populations from 1950\u0026ndash;2000 and found that the genetic diversity of European soybean germplasm was significantly lower than that of North American and Asian soybean germplasm.\u003c/p\u003e \u003cp\u003ePrevious analysis of smaller-scale Chinese and European soybean populations often revealed clearer population structures (Saleem et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yao et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, our study found significant gene flow between Chinese and European soybean germplasms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C), making it challenging to separate them using conventional population structure analysis. Liu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provided a thorough overview of the global spread of soybeans originating from China. However, their study was constrained by the limited variety of European soybean germplasm available, leading them to use soybean varieties from southern Sweden as a proxy for European germplasm. Despite these limitations, they asserted that the European soybean lineage can be traced back to Northeast China and North America, with the North American soybean germplasm itself having roots in Northeast China (Gizlice et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). In contrast, our research utilized a broader spectrum of European germplasm, comprising 797 soybean varieties preserved by the Chinese National Soybean GeneBank (CNSGB). This extensive collection facilitated a more comprehensive comparison between the soybean germplasms of Northeast China and Europe. Our findings indicate that certain European soybean germplasms exhibit minimal genetic differences when compared to the Northeast Chinese soybean germplasm, thereby providing molecular evidence supporting the Northeast Chinese ancestry of some European soybeans. This insight enhances our understanding of the genetic continuity and diversity resulting from decades of breeding work, underscoring the close genetic ties between European and Northeast Chinese soybeans.\u003c/p\u003e \u003cp\u003eSelective Sweeps in CN and EU collections\u003c/p\u003e \u003cp\u003eDuring the domestication of soybean, some genes associated with certain traits were selected and fixed, resulting in a significant reduction in genetic diversity in the affected regions (Goettel et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qin et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Selective sweep analysis can be used to locate these selected regions, and in combination with previously identified loci, can help infer differences in selection pressures between populations or aid in the discovery of new genes (Wen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, we used F\u003csub\u003eST\u003c/sub\u003e, pi, and XP-CLR to perform selective sweep analysis on Chinese Northeast and European soybean populations, which minimized the occurrence of false positives and facilitated the mapping and genetic analysis of the identified regions.\u003c/p\u003e \u003cp\u003eBuilding on the foundation laid by genome-wide association studies and QTL mapping, our investigation delves into the genetic underpinnings of soybean adaptation to diverse climatic conditions. Several QTLs related to flowering and maturity were identified through a selective sweep, echoing the discoveries in Saleem's study (Saleem et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, our research unveiled a region with pronounced selection signals in the vicinity of loci \u003cem\u003eE4\u003c/em\u003e, in addition to a robust selection region in the \u003cem\u003eE1\u003c/em\u003e loci. This observation aligns with the introduction of early-maturing European soybeans into our study, highlighting the genetic influence of breeding efforts aimed at adapting to different climatic conditions. Furthermore, we identified a strong selection signal adjacent to PRR genes, notable for their functional significance in the \u003cem\u003ePRR3/7\u003c/em\u003e subclade, particularly in response to long-day conditions (Lu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings underscore the profound impact of domestication and breeding practices on the genetic variation observed within soybean populations across China and Europe, as they have been tailored to thrive under the distinct latitudinal challenges presented by their respective environments.\u003c/p\u003e \u003cp\u003eSeed protein content is one of the most important breeding objectives for soybeans (Singer et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, we identified six protein-related QTLs that have undergone selection in Chinese germplasm, yet none were identified within European germplasm. Despite this, preliminary findings indicate that European varieties generally possess higher protein content. This discrepancy leads us to suspect that there are additional protein-related QTLs and genes in European soybeans that remain undiscovered. This possibility highlights the need for further genetic exploration to fully understand and utilize the genetic potential of European soybean varieties for protein content enhancement. In addition, seven QTLs related to seed amino acid content were identified in Chinese germplasm, indicating that Chinese Northeast soybeans may have undergone specific selection for some amino acid content.\u003c/p\u003e \u003cp\u003eThe Northeast region of China is renowned as a major hub for high-oil soybean production. Breeding efforts in this area have been focused on enhancing the oil composition of soybeans towards healthier profiles, a trend also reflected in recent European varieties. Our research has identified several oil-related Quantitative Trait Loci (QTLs) in this region, emphasizing its importance in soybean oil production (Song et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoybean oil typically comprises 21.5% oleic acid, a beneficial unsaturated fatty acid, and 12.2% palmitic acid, a saturated fatty acid which, while stabilizing the oil, can be detrimental to health if consumed excessively (Abdelghany et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Julibert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the health implications, increasing the proportion of unsaturated fatty acids is a significant breeding target to enhance the nutritional value of soybean oil (Thelen \u0026amp; Ohlrogge \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In line with these objectives, our study identified two QTLs related to seed oleic acid content in Chinese germplasm. Furthermore, we discovered an additional QTL in newly introduced European germplasm, indicating potential genetic avenues for improving oil quality in both regions. This cross-regional genetic investigation could further enhance the quality of soybeans by informing targeted breeding strategies.\u003c/p\u003e \u003cp\u003eWe also identified some QTL related to nutrient use efficiency and accumulation. The regulation mechanism of nutrient accumulation in soybean tissues is very complex and may vary depending on the soil and environmental conditions in different regions, leading to different selection directions (Ray et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dhanapal et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZDX1 soybean SNP array\u003c/p\u003e \u003cp\u003eThe ZDX1 soybean SNP array was developed using the largest scale of soybean core germplasm to date, encompassing 2214 soybean accessions from China and worldwide. This revolutionary array is capable of accurately identifying soybean germplasm from both China and abroad. When compared to other soybean array like SoySNP50K, 180 K AXIOM\u0026reg;, and NJAU 355 K SoySNP, the ZDX1 array boasts 80% unique SNPs, providing greater coverage of the soybean genome (Sun et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Moreover, its functional sites can efficiently and precisely identify crucial agronomic traits.\u003c/p\u003e \u003cp\u003eSoybean cyst nematode is a widespread issue that continues to affect soybean production regions across the world, and it is rapidly expanding to other areas (Tylka \u0026amp; Marett \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, using the ZDX1 array to examine functional loci, we discovered 24 European varieties with resistance to soybean cyst nematode (Table S3). However, we also observed that the genetic diversity and quantity of resistance to soybean cyst nematode may be inadequate in European germplasm. This inadequacy may hinder the future expansion of soybean cultivation in Europe.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to explore the genetic differences between soybeans from Northeast China and Europe. Our findings revealed that European soybean varieties exhibit relatively lower genetic diversity compared to the Chinese collections, which include elite cultivars, landraces, and wild accessions. This observation suggests a narrower genetic base within European varieties. Additionally, population structure analysis suggested that these European varieties likely originated from China, underscoring a historical connection in soybean cultivation between the regions. Through a combined analysis using F\u003csub\u003eST\u003c/sub\u003e, pi, and XP-CLR, we identified distinct breeding targets between the Northeast Chinese and European varieties. This comparison provides insights into the developmental trajectory and adaptation strategies of soybean breeding in different geographical contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLijuan Qiu and Zhangxiong Liu conceived and designed the experiments, analyzed the data, prepared figures and/or tables, and wrote or reviewed the draft manuscript. Jiangyuan Xu, Xindong Yao, and Yuqing Lu conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, and wrote or reviewed the draft manuscript. Rittler Leopold analyzed the data and prepared figures and/or tables. Yongzhe Gu, Ming Yuan, Yong Zhang, Rujian Sun, Yongguo Xue, Yeli Liu, Dezhi Han, Jinxing Wang, and Huawei Gao performed the experiments, analyzed the data, and wrote or reviewed the draft manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e\u0026dagger;Jiangyuan Xu, Xindong Yao and Yuqing Lu contributed equally to this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelghany AM, Zhang S, Azam M, Shaibu AS, Feng Y, Li Y, Tian Y, Hong H, Li B, Sun J (2020) Profiling of seed fatty acid composition in 1025 Chinese soybean accessions from diverse ecoregions. The Crop Journal 8 (4):635-644. doi:https://doi.org/10.1016/j.cj.2019.11.002\u003c/li\u003e\n\u003cli\u003eAlexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. 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International Journal of Genomics 2018:8561458. doi:10.1155/2018/8561458\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":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic diversity, Selective sweep analysis, European soybean, Chinese soybean","lastPublishedDoi":"10.21203/rs.3.rs-4647180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4647180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough soybeans (\u003cem\u003eGlycine max\u003c/em\u003e [L.] Merr.) originated in China and have spread worldwide, artificial selection for different breeding targets and methods in various regions can alter the genetic makeup of soybeans, enabling them to adapt to different environments. China has established a soybean germplasm gene bank that stores over 30,000 soybean germplasms from all over the world, but it contains few modern European varieties. The selective sweep analysis is an effective method for evaluating genetic diversity among populations and subpopulations. To compare the genetic diversity between Chinese and European germplasms, we genotyped 797 European varieties, 804 Chinese elite cultivars and landraces, and 54 Chinese wild varieties using the ZDX1 array, respectively. An analysis of 158,315 SNPs demonstrated a higher genetic diversity in Chinese wild soybeans and cultivars. Moreover, population structure findings indicated that European varieties possess partial Chinese ancestry. The joint analysis of pi, F\u003csub\u003eST\u003c/sub\u003e and XP-CLR identified 140 selected regions between Chinese and European germplasms in total. Specifically, the Chinese collection had 124 regions distributed across 15 chromosomes, while the European collection had 16 regions spread over 10 chromosomes. The QTLs identified within these selected regions highlight the significant differences in breeding targets across regions, providing a scientific basis for both Chinese and European breeders to utilize these germplasm resources.\u003c/p\u003e","manuscriptTitle":"A comparison of Chinese wild and cultivar soybean with European soybean collections on genetic diversity by Genome-Wide Scan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 21:18:24","doi":"10.21203/rs.3.rs-4647180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-30T02:41:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-30T01:40:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298437508863829863220495704564964035228","date":"2024-09-19T09:20:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T07:45:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103023977744052616808600547698336898393","date":"2024-09-13T11:29:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107697421203933896971322288025921511112","date":"2024-09-12T06:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327646595778594513996684401524308119096","date":"2024-07-05T19:03:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-04T13:48:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-03T15:38:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-03T15:38:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2024-06-27T08:59:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"92abdd28-86dd-4053-813f-30009934e3c9","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:01:35+00:00","versionOfRecord":{"articleIdentity":"rs-4647180","link":"https://doi.org/10.1007/s10722-024-02294-8","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2025-01-08 15:57:27","publishedOnDateReadable":"January 8th, 2025"},"versionCreatedAt":"2024-07-29 21:18:24","video":"","vorDoi":"10.1007/s10722-024-02294-8","vorDoiUrl":"https://doi.org/10.1007/s10722-024-02294-8","workflowStages":[]},"version":"v1","identity":"rs-4647180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4647180","identity":"rs-4647180","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}