Joint genetic control of isoflavones and soyasaponins revealed by mGWAS, genomic prediction, and SHAP-guided allele stacking

Joint genetic control of isoflavones and soyasaponins revealed by mGWAS, genomic prediction, and SHAP-guided allele stacking | 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 Article Joint genetic control of isoflavones and soyasaponins revealed by mGWAS, genomic prediction, and SHAP-guided allele stacking Hakyung Kwon, Seung Yeob Song, Yeonghun Cho, Ji Eun Ra, Jungmin Ha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8256309/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Isoflavones and soyasaponins are two classes of health-promoting specialized metabolites in soybean, and improving them simultaneously is a key breeding goal. Emerging evidence indicates that these two metabolite classes can act synergistically in vivo and in vitro, making their simultaneous enhancement an increasingly important breeding objective. However, despite extensive studies on each pathway independently, the genetic basis underlying joint variation of isoflavones and soyasaponins remains poorly understood. Here, we profiled 17 metabolites (12 isoflavones and 5 soyasaponins) across 376 accessions of the Korean soybean core collection using UPLC. We characterized metabolite distributions, correlations, and presence–absence patterns, and performed multi-metabolite Genome-Wide Association Study (GWAS), identifying 70 high-confidence loci. These included previously reported major loci as well as eight novel loci for isoflavones and thirteen for soyasaponins. Five genomic regions showed shared linkage disequilibrium (LD) structure between the two pathways, and we identified candidate genes for high-confidence loci. We next compared FT-IR–based phenomic prediction with GWAS-informed genomic prediction, finding that genomic prediction consistently outperformed phenomic prediction and achieved moderate to high accuracy, indicating strong genetic determinism. Finally, we applied an XGBoost– SHapley Additive exPlanations (SHAP) framework to estimate the extent to which favorable alleles could be combined in silico. Single-trait allele stacking pointed to CMJ_115, CMJ_068, and CMJ_236 as the best-performing accessions for Acetyl-daidzin, Malonyl-daidzin, and Soyasaponin-ab, respectively. Multi-trait optimization produced a virtual genotype most similar to CMJ_317, suggesting this accession as a practical parent for jointly improving both metabolite classes. Overall, our findings provide a population-scale map of diversity, genetic factors, and achievable breeding gains for functional soybean improvement. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Biological sciences/Plant sciences soybean metabolites isoflavone soyasaponin mGWAS genomic prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Soybean ( Glycine max ) is a globally important crop valued not only for its high-quality plant protein and vegetable oil, but also for its diverse bioactive metabolites that contribute to human health [1,2]. While soybean-derived products such as soybean oil, tofu, fermented pastes, and soy sauce have long served as staple dietary components, soybean is now increasingly consumed as a health-promoting ingredient[3–5]. As interest in functional foods has expanded, soybean has emerged as a major source of physiologically active phytochemicals, most notably isoflavones and soyasaponins, which exhibit broad effects on human metabolism, immunity, and chronic disease risk[2,3,6]. Isoflavones are abundant in soybean seeds but vary widely among genotypes, with concentrations spanning more than a 20-fold range across global germplasm[7,8]. The major aglycones—daidzein, genistein, and glycitein—occur predominantly as glycosides, acetylglycosides, and malonylglycosides, generating twelve molecular forms with malonylated conjugates representing 60–80% of total isoflavones [9,10]. Isoflavones have been associated with a wide range of health benefits, including alleviation of menopausal symptoms, antioxidant activity, improved bone metabolism, and anticancer effects [11–14]. These functions arise from multiple biological mechanisms, including estrogen receptor modulation, regulation of oxidative stress, and interaction with cell-signaling pathways such as MAPK and NF-κB [15]. Given these roles, isoflavone content has become a major target for soybean quality improvement and a subject of extensive genetic research. Soyasaponins, a class of triterpenoid glycosides unique to soybean, exhibit substantial quantitative and compositional diversity[16,17]. They typically account for 0.2–0.5% of seed weight but can exceed 2% in hypocotyl tissue[18,19]. Soyasaponins are broadly divided into group A bisdesmosides and DDMP-conjugated precursors, the latter being further converted into groups B and E depending on their terminal sugar moieties[20,21]. Group A acetylated saponins have traditionally been associated with bitterness and astringency, whereas DDMP- and B-type saponins are linked to desirable physiological effects, including cholesterol lowering, anti-inflammatory and antioxidant activities, and antiviral properties[22,23]. Recent reports, however, indicate that certain A-group conjugates—such as the Ab chemotype—also possess distinct bioactivities, such as strong BMP-2–mediated osteogenic effects, indicating that A-type saponins themselves represent important functional components[24]. Although the biological properties of isoflavones and soyasaponins have been extensively studied, emerging evidence suggests that these two metabolite classes may act synergistically. Recent in vivo studies reported that combined supplementation of isoflavones and saponins significantly suppressed NNK-induced lung tumor formation in mice, whereas neither compound alone produced significant effects[25]. Cellular studies have further shown that both isoflavones and group A saponins enhance osteoblast differentiation via increased alkaline phosphatase activity[24]. These findings raise the possibility that soyasaponins may enhance the bioavailability or physiological activity of isoflavones, indicating an integrated functional relationship between the two pathways. Despite this, the genetic basis underlying joint variation in isoflavone and soyasaponin profiles remains virtually unexplored. Genetic studies over the past two decades have identified numerous loci controlling isoflavone biosynthesis and accumulation, including QTLs on nearly all soybean chromosomes and candidate genes such as IFS, CHS, IOMT, β-glucosidase, GST, and several transcription factors, particularly members of the R2R3-MYB and zinc-finger protein families[26–33]. Likewise, major loci determining soyasaponin composition have been mapped, including Sg-1, Sg-3, Sg-4, Sg-5, and Sg-6, which encode UDP-glycosyltransferases, oxidosqualene cyclases, and P450 enzymes involved in aglycone formation and sugar-chain tailoring[17,34–40]. However, these studies have examined isoflavones and soyasaponins independently. Given that specialized metabolic pathways can share precursors, compete for glycosylation capacity, or reside within linked genomic regions, it is not yet known whether the genetic determinants of isoflavone and soyasaponin variation interact, overlap, or influence each other. Consequently, it remains uncertain whether high accumulation of both metabolite classes can be achieved within the same genetic background or how these pathways might be jointly optimized through breeding. To address these questions, genetically representative germplasm is required. The Korean soybean core collection, constructed from more than 2,872 accessions while retaining over 99% of total genetic diversity, provides a powerful resource for dissecting metabolic, phenotypic, and genomic variation with minimal redundancy[41]. Core collections have previously enabled high-resolution trait dissection and identification of novel alleles in soybean for seed composition, stress tolerance, and agronomic traits[42–45]. Leveraging such a collection allows comprehensive evaluation of natural variation in isoflavone and soyasaponin profiles and supports integrative genomic analysis to identify loci governing their independent and joint accumulation. In this study, we conducted a comprehensive metabolite–genome analysis of isoflavones and soyasaponins across 376 accessions of the Korean soybean core collection using high-coverage whole-genome sequencing and UPLC–based metabolite profiling. Specifically, we (i) quantified 12 isoflavone compounds and 5 major soyasaponins, (ii) characterized their distribution, correlations, and presence–absence patterns, (iii) performed single-trait and multi-trait Genome-Wide Association Study (GWAS) to uncover loci controlling each metabolite class, and (iv) examined whether loci for isoflavone and soyasaponin pathways show genomic co-localization, independence, or physical linkage, and (v) evaluated the predictive power of GWAS-identified variants and assessed the extent to which breeding could elevate metabolite levels using genomic prediction and allele-stacking simulations. By integrating metabolic phenotypes with genome-wide variation, this study aims to reveal whether high isoflavone and high soyasaponin chemotypes can co-occur, identify genetic architectures that enable or constrain simultaneous improvement, and provide strategic insights for breeding soybean cultivars optimized for functional health applications. Materials and Methods Genotype analysis SNP variant data were obtained from the Soybean Haplotype Map Project , which provides genome-wide variation derived from resequencing of 781 soybean accessions [46]. Genotype information corresponding to the 376 Korean soybean core collection accessions was extracted from this dataset for downstream analyses. Only biallelic SNPs located on the 20 soybean chromosomes were retained, while variants on unanchored scaffolds and non-chromosomal contigs were removed. Because soybean is predominantly selfing and exhibits high levels of homozygosity, all heterozygous genotype calls were treated as missing to minimize false heterozygosity arising from misalignment, structural variation, or multi-copy loci. linkage disequilibrium (LD) pruning was performed with PLINK 1.9 using a sliding-window approach and an r² threshold of 0.8[47]. The resulting LD-pruned SNP set was used for both principal component analysis (PCA) and ancestry inference. Population structure was estimated using ADMIXTURE (v1.3) across K = 1–10 , and the number of ancestral clusters was determined by selecting the K value that produced the lowest cross-validation error[48]. UPLC-based targeted metabolite profiling Each seeds were finely ground, and for targeted metabolomics, 0.5 g of powder was extracted with 20 mL of 100% methanol on a shaking incubator for 60 min at room temperature. The extracts were centrifuged at 7,800 rpm for 5 min, and the supernatant was filtered through a 0.2 μm PTFE membrane. Targeted quantification of 12 isoflavones and 5 soyasaponins was performed using a Thermo UltiMate 3000 UHPLC system fitted with a HALO C18 column (2.1 × 100 mm, 2.7 μm). Chromatographic separation employed 0.1% acetic acid in water (solvent A) and 0.1% acetic acid in acetonitrile (solvent B), and compounds were detected at 254 nm using a diode-array detector. Calibration curves were generated from authentic standards across a 0–100 μg/mL range (five concentration points), and quantification was conducted in Chromeleon 7 using internal-standard normalization and regression models with R² ≥ 0.999 for all analytes. Quantified isoflavones included daidzin, glycitin, genistin, malonyl-daidzin, malonyl-glycitin, malonyl-genistin, acetyl-daidzin, acetyl-glycitin, acetyl-genistin, daidzein, glycitein, and genistein. Soyasaponins Aa, Ab, Ba, Bb, and Bb′ were quantified using the same UHPLC method. FT-IR-based phenomic profiling For untargeted FT-IR metabolite profiling, 20 mg of powdered sample was mixed with 200 μL of 20% methanol, thoroughly vortexed, incubated for 30 min at 50°C with intermittent agitation, and centrifuged twice (13,000 rpm, 15 min followed by 5 min) to remove particulates before storage at –20°C. Dried methanolic extracts were analyzed using a Tensor II FT-IR spectrometer equipped with a DTGS detector. Spectra were collected from 4000 to 400 cm⁻¹ with 4 cm⁻¹ resolution over 128 scans. Baseline correction, area normalization, and second-derivative transformation were performed in OPUS Lab 7.0 prior to multivariate analysis. Phenotype processing and statistical analysis Compounds undetected in all accessions, daidzein, genistein, glycitein, malonyl genistin, acetyl genistin, and acetyl glycitin, were excluded from downstream analyses. Total isoflavone and total soyasaponin contents were calculated as the sum of all quantified compounds within each pathway for each accession. All statistical analyses were conducted in R v4.5.2 [49]. Summary statistics, including means, standard deviations, and coefficients of variation, were computed using base R functions. Pairwise Pearson correlation coefficients were calculated using the cor function in the stats package, and p-values were adjusted for multiple testing using the false-discovery-rate method implemented in p.adjust. Isoflavone metabolic modules were inferred directly from the correlation structure. Presence–absence matrices were generated by binarizing each metabolite as detected or undetected, and chemotypes were enumerated by identifying all unique combinations of detectable compounds across accessions. For each chemotype, its frequency among the 376 accessions was recorded, and the dominant patterns were defined based on their prevalence. The contribution of individual metabolites to pathway-level variation was assessed by correlating compound abundances with total pathway output using linear models and Pearson correlations. Genome-wide association study (GWAS) GWAS was conducted using the GAPIT3, employing four different statistical models: the mixed linear model (MLM), FarmCPU, BLINK, and MLMM [50]. For population structure correction, individual ancestry coefficients (Q-matrix) were obtained from ADMIXTURE using the LD-pruned SNP set[48]. Cross-validation identified K = 5 as the optimal number of ancestral clusters, and the corresponding five-component Q-matrix was included as fixed covariates in all models. To further control for relatedness among accessions, a kinship matrix computed from the filtered genotype data was incorporated jointly with the Q-matrix. Independent association signals were first identified by clumping significant SNPs in PLINK 1.9, which groups nearby variants that are in linkage disequilibrium (LD) with the most significant marker in a given region[47]. A physical window of 1 kb was used to ensure that only the strongest SNP within each tightly linked cluster was retained as the lead signal, thereby removing redundant secondary peaks arising from local LD. To characterize the genomic interval represented by each lead SNP, pairwise LD was quantified in PLINK for all neighboring variants located within ±500 kb of the lead position. SNPs showing r² ≥ 0.2 with the lead SNP were considered part of the same LD block, and this block defined the physical extent of the association signal. For traits with multiple lead SNPs, these LD blocks were compared for physical overlap. Overlapping blocks were merged and treated as a single locus. The final set of association intervals for each phenotype consisted of non-overlapping LD-supported genomic regions, each representing an independent association signal. Candidate gene identification Candidate genes within each GWAS interval were identified through an integrative annotation-based approach. Gene models were retrieved according to the physical boundaries of each association peak, which were defined by the extent of linkage disequilibrium surrounding the lead SNP. Association signals represented by a single SNP positioned in an intergenic region were classified as putative regulatory variants and were not considered in the downstream gene-level prioritization. For all other loci, every annotated gene located within the LD-defined interval was evaluated. Functional annotation and predicted biochemical roles were obtained from the soybean reference genome (Wm82.a2), which corresponds to the version used for variant calling[51]. These annotations were cross-referenced with KEGG pathway assignments and previously reported functional studies to identify known or putative biosynthetic genes[52]. Arabidopsis homologs of each soybean gene were identified using BLASTP, and the scientific literature for each homolog was surveyed to assess prior implication in flavonoid, phenylpropanoid, and terpenoid pathways[53]. To prioritize candidates expressed in the tissue relevant to trait manifestation, expression profiles during soybean seed development were obtained from SoyBase[54]. Co-expression analysis was then conducted using ATTED-II to quantify the degree of coordinated expression between each candidate gene and known pathway genes[55]. Genes that showed both seed-stage expression and enriched co-expression with established flavonoid, isoflavone, or soyasaponin pathway genes were given the highest priority. However, genes lacking strong co-expression signals were also retained as candidates when they had clear functional support from Arabidopsis literature together with detectable expression during soybean seed development. Genomic and phenomic prediction modeling To evaluate the predictive capacity of high-dimensional phenomic and genomic signals and to identify allele combinations that maximize target metabolite levels, we applied FT-IR–based phenomic prediction, GWAS-informed genomic prediction. All predictive models were implemented using Random Forest regression (ranger v3.2.4)[56]. Prediction uncertainty was assessed using 20 bootstrap Random Forest models , and the distribution of bootstrap predictions was used to derive mean values and 95% confidence intervals. Raw FT-IR spectra were processed through second-derivative transformation and wavelength alignment. For each trait, Random Forest variable importance was computed, and the top 5% most informative wavelengths were selected and incorporated into the model using scaled importance values as predictor weights. For genomic prediction, trait-specific SNP sets were defined using GWAS results with the top 5% SNPs in p-values. SNP-level weights were derived by combining the absolute GWAS effect size, −log10(P), and trait-specific prediction accuracy, each scaled and normalized. These weights were used during Random Forest model fitting. XGBoost-SHAP-guided allele stacking and optimal genotype search To identify genotype configurations that maximize predicted metabolite levels, genomic prediction was performed using XGBoost regression models trained on GWAS-filtered SNPs (P < 0.05) for each metabolite trait[57]. Feature contributions were estimated using SHapley Additive exPlanations (SHAP) values, and the top SHAP-ranked SNPs from the three focal metabolites (Acetyl-daidzin, Malonyl-daidzin, Soyasaponin-ab) were combined to define a 579-SNP search space for allele-stacking simulations[58]. To identify allele combinations maximizing predicted trait values, we applied a greedy coordinate-wise search: starting from the real accession with the highest model-predicted value, each SNP genotype (0/1/2) was iteratively replaced with the alternative genotypes, and changes were retained only when they increased the objective value. Genetic similarity between optimized genotypes and the 376 real accessions was assessed by Hamming distance across the 579 SNPs. Optimal genotypes were appended to the real genotype matrix, and a neighbor-joining tree was constructed from the allele-mismatch distance matrix to visualize their phylogenetic placement relative to existing diversity. Results Genotype Analysis Whole-genome resequencing of 376 Korean soybean core collection accessions yielded 10,489,771 SNPs across the 20 chromosomes. As expected for soybean, linkage disequilibrium (LD) in the unpruned dataset was high at short distances (r² ≥ 0.2 within 125 kb) and decayed slowly to ~0.1 around 900 kb (Fig. 1a). Many adjacent SNPs were separated by ≤10 bp, with a median inter-marker distance of 36 bp, indicating substantial short-range redundancy (Fig. 1b). To improve computational efficiency and statistical power, markers were LD-pruned at r² < 0.8, resulting in a final working set of 976,378 SNPs. After pruning, the LD decay curve flattened and pairwise r² dropped below 0.2 at ≤125 kb, confirming effective redundancy reduction (Fig. 1a). The median inter-SNP spacing increased to 414 bp and exhibited a more uniform genome-wide distribution (Figure 1b). Population structure analysis on the pruned panel supported K = 5, and the resulting Q matrix was incorporated into all GWAS models (Fig. 2). UPLC-based targeted metabolite profiling Isoflavones A total of 12 isoflavone compounds were profiled across 376 accessions. Three aglycones (daidzein, genistein, and glycitein) and three conjugated forms (malonyl genistin, acetyl genistin, and acetyl glycitin) were undetectable in all samples, leaving six quantifiable glycosides (Fig. 3a). Correlation analysis resolved the six detectable compounds into two robust and largely independent metabolic modules; glycitein module and daidzein/genistein module (Fig. 3b). The glycitein module, consisting of glycitin and malonyl glycitin, displayed extremely strong internal correlation (r = 0.97), reflecting their shared biosynthetic steps. The daidzein/genistein module, comprising daidzin, malonyl daidzin, acetyl daidzin, and genistin, also exhibited tight cohesion (up to r = 0.99). Cross-module correlations were weak (|r| ≤ 0.12, FDR > 0.05), demonstrating that the glycitein-derived and daidzein/genistein-derived branches vary independently. Presence–absence combinations further highlighted the constrained combinatorial architecture of the pathway (Fig. 3c). Despite six detectable glycosides, only a small subset of the theoretical 64 presence–absence combinations occurred. Three major chemotypes dominated the panel; i) the full daidzein-derived glycoside suite (n = 186), ii) the same pattern with malonyl glycitin added (n = 64), and iii) a rarer chemotype containing both malonyl glycitin and glycitin (n = 37). Remaining chemotypes were very infrequent and dispersed across the panel, and none of these low-frequency patterns exhibited elevated total isoflavone levels. This concentration of high accumulation within the dominant daidzein-derived chemotypes highlights the restricted combinatorial diversity and strong modular organization of the isoflavone pathway. Analysis of quantitative variation showed that both compound-level dispersion and total isoflavone accumulation were dominated by the daidzein/genistein-derived module. Acetyl daidzin and malonyl daidzin were not only the most abundant compounds (means 0.33 and 0.20 mg/ml) but also the most variable (SD = 0.20 and 0.16). These same metabolites, together with genistin and daidzin, showed exceptionally strong correlations with total isoflavone content (acetyl daidzin r = 0.96; malonyl daidzin r = 0.95; genistin r = 0.91; daidzin r = 0.89), whereas the glycitein module contributed minimally (r ≤ 0.49). Collectively, these observations demonstrate that both the abundance and the quantitative diversity of the isoflavone pathway are primarily driven by variation in the daidzein-derived branch. Soyasaponins A total of five soyasaponins (Aa, Ab, Ba, Bb, and Bb′) were quantified across 376 accessions (Fig. 4a). The A-group saponins showed the widest range, with Aa and Ab exhibiting mean abundances of 0.23 and 0.45 mg/ml (SD = 0.30 and 0.50). Bb and Bb′ were consistently detected (means = 0.14 and 0.12 mg/ml), whereas Ba was rare (mean = 0.03 mg/ml, SD = 0.05). These distributions indicate that the dominant source of variation lies within the A-group branch. Correlation analysis revealed a simple but robust quantitative structure (Fig. 4b). Bb and Bb′ were tightly correlated (r = 0.79), forming a cohesive B-group submodule, whereas Aa and Ab were strongly anticorrelated (r = –0.68), consistent with their mutually exclusive chemotypes. Cross-group correlations were weak to moderate (|r| ≤ 0.31), indicating that the A-group chemotype switch and the conserved B-group branch represent two largely independent axes of variation within the saponin pathway. Presence–absence combinations further revealed a constrained chemotype architecture (Fig. 4c). Despite five detectable compounds, only a small subset of the theoretical 32 presence–absence combinations occurred. The population was structured around two dominant and mutually exclusive A-group chemotypes—Aa-type (46%) and Ab-type (56%)—while Bb was almost ubiquitous (~99.5%). Ba and Bb′ appeared only in low-frequency, accession-specific patterns. None of these rare chemotypes exhibited elevated total soyasaponin levels, indicating that pathway flux is concentrated within a small number of dominant metabolic configurations. Analysis of pathway-level variation showed that total soyasaponin accumulation was primarily driven by the Ab-dominated branch. Ab was both the most abundant (mean = 0.45 mg/ml) and the most variable (SD = 0.501), and it showed the strongest correlation with total soyasaponin content (r = 0.761). Bb also contributed substantially (r = 0.552), with Bb′ showing a modest association (r = 0.306). In contrast, Aa and Ba contributed minimally to total accumulation (r = –0.092 and 0.078). Together, these findings demonstrate that although the Aa–Ab chemotype switch defines the structural foundation of saponin diversity, the Ab–Bb axis is the primary quantitative driver of total soyasaponin levels. Isoflavone and Soyasaponin relationship The relationship between isoflavone and soyasaponin accumulation was evaluated at the compound, module, and pathway levels. Correlation analysis of individual compounds showed that cross-pathway associations were generally very weak, with most isoflavone–soyasaponin pairs displaying near-zero correlation coefficients (|r| < 0.2) (Supplementary Table S1). This pattern indicates that the two metabolic pathways vary largely independently at the compound level. Consistently, presence–absence association tests revealed almost no significant co-occurrence patterns between individual compounds from the two pathways, except for a single enriched pair (Daidzin–Soyasaponin_bb′; FDR = 0.005), supporting an overall independence between pathways in terms of detection frequency. In contrast, total isoflavone content and total soyasaponin content exhibited a weak but noticeable positive correlation (r = 0.30), suggesting partial convergence or co-regulation at the whole-pathway accumulation level (Supplementary Fig. S1). Taken together, these results indicate that although individual compounds in the isoflavone and soyasaponin pathways behave almost independently, their total accumulation levels share a small degree of coordinated variation across accessions. Genome-wide association study (GWAS) Across the four GWAS models (MLM, MLMM, FarmCPU, and BLINK), a total of 1,799 significant SNPs were detected for isoflavone and soyasaponin traits (Supplementary Table S2,3). After LD-based merging, these signals collapsed into 1,315 unique loci, of which 70 were supported by at least two models and considered high-confidence associations (Table 1,2). LD intervals surrounding the lead SNPs ranged from 299 kb to 5.2 Mb, reflecting the characteristic long-range LD structure of the soybean genome. Isoflavones exhibiting high phenotypic diversity, particularly Acetyl daidzin and malonyl daidzin, showed abundant GWAS peaks. Three high-confidence loci, Glycitin_4_1 and Malonyl_daidzin_16_1 were consistently detected across all models, with the strongest locus explaining up to 24.13% of phenotypic variance (Table 1) Several loci co-localized with previously reported QTL or GWAS signals associated with phenylpropanoid or isoflavone biosynthesis, while 8 loci appear to be novel. Consistent with the mutually exclusive Aa/Ab chemotypes, the GWAS for A-group soyasaponins identified a single dominant region with extremely high significance (−log₁₀P up to 65.53 for Soyasaponin_Aa_7_2 using MLMM), corresponding to the classical sg-1 locus (Table 2). Although sg-1 clearly controls the presence–absence chemotype, its quantitative effect was modest, with the highest PVE for this region reaching 31.03% in Soyasaponin_Ab_7_2, indicating that A-group abundance is influenced by additional loci. Indeed, several other regions exhibited substantial quantitative effects, with PVE values reaching up to 67.45% at Soyasaponin_Ab_18_1 using MLMM, highlighting polygenic control of abundance beyond the major chemotype locus. Additional loci associated with Ba and Bb′ reflected their presence–absence variation across the panel. Importantly, several GWAS signals from different traits converged within shared LD intervals. In total, 27 distinct trait–locus associations collapsed into nine genomic regions, five of which were jointly detected for both isoflavone and soyasaponin traits (Supplementary Table S4). These loci were positioned within overlapping LD blocks across traits, suggesting potential physical linkage or coordinated regulation between the pathways. Notably, on chromosome 15, the major Soyasaponin_Aa_15_1 locus (17,454,177–20,341,457 bp) entirely encompassed the Acetyl_daidzin_15_1 interval (17,949,582–19,722,979 bp). Similarly, the major Soyasaponin_Ab_18_1 locus on chromosome 18 co-localized with both the Malonyl_glycitin_18_1 and Acetyl_daidzin_18_1 regions. These results indicate that favorable alleles for both traits can co-occur within the same haplotypes, although the degree to which this facilitates or constrains breeding will depend on the underlying linkage relationships. Candidate gene identification The physical size of 70 high-confidence loci varied widely, ranging from broad LD blocks containing up to 573 genes to extremely narrow intervals defined by only a few SNPs (Table 1,2). Notably, 5 loci were represented by a single significant SNPs, and were located in intergenic regions. In such cases, where no protein-coding genes reside in the interval, the association is most likely driven by regulatory variants influencing expression of nearby biosynthetic or regulatory genes. Comparison of the detected GWAS signals with established pathway knowledge, including KEGG annotations and previously reported functional studies, showed that 19 loci co-localized with genes whose biochemical roles have been characterized (Table 1,2, Supplementary Table S5,6). These included key biosynthetic or regulatory components such as caffeoyl-CoA O-methyltransferase (OMT), flavonol synthase (FLS), cinnamate 4-hydroxylase (C4H), and the transcriptional repressor MYB4 (Supplemenatary Table S5). For soyasaponins, the classical sg-1 locus (Glyma.07G254600; UGT73F2/4) was located within one of the intervals, consistent with its known role in terminal sugar modification, and additional UDP glycosyltransferases(UGTs) were also recovered within other saponin-associated regions (Supplemenatary Table S6). In contrast, the remaining 51 loci did not contain any functionally established biosynthetic or regulatory genes for isoflavone and soyasaponin, and thus represent regions whose underlying molecular basis remains unresolved. For the 46 multi-gene loci without known pathway genes, candidate genes were prioritized through an integrative framework. First, all soybean genes within each interval were examined for Arabidopsis homologs, and literature describing those homologs was surveyed for evidence of involvement in flavonoid, phenylpropanoid, terpenoid, or metabolic regulation pathways. Because individual Arabidopsis genes often correspond to several soybean paralogs, seed-stage expression was evaluated early in the prioritization process to exclude candidates lacking expression in the relevant tissue. Among the expressed genes, genes forming enriched co-expression relationships with flavonoid, isoflavone, or soyasaponin biosynthetic genes were further prioritized. When soybean expression and co-expression patterns aligned with Arabidopsis functional evidence, these genes were classified as high-confidence candidates. Across these novel multi-gene intervals, 487 candidate genes had Arabidopsis homologs implicated in relevant pathways, with 43 showing strong seed expression and 10 demonstrating pathway-enriched co-expression (Supplementary Table S5,6). In addition, 5 intergenic single-SNP intervals likely represent cis-regulatory contributors rather than structural gene candidates. The prioritized candidates outline key genomic components that likely contribute to the quantitative diversity of isoflavone and soyasaponin biosynthesis. Genomic and phenomic prediction modeling Genomic prediction using trait-specific GWAS SNPs showed consistently strong performance across both isoflavones and soyasaponins, with most traits reaching correlations of 0.70–0.90 across cross-validation folds (Fig. 5). Soyasaponins in particular showed the highest predictability, exceeding r = 0.90, indicating that the major-effect loci detected by GWAS capture substantial genetic control over their accumulation. In comparison, FT-IR phenomic prediction exhibited more variable accuracy. Metabolites with clear spectral signatures, for example, Acetyl-daidzin and Genistin, were moderately predictable, whereas low-abundance compounds such as Glycitin showed weak performance. Bootstrap resampling with 20 Random Forest refits confirmed that genomic predictions were stable with narrow confidence intervals, while phenomic models exhibited broader uncertainty. Overall, these results demonstrate that GWAS-informed genomic models provide reliable and trait-specific predictive power, whereas FT-IR spectra capture broader chemical variation but cannot fully resolve compound-specific differences. XGBoost-SHAP-guided allele stacking and optimal genotype search We used SHAP-guided allele stacking to estimate the maximum achievable levels for each metabolite and to predict the phenotypes obtainable by combining favorable SNPs (Fig. 6). For single traits, the SHAP-informed stacking results consistently pointed to the same top-performing accessions present in the panel: CMJ_115 for Acetyl-daidzin, CMJ_068 for Malonyl-daidzin, and CMJ_236 for Soyasaponin-ab. The predicted single-trait optima remained close to these observed values and did not exceed the highest phenotype recorded in the population. For the multi-trait scenario, we generated an in silico genotype optimized to maximize all three metabolites simultaneously. This virtual genotype showed predicted levels of approximately 1.16 for Acetyl-daidzin, 0.76 for Malonyl-daidzin, and 2.16 for Soyasaponin-ab. A genome-wide comparison revealed that CMJ_317 was the closest real accession to this multi-trait optimum, despite differing at 268 of the 579 SNPs considered. These results identify CMJ_317 as the most similar real genotype to the modeled multi-trait optimum. Discussion The increasing interest in soybean-derived functional compounds has stimulated substantial research on individual classes of isoflavones and soyasaponins, yet efforts to simultaneously enhance both pathways within a single cultivar have been limited. Despite evidence that these metabolites can exert complementary physiological effects, most previous studies have focused on either pathway in isolation, and comprehensive analyses integrating phenotypic diversity, chemotype structure, and genome-wide regulation across large germplasm panels remain limited. By utilizing a well-characterized core collection of 376 Korean soybean accessions, we provide an integrated view of how these two major bioactive pathways are organized phenotypically and genetically, and we identify key points for breeding cultivars with coordinated improvements in both metabolite classes. Our phenotypic analyses revealed that both pathways display constrained chemotypes. For isoflavones, only three dominant chemotypes accounted for approximately 88% of the accessions (Fig. 3c). Rare chemotypes showed consistently lower total accumulation, suggesting that the standing diversity in cultivated soybean is biased toward high-accumulation configurations. This trend aligns with the biological roles of isoflavones in UV protection, antioxidative defense, and stress adaptation, although the relative contributions of domestication, breeding history, and ecological selection remain uncertain[59–61]. Within this restricted chemotype space, quantitative variation was strongly concentrated in the daidzein-derived branch (Fig. 3a,c). Acetyl daidzin and malonyl daidzin were the most abundant and the most variable compounds, and together they explained the majority of variation in total isoflavone content. These observations indicate that pathway-level diversity is shaped primarily by flux through a small number of major conjugates, highlighting the daidzein branch as a key leverage point for metabolic improvement. Although the physiological activities of acetyl daidzin and malonyl daidzin are less well characterized than those of their aglycone counterparts, their dominant contribution to pathway-level variation suggests that further functional investigation of these conjugates could expand the utility of high-isoflavone soybean cultivars in food and health-oriented applications. Although modest positive correlations were observed between specific isoflavone and saponin compounds, the overall correlation between pathway totals was weak (r = 0.3), indicating limited metabolic coupling (Supplementary Fig.1). Several factors may account for this independence, including minimal precursor competition, differences in temporal accumulation patterns, and distinct transcriptional regulatory networks governing each pathway. From a breeding perspective, this independence implies that simultaneous enhancement of isoflavone and soyasaponin accumulation is feasible without strong antagonistic trade-offs. The genetic architecture uncovered by GWAS revealed clear loci controlling variation in both pathways (Table 1,2). Classical loci previously reported in isoflavone or soyasaponin variation, including the sg-1 region underlying the Aa/Ab chemotype split, were robustly rediscovered, validating both the phenotypic dataset and our analytical framework (Table 2). Beyond these known regions, we identified multiple novel loci, several of which showed strong evidence of functional relevance. Among these, an R2R3-MYB transcription factor orthologous to Arabidopsis MYB5 emerged as a compelling candidate, harboring multiple missense variants and exhibiting strong seed-stage expression and enriched co-expression with flavonoid and phenylpropanoid genes (Supplementary Fig. 2). Such transcriptional regulators represent previously uncharacterized nodes that may modulate flux through the isoflavone pathway. Although isoflavone and soyasaponin traits were largely controlled by distinct sets of loci, a small number of genomic regions showed associations with traits from both pathways (Supplementary Table S4). Five such loci were detected, but their candidate genes differed and displayed limited haplotype-level linkage, suggesting local genomic clustering rather than true pleiotropy. These regions may nevertheless complicate breeding strategies due to potential linkage drag and therefore warrant further investigation. Although SHAP-guided allele stacking identified realistic upper-bound predictions, the simulated optima did not surpass the highest observed phenotypes for any trait, indicating that further genetic gains may require broader allelic diversity or haplotype configurations not present in the current panel. The multi-trait optimum differed from CMJ_317 at 268 SNPs, suggesting that additional recombination or introgression would be needed to fully realize the modeled genotype (Fig. 6). Nonetheless, CMJ_317 represents a practical starting parent for breeding toward simultaneous enhancement of Acetyl-daidzin, Malonyl-daidzin, and Soyasaponin-ab, and stepwise introgression of the remaining favorable alleles may enable development of new varieties approaching the predicted multi-trait optimum. Several other considerations point to opportunities for improving metabolite prediction and allele-stacking outcomes. Some association signals mapped to intergenic regions, suggesting the presence of regulatory variants that require functional validation, while others involved low-frequency alleles that may not have been sufficiently represented for the models to learn their effects. Expanding population size and genetic diversity through additional landraces, breeding lines, and wild relatives, will be essential to capture these rare or favorable haplotypes and improve model accuracy. Complementary approaches such as fine-mapping, CRISPR-based perturbation, and integrated multi-omics (transcriptome, proteome, and others) will also be necessary to resolve causal variants and mechanistic control points with greater precision, ultimately enabling more reliable predictions of how far breeding can push these metabolites. In summary, this study provides a comprehensive framework for understanding the phenotypic, chemotypic, and genetic organization of isoflavone and soyasaponin pathways in soybean. By delineating the dominant metabolic modules, rediscovering classical loci alongside newly implicated regulatory candidates, and demonstrating the feasibility of machine learning–assisted allele stacking, we establish a foundation for the coordinated improvement of both pathways. These insights advance our understanding of soybean specialized metabolism and offer concrete strategies for developing high-value cultivars with enhanced functional compound profiles. Declarations Author contributions H.K.: Conceptualization;Software; Formal analysis; Investigation; Validation; Visualization; Methodology; Writing – original draft; Writing – review & editing SY.S.: Conceptualization; Resources; Data curation; Funding acquisition Y.C.: Software; Formal analysis; Investigation; Visualization JE.R.: Resources; Data curation; J.H.: Conceptualization; Supervision; Funding acquisition; Methodology; Writing – review & editing; Project administration Data availability All data generated or analyzed during this study are included in this published article and its supplementary information files. Funding This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. RS-2025-00853272)" Rural Development Administration, Republic of Korea. 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Summary of isoflavonoid GWAS loci and corresponding candidate genes True GWAS name Novel # of genes Candidate gene Chromosome Block_start Block_end Supporting SNP Model P.value block_start block_end Acetyl_daidzin_1_1 O 0 regulatory element 1 4,742,374 4,742,374 Chr01:4742374:A:C FarmCPU 9.54E-19 4,742,374 4,742,374 Chr01:4742374:A:C MLM 2.02E-12 4,742,374 4,742,374 Acetyl_daidzin_1_2 O 71 Glyma.01G062200 1 7,334,452 9,097,388 Chr01:8139098:A:G MLM 8.3E-91 7,334,452 9,097,388 Chr01:7565616:A:G FarmCPU 9.54E-19 7,526,367 8,539,812 Acetyl_daidzin_1_3 X 9 - 1 33,070,162 33,369,200 Chr01:33163393:A:G FarmCPU 9.54E-19 33,070,162 33,369,200 Chr01:33070162:A:G MLM 2.33E-09 33,070,162 33,369,200 Acetyl_daidzin_1_4 X 44 Glyma.01G106000 1 35,102,164 37,000,839 Chr01:36009539:C:T MLM 1.55E-09 35,102,164 37,000,839 Chr01:35865390:A:G FarmCPU 9.54E-19 35,865,390 35,865,390 Acetyl_daidzin_1_5 X 74 Glyma.01G123900 1 41,932,012 44,341,861 Chr01:42899424:C:T MLM 1.35E-13 41,932,012 43,765,065 Chr01:43566183:A:G FarmCPU 9.54E-19 42,607,700 44,341,861 Acetyl_daidzin_2_1 X 244 Glyma.02G036400 2 2,103,061 4,371,582 Chr02:3103043:C:T MLM 1.09E-08 2,103,061 3,903,128 Chr02:4371582:C:T FarmCPU 9.54E-19 3,674,885 4,371,582 Chr02:4371582:C:T MLM 2.02E-12 3,674,885 4,371,582 Acetyl_daidzin_4_1 X 514 Glyma.04G008600 4 87,887 4,225,512 Chr04:376298:C:T MLM 1.75E-14 87,887 1,372,076 Chr04:2240055:G:T FarmCPU 9.54E-19 1,349,017 3,234,687 Chr04:3236177:C:G MLM 1.82E-10 2,239,771 4,225,512 Acetyl_daidzin_4_2 O 0 regulatory element 4 37,087,005 37,087,005 Chr04:37087005:C:T FarmCPU 9.54E-19 37,087,005 37,087,005 Chr04:37087005:C:T MLM 2.02E-12 37,087,005 37,087,005 Acetyl_daidzin_4_3 X 171 Glyma.04G205100 4 47,223,303 48,807,135 Chr04:47874552:G:T MLM 4.45E-10 47,223,303 48,807,135 Chr04:47874552:G:T MLMM 9E-11 47,223,303 48,807,135 Acetyl_daidzin_5_1 X 144 Glyma.05G039400 5 2,319,438 3,714,546 Chr05:2319438:A:T MLM 4.92E-08 2,319,438 3,292,288 Chr05:3376030:C:G FarmCPU 9.54E-19 2,424,436 3,714,546 Acetyl_daidzin_7_1 X 119 Glyma.07G214700 7 37,358,480 38,892,392 Chr07:37358480:A:G FarmCPU 9.54E-17 37,358,480 38,154,923 Chr07:38187424:A:G MLM 4.92E-08 37,448,780 38,892,392 Acetyl_daidzin_9_1 X 147 Glyma.09G012900 9 566,309 1,793,348 Chr09:986453:A:T FarmCPU 9.54E-19 566,309 1,793,348 Chr09:837687:C:T MLM 2.9E-10 837,687 837,687 Acetyl_daidzin_13_1 O 58 Glyma.13G082300 13 18,404,379 19,501,606 Chr13:18690947:C:T MLM 2.84E-12 18,404,379 18,865,899 Chr13:19501606:C:T FarmCPU 9.54E-19 18,845,032 19,501,606 Acetyl_daidzin_14_1 X 46 Glyma.14G120900 14 15,010,098 17,712,775 Chr14:15913030:C:T MLM 7.83E-13 15,010,098 16,911,740 Chr14:16731540:A:C FarmCPU 9.54E-19 15,733,151 17,712,775 Chr14:17056411:A:G MLM 4.89E-10 16,059,634 17,712,775 Chr14:17167225:C:T MLM 9.1E-24 16,180,772 17,712,775 Acetyl_daidzin_15_1 X 59 Glyma.15G184300 15 17,949,582 19,722,979 Chr15:18731695:A:G FarmCPU 9.54E-19 17,949,582 19,722,979 Chr15:18506708:C:T MLM 5.16E-09 18,506,708 18,506,708 Acetyl_daidzin_16_1 X 134 Glyma.16G150600 16 32,966,320 34,457,317 Chr16:33955844:C:G FarmCPU 6.74E-09 32,966,320 34,457,317 Chr16:33914105:G:T MLM 3.21E-15 33,099,075 34,385,943 Acetyl_daidzin_18_1 X 390 Glyma.18G040700 18 1,179,573 4,827,584 Chr18:2138965:A:C MLM 9.56E-14 1,179,573 3,138,150 Chr18:2885208:A:G FarmCPU 1.48E-08 1,891,026 3,879,302 Chr18:3828776:A:G MLM 2.96E-08 2,829,171 4,827,584 Acetyl_daidzin_18_2 X 52 Glyma.18G159500 18 35,394,788 37,506,336 Chr18:36393119:C:T FarmCPU 6.75E-11 35,394,788 37,391,087 Chr18:36613555:A:G MLM 1.61E-13 35,660,177 37,506,336 Chr18:36553877:C:T MLM 1.88E-10 36,553,877 36,553,877 Acetyl_daidzin_20_1 X 37 - 20 23,362,065 24,717,382 Chr20:23807775:C:T FarmCPU 0.000000015 23,362,065 24,717,382 Chr20:23869165:C:T MLM 2.51E-08 23,362,065 24,717,382 Daidzin_2_1 X 107 Glyma.02G005700 2 631 1,020,334 Chr02:287254:A:T Blink 6.09E-18 631 1,020,334 Chr02:648719:A:G FarmCPU 7.57E-12 631 1,020,334 Daidzin_5_1 X 0 regulatory element 5 6,390,474 6,390,474 Chr05:6390474:C:T Blink 2E-14 6,390,474 6,390,474 Chr05:6390474:C:T MLMM 4.88E-12 6,390,474 6,390,474 Daidzin_14_1 X 45 Glyma.14G112400 14 12,994,754 14,280,241 Chr14:13713886:A:G Blink 6.09E-16 12,994,754 14,280,241 Chr14:13713886:A:G MLMM 5.43E-09 12,994,754 14,280,241 Daidzin_14_2 X 35 - 14 29,793,549 31,632,724 Chr14:30643399:A:G Blink 6.09E-18 29,793,549 31,632,724 Chr14:30643399:A:G MLMM 1.28E-09 29,793,549 31,632,724 Daidzin_15_1 X 75 - 15 43,634,076 45,050,243 Chr15:44517734:C:G Blink 6.09E-18 43,634,076 45,050,243 Chr15:44517734:C:G MLMM 1.38E-19 43,634,076 45,050,243 Daidzin_18_1 X 153 Glyma.18G220500 18 49,895,027 51,695,849 Chr18:50696088:A:G Blink 7.57E-11 49,895,027 51,695,849 Chr18:50696088:A:G FarmCPU 4.22E-09 49,895,027 51,695,849 Genistin_3_1 O 83 Glyma.03G113600 3 31,355,386 32,853,434 Chr03:32267163:C:T MLM 2.3E-10 31,355,386 32,853,434 Chr03:31758815:A:G FarmCPU 1.65E-08 31,758,815 31,758,815 Chr03:31758815:A:G MLMM 4.96E-10 31,758,815 31,758,815 Chr03:31758815:A:G MLM 2.71E-15 31,758,815 31,758,815 Genistin_7_1 X 379 Glyma.07G071000 7 5,706,709 9,697,087 Chr07:6701412:A:T MLM 9.57E-09 5,706,709 7,699,182 Chr07:7782208:C:T FarmCPU 2.32E-11 6,921,401 8,781,733 Chr07:8636668:A:T MLM 6.93E-26 8,460,406 8,710,959 Chr07:9431341:A:G MLM 5.32E-09 8,553,528 9,697,087 Genistin_7_2 X 297 Glyma.07G132600 7 15,195,644 19,251,649 Chr07:15690617:A:G MLM 1.66E-16 15,195,644 16,660,146 Chr07:15690617:A:G MLMM 1.19E-09 15,195,644 16,660,146 Chr07:17407305:A:G FarmCPU 2.02E-08 16,468,280 18,382,935 Chr07:18353888:C:T MLM 3.31E-43 17,721,590 19,251,649 Genistin_16_1 O 257 Glyma.16G150600 16 30,310,478 32,893,478 Chr16:30378466:A:G MLM 2.48E-10 30,310,478 31,329,735 Chr16:31930954:A:G FarmCPU 4.37E-10 30,942,337 32,893,478 Chr16:30676060:A:T MLM 6.48E-11 30,561,521 30,727,694 Genistin_19_1 X 66 Glyma.19G018300 19 1,673,173 2,659,673 Chr19:1673173:C:T FarmCPU 6.12E-13 1,673,173 2,659,673 Chr19:1673173:C:T MLM 3.21E-29 1,673,173 2,659,673 Genistin_20_1 O 184 Glyma.20G114200 20 34,056,113 35,776,931 Chr20:34852421:A:G MLM 0.000000045 34,056,113 35,776,931 Chr20:34521942:C:G FarmCPU 9.04E-11 34,380,166 35,497,193 Chr20:34521942:C:G MLMM 2.7E-10 34,380,166 35,497,193 Glycitin_1_1 X 176 Glyma.01G017100 1 1,097,867 3,090,799 Chr01:1389012:C:G Blink 7.55E-10 1,097,867 1,572,843 Chr01:2237373:C:T FarmCPU 8.89E-11 1,314,162 3,090,799 Glycitin_1_2 X 83 Glyma.01G106000 1 35,119,917 38,246,565 Chr01:36117212:A:G FarmCPU 1.99E-08 35,119,917 37,117,096 Chr01:37735698:A:T Blink 8.11E-16 36,752,895 38,246,565 Glycitin_4_1 X 92 Glyma.04G154800 4 35,222,713 40,421,262 Chr04:36181320:A:G MLM 3.06E-11 35,222,713 37,058,733 Chr04:37681298:A:G MLMM 3.74E-35 36,719,334 38,494,262 Chr04:37681298:A:G Blink 2.07E-50 36,719,334 38,494,262 Chr04:37681298:A:G FarmCPU 5.77E-20 36,719,334 38,494,262 Chr04:37681298:A:G MLM 1.91E-13 36,719,334 38,494,262 Chr04:38995245:G:T MLM 1.3E-09 37,995,292 39,994,701 Chr04:39432514:A:C MLM 3.08E-08 38,432,540 40,421,262 Glycitin_6_1 X 9 Glyma.06G220700 6 32,402,502 33,507,967 Chr06:33112569:C:T Blink 4.69E-23 32,402,502 33,507,967 Chr06:33112569:C:T FarmCPU 9.02E-09 32,402,502 33,507,967 Glycitin_9_1 X 140 Glyma.09G049500 9 3,767,928 5,283,968 Chr09:4684644:A:G FarmCPU 2.72E-16 3,767,928 5,283,968 Chr09:4684644:A:G MLM 4.6E-11 3,767,928 5,283,968 Chr09:4656231:G:T Blink 7.88E-24 3,915,352 5,283,968 Chr09:4656231:G:T MLM 1.97E-12 3,915,352 5,283,968 Chr09:4656231:G:T FarmCPU 3.29E-16 3,915,352 5,283,968 Glycitin_12_1 X 96 Glyma.12G142900 12 15,937,915 19,563,868 Chr12:16897163:A:G Blink 1.82E-12 15,937,915 17,896,899 Chr12:18564409:A:G MLMM 8.62E-12 17,564,414 19,563,868 Glycitin_13_1 X 83 Glyma.13G004100 13 723,424 3,066,096 Chr13:1722722:C:T MLM 1.02E-10 723,424 2,475,526 Chr13:2361342:A:G MLMM 1.49E-09 1,361,938 3,066,096 Glycitin_13_2 X 103 Glyma.13G032200 13 9,109,580 12,369,941 Chr13:10108633:A:G Blink 7.78E-31 9,109,580 11,107,957 Chr13:10108633:A:G MLM 1.96E-11 9,109,580 11,107,957 Chr13:10736041:A:G MLM 8.31E-12 9,736,882 11,730,518 Chr13:11240279:A:G MLM 1.33E-08 10,241,767 12,229,805 Chr13:11385802:A:T MLM 2.88E-09 10,388,827 12,369,941 Glycitin_13_3 X 200 Glyma.13G173500 13 27,172,690 29,066,014 Chr13:27845274:C:T Blink 5.77E-27 27,172,690 28,842,664 Chr13:27845274:C:T MLM 1.23E-10 27,172,690 28,842,664 Chr13:28578286:C:T MLM 2.08E-08 27,578,452 29,066,014 Glycitin_15_1 X 172 Glyma.15G034900 15 2,504,554 3,824,475 Chr15:3180891:C:T Blink 3.38E-14 2,504,554 3,824,475 Chr15:3180891:C:T FarmCPU 4.03E-13 2,504,554 3,824,475 Glycitin_15_2 X 152 Glyma.15G080000 15 5,618,932 6,816,864 Chr15:6482890:C:T Blink 1.5E-44 5,618,932 6,816,864 Chr15:6482890:C:T FarmCPU 3.24E-25 5,618,932 6,816,864 Glycitin_16_1 X 260 Glyma.16G150600 16 32,085,571 34,985,316 Chr16:32681087:G:T MLM 4.06E-08 32,085,571 33,646,986 Chr16:34129142:A:C Blink 3.98E-14 33,282,191 34,985,316 Chr16:34141604:A:G MLM 3.06E-08 33,282,191 34,970,876 Glycitin_16_2 X 217 Glyma.16G196700 16 35,179,776 36,987,137 Chr16:36069498:C:T FarmCPU 3.89E-10 35,179,776 36,987,137 Chr16:36069498:C:T MLM 1.74E-10 35,179,776 36,987,137 Chr16:36507395:A:T MLM 3.45E-09 35,713,237 36,569,476 Chr16:36090865:C:T FarmCPU 1.17E-09 35,713,237 36,569,476 Glycitin_17_1 X 235 Glyma.17G050500 17 2,514,466 4,394,755 Chr17:2885405:A:C MLMM 7.06E-17 2,514,466 3,879,830 Chr17:4199484:A:G MLM 6.86E-10 3,419,715 4,394,755 Glycitin_18_1 X 50 Glyma.18G173300 18 40,404,169 42,142,590 Chr18:41404121:C:T MLM 1.2E-09 40,404,169 42,142,590 Chr18:41404121:C:T MLMM 1.5E-12 40,404,169 42,142,590 Malonyl_daidzin_4_1 O 231 Glyma.04G018600 4 1,349,017 3,234,687 Chr04:2240055:G:T Blink 5.52E-09 1,349,017 3,234,687 Chr04:2240055:G:T FarmCPU 3.59E-08 1,349,017 3,234,687 Malonyl_daidzin_16_1 X 182 Glyma.16G150600 16 29,976,517 31,944,614 Chr16:30950699:A:G Blink 2.18E-08 29,976,517 31,944,614 Chr16:30950699:A:G FarmCPU 1.1E-12 29,976,517 31,944,614 Chr16:30950699:A:G MLM 5.71E-09 29,976,517 31,944,614 Chr16:30950699:A:G MLMM 6.39E-11 29,976,517 31,944,614 Malonyl_glycitin_11_1 X 146 Glyma.11G107100 11 7,205,614 8,345,704 Chr11:7869905:C:T MLM 1.87E-10 7,205,614 7,869,905 Chr11:8259013:A:G MLMM 2.63E-14 7,626,429 8,344,956 Chr11:8196966:G:T MLM 7.07E-10 7,626,429 8,345,704 Chr11:8259013:A:G MLM 4.22E-10 7,626,429 8,344,956 Malonyl_glycitin_13_1 X 276 Glyma.13G173500 13 27,172,690 29,784,782 Chr13:27845274:C:T MLM 7.51E-10 27,172,690 28,842,664 Chr13:28578286:C:T MLMM 3.98E-12 27,578,452 29,066,014 Chr13:28562762:C:T MLM 8.81E-09 27,578,063 29,066,014 Chr13:28578286:C:T MLM 2.04E-10 27,578,452 29,066,014 Chr13:28790951:A:G MLM 1.7E-11 27,795,307 29,784,782 Malonyl_glycitin_18_1 X 573 Glyma.18G029000 18 24,064 5,054,348 Chr18:529017:A:T MLM 3.84E-15 24,064 1,518,137 Chr18:2138965:A:C MLMM 2.84E-16 1,179,573 3,138,150 Chr18:965331:A:C MLM 7.98E-09 24,064 1,961,100 Chr18:2138965:A:C MLM 4.18E-22 1,179,573 3,138,150 Chr18:4059639:G:T MLM 0.000000002 3,066,156 5,054,348 Table 2. Summary of soyasaponin GWAS loci and corresponding candidate genes True GWAS name Novel # of genes Candidate gene Chromosome Block_start Block_end Supporting SNP Model P.value LD_start LD_end Soyasaponin_aa_1_1 O 47 2 1 9,674,258 11,644,447 Chr01:10644931:C:T Blink 2.51E-21 9,674,258 11,644,447 Chr01:10690289:C:T MLM 3.82E-09 9,690,465 11,270,098 Soyasaponin_aa_1_2 O 25 - 1 35,870,925 37,000,839 Chr01:36678490:A:C Blink 2.51E-21 35,870,925 37,000,839 Chr01:36084764:C:T MLM 2.06E-08 35,960,769 36,084,764 Soyasaponin_aa_2_1 O 111 - 2 5,159,851 6,037,828 Chr02:5987597:C:T Blink 2.51E-21 5,159,851 6,037,828 Chr02:6025355:A:C MLM 2.84E-09 5,159,851 6,037,828 Soyasaponin_aa_6_1 x 77 - 6 14,865,975 15,801,749 Chr06:14918909:G:T MLMM 1.55E-13 14,865,975 15,801,749 Chr06:14984595:A:G MLM 4.5E-14 14,903,612 15,801,749 Soyasaponin_aa_7_1 O 216 Glyma.07G215500 7 39,280,666 42,124,839 Chr07:40280651:A:G MLMM 1.84E-11 39,280,666 41,280,542 Chr07:40097038:C:T MLM 5.48E-22 40,097,038 40,097,038 Chr07:41576567:C:T MLM 5.48E-22 41,139,278 41,576,567 Chr07:42124839:A:C Blink 2.51E-21 41,211,575 42,124,839 Soyasaponin_aa_7_2 x 269 Glyma.07G254600 7 42,272,244 44,260,461 Chr07:43271899:C:T Blink 6.27E-19 42,272,244 44,260,461 Chr07:43062404:A:C MLM 3.72E-18 42,348,734 43,773,076 Chr07:43134737:A:G MLM 3.43E-29 42,401,716 43,553,458 Chr07:43148354:C:T MLM 2.43E-27 42,401,716 43,553,458 Chr07:43134737:A:G MLMM 2.96E-66 42,401,716 43,553,458 Chr07:43134737:A:G Blink 2.51E-21 42,401,716 43,553,458 Soyasaponin_aa_11_1 x 74 - 11 13,712,205 15,403,620 Chr11:14405373:A:C MLM 2.81E-09 13,712,205 15,403,620 Chr11:14301944:A:G MLMM 5.41E-15 13,712,205 15,300,911 Soyasaponin_aa_12_1 O 0 regulatory element 12 2,442,972 2,442,972 Chr12:2442972:A:G Blink 2.51E-21 2,442,972 2,442,972 Chr12:2442972:A:G MLM 1.17E-10 2,442,972 2,442,972 Soyasaponin_aa_15_1 O 96 - 15 17,454,177 20,341,457 Chr15:17949582:C:T Blink 3.85E-13 17,454,177 18,946,191 Chr15:18773353:A:G MLM 1.27E-09 17,949,582 19,772,658 Chr15:19342069:A:G Blink 5.36E-11 18,398,731 20,341,457 Soyasaponin_ab_7_1 O 162 Glyma.07G215500 7 38,395,673 40,795,841 Chr07:39393415:A:G FarmCPU 1.84E-08 38,395,673 40,393,112 Chr07:39796478:C:T Blink 3.89E-11 38,798,377 40,795,841 Soyasaponin_ab_7_2 X 381 Glyma.07G254600 7 41,015,527 44,129,252 Chr07:42015087:A:T MLMM 1.43E-12 41,015,527 43,012,622 Chr07:43129404:A:C FarmCPU 2.27E-09 42,129,788 44,129,252 Chr07:43134737:A:G Blink 5.12E-24 42,401,716 43,553,458 Chr07:43134737:A:G FarmCPU 3.67E-49 42,401,716 43,553,458 Chr07:43148354:C:T FarmCPU 1.97E-51 42,401,716 43,553,458 Chr07:43134737:A:G MLMM 2.16E-60 42,401,716 43,553,458 Soyasaponin_ab_18_1 O 191 Glyma.18G052400 18 3,829,796 5,827,277 Chr18:4829576:A:G FarmCPU 5.76E-09 3,829,796 5,827,277 Chr18:4976093:C:T MLMM 3.44E-11 4,976,093 4,976,093 Soyasaponin_ba_1_1 O 164 Glyma.01G046300 1 3,678,263 6,139,393 Chr01:4677203:C:G FarmCPU 1.17E-09 3,678,263 5,583,655 Chr01:5345147:A:G MLM 3.62E-14 4,347,457 5,583,655 Chr01:6139393:A:C MLM 4.91E-13 5,286,692 6,139,393 Chr01:5905557:C:T MLM 1.33E-29 5,498,974 5,905,557 Soyasaponin_bb_1_2 O 324 - 1 52,510,109 55,148,472 Chr01:53159832:A:G Blink 1.42E-103 52,510,109 53,828,108 Chr01:54362744:C:G MLMM 4.16E-15 53,611,380 55,148,472 Chr01:54362744:C:G Blink 1.42E-101 53,611,380 55,148,472 Soyasaponin_bb_18_1 O 107 Glyma.18G198400 18 46,690,656 48,742,418 Chr18:47689145:A:G MLMM 7.18E-14 46,690,656 48,266,685 Chr18:47785202:A:G Blink 1.42E-103 46,851,823 48,742,418 Soyasaponin_bb_prime_6_1 X 73 - 6 20,148,157 22,147,839 Chr06:21148000:A:T FarmCPU 8.77E-11 20,148,157 22,147,839 Chr06:21148000:A:T MLMM 1.51E-09 20,148,157 22,147,839 Soyasaponin_total_7_1 X 195 Glyma.07G254600 7 42,348,734 43,773,076 Chr07:43062404:A:C Blink 3.56E-14 42,348,734 43,773,076 Chr07:43062404:A:C FarmCPU 4.6E-18 42,348,734 43,773,076 Chr07:43062404:A:C MLM 2.76E-10 42,348,734 43,773,076 Chr07:43148354:C:T FarmCPU 2.1E-14 42,401,716 43,553,458 Chr07:43134737:A:G MLM 4.66E-12 42,401,716 43,553,458 Chr07:43148354:C:T MLM 4.3E-12 42,401,716 43,553,458 Chr07:43148354:C:T MLMM 2.19E-12 42,401,716 43,553,458 Soyasaponin_total_14_1 O 165 - 14 1,160,815 2,378,373 Chr14:1243861:C:T Blink 1.34E-08 1,160,815 1,923,828 Chr14:1243861:C:T FarmCPU 0.000000017 1,160,815 1,923,828 Chr14:2096021:A:G Blink 1.29E-08 1,619,891 2,378,373 Soyasaponin_total_17_1 O 0 regulatory element 17 15,310,650 15,310,650 Chr17:15310650:C:T Blink 4.46E-18 15,310,650 15,310,650 Chr17:15310650:C:T FarmCPU 3.73E-25 15,310,650 15,310,650 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx SupplementaryFigures.docx Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 03 Dec, 2025 Submission checks completed at journal 03 Dec, 2025 First submitted to journal 02 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8256309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583584671,"identity":"76e6f6db-83b2-4660-857c-34e56ae79f53","order_by":0,"name":"Hakyung Kwon","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Hakyung","middleName":"","lastName":"Kwon","suffix":""},{"id":583584672,"identity":"15f6087f-8a86-4069-a476-96805e903b03","order_by":1,"name":"Seung Yeob Song","email":"","orcid":"","institution":"Rural Development Administration","correspondingAuthor":false,"prefix":"","firstName":"Seung","middleName":"Yeob","lastName":"Song","suffix":""},{"id":583584673,"identity":"d91705bb-e2ba-4760-816b-cb9ea122010c","order_by":2,"name":"Yeonghun Cho","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Yeonghun","middleName":"","lastName":"Cho","suffix":""},{"id":583584674,"identity":"20b7a8e1-8456-4bd9-b18d-c4c7b73b0014","order_by":3,"name":"Ji Eun Ra","email":"","orcid":"","institution":"Rural Development Administration","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Eun","lastName":"Ra","suffix":""},{"id":583584675,"identity":"361ded06-3d94-4686-8e0c-02d2efe0ca8a","order_by":4,"name":"Jungmin Ha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RMQrCMBSA4VcC6RJxLRTsFVIKFVHxKkrBKRVdnAtCXQTXeItOnQtZxa4d3AodxEHI6mAqolvo6JCfkCahHwQCYDL9YVbSzrQdqPic4c4Ez7uRbxQI7UYQj8WtWl9haJ+l3KTg9RO8fGgvxlfLMaMNjA5x7p5S8HmBBdcTFgaMCqBFnKNeClYG9k57sR8p77VUZNaFBPWbVAxcRRYZYKEnh3uIFCG0akKXXJyICxxpib9ngWRPMaBlVEuynUyP+zTQkwSwo77ks1drpAUAnvpF+womk8lkghc/SEM9dlyzngAAAABJRU5ErkJggg==","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Jungmin","middleName":"","lastName":"Ha","suffix":""}],"badges":[],"createdAt":"2025-12-02 05:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8256309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8256309/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-50166-1","type":"published","date":"2026-04-25T15:58:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101771407,"identity":"fcc59a28-63dc-4841-9261-eea8efa7377e","added_by":"auto","created_at":"2026-02-03 13:21:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102833,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide LD decay and SNP spacing before and after LD pruning. (a) Linkage disequilibrium (LD) decay curves depicting the relationship between pairwise r² and physical distance across all chromosomes before (gray) and after (blue) LD pruning. (b) Distribution of inter-SNP distances before (gray) and after (blue) pruning.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/fbedbb3ab8ea5cbc537120bf.png"},{"id":101771409,"identity":"26db54a0-006a-485d-b178-22ea2ab3e5d7","added_by":"auto","created_at":"2026-02-03 13:21:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69960,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure of the 376-accession Korean soybean core collection. (a) Cross-validation error plot from ADMIXTURE analysis showing that prediction error decreased sharply up to K = 5, supporting five major ancestral components. (b) Population structure bar plot for K = 5, where each bar represents one accession and colors denote ancestry proportions. The panel separates clearly into four to five genetic clusters.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/7b3a43a5bf9b5f9d8fecc37a.png"},{"id":101771413,"identity":"7dcaea64-8a03-4f68-a371-6277b681b429","added_by":"auto","created_at":"2026-02-03 13:21:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124031,"visible":true,"origin":"","legend":"\u003cp\u003eIsoflavone variation and correlation structure across the soybean panel. (a) Histogram showing the distribution of each detected isoflavone glycoside. (b) Pairwise correlation heatmap, with color scale indicating Pearson correlation coefficients. Daidzin, malonyl daidzin, acetyl daidzin, and genistin form a positively correlated module, while glycitein-derived conjugates form a second independent module. (c) Mean metabolite content stratified by presence–absence combinations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/7e10d078780d8a0bd8195395.png"},{"id":101771414,"identity":"7937e252-87b5-414e-a785-35f38a54acaf","added_by":"auto","created_at":"2026-02-03 13:21:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSoyasaponin variation, correlation patterns, and chemotype structure.\u003c/strong\u003e (a) Histogram of abundance for five quantified soyasaponins (Aa, Ab, Ba, Bb, Bb′). (b) Correlation heatmap illustrating the negative association between Aa and Ab and additional relationships among B-group saponins. (c) Mean content plotted by presence–absence chemotypes, showing near-mutual exclusivity between Aa and Ab and substantial variation within each chemotype.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/b0a5384ab605079d5c4121e1.png"},{"id":101880480,"identity":"29662f09-df55-4567-8129-82927e87a91e","added_by":"auto","created_at":"2026-02-04 15:02:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48921,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of genomic and phenomic prediction accuracy across metabolites. Bar plots summarizing prediction performance (Pearson correlation) for 12 isoflavones and 5 soyasaponins. Genomic prediction consistently outperformed FT-IR–based phenomic prediction.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/c47e894eb6010904d1a69300.png"},{"id":101771408,"identity":"378bada9-67f0-40e5-b0ce-48d2ea707742","added_by":"auto","created_at":"2026-02-03 13:21:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":28471,"visible":true,"origin":"","legend":"\u003cp\u003eObserved metabolite levels in the closest accessions to each SHAP-guided optimal genotype, compared with the predicted multi-trait best genotype. Bar plots show the observed concentrations of Acetyl-daidzin, Malonyl-daidzin, and Soyasaponin-ab in four accessions identified as the closest real genotypes to SHAP-guided allele-stacking scenarios (CMJ_115 for Acetyl-daidzin, CMJ_068 for Malonyl-daidzin, CMJ_236 for Soyasaponin-ab, and CMJ_317 for the multi-trait scenario). The rightmost bars represent the predicted metabolite levels of the virtual multi-trait optimal genotype generated by XGBoost–SHAP allele stacking. This comparison illustrates how the highest-performing real accessions currently distribute across traits and how far metabolite levels could increase through the introduction of favorable alleles predicted by the model.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/207528e8bde3e8be28c73105.png"},{"id":107928031,"identity":"29acdf5f-d7c9-459d-99d1-e1ab7ee67e97","added_by":"auto","created_at":"2026-04-27 16:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1316239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/bc6e98a3-f1f3-41b3-b5f8-1d3151d33e04.pdf"},{"id":101771410,"identity":"2584b219-ba41-4106-a084-d36b2c382c72","added_by":"auto","created_at":"2026-02-03 13:21:07","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":116138,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/b2de1b4e422f79f7c921c795.xlsx"},{"id":101771412,"identity":"b9d0de63-8715-4c86-a674-2390dfceb36a","added_by":"auto","created_at":"2026-02-03 13:21:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1404058,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8256309/v1/3d33e6fef2b1ee67b1c99ed5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint genetic control of isoflavones and soyasaponins revealed by mGWAS, genomic prediction, and SHAP-guided allele stacking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoybean (\u003cem\u003eGlycine max\u003c/em\u003e) is a globally important crop valued not only for its high-quality plant protein and vegetable oil, but also for its diverse bioactive metabolites that contribute to human health [1,2]. While soybean-derived products such as soybean oil, tofu, fermented pastes, and soy sauce have long served as staple dietary components, soybean is now increasingly consumed as a health-promoting ingredient[3\u0026ndash;5]. As interest in functional foods has expanded, soybean has emerged as a major source of physiologically active phytochemicals, most notably isoflavones and soyasaponins, which exhibit broad effects on human metabolism, immunity, and chronic disease risk[2,3,6].\u003c/p\u003e \u003cp\u003eIsoflavones are abundant in soybean seeds but vary widely among genotypes, with concentrations spanning more than a 20-fold range across global germplasm[7,8]. The major aglycones\u0026mdash;daidzein, genistein, and glycitein\u0026mdash;occur predominantly as glycosides, acetylglycosides, and malonylglycosides, generating twelve molecular forms with malonylated conjugates representing 60\u0026ndash;80% of total isoflavones [9,10]. Isoflavones have been associated with a wide range of health benefits, including alleviation of menopausal symptoms, antioxidant activity, improved bone metabolism, and anticancer effects [11\u0026ndash;14]. These functions arise from multiple biological mechanisms, including estrogen receptor modulation, regulation of oxidative stress, and interaction with cell-signaling pathways such as MAPK and NF-κB [15]. Given these roles, isoflavone content has become a major target for soybean quality improvement and a subject of extensive genetic research.\u003c/p\u003e \u003cp\u003eSoyasaponins, a class of triterpenoid glycosides unique to soybean, exhibit substantial quantitative and compositional diversity[16,17]. They typically account for 0.2\u0026ndash;0.5% of seed weight but can exceed 2% in hypocotyl tissue[18,19]. Soyasaponins are broadly divided into group A bisdesmosides and DDMP-conjugated precursors, the latter being further converted into groups B and E depending on their terminal sugar moieties[20,21]. Group A acetylated saponins have traditionally been associated with bitterness and astringency, whereas DDMP- and B-type saponins are linked to desirable physiological effects, including cholesterol lowering, anti-inflammatory and antioxidant activities, and antiviral properties[22,23]. Recent reports, however, indicate that certain A-group conjugates\u0026mdash;such as the Ab chemotype\u0026mdash;also possess distinct bioactivities, such as strong BMP-2\u0026ndash;mediated osteogenic effects, indicating that A-type saponins themselves represent important functional components[24].\u003c/p\u003e \u003cp\u003eAlthough the biological properties of isoflavones and soyasaponins have been extensively studied, emerging evidence suggests that these two metabolite classes may act synergistically. Recent in vivo studies reported that combined supplementation of isoflavones and saponins significantly suppressed NNK-induced lung tumor formation in mice, whereas neither compound alone produced significant effects[25]. Cellular studies have further shown that both isoflavones and group A saponins enhance osteoblast differentiation via increased alkaline phosphatase activity[24]. These findings raise the possibility that soyasaponins may enhance the bioavailability or physiological activity of isoflavones, indicating an integrated functional relationship between the two pathways. Despite this, the genetic basis underlying joint variation in isoflavone and soyasaponin profiles remains virtually unexplored.\u003c/p\u003e \u003cp\u003eGenetic studies over the past two decades have identified numerous loci controlling isoflavone biosynthesis and accumulation, including QTLs on nearly all soybean chromosomes and candidate genes such as IFS, CHS, IOMT, β-glucosidase, GST, and several transcription factors, particularly members of the R2R3-MYB and zinc-finger protein families[26\u0026ndash;33]. Likewise, major loci determining soyasaponin composition have been mapped, including Sg-1, Sg-3, Sg-4, Sg-5, and Sg-6, which encode UDP-glycosyltransferases, oxidosqualene cyclases, and P450 enzymes involved in aglycone formation and sugar-chain tailoring[17,34\u0026ndash;40]. However, these studies have examined isoflavones and soyasaponins independently. Given that specialized metabolic pathways can share precursors, compete for glycosylation capacity, or reside within linked genomic regions, it is not yet known whether the genetic determinants of isoflavone and soyasaponin variation interact, overlap, or influence each other. Consequently, it remains uncertain whether high accumulation of both metabolite classes can be achieved within the same genetic background or how these pathways might be jointly optimized through breeding.\u003c/p\u003e \u003cp\u003eTo address these questions, genetically representative germplasm is required. The Korean soybean core collection, constructed from more than 2,872 accessions while retaining over 99% of total genetic diversity, provides a powerful resource for dissecting metabolic, phenotypic, and genomic variation with minimal redundancy[41]. Core collections have previously enabled high-resolution trait dissection and identification of novel alleles in soybean for seed composition, stress tolerance, and agronomic traits[42\u0026ndash;45]. Leveraging such a collection allows comprehensive evaluation of natural variation in isoflavone and soyasaponin profiles and supports integrative genomic analysis to identify loci governing their independent and joint accumulation.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a comprehensive metabolite\u0026ndash;genome analysis of isoflavones and soyasaponins across 376 accessions of the Korean soybean core collection using high-coverage whole-genome sequencing and UPLC\u0026ndash;based metabolite profiling. Specifically, we (i) quantified 12 isoflavone compounds and 5 major soyasaponins, (ii) characterized their distribution, correlations, and presence\u0026ndash;absence patterns, (iii) performed single-trait and multi-trait Genome-Wide Association Study (GWAS) to uncover loci controlling each metabolite class, and (iv) examined whether loci for isoflavone and soyasaponin pathways show genomic co-localization, independence, or physical linkage, and (v) evaluated the predictive power of GWAS-identified variants and assessed the extent to which breeding could elevate metabolite levels using genomic prediction and allele-stacking simulations. By integrating metabolic phenotypes with genome-wide variation, this study aims to reveal whether high isoflavone and high soyasaponin chemotypes can co-occur, identify genetic architectures that enable or constrain simultaneous improvement, and provide strategic insights for breeding soybean cultivars optimized for functional health applications.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eGenotype analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNP variant data were obtained from the \u003cstrong\u003eSoybean Haplotype Map Project\u003c/strong\u003e, which provides genome-wide variation derived from resequencing of 781 soybean accessions [46]. Genotype information corresponding to the \u003cstrong\u003e376 Korean soybean core collection accessions\u003c/strong\u003e was extracted from this dataset for downstream analyses. Only biallelic SNPs located on the 20 soybean chromosomes were retained, while variants on unanchored scaffolds and non-chromosomal contigs were removed. Because soybean is predominantly selfing and exhibits high levels of homozygosity, all heterozygous genotype calls were treated as missing to minimize false heterozygosity arising from misalignment, structural variation, or multi-copy loci.\u003c/p\u003e\n\u003cp\u003elinkage disequilibrium (LD) pruning was performed with PLINK 1.9 using a sliding-window approach and an r\u0026sup2; threshold of 0.8[47]. The resulting LD-pruned SNP set was used for both principal component analysis (PCA) and ancestry inference. Population structure was estimated using ADMIXTURE (v1.3) across \u003cstrong\u003eK = 1\u0026ndash;10\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e and the number of ancestral clusters was determined by selecting the K value that produced the lowest cross-validation error[48].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUPLC-based targeted metabolite profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach seeds were finely ground, and for targeted metabolomics, 0.5 g of powder was extracted with 20 mL of 100% methanol on a shaking incubator for 60 min at room temperature. The extracts were centrifuged at 7,800 rpm for 5 min, and the supernatant was filtered through a 0.2 \u0026mu;m PTFE membrane. Targeted quantification of 12 isoflavones and 5 soyasaponins was performed using a Thermo UltiMate 3000 UHPLC system fitted with a HALO C18 column (2.1 \u0026times; 100 mm, 2.7 \u0026mu;m). Chromatographic separation employed 0.1% acetic acid in water (solvent A) and 0.1% acetic acid in acetonitrile (solvent B), and compounds were detected at 254 nm using a diode-array detector. Calibration curves were generated from authentic standards across a 0\u0026ndash;100 \u0026mu;g/mL range (five concentration points), and quantification was conducted in Chromeleon 7 using internal-standard normalization and regression models with R\u0026sup2; \u0026ge; 0.999 for all analytes. Quantified isoflavones included daidzin, glycitin, genistin, malonyl-daidzin, malonyl-glycitin, malonyl-genistin, acetyl-daidzin, acetyl-glycitin, acetyl-genistin, daidzein, glycitein, and genistein. Soyasaponins Aa, Ab, Ba, Bb, and Bb\u0026prime; were quantified using the same UHPLC method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFT-IR-based phenomic profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor untargeted FT-IR metabolite profiling, 20 mg of powdered sample was mixed with 200 \u0026mu;L of 20% methanol, thoroughly vortexed, incubated for 30 min at 50\u0026deg;C with intermittent agitation, and centrifuged twice (13,000 rpm, 15 min followed by 5 min) to remove particulates before storage at \u0026ndash;20\u0026deg;C. Dried methanolic extracts were analyzed using a Tensor II FT-IR spectrometer equipped with a DTGS detector. Spectra were collected from 4000 to 400 cm⁻\u0026sup1; with 4 cm⁻\u0026sup1; resolution over 128 scans. Baseline correction, area normalization, and second-derivative transformation were performed in OPUS Lab 7.0 prior to multivariate analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotype processing and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompounds undetected in all accessions, daidzein, genistein, glycitein, malonyl genistin, acetyl genistin, and acetyl glycitin, were excluded from downstream analyses. Total isoflavone and total soyasaponin contents were calculated as the sum of all quantified compounds within each pathway for each accession.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted in R v4.5.2 [49]. Summary statistics, including means, standard deviations, and coefficients of variation, were computed using base R functions. Pairwise Pearson correlation coefficients were calculated using the cor function in the stats package, and p-values were adjusted for multiple testing using the false-discovery-rate method implemented in p.adjust. Isoflavone metabolic modules were inferred directly from the correlation structure. Presence\u0026ndash;absence matrices were generated by binarizing each metabolite as detected or undetected, and chemotypes were enumerated by identifying all unique combinations of detectable compounds across accessions. For each chemotype, its frequency among the 376 accessions was recorded, and the dominant patterns were defined based on their prevalence. The contribution of individual metabolites to pathway-level variation was assessed by correlating compound abundances with total pathway output using linear models and Pearson correlations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide association study (GWAS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS was conducted using the GAPIT3, employing four different statistical models: the mixed linear model (MLM), FarmCPU, BLINK, and MLMM [50]. For population structure correction, individual ancestry coefficients (Q-matrix) were obtained from ADMIXTURE using the LD-pruned SNP set[48]. Cross-validation identified K = 5 as the optimal number of ancestral clusters, and the corresponding five-component Q-matrix was included as fixed covariates in all models. To further control for relatedness among accessions, a kinship matrix computed from the filtered genotype data was incorporated jointly with the Q-matrix.\u003c/p\u003e\n\u003cp\u003eIndependent association signals were first identified by clumping significant SNPs in PLINK 1.9, which groups nearby variants that are in linkage disequilibrium (LD) with the most significant marker in a given region[47]. A physical window of 1 kb was used to ensure that only the strongest SNP within each tightly linked cluster was retained as the lead signal, thereby removing redundant secondary peaks arising from local LD. To characterize the genomic interval represented by each lead SNP, pairwise LD was quantified in PLINK for all neighboring variants located within \u0026plusmn;500 kb of the lead position. SNPs showing r\u0026sup2; \u0026ge; 0.2 with the lead SNP were considered part of the same LD block, and this block defined the physical extent of the association signal. For traits with multiple lead SNPs, these LD blocks were compared for physical overlap. Overlapping blocks were merged and treated as a single locus. The final set of association intervals for each phenotype consisted of non-overlapping LD-supported genomic regions, each representing an independent association signal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate gene identification\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCandidate genes within each GWAS interval were identified through an integrative annotation-based approach. Gene models were retrieved according to the physical boundaries of each association peak, which were defined by the extent of linkage disequilibrium surrounding the lead SNP. Association signals represented by a single SNP positioned in an intergenic region were classified as putative regulatory variants and were not considered in the downstream gene-level prioritization. For all other loci, every annotated gene located within the LD-defined interval was evaluated.\u003c/p\u003e\n\u003cp\u003eFunctional annotation and predicted biochemical roles were obtained from the soybean reference genome (Wm82.a2), which corresponds to the version used for variant calling[51]. These annotations were cross-referenced with KEGG pathway assignments and previously reported functional studies to identify known or putative biosynthetic genes[52]. Arabidopsis homologs of each soybean gene were identified using BLASTP, and the scientific literature for each homolog was surveyed to assess prior implication in flavonoid, phenylpropanoid, and terpenoid pathways[53]. To prioritize candidates expressed in the tissue relevant to trait manifestation, expression profiles during soybean seed development were obtained from SoyBase[54]. Co-expression analysis was then conducted using ATTED-II to quantify the degree of coordinated expression between each candidate gene and known pathway genes[55].\u0026nbsp;Genes that showed both seed-stage expression and enriched co-expression with established flavonoid, isoflavone, or soyasaponin pathway genes were given the highest priority. However, genes lacking strong co-expression signals were also retained as candidates when they had clear functional support from Arabidopsis literature together with detectable expression during soybean seed development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic and phenomic prediction modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictive capacity of high-dimensional phenomic and genomic signals and to identify allele combinations that maximize target metabolite levels, we applied FT-IR\u0026ndash;based phenomic prediction, GWAS-informed genomic prediction. All predictive models were implemented using Random Forest regression (ranger v3.2.4)[56]. Prediction uncertainty was assessed using \u003cstrong\u003e20 bootstrap Random Forest models\u003c/strong\u003e, and the distribution of bootstrap predictions was used to derive mean values and 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eRaw FT-IR spectra were processed through second-derivative transformation and wavelength alignment. For each trait, Random Forest variable importance was computed, and the top 5% most informative wavelengths were selected and incorporated into the model using scaled importance values as predictor weights.\u003c/p\u003e\n\u003cp\u003eFor genomic prediction, trait-specific SNP sets were defined using GWAS results with the top 5% SNPs in p-values. SNP-level weights were derived by combining the absolute GWAS effect size, \u0026minus;log10(P), and trait-specific prediction accuracy, each scaled and normalized. These weights were used during Random Forest model fitting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXGBoost-SHAP-guided allele stacking and optimal genotype search\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify genotype configurations that maximize predicted metabolite levels, genomic prediction was performed using XGBoost regression models trained on GWAS-filtered SNPs (P \u0026lt; 0.05) for each metabolite trait[57]. Feature contributions were estimated using SHapley Additive exPlanations (SHAP) values, and the top SHAP-ranked SNPs from the three focal metabolites (Acetyl-daidzin, Malonyl-daidzin, Soyasaponin-ab) were combined to define a 579-SNP search space for allele-stacking simulations[58]. To identify allele combinations maximizing predicted trait values, we applied a greedy coordinate-wise search: starting from the real accession with the highest model-predicted value, each SNP genotype (0/1/2) was iteratively replaced with the alternative genotypes, and changes were retained only when they increased the objective value. Genetic similarity between optimized genotypes and the 376 real accessions was assessed by Hamming distance across the 579 SNPs. Optimal genotypes were appended to the real genotype matrix, and a neighbor-joining tree was constructed from the allele-mismatch distance matrix to visualize their phylogenetic placement relative to existing diversity.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGenotype Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole-genome resequencing of 376 Korean soybean core collection accessions yielded 10,489,771 SNPs across the 20 chromosomes. As expected for soybean, linkage disequilibrium (LD) in the unpruned dataset was high at short distances (r\u0026sup2; \u0026ge; 0.2 within 125 kb) and decayed slowly to ~0.1 around 900 kb (Fig. 1a). Many adjacent SNPs were separated by \u0026le;10 bp, with a median inter-marker distance of 36 bp, indicating substantial short-range redundancy (Fig. 1b).\u003c/p\u003e\n\u003cp\u003eTo improve computational efficiency and statistical power, markers were LD-pruned at r\u0026sup2; \u0026lt; 0.8, resulting in a final working set of 976,378 SNPs. After pruning, the LD decay curve flattened and pairwise r\u0026sup2; dropped below 0.2 at \u0026le;125 kb, confirming effective redundancy reduction (Fig. 1a). The median inter-SNP spacing increased to 414 bp and exhibited a more uniform genome-wide distribution (Figure 1b). Population structure analysis on the pruned panel supported K = 5, and the resulting Q matrix was incorporated into all GWAS models (Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUPLC-based targeted metabolite profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsoflavones\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 12 isoflavone compounds were profiled across 376 accessions. Three aglycones (daidzein, genistein, and glycitein) and three conjugated forms (malonyl genistin, acetyl genistin, and acetyl glycitin) were undetectable in all samples, leaving six quantifiable glycosides (Fig. 3a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelation analysis resolved the six detectable compounds into two robust and largely independent metabolic modules; glycitein module and daidzein/genistein module (Fig. 3b). The glycitein module, consisting of glycitin and malonyl glycitin, displayed extremely strong internal correlation (r = 0.97), reflecting their shared biosynthetic steps. The daidzein/genistein module, comprising daidzin, malonyl daidzin, acetyl daidzin, and genistin, also exhibited tight cohesion (up to r = 0.99). Cross-module correlations were weak (|r| \u0026le; 0.12, FDR \u0026gt; 0.05), demonstrating that the glycitein-derived and daidzein/genistein-derived branches vary independently.\u003c/p\u003e\n\u003cp\u003ePresence\u0026ndash;absence combinations further highlighted the constrained combinatorial architecture of the pathway (Fig. 3c). Despite six detectable glycosides, only a small subset of the theoretical 64 presence\u0026ndash;absence combinations occurred. Three major chemotypes dominated the panel; i) the full daidzein-derived glycoside suite (n = 186), ii) the same pattern with malonyl glycitin added (n = 64), and iii) a rarer chemotype containing both malonyl glycitin and glycitin (n = 37). Remaining chemotypes were very infrequent and dispersed across the panel, and none of these low-frequency patterns exhibited elevated total isoflavone levels. This concentration of high accumulation within the dominant daidzein-derived chemotypes highlights the restricted combinatorial diversity and strong modular organization of the isoflavone pathway.\u003c/p\u003e\n\u003cp\u003eAnalysis of quantitative variation showed that both compound-level dispersion and total isoflavone accumulation were dominated by the daidzein/genistein-derived module. Acetyl daidzin and malonyl daidzin were not only the most abundant compounds (means 0.33 and 0.20 mg/ml) but also the most variable (SD = 0.20 and 0.16). These same metabolites, together with genistin and daidzin, showed exceptionally strong correlations with total isoflavone content (acetyl daidzin r = 0.96; malonyl daidzin r = 0.95; genistin r = 0.91; daidzin r = 0.89), whereas the glycitein module contributed minimally (r \u0026le; 0.49). Collectively, these observations demonstrate that both the abundance and the quantitative diversity of the isoflavone pathway are primarily driven by variation in the daidzein-derived branch.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoyasaponins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of five soyasaponins (Aa, Ab, Ba, Bb, and Bb\u0026prime;) were quantified across 376 accessions (Fig. 4a). The A-group saponins showed the widest range, with Aa and Ab exhibiting mean abundances of 0.23 and 0.45 mg/ml (SD = 0.30 and 0.50). Bb and Bb\u0026prime; were consistently detected (means = 0.14 and 0.12 mg/ml), whereas Ba was rare (mean = 0.03 mg/ml, SD = 0.05). These distributions indicate that the dominant source of variation lies within the A-group branch.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed a simple but robust quantitative structure (Fig. 4b). Bb and Bb\u0026prime; were tightly correlated (r = 0.79), forming a cohesive B-group submodule, whereas Aa and Ab were strongly anticorrelated (r = \u0026ndash;0.68), consistent with their mutually exclusive chemotypes. Cross-group correlations were weak to moderate (|r| \u0026le; 0.31), indicating that the A-group chemotype switch and the conserved B-group branch represent two largely independent axes of variation within the saponin pathway.\u003c/p\u003e\n\u003cp\u003ePresence\u0026ndash;absence combinations further revealed a constrained chemotype architecture (Fig. 4c). Despite five detectable compounds, only a small subset of the theoretical 32 presence\u0026ndash;absence combinations occurred. The population was structured around two dominant and mutually exclusive A-group chemotypes\u0026mdash;Aa-type (46%) and Ab-type (56%)\u0026mdash;while Bb was almost ubiquitous (~99.5%). Ba and Bb\u0026prime; appeared only in low-frequency, accession-specific patterns. None of these rare chemotypes exhibited elevated total soyasaponin levels, indicating that pathway flux is concentrated within a small number of dominant metabolic configurations.\u003c/p\u003e\n\u003cp\u003eAnalysis of pathway-level variation showed that total soyasaponin accumulation was primarily driven by the Ab-dominated branch. Ab was both the most abundant (mean = 0.45 mg/ml) and the most variable (SD = 0.501), and it showed the strongest correlation with total soyasaponin content (r = 0.761). Bb also contributed substantially (r = 0.552), with Bb\u0026prime; showing a modest association (r = 0.306). In contrast, Aa and Ba contributed minimally to total accumulation (r = \u0026ndash;0.092 and 0.078). Together, these findings demonstrate that although the Aa\u0026ndash;Ab chemotype switch defines the structural foundation of saponin diversity, the Ab\u0026ndash;Bb axis is the primary quantitative driver of total soyasaponin levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsoflavone and Soyasaponin relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between isoflavone and soyasaponin accumulation was evaluated at the compound, module, and pathway levels. Correlation analysis of individual compounds showed that cross-pathway associations were generally very weak, with most isoflavone\u0026ndash;soyasaponin pairs displaying near-zero correlation coefficients (|r| \u0026lt; 0.2) (Supplementary Table S1). This pattern indicates that the two metabolic pathways vary largely independently at the compound level. Consistently, presence\u0026ndash;absence association tests revealed almost no significant co-occurrence patterns between individual compounds from the two pathways, except for a single enriched pair (Daidzin\u0026ndash;Soyasaponin_bb\u0026prime;; FDR = 0.005), supporting an overall independence between pathways in terms of detection frequency.\u003c/p\u003e\n\u003cp\u003eIn contrast, total isoflavone content and total soyasaponin content exhibited a weak but noticeable positive correlation (r = 0.30), suggesting partial convergence or co-regulation at the whole-pathway accumulation level (Supplementary Fig. S1). Taken together, these results indicate that although individual compounds in the isoflavone and soyasaponin pathways behave almost independently, their total accumulation levels share a small degree of coordinated variation across accessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide association study (GWAS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross the four GWAS models (MLM, MLMM, FarmCPU, and BLINK), a total of 1,799 significant SNPs were detected for isoflavone and soyasaponin traits (Supplementary Table S2,3). After LD-based merging, these signals collapsed into 1,315 unique loci, of which 70 were supported by at least two models and considered high-confidence associations (Table 1,2). LD intervals surrounding the lead SNPs ranged from 299 kb to 5.2 Mb, reflecting the characteristic long-range LD structure of the soybean genome.\u003c/p\u003e\n\u003cp\u003eIsoflavones exhibiting high phenotypic diversity, particularly Acetyl daidzin and malonyl daidzin, showed abundant GWAS peaks. Three high-confidence loci, Glycitin_4_1 and Malonyl_daidzin_16_1 were consistently detected across all models, with the strongest locus explaining up to 24.13% of phenotypic variance (Table 1) Several loci co-localized with previously reported QTL or GWAS signals associated with phenylpropanoid or isoflavone biosynthesis, while 8 loci appear to be novel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsistent with the mutually exclusive Aa/Ab chemotypes, the GWAS for A-group soyasaponins identified a single dominant region with extremely high significance (\u0026minus;log₁₀P up to 65.53 for Soyasaponin_Aa_7_2 using MLMM), corresponding to the classical \u003cem\u003esg-1\u0026nbsp;\u003c/em\u003elocus (Table 2). Although \u003cem\u003esg-1\u0026nbsp;\u003c/em\u003eclearly controls the presence\u0026ndash;absence chemotype, its quantitative effect was modest, with the highest PVE for this region reaching 31.03% in Soyasaponin_Ab_7_2, indicating that A-group abundance is influenced by additional loci. Indeed, several other regions exhibited substantial quantitative effects, with PVE values reaching up to 67.45% at Soyasaponin_Ab_18_1 using MLMM, highlighting polygenic control of abundance beyond the major chemotype locus. Additional loci associated with Ba and Bb\u0026prime; reflected their presence\u0026ndash;absence variation across the panel.\u003c/p\u003e\n\u003cp\u003eImportantly, several GWAS signals from different traits converged within shared LD intervals. In total, 27 distinct trait\u0026ndash;locus associations collapsed into nine genomic regions, five of which were jointly detected for both isoflavone and soyasaponin traits (Supplementary Table S4). These loci were positioned within overlapping LD blocks across traits, suggesting potential physical linkage or coordinated regulation between the pathways. Notably, on chromosome 15, the major \u003cem\u003eSoyasaponin_Aa_15_1\u003c/em\u003e locus (17,454,177\u0026ndash;20,341,457 bp) entirely encompassed the \u003cem\u003eAcetyl_daidzin_15_1\u003c/em\u003e interval (17,949,582\u0026ndash;19,722,979 bp). Similarly, the major \u003cem\u003eSoyasaponin_Ab_18_1\u003c/em\u003e locus on chromosome 18 co-localized with both the \u003cem\u003eMalonyl_glycitin_18_1\u003c/em\u003e and \u003cem\u003eAcetyl_daidzin_18_1\u003c/em\u003e regions. These results indicate that favorable alleles for both traits can co-occur within the same haplotypes, although the degree to which this facilitates or constrains breeding will depend on the underlying linkage relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate gene identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe physical size of 70 high-confidence loci varied widely, ranging from broad LD blocks containing up to 573 genes to extremely narrow intervals defined by only a few SNPs\u0026nbsp;(Table 1,2). Notably, 5 loci were represented by a single significant SNPs, and were located in intergenic regions. In such cases, where no protein-coding genes reside in the interval, the association is most likely driven by regulatory variants influencing expression of nearby biosynthetic or regulatory genes.\u003c/p\u003e\n\u003cp\u003eComparison of the detected GWAS signals with established pathway knowledge, including KEGG annotations and previously reported functional studies, showed that 19 loci co-localized with genes whose biochemical roles have been characterized (Table 1,2, Supplementary Table S5,6). These included key biosynthetic or regulatory components such as caffeoyl-CoA O-methyltransferase (OMT), flavonol synthase (FLS), cinnamate 4-hydroxylase (C4H), and the transcriptional repressor MYB4 (Supplemenatary Table S5). For soyasaponins, the classical sg-1 locus (Glyma.07G254600; UGT73F2/4) was located within one of the intervals, consistent with its known role in terminal sugar modification, and additional UDP glycosyltransferases(UGTs) were also recovered within other saponin-associated regions (Supplemenatary Table S6). In contrast, the remaining 51 loci did not contain any functionally established biosynthetic or regulatory genes for isoflavone and soyasaponin, and thus represent regions whose underlying molecular basis remains unresolved.\u003c/p\u003e\n\u003cp\u003eFor the 46 multi-gene loci without known pathway genes, candidate genes were prioritized through an integrative framework. First, all soybean genes within each interval were examined for Arabidopsis homologs, and literature describing those homologs was surveyed for evidence of involvement in flavonoid, phenylpropanoid, terpenoid, or metabolic regulation pathways. Because individual Arabidopsis genes often correspond to several soybean paralogs, seed-stage expression was evaluated early in the prioritization process to exclude candidates lacking expression in the relevant tissue. Among the expressed genes, genes forming enriched co-expression relationships with flavonoid, isoflavone, or soyasaponin biosynthetic genes were further prioritized. When soybean expression and co-expression patterns aligned with Arabidopsis functional evidence, these genes were classified as high-confidence candidates.\u003c/p\u003e\n\u003cp\u003eAcross these novel multi-gene intervals, 487\u0026nbsp;candidate genes had Arabidopsis homologs implicated in relevant pathways,\u0026nbsp;with 43 showing strong seed expression and 10 demonstrating pathway-enriched co-expression (Supplementary Table S5,6). In addition, 5 intergenic single-SNP intervals likely represent cis-regulatory contributors rather than structural gene candidates. The prioritized candidates outline key genomic components that likely contribute to the quantitative diversity of isoflavone and soyasaponin biosynthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic and phenomic prediction modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic prediction using trait-specific GWAS SNPs showed consistently strong performance across both isoflavones and soyasaponins, with most traits reaching correlations of 0.70\u0026ndash;0.90 across cross-validation folds (Fig. 5). Soyasaponins in particular showed the highest predictability, exceeding r = 0.90, indicating that the major-effect loci detected by GWAS capture substantial genetic control over their accumulation. In comparison, FT-IR phenomic prediction exhibited more variable accuracy. Metabolites with clear spectral signatures, for example, Acetyl-daidzin and Genistin, were moderately predictable, whereas low-abundance compounds such as Glycitin showed weak performance. Bootstrap resampling with 20 Random Forest refits confirmed that genomic predictions were stable with narrow confidence intervals, while phenomic models exhibited broader uncertainty. Overall, these results demonstrate that GWAS-informed genomic models provide reliable and trait-specific predictive power, whereas FT-IR spectra capture broader chemical variation but cannot fully resolve compound-specific differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXGBoost-SHAP-guided allele stacking and optimal genotype search\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used SHAP-guided allele stacking to estimate the maximum achievable levels for each metabolite and to predict the phenotypes obtainable by combining favorable SNPs (Fig. 6). For single traits, the SHAP-informed stacking results consistently pointed to the same top-performing accessions present in the panel: CMJ_115 for Acetyl-daidzin, CMJ_068 for Malonyl-daidzin, and CMJ_236 for Soyasaponin-ab. The predicted single-trait optima remained close to these observed values and did not exceed the highest phenotype recorded in the population.\u003c/p\u003e\n\u003cp\u003eFor the multi-trait scenario, we generated an in silico genotype optimized to maximize all three metabolites simultaneously. This virtual genotype showed predicted levels of approximately 1.16 for Acetyl-daidzin, 0.76 for Malonyl-daidzin, and 2.16 for Soyasaponin-ab. A genome-wide comparison revealed that CMJ_317 was the closest real accession to this multi-trait optimum, despite differing at 268 of the 579 SNPs considered. These results identify CMJ_317 as the most similar real genotype to the modeled multi-trait optimum.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe increasing interest in soybean-derived functional compounds has stimulated substantial research on individual classes of isoflavones and soyasaponins, yet efforts to simultaneously enhance both pathways within a single cultivar have been limited. Despite evidence that these metabolites can exert complementary physiological effects, most previous studies have focused on either pathway in isolation, and comprehensive analyses integrating phenotypic diversity, chemotype structure, and genome-wide regulation across large germplasm panels remain limited. By utilizing a well-characterized core collection of 376 Korean soybean accessions, we provide an integrated view of how these two major bioactive pathways are organized phenotypically and genetically, and we identify key points for breeding cultivars with coordinated improvements in both metabolite classes.\u003c/p\u003e\n\u003cp\u003eOur phenotypic analyses revealed that both pathways display constrained chemotypes. For isoflavones, only three dominant chemotypes accounted for approximately 88% of the accessions (Fig. 3c). Rare chemotypes showed consistently lower total accumulation, suggesting that the standing diversity in cultivated soybean is biased toward high-accumulation configurations. This trend aligns with the biological roles of isoflavones in UV protection, antioxidative defense, and stress adaptation, although the relative contributions of domestication, breeding history, and ecological selection remain uncertain[59\u0026ndash;61].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin this restricted chemotype space, quantitative variation was strongly concentrated in the daidzein-derived branch (Fig. 3a,c). Acetyl daidzin and malonyl daidzin were the most abundant and the most variable compounds, and together they explained the majority of variation in total isoflavone content. These observations indicate that pathway-level diversity is shaped primarily by flux through a small number of major conjugates, highlighting the daidzein branch as a key leverage point for metabolic improvement. Although the physiological activities of acetyl daidzin and malonyl daidzin are less well characterized than those of their aglycone counterparts, their dominant contribution to pathway-level variation suggests that further functional investigation of these conjugates could expand the utility of high-isoflavone soybean cultivars in food and health-oriented applications.\u003c/p\u003e\n\u003cp\u003eAlthough modest positive correlations were observed between specific isoflavone and saponin compounds, the overall correlation between pathway totals was weak (r = 0.3), indicating limited metabolic coupling (Supplementary Fig.1). Several factors may account for this independence, including minimal precursor competition, differences in temporal accumulation patterns, and distinct transcriptional regulatory networks governing each pathway. From a breeding perspective, this independence implies that simultaneous enhancement of isoflavone and soyasaponin accumulation is feasible without strong antagonistic trade-offs.\u003c/p\u003e\n\u003cp\u003eThe genetic architecture uncovered by GWAS revealed clear loci controlling variation in both pathways (Table 1,2). Classical loci previously reported in isoflavone or soyasaponin variation, including the sg-1 region underlying the Aa/Ab chemotype split, were robustly rediscovered, validating both the phenotypic dataset and our analytical framework (Table 2). Beyond these known regions, we identified multiple novel loci, several of which showed strong evidence of functional relevance. Among these, an R2R3-MYB transcription factor orthologous to Arabidopsis MYB5 emerged as a compelling candidate, harboring multiple missense variants and exhibiting strong seed-stage expression and enriched co-expression with flavonoid and phenylpropanoid genes (Supplementary Fig. 2). Such transcriptional regulators represent previously uncharacterized nodes that may modulate flux through the isoflavone pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough isoflavone and soyasaponin traits were largely controlled by distinct sets of loci, a small number of genomic regions showed associations with traits from both pathways (Supplementary Table S4). Five such loci were detected, but their candidate genes differed and displayed limited haplotype-level linkage, suggesting local genomic clustering rather than true pleiotropy. These regions may nevertheless complicate breeding strategies due to potential linkage drag and therefore warrant further investigation.\u003c/p\u003e\n\u003cp\u003eAlthough SHAP-guided allele stacking identified realistic upper-bound predictions, the simulated optima did not surpass the highest observed phenotypes for any trait, indicating that further genetic gains may require broader allelic diversity or haplotype configurations not present in the current panel. The multi-trait optimum differed from CMJ_317 at 268 SNPs, suggesting that additional recombination or introgression would be needed to fully realize the modeled genotype (Fig. 6). Nonetheless, CMJ_317 represents a practical starting parent for breeding toward simultaneous enhancement of Acetyl-daidzin, Malonyl-daidzin, and Soyasaponin-ab, and stepwise introgression of the remaining favorable alleles may enable development of new varieties approaching the predicted multi-trait optimum.\u003c/p\u003e\n\u003cp\u003eSeveral other considerations point to opportunities for improving metabolite prediction and allele-stacking outcomes. Some association signals mapped to intergenic regions, suggesting the presence of regulatory variants that require functional validation, while others involved low-frequency alleles that may not have been sufficiently represented for the models to learn their effects. Expanding population size and genetic diversity through additional landraces, breeding lines, and wild relatives, will be essential to capture these rare or favorable haplotypes and improve model accuracy. Complementary approaches such as fine-mapping, CRISPR-based perturbation, and integrated multi-omics (transcriptome, proteome, and others) will also be necessary to resolve causal variants and mechanistic control points with greater precision, ultimately enabling more reliable predictions of how far breeding can push these metabolites.\u003c/p\u003e\n\u003cp\u003eIn summary, this study provides a comprehensive framework for understanding the phenotypic, chemotypic, and genetic organization of isoflavone and soyasaponin pathways in soybean. By delineating the dominant metabolic modules, rediscovering classical loci alongside newly implicated regulatory candidates, and demonstrating the feasibility of machine learning\u0026ndash;assisted allele stacking, we establish a foundation for the coordinated improvement of both pathways. These insights advance our understanding of soybean specialized metabolism and offer concrete strategies for developing high-value cultivars with enhanced functional compound profiles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.K.:\u0026nbsp; Conceptualization;Software; Formal analysis; Investigation; Validation; Visualization; Methodology; Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eSY.S.: Conceptualization; Resources; Data curation; Funding acquisition\u003c/p\u003e\n\u003cp\u003eY.C.: Software; Formal analysis; Investigation; Visualization\u003c/p\u003e\n\u003cp\u003eJE.R.: Resources; Data curation;\u003c/p\u003e\n\u003cp\u003eJ.H.: Conceptualization; Supervision; Funding acquisition; Methodology; Writing \u0026ndash; review \u0026amp; editing; Project administration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was carried out with the support of \u0026quot;Cooperative Research Program for Agriculture Science and Technology Development (Project No. RS-2025-00853272)\u0026quot; Rural Development Administration, Republic of Korea.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Regional Innovation System \u0026amp; Education(RISE) program through the Gangwon RISE Center, funded by the Ministry of Education(MOE) and the Gangwon State(G.S.), Republic of Korea (2025-RISE-10-005)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u0026nbsp;\u003c/strong\u003eand requests for materials should be addressed to J.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReprints and permissions information\u0026nbsp;\u003c/strong\u003eis available at\u0026nbsp;www.nature.com/reprints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEl-Shemy, H. \u003cem\u003eSoybean and Nutrition\u003c/em\u003e. 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Preprint at https://doi.org/10.48550/arXiv.1705.07874 (2017).\u003c/li\u003e\n\u003cli\u003eWang, M., Wang, Y., Xie, C., Wang, P. \u0026amp; Yang, R. The regulation of UV-B - Triggered ABA signal on isoflavones synthesis in soybean suspension cells. \u003cem\u003ePlant Physiology and Biochemistry\u003c/em\u003e \u003cstrong\u003e222\u003c/strong\u003e, 109728 (2025).\u003c/li\u003e\n\u003cli\u003eChoi, Y.-M. \u003cem\u003eet al.\u003c/em\u003e Isoflavones, anthocyanins, phenolic content, and antioxidant activities of black soybeans (Glycine max (L.) Merrill) as affected by seed weight. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 19960 (2020).\u003c/li\u003e\n\u003cli\u003eSohn, S. I. \u003cem\u003eet al.\u003c/em\u003e Metabolic Engineering of Isoflavones: An Updated Overview. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, (2021).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Summary of isoflavonoid GWAS loci and corresponding candidate genes\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTrue GWAS name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003eNovel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e# of genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCandidate gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eBlock_start\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eBlock_end\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSupporting SNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP.value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eblock_start\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eblock_end\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_1_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eregulatory element\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4,742,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4,742,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:4742374:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,742,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,742,374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:4742374:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.02E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,742,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,742,374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_1_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.01G062200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7,334,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9,097,388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:8139098:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.3E-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,334,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e9,097,388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:7565616:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,526,367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,539,812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_1_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33,070,162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33,369,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:33163393:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,070,162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,369,200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:33070162:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.33E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,070,162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,369,200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_1_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.01G106000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35,102,164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37,000,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:36009539:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.55E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,102,164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,000,839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:35865390:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,865,390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,865,390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_1_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.01G123900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e41,932,012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e44,341,861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:42899424:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.35E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e41,932,012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e43,765,065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:43566183:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e42,607,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e44,341,861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_2_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.02G036400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2,103,061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4,371,582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr02:3103043:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.09E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,103,061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,903,128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr02:4371582:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,674,885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,371,582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr02:4371582:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.02E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,674,885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,371,582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_4_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.04G008600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e87,887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4,225,512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:376298:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.75E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e87,887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,372,076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:2240055:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,349,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,234,687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:3236177:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.82E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,239,771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,225,512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_4_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eregulatory element\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37,087,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37,087,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:37087005:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,087,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,087,005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:37087005:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.02E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,087,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,087,005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_4_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.04G205100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e47,223,303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e48,807,135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:47874552:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.45E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e47,223,303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e48,807,135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:47874552:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e47,223,303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e48,807,135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_5_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.05G039400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2,319,438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3,714,546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr05:2319438:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.92E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,319,438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,292,288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr05:3376030:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,424,436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,714,546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_7_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G214700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37,358,480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e38,892,392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:37358480:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,358,480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,154,923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:38187424:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.92E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,448,780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,892,392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_9_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.09G012900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e566,309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1,793,348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr09:986453:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e566,309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,793,348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr09:837687:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.9E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e837,687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e837,687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_13_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.13G082300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e18,404,379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19,501,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:18690947:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.84E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e18,404,379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e18,865,899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:19501606:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e18,845,032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e19,501,606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_14_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.14G120900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 8px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e15,010,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e17,712,775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:15913030:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.83E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e15,010,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e16,911,740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:16731540:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e15,733,151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17,712,775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:17056411:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.89E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e16,059,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17,712,775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:17167225:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.1E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e16,180,772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17,712,775\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_15_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.15G184300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e17,949,582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19,722,979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:18731695:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.54E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17,949,582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e19,722,979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:18506708:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.16E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e18,506,708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e18,506,708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_16_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.16G150600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e32,966,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34,457,317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:33955844:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.74E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e32,966,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e34,457,317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:33914105:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.21E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,099,075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e34,385,943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_18_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.18G040700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1,179,573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4,827,584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:2138965:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.56E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,179,573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,138,150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:2885208:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.48E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,891,026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,879,302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:3828776:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.96E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,829,171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,827,584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_18_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.18G159500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35,394,788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37,506,336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:36393119:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.75E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,394,788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,391,087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:36613555:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.61E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,660,177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,506,336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:36553877:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.88E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,553,877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,553,877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetyl_daidzin_20_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e23,362,065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e24,717,382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr20:23807775:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000000015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e23,362,065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e24,717,382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr20:23869165:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.51E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e23,362,065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e24,717,382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDaidzin_2_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.02G005700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1,020,334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr02:287254:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.09E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,020,334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr02:648719:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.57E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,020,334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDaidzin_5_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eregulatory element\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6,390,474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6,390,474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr05:6390474:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6,390,474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6,390,474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr05:6390474:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.88E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6,390,474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6,390,474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDaidzin_14_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.14G112400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e12,994,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e14,280,241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:13713886:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.09E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e12,994,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e14,280,241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:13713886:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.43E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e12,994,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e14,280,241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDaidzin_14_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e29,793,549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31,632,724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:30643399:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.09E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,793,549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,632,724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr14:30643399:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.28E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,793,549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,632,724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDaidzin_15_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e43,634,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e45,050,243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:44517734:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.09E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e43,634,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e45,050,243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:44517734:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.38E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e43,634,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e45,050,243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eDaidzin_18_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.18G220500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e49,895,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e51,695,849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:50696088:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.57E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e49,895,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e51,695,849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:50696088:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.22E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e49,895,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e51,695,849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGenistin_3_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.03G113600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31,355,386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e32,853,434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr03:32267163:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.3E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,355,386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e32,853,434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr03:31758815:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.65E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,758,815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,758,815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr03:31758815:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.96E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,758,815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,758,815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr03:31758815:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.71E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,758,815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,758,815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGenistin_7_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G071000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5,706,709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9,697,087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:6701412:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.57E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,706,709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,699,182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:7782208:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.32E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6,921,401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,781,733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:8636668:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.93E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,460,406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,710,959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:9431341:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.32E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,553,528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e9,697,087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGenistin_7_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G132600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e15,195,644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19,251,649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:15690617:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.66E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e15,195,644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e16,660,146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:15690617:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.19E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e15,195,644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e16,660,146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:17407305:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.02E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e16,468,280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e18,382,935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr07:18353888:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.31E-43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17,721,590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e19,251,649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGenistin_16_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.16G150600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e30,310,478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e32,893,478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:30378466:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.48E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e30,310,478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,329,735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:31930954:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.37E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e30,942,337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e32,893,478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:30676060:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.48E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e30,561,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e30,727,694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGenistin_19_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.19G018300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1,673,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2,659,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr19:1673173:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.12E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,673,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,659,673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr19:1673173:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.21E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,673,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,659,673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGenistin_20_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.20G114200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34,056,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35,776,931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr20:34852421:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000000045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e34,056,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,776,931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr20:34521942:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.04E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e34,380,166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,497,193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr20:34521942:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.7E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e34,380,166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,497,193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_1_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.01G017100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1,097,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3,090,799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:1389012:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.55E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,097,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,572,843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:2237373:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.89E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,314,162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,090,799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_1_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.01G106000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35,119,917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e38,246,565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:36117212:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.99E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,119,917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,117,096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr01:37735698:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.11E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,752,895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,246,565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_4_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 7px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.04G154800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35,222,713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 7px;\"\u003e\n \u003cp\u003e40,421,262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:36181320:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.06E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,222,713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,058,733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:37681298:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.74E-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,719,334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,494,262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:37681298:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.07E-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,719,334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,494,262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:37681298:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.77E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,719,334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,494,262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:37681298:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.91E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,719,334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,494,262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:38995245:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.3E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e37,995,292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e39,994,701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:39432514:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.08E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e38,432,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e40,421,262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_6_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.06G220700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e32,402,502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e33,507,967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr06:33112569:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.69E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e32,402,502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,507,967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr06:33112569:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9.02E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e32,402,502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,507,967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_9_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.09G049500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 8px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3,767,928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5,283,968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr09:4684644:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.72E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,767,928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,283,968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr09:4684644:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.6E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,767,928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,283,968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr09:4656231:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.88E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,915,352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,283,968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr09:4656231:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.97E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,915,352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,283,968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr09:4656231:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.29E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,915,352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,283,968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_12_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.12G142900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n 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style=\"width: 8px;\"\u003e\n \u003cp\u003e8.62E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e17,564,414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e19,563,868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_13_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.13G004100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e723,424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3,066,096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:1722722:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.02E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e723,424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,475,526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:2361342:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.49E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,361,938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,066,096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_13_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.13G032200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 8px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9,109,580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e12,369,941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:10108633:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.78E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e9,109,580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11,107,957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:10108633:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.96E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e9,109,580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11,107,957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:10736041:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.31E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e9,736,882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11,730,518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:11240279:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.33E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e10,241,767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e12,229,805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:11385802:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.88E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e10,388,827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e12,369,941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_13_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.13G173500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e27,172,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e29,066,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:27845274:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.77E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,172,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e28,842,664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:27845274:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.23E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,172,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e28,842,664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:28578286:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.08E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,578,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,066,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_15_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.15G034900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2,504,554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3,824,475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:3180891:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.38E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,504,554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,824,475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:3180891:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.03E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,504,554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,824,475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_15_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.15G080000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5,618,932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6,816,864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:6482890:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.5E-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,618,932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6,816,864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr15:6482890:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.24E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,618,932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6,816,864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_16_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.16G150600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e32,085,571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e34,985,316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:32681087:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.06E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e32,085,571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,646,986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:34129142:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.98E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,282,191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e34,985,316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:34141604:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.06E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e33,282,191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e34,970,876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_16_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.16G196700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35,179,776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e36,987,137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:36069498:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.89E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,179,776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,987,137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:36069498:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.74E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,179,776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,987,137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:36507395:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.45E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,713,237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,569,476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:36090865:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.17E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e35,713,237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e36,569,476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_17_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.17G050500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2,514,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4,394,755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr17:2885405:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.06E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2,514,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,879,830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr17:4199484:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.86E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,419,715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4,394,755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGlycitin_18_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.18G173300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e40,404,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e42,142,590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:41404121:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.2E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e40,404,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e42,142,590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:41404121:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.5E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e40,404,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e42,142,590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMalonyl_daidzin_4_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.04G018600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1,349,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3,234,687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:2240055:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.52E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,349,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,234,687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr04:2240055:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.59E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,349,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,234,687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMalonyl_daidzin_16_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.16G150600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e29,976,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31,944,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:30950699:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.18E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,976,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,944,614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:30950699:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.1E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,976,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,944,614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:30950699:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.71E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,976,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,944,614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr16:30950699:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6.39E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,976,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31,944,614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMalonyl_glycitin_11_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.11G107100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 8px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7,205,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e8,345,704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr11:7869905:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.87E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,205,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,869,905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr11:8259013:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.63E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,626,429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,344,956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr11:8196966:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.07E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,626,429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,345,704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr11:8259013:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.22E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7,626,429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8,344,956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMalonyl_glycitin_13_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.13G173500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 8px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e27,172,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e29,784,782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:27845274:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.51E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,172,690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e28,842,664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:28578286:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.98E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,578,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,066,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:28562762:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.81E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,578,063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,066,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:28578286:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.04E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,578,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,066,014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr13:28790951:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.7E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27,795,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e29,784,782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMalonyl_glycitin_18_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 4px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.18G029000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 8px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e24,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5,054,348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:529017:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.84E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e24,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,518,137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:2138965:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.84E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,179,573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,138,150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:965331:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7.98E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e24,064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,961,100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:2138965:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.18E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1,179,573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,138,150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eChr18:4059639:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000000002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3,066,156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5,054,348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Summary of soyasaponin GWAS loci and corresponding candidate genes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eTrue GWAS name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eNovel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e# of genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCandidate gene\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlock_start\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eBlock_end\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eSupporting SNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eP.value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eLD_start\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eLD_end\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_1_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e9,674,258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e11,644,447\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:10644931:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.51E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e9,674,258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11,644,447\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:10690289:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.82E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e9,690,465\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e11,270,098\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_1_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e35,870,925\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e37,000,839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:36678490:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.51E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e35,870,925\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e37,000,839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:36084764:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.06E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e35,960,769\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e36,084,764\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_2_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5,159,851\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e6,037,828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr02:5987597:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.51E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,159,851\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e6,037,828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr02:6025355:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.84E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,159,851\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e6,037,828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_6_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e14,865,975\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e15,801,749\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr06:14918909:G:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.55E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e14,865,975\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e15,801,749\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr06:14984595:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.5E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e14,903,612\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e15,801,749\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_7_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G215500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003e39,280,666\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003e42,124,839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:40280651:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.84E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e39,280,666\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e41,280,542\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:40097038:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.48E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e40,097,038\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e40,097,038\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:41576567:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.48E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e41,139,278\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e41,576,567\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:42124839:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.51E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e41,211,575\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,124,839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_7_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 7px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 6px;\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G254600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 6px;\"\u003e\n \u003cp\u003e42,272,244\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 6px;\"\u003e\n \u003cp\u003e44,260,461\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43271899:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6.27E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,272,244\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e44,260,461\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43062404:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.72E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,348,734\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,773,076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43134737:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.43E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43148354:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.43E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43134737:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.96E-66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43134737:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.51E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_11_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e13,712,205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e15,403,620\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr11:14405373:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.81E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e13,712,205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e15,403,620\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr11:14301944:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.41E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e13,712,205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e15,300,911\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_12_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eregulatory element\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2,442,972\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2,442,972\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr12:2442972:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.51E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2,442,972\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2,442,972\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr12:2442972:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.17E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2,442,972\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2,442,972\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_aa_15_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e17,454,177\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e20,341,457\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr15:17949582:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.85E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e17,454,177\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e18,946,191\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr15:18773353:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.27E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e17,949,582\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e19,772,658\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr15:19342069:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.36E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e18,398,731\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e20,341,457\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_ab_7_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G215500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e38,395,673\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e40,795,841\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:39393415:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.84E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e38,395,673\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e40,393,112\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:39796478:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.89E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e38,798,377\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e40,795,841\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_ab_7_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 7px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 6px;\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G254600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 6px;\"\u003e\n \u003cp\u003e41,015,527\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 6px;\"\u003e\n \u003cp\u003e44,129,252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:42015087:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.43E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e41,015,527\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,012,622\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43129404:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.27E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,129,788\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e44,129,252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43134737:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.12E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43134737:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.67E-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43148354:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.97E-51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43134737:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.16E-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_ab_18_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.18G052400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3,829,796\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5,827,277\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr18:4829576:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.76E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e3,829,796\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,827,277\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr18:4976093:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.44E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e4,976,093\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e4,976,093\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_ba_1_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.01G046300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3,678,263\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 6px;\"\u003e\n \u003cp\u003e6,139,393\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:4677203:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.17E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e3,678,263\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,583,655\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:5345147:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.62E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e4,347,457\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,583,655\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:6139393:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.91E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,286,692\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e6,139,393\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:5905557:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.33E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,498,974\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e5,905,557\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_bb_1_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e52,510,109\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e55,148,472\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:53159832:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.42E-103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e52,510,109\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e53,828,108\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:54362744:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.16E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e53,611,380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e55,148,472\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr01:54362744:C:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.42E-101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e53,611,380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e55,148,472\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_bb_18_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.18G198400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e46,690,656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e48,742,418\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr18:47689145:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7.18E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e46,690,656\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e48,266,685\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr18:47785202:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.42E-103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e46,851,823\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e48,742,418\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_bb_prime_6_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e20,148,157\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e22,147,839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr06:21148000:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8.77E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e20,148,157\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22,147,839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr06:21148000:A:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.51E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e20,148,157\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e22,147,839\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_total_7_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 7px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 6px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGlyma.07G254600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 6px;\"\u003e\n \u003cp\u003e42,348,734\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 6px;\"\u003e\n \u003cp\u003e43,773,076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43062404:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.56E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,348,734\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,773,076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43062404:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.6E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,348,734\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,773,076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43062404:A:C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.76E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,348,734\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,773,076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43148354:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.1E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43134737:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.66E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43148354:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.3E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr07:43148354:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eMLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.19E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e42,401,716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e43,553,458\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_total_14_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 7px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1,160,815\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2,378,373\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr14:1243861:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.34E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1,160,815\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1,923,828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr14:1243861:C:T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eFarmCPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e0.000000017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1,160,815\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1,923,828\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eChr14:2096021:A:G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003eBlink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.29E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e1,619,891\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2,378,373\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSoyasaponin_total_17_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003eregulatory element\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 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Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"soybean, metabolites, isoflavone, soyasaponin, mGWAS, genomic prediction","lastPublishedDoi":"10.21203/rs.3.rs-8256309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8256309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIsoflavones and soyasaponins are two classes of health-promoting specialized metabolites in soybean, and improving them simultaneously is a key breeding goal. Emerging evidence indicates that these two metabolite classes can act synergistically in vivo and in vitro, making their simultaneous enhancement an increasingly important breeding objective. However, despite extensive studies on each pathway independently, the genetic basis underlying joint variation of isoflavones and soyasaponins remains poorly understood. Here, we profiled 17 metabolites (12 isoflavones and 5 soyasaponins) across 376 accessions of the Korean soybean core collection using UPLC. We characterized metabolite distributions, correlations, and presence\u0026ndash;absence patterns, and performed multi-metabolite Genome-Wide Association Study (GWAS), identifying 70 high-confidence loci. These included previously reported major loci as well as eight novel loci for isoflavones and thirteen for soyasaponins. Five genomic regions showed shared linkage disequilibrium (LD) structure between the two pathways, and we identified candidate genes for high-confidence loci. We next compared FT-IR\u0026ndash;based phenomic prediction with GWAS-informed genomic prediction, finding that genomic prediction consistently outperformed phenomic prediction and achieved moderate to high accuracy, indicating strong genetic determinism. Finally, we applied an XGBoost\u0026ndash; SHapley Additive exPlanations (SHAP) framework to estimate the extent to which favorable alleles could be combined in silico. Single-trait allele stacking pointed to CMJ_115, CMJ_068, and CMJ_236 as the best-performing accessions for Acetyl-daidzin, Malonyl-daidzin, and Soyasaponin-ab, respectively. Multi-trait optimization produced a virtual genotype most similar to CMJ_317, suggesting this accession as a practical parent for jointly improving both metabolite classes. Overall, our findings provide a population-scale map of diversity, genetic factors, and achievable breeding gains for functional soybean improvement.\u003c/p\u003e","manuscriptTitle":"Joint genetic control of isoflavones and soyasaponins revealed by mGWAS, genomic prediction, and SHAP-guided allele stacking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 13:21:00","doi":"10.21203/rs.3.rs-8256309/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-19T03:41:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T15:09:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T14:18:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155222342365017264872345725209996718911","date":"2026-02-13T01:22:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T13:57:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64425225378425782074969698869518703081","date":"2026-01-31T00:29:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233995405446969402727665819172052967385","date":"2026-01-30T11:19:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126323717007880386666878270152568254517","date":"2026-01-29T14:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T08:15:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T11:55:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-03T11:55:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-02T05:42:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7116abe9-8897-4e19-b40a-f664e455f7ae","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":62091670,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62091671,"name":"Biological sciences/Genetics"},{"id":62091672,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-27T16:04:27+00:00","versionOfRecord":{"articleIdentity":"rs-8256309","link":"https://doi.org/10.1038/s41598-026-50166-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-25 15:58:05","publishedOnDateReadable":"April 25th, 2026"},"versionCreatedAt":"2026-02-03 13:21:00","video":"","vorDoi":"10.1038/s41598-026-50166-1","vorDoiUrl":"https://doi.org/10.1038/s41598-026-50166-1","workflowStages":[]},"version":"v1","identity":"rs-8256309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8256309","identity":"rs-8256309","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}